Proceedings

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Precision Horticulture
Modeling and Geo-statistics
Precision Dairy and Livestock Management
Decision Support Systems in Precision Agriculture
Precision Agriculture and Climate Change
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change)
Extension or Outreach Education of Precision Agriculture
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change)
Global Proliferation of Precision Agriculture and its Applications
Food Security and Precision Agriculture
Precision Crop Protection
Precision Nutrient Management
Precision Livestock Management
Geospatial Data
Sensor Application in Managing In-season Crop Variability
Unmanned Aerial Systems
Remote Sensing for Nitrogen Management
Big Data, Data Mining and Deep Learning
Precision Dairy and Livestock Management
Decision Support Systems
Profitability, Sustainability, and Adoption
Agricultural Education
In-Season Nitrogen Management
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Authors
Abban-Baidoo, E
Abd Aziz, S
Abdol Lajis, G
Abonyi, J
Abu Seman, I
Acuna, T
Adamchuk, V
Adamchuk, V
Adamchuk, V.I
Adamchuk, V.I
Adamchuk, V.I
Adamchuk, V.I
Aduramigba-Modupe, V
Ahamed, T
Ahmed, M
Aizpurua, A
Aizpurua, A
Akune, V.S
Al-Busaidi, A
Alabi, T
Albrecht, H
Albrigo, L.G
Alderman, P
Alfonso, F
Alheidary, M.H
Alheit, K.V
Amaral, L.R
Andersen, P
Andrade, R.G
Aranguren, M
Araujo, R
Archontoulis, S
Arnall, B
B, K
Bae, K
Baeck, P
Baghernejad, M
Bajwa, S
Balboa, G
Balmos, A
Bareth, G
Barros, M.F
Barwick, J.D
Basso, B
Bassoi, L.H
Bastos, L
Batchelor, W.D
Bauer, P.J
Bauer, P.J
Bazakos, M
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bean, G
Bean, G.M
Been, T
Beeri, O
Bejo, S
Belasque Jr., J
Bell, G.E
Bell, G.E
Bellenguez, R
Beltarre, G
Benavente, J.C
Benbihi, A
Beneduzzi, H.M
Benez, S.H
Bennur, P
Benő, A
Berdugo, C
Berg, A
Berger, A.G
Bernardi, A.C
Bernardi, A.C
Besga, G
Betteridge, K
Betzek, N.M
Betzek, N.M
Betzek, N.M
Betzek, N.M
Bishop, T.F
Bishop-Hurley, G.J
Biswas, A
Biswas, A
Blacker, C
Blommaert, J
Bodas, V
Bodson, B
Bohman, B
Boiko, I
Boini, A
Bojer, O.M
Boonen, M
Bourgain, O
Bouroubi, M.Y
Bouroubi, Y
Bouroubi, Y
Boydston, R
Bresilla, K
Bronson, K
Brorsen, B.W
Buckmaster, D
Bugnet, P
Burris, E
Busemeyer, L
Busscher, W.J
Bélec, C
Calera, A
Calera, M
Callegari, D
Camberato, J
Camberato, J.J
Camberato, J.J
Cammarano, D
Campos, I
Campos, L.B
Campoy, J
Cao, Q
Cao, Q
Carrillo Romero, G
Carroll, S
Carter, P
Carter, P.R
Castell, A
Castellón, A
Cavayas, F
Cendrero Mateo, M.P
Chae, Y
Chaplin, Y
Charvat Jr., K
Charvat, K
Charvat, K
Charvat, K
Chen, L
Chen, L
Chen, L
Chen, S
Chen, T
Chen, X
Chen, X
Chiang, R.C
Chikowo, R
Choudhari, D.D
Chung, S
Chung, S
Ciampitti, I
Citon, L.C
Claupein, W
Clay, D.E
Clay, S.A
Codjia, C
Coelho, A
Cointault, F
Colley III, R
Colley III, R
Cooper, J
Cordero, E
Corrêdo, L
Cosby, A.M
Craker, B.E
Cugnasca, C.E
Cugnasca, C.E
Cui, Z
Cuitiva Baracaldo, R
Cunha, T.F
Cunha, T.F
Dall'Agnol, R.W
Dallago, G.M
Danford, D.D
Dao, T.H
Dao, T.H
Das, K
Davis, J
Dehne, H
Delalieux, S
Delauré, B
Delgado, J.A
Deng, L
Destain, J
Destain, M
Diaz-Zorita, M
Dima, C
Dima, C.S
Dobos, R
Donald, G.E
Donatti, C
Dong, R
Dong, R
Douridas, N
Douzals, J
Draganova, I
Draye, X
Drexler, D
Drummond, S.T
Drummond, S.T
Duarte, C
Duhachek, G
Dumont, B
Dunbabin, M
Duncan, S
Durand, P
Duval, C
Duval, C
Duval, C
Duval, C
Duval, C
Dyrmann, M
Ehsani, R
Ehsani, R
Ehsani, R
Eitelwein, M.T
Ellixson, A
Emadi, M.M
Enger, B.D
English, B.C
English, B.C
English, B.C
English, B.C
Erickson, B
Esau, K
Esquivel, W
Evans, D.E
Fageria, N.K
Fajardo, M
Fajardo, M
Farooque, A
Fassana, N
Fassinou Hotegni, N
Fasso, W
Fausti, S
Fergugson, R.B
Fergugson, R.B
Ferguson, R
Ferguson, R.B
Ferguson, R.B
Ferguson, R.B
Ferguson, R.B
Fernandez, F
Fernandez, F.G
Fernandez, F.G
Ferraz, M.N
Ferraz, M.N
Ferreyra, R
Figueiredo, D.M
Filippi, P
Fixen, P
Fleming, K
Fodjo Kamdem, M
Fornale, M
Fountas, S
Francisco, E
Franzen, D
Franzen, D.W
Franzen, D.W
Fraser, E
Freeman, M
Frimpong, K.A
Fu, X
Fulton, J
Fulton, J
Fulton, J.P
Fulton, J.P
Fumery, J
Galzki, J
Gamble, A
Gavioli, A
Gavioli, A
Gavioli, A
Gavioli, A
Gavioli, A
Gaynor, P
Gerighausen, H
Ghinassi, G.P
Gnip, P
Gnip, P
Gnyp, M.L
Goeringer, P
Goffart, J
Gonzalez-Mora, J
Gore, A.K
Gosselin, C
Graeff, S
Grappadelli, L.C
Grego, C.R
Gregory, S
Grignani, C
Grove, J
Guo, J
Guo, Y
Guppy, C.N
Guppy, C.N
Haak, D
Hand, K.J
Hannah, L
Harper, D.C
Hartschuh, J.M
Hatfield, J
Hatfield, J.L
Hauser, J.S
Hayhurst, K
Hazra, J
Hazzoumi, Z
Hedley, M.J
Helga, W
Henry, B
Herold, L
Hijmans, R.J
Hillnhuetter, C
Hinch, G.N
Hinsinger, P
Hock, M.W
Hoffman, E
Hoffmann, C
Hoffmann, W.C
Holland, K.H
Hong, S
Hongo, C
Horakova, S
Huang, H
Huang, H
Huang, L
Huang, S
Huang, W
Huang, Y
Huh, Y
Hunsche, M
Hur, S
Hutchinson, A
Ikpi, A.E
Inamasu, R.Y
Inamasu, R.Y
Inamasu, R.Y
Irvine, L
Jacquemin, G
James, P
Jarolimek, J
Jasper, J
Jasse, E.P
Jayasuriya, H
Jensen, N
Jermy, M
Jezek, J
Jha, S
Ji, W
Ji, W
Jiang, H
Jiang, R
Jimenez, N
Johnson, A
Johnson, R.M
Jones, E.J
Jorgensen, R.N
Journaux, L
KC, K
Kabaliuk, N
Kaiser, D
Kalmar, J
Kantipudi, K
Kaur, G
Kavanagh, R
Kechadi, M
Keller, B
Kempenaar, C
Kepka, M
Khosla, R
Khosla, R
Khosla, R
Khosla, R
Khosla, R
Khosla, R
Khosro Anjom, F
Khot, L
Khot, L
Khun, K
Kidd, J
Kim, H
Kitchen, N.R
Kitchen, N.R
Kitchen, N.R
Kitchen, N.R
Kitchen, N.R
Kitchen, N.R
Kitchen, N.R
Kitchen, N.R
Klose, R
Kocks, C
Kocsis, M
Kong, J
Kovacs, A.J
Krivanek, Z
Krogmeier, J
Kross, A
Krueger Shvetsova, E
Krueger, E
Kumar, A
Kurtener, D
Kurtener, D
Kurtener, D
Kurtener, D
L, M
LAK, M
LAWAL, J
LAWAL, J
Laacouri, A
Laacouri, A
Laboski, C
Laboski, C.A
Laboski, C.A
Lacey, R
Lacroix, R
Lai, C
Lamb, D.W
Lamb, D.W
Lamb, D.W
Lamb, D.W
Lamb, D.W
Lambert, D.M
Lambert, D.M
Lambert, D.M
Lampinen, B
Lan, Y
Lan, Y
Lapen, D
Larbi, P.A
Larkin, S.L
Larkin, S.L
Larson, J.A
Larson, J.A
Larson, J.A
Larson, J.A
Lauzon, S
Le-Khac, N
Lebeau, F
Lee, D
Lee, J
Lee, W
Leese, S
Levow, G
Li, D
Li, F
Li, S
Li, S
Liakos, V
Liakos, V
Liang, X
Lilienthal, H
Lindblom, J
Linz, A
Liu, X
Livens, S
Llorens, J
Llorens, J
Longchamps, L
Longchamps, L
Longchamps, L
Lopez, H
Lowrance, C
Lu, J
Luck, J.D
Lukas, V
Luker, E
Lum, C
Lundström, C
MECHRI, M
Ma, W
Mackenzie, M
Magalhaes, P.G
Magalhaes, P.S
Magalhães, P.G
Magalhães, P.S
Mahlein, A
Maldaner, L
Manfrini, L
Marcassa, L
Marin, A
Marjerison, R
Marra, M.C
Marra, M.C
Martello, M
Martin, D.L
Martin, D.L
Martin, R
Martin, S.W
Martinon, V
Martre, P
Maxwell, T
May-tal, S
McCarter, K.S
McLendon, A
McNairn, H
McNeill, D
Meitalovs, J
Melnitchouck, A
Melnitchouck, A
Mendez, L
Meng, L
Mercuri, P
Mi, G
Miao, Y
Miao, Y
Miao, Y
Miao, Y
Miao, Y
Michelon, G.K
Michelon, G.K
Miklas, P.N
Milics, G
Millen, J.A
Min, C
Miniotti, E.F
Mishamo, M
Mishra, A
Mishra, A
Mishra, A.K
Miteran, J
Moebiu-Clune, B
Moebius-Clune, D
Moeller, K
Molin, J
Molin, J
Molin, J.P
Molin, J.P
Mooney, D.F
Mooney, D.F
Mooney, D.F
Moorhead, R.J
Moorhead, R.J
Morandi, B
Morellas, V
Moretti, B
Morris, C
Moshia, M.E
Moss, J.Q
Moss, J.Q
Moss, J.Q
Mostaço, G.M
Moyle, J
Mueller-Linow, M
Mulla, D
Mulla, D
Mulla, D
Mulla, D
Mulla, D.J
Mulla, D.J
Mulla, D.J
Mullen, R.W
Mullenix, D
Muller, O
Mulligan, M
Munar Vivas, O
Musil, M
Nabizadeh, E
Nafziger, E
Nafziger, E.D
Nafziger, E.D
Nagel, P
Naime, J.D
Nakazawa, P.H
Narayana, C
Nayse, S.P
Nelson, K.J
Neményi, M
Neupane, D
Neves, D.C
Ngo, V.M
Nguyen-Xuan, T
Nigon, T
Nigon, T
Nobakhti, A
Noga, G
Norwood, S.H
Nowatzki, J
Nunes, L
Nuyttens, D
Nyeki, A
Nysten, S
O'Connor, C
Oerke, E
Oerke, E
Ogasawara, C
Okoruwa, V.O
Oksanen, T
Olayide, O.E
Omodele, T
Orellana, J
Ortega, R.A
Ortiz, B.V
Osann, A
Ossowski, M
Pagani, A
Pagni, P
Pan, L
Pan, X
Papanikolopoulos, N
Parajulee, M
Pauly, K
Pawar, S.N
Paxton, K.W
Payton, M.E
Pecchioni, N
Pena-Yewtukhiw, E.M
Pendke, M.S
Perez, N.B
Perez, V
Perez-Parmo, R
Perry, C
Perulli, G
Pfenning, J
Phillippi, E
Phillips, S
Phillips, S.B
Pieruschka, R
Pinkston, P
Pinto, F
Pl, L
Plaza, C
Poelling, B
Port, K
Port, K
Porter, L
Porter, W
Porto, A.J
Portz, G
Post, S
Pourshamsaei, H
Pradalier, C
Prasad, R
Price, R.R
Prince Czarnecki, J.M
Puntel, L
Queiros, L.R
Quirós, J.J
R, C
Rabe, N
Rabello, L.M
Ragab, R
Rahe, F
Rainbow, R
Ransom, C.J
Ransom, C.J
Rascher, U
Raun, W.R
Raun, W.R
Raun, W.R
Raz, J
Reeves, J.M
Reich, R
Rejesus, R
Rejesus, R.M
Rennó, L.N
Resende, A.V
Reyes, J.F
Reynolds, D.B
Reznik, T
Rhea, S.T
Richard, A
Roberts, D
Roberts, D.C
Roberts, D.F
Roberts, J
Roberts, R.K
Roberts, R.K
Roberts, R.K
Roberts, R.K
Robinette, M
Roehrdanz, P
Rojo, F
Romanelli, T.L
Romani, M
Rosen, C
Ruckelshausen, A
Rud, R
Rudy, H
Rumpf, T
Rund, Q
Rydahl, P
Sacco, D
Saenz, L
Saifuzzaman, M
Samiappan, S
Sanches, G.M
Sanchez, S
Sanderson, R
Sankaran, S
Santana Neto, A.J
Santos, A.B
Santos, H.P
Santos, R.A
Sapkota, T.B
Saraswat, D
Sauer, B
Sawyer, J
Sawyer, J.E
Sawyer, J.E
Scharf, P
Scharf, P.C
Schenatto, K
Schenatto, K
Schenatto, K
Schenatto, K
Schenatto, K
Schenatto, K
Schepers, J.S
Scheve, A
Schickling, A
Schindelbeck, R
Schmidt, J.P
Schneider, D
Schneider, S
Schnug, E
Schroeder, M.A
Schuenemann, G.M
Schumacher, L
Schumann, A
Schurr, U
Segarra, E
Sela, S
Sessitsch, A
Shahar, Y
Shanahan, J
Shanahan, J.F
Shanahan, J.F
Shannon, K
Sharda, A
Sharma, A
Shaver, T
Shaw-Feather, C
Shearer, S.A
Sheridan, A
Shi, W
Shi, Y
Shinde, G.U
Shinde, S
Shiratsuchi, L
Shoups, D
Shrestha, R
Sigit, G
Sikora, R.A
Silveira, R.R
Sima, A
Simek, P
Sinfort, C
Singh, G
Singh, J
Singh, M
Sisák, I
Skouby, D
Slater, G
Slaughter, D
Sogbedji, J.M
Solie, J.B
Solie, J.B
Solie, J.B
Sorensen, M.D
Sousa, R.V
Souza, E.G
Souza, E.G
Souza, E.G
Souza, E.G
Souza, E.G
Souza, E.G
Souza, I.R
Souza, W.J
Spekken, M
Splichal, M
Sripada, R.P
Stafford, K.J
Stalidzans, E
Stanitsas, P
Steiner, U
Stelford, M.W
Stiehl, D
Stoces, M
Stone, K
Stone, M.L
Strickland, E.E
Suddth, K.S
Sudduth, K.A
Sudduth, K.A
Sudduth, K.A
Suh, C
Sun, Z
Sunohara, M
Sutherland, A
Swain, D
Szabó, K
Ta, S
Tamura, E
Tangerino, G.T
Tatge, J
Tavares, T
Tavares, T.R
Taylor, D
Taylor, J
Taylor, R.K
Taylor, R.K
Tekin, A
Tenni, D
Thiel, M
Thomason, W.E
Thompson, L.J
Thorson, N
Tian, Y
Tian, Y
Torbert, H
Trautz, D
Tremblay, N
Tremblay, N
Trevisan, R.G
Trindall, J
Troesch, A.M
Trotter, M
Trotter, M
Trotter, M.G
Trotter, M.G
Trotter, M.G
Trotter, M.G
Trotter, T
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Vallespi Gonzalez, C
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Velandia, M
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Velandia, M
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Vellidis, G
Verstynen, H
Vetsch, J
Viator, R.P
Vieri, M.P
Vigil, M
Vigneault, P
Vigneault, P
Vilela, M.D
Villodre, J
Virgawati, S
Voicu, A
Wadhai, V.M
Wagner, P
Walthall, C
Wang, C
Wang, C
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Wang, C
Wang, J
Wang, J
Wang, N
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Wang, X
Wang, X
Wang, Y
Ward, M.D
Watcharaanantapong, P
Weckler, P
Weckler, P
Weiss, U
Welch, M
Werner, A
Werner, A
Westbrook, J
Westbrook, J
Westerdijk, K
Westfall, D
Westfall, D
Wetterich, C
Whelan, B
Whelan, B.M
Williams, R
Wilson, G.L
Wilson, J.A
Winstead, A.T
Worosz, M
Wright, T.M
Wu, D
Wurbs, A
Xia, T
YI, S
Yang, C
Yang, C
Yang, C
Yang, L
Yao, Y
Yi, T
Yoo, H
Yost, M
Yost, M.A
Yule, I.J
Yule, I.J
Yule, I.J
Zacepins, A
Zach, D
Zaller, M
Zaman, Q
Zaman, Q.U
Zermas, D
Zhang, H
Zhang, H
Zhang, R
Zhang, R
Zhao, C
Zhao, C
Zhou, J
Zhu, Y
Zikan, A
Zotarelli, L
da Silva, L.D
de Azevedo, K.K
de Menezes, P.L
de Sousa, M.G
de Souza, E.G
http://icons.paqinteractive.com/16x16/ac, G
http://icons.paqinteractive.com/16x16/ac, G
http://icons.paqinteractive.com/16x16/ac, G
http://icons.paqinteractive.com/16x16/ac, G
maddalon, J
neogi, N
van Es, H
van Es, H.M
van Evert, F
van Vliet, L
wang, X
Topics
Sensor Application in Managing In-season Crop Variability
Precision Nutrient Management
Precision Horticulture
Modeling and Geo-statistics
Precision Livestock Management
Extension or Outreach Education of Precision Agriculture
Precision Crop Protection
Decision Support Systems
Agricultural Education
Geospatial Data
Big Data, Data Mining and Deep Learning
In-Season Nitrogen Management
Decision Support Systems in Precision Agriculture
Precision Agriculture and Climate Change
Unmanned Aerial Systems
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change)
Precision Dairy and Livestock Management
Global Proliferation of Precision Agriculture and its Applications
Profitability, Sustainability, and Adoption
Remote Sensing for Nitrogen Management
Precision Dairy and Livestock Management
Food Security and Precision Agriculture
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change)
Type
Oral
Poster
Year
2010
2024
2018
2016
2012
2008
2022
Home » Topics » Results

Topics

Filter results214 paper(s) found.

1. Saltmed Model As An Integrated Management Tool For Precision Management Of Water, Crop, Soil, And Fertilizers

                 SALTMED-2009: A modelling tool for Precision Agriculture                                                    R. Ragab Centre for Ecology and H... R. Ragab

2. Nugis: The Development Of A Nutrient Use Geographic Information System

NuGIS is a project of the International Plant Nutrition Institute (IPNI). The goal was to examine sources of nutrients (fertilizers and manure) and compare this to crop removal. The project used GIS and database analysis to create maps at the state and county level and then used GIS to migrate the budget analysis to the local watershed and regional watershed levels. This paper will cover the sources of data used, how the data was processed to generate county level numbers, and how GIS was use... Q. Rund, R. Williams

3. A Crop And Soil Strategy For Sensor-based Variable-rate Nitrogen Management

Crop-based active canopy sensors and soil-based management zones (MZ) are currently being studied as tools to direct in-season variable-rate N application. Some have suggested the integration of these tools as a more robust decision tool for guiding spatially variable N rates. The objectives of this study were to identify (1) soil variables useful for MZ delineation and (2) determine if MZ could be useful in identifying field areas wi... D.F. Roberts, J.F. Shanahan, R.B. Fergugson, V.I. Adamchuk, N.R. Kitchen

4. Fluorescence Imaging Spectroscopy Applied To Citrus Diseases

Diseases are one of the most serious threats for citrus production worldwide. Sao Paulo, Brazil and Florida, USA, are the most important citrus producers and, both, are making efforts for citrus diseases control. Citrus canker is one of the serious diseases, caused by the Xanthomonas citri subsp. citri bacteria, that infects citrus trees and relatives, causing a large economic loss in the citrus juice production. Another important disease affecting the citrus production worldwide is the Huang... C. Wetterich, J. Belasque jr., L. Marcassa

5. Hyperspectral Imaging Of Sugar Beet Symptoms Caused By Soil-borne Organisms

The soil-borne pathogen Rhizoctonia solani and the plant parasitic nematode Heterodera schachtii are the most important constraints in sugar beet production worldwide. Symptoms caused by fungal infection are yellowing of leaves and rotting of the beet tuber late in the cropping season. Nematode afflicted plants show stunted growth early in the cropping season and also leaf wilting late in the season when water stress often sets in. Due to the low mobility of soil-borne organisms, they are ide... C. Hillnhuetter, A. Mahlein, R.A. Sikora, E. Oerke

6. Using An Active Crop Sensor To Detect Variability Of Nitrogen Supply On Sugar Cane Fields

Nitrogen management has been intensively studied on several crops and recently associated with variable rate application on-the-go based on crop sensors. On sugar cane those studies are yet scarce and as a biofuel crop the input of energy matters, looking for a high positive balance of biofuel production and low carbon emission on the whole production system. This paper shows the first results obtained using a nitrogen and biomass sensor (N-SensorTM ALS, Yara International ASA) aiming to indi... J. Molin, G. Portz, J. Jasper

7. Comparative Analysis Of Different Approaches

The efficiency of variable rate seeding (VRS) was confirmed in various crops. It is proven that corn requires increasing seeding rates in high-yielding zones, whereas soybeans need lower rates. However, the data for wheat appeared to be controversial. The aim of our experiment was to determine the most efficient strategy for variable rate fertilization and seeding in spring wheat in the conditions of Canadian Prairies. Two approaches were tested: based on Normalize Difference Vegetation Index... A. Melnitchouck

8. Primary Framework Of Diagnosis And Management For Wheat Production Based On The Online Telemonitoring Networks

  PRIMARY FRAMEWORK OF DIAGNOSIS AND MANAGEMENT FOR WHEAT PRODUCTION BASED ON THE ONLINE TELEMONITORING NETWORKS   Sun Zhong-fu, Du Ke-ming, Zhang Yan, Liang Ju-bao   Inst. of Environ. & Sustainable Develop. in Agriculture£¨IEDA£© Chinese... Z. Sun, ,

9. Quantifying Spatial Variability Of Indigenous Nitrogen Supply For Precision Nitrogen Management In North China Plain

... Y. Miao, Q. Cao, Z. Cui, F. Li, T.H. Dao, R. Khosla, X. Chen

10. Precision Manure Management: It Matters Where You Put Your Manure

“Precision fertilizer management” has been around for more than a decade and is practiced widely in Colorado and elsewhere. By precision, we mean application of fertilizer at the right time, in the right place, and in the right amount. However, “Precision Manure Management” is a relatively new concept that converge the best manure management practices with precision nutrient management practices, such as variable rate nutrient application across site-specific managemen... M.E. Moshia, R. Khosla, J. Davis, D. Westfall

11. Smoothness Index Of Thematic Maps

A thematic map shows the spatial distribution of one or more specific data themes for standard geographic areas. The thematic maps are generated to represent the studied variables, so interpolators are used to determine their values in places not sampled. It is usuall... C.L. Bazzi, E.G. Souza, D. Stiehl

12. Thematic And Profitability Maps For Precision Agriculture

Yield maps became economically feasible to farmers with the technological advances in precision agriculture. The evidence of its profitability, however, is still unknown and, rarely, yield variability has been correlated to profitable variability. Differently ... E.G. Souza, C.L. Bazzi, M.A. Uribe-opazo

13. Developing An Active Crop Sensor-based In-season Nitrogen Management Strategy For Rice In Northeast China

  Crop sensor-based in-season N management strategies have been successfully developed and evaluated for winter wheat around the world, but little has been reported for rice. The objective of this study was to develop an active crop sensor-based in-season N management strategy for upland rice in ... Y. Yao, Y. Miao, S. Huang, M.L. Gnyp, R. Jiang, X. Chen, G. Bareth

14. Economic Profitability Of Site-specific Pesticide Management At The Farm Scale For Crop Systems In Haute-Normandie (France)

 Modern agriculture requires decision making criteria applicable to different scales of territory in order to reconcile productivity and respect of the environment, particularly for pest management. Taking into account the recent ... O. Bourgain, C. Duval, J. Llorens

15. Pasture Yield Measurement With The C-DAX Pasture Meter

A system of pasture yield measurement was developed for New Zealand’s pasture based, rotationally grazed farming systems. Pasture yield measurement is complex because the pasture biomass has to be measured in-situ,  pre and post grazing so that pasture consumption and utilisation can be calculated. The “Pasture Meter” was initially developed by Massey University and subsequently commercialised b... I.J. Yule

16. Monitoring Dairy Cow Activity With GPS-tracking And Supporting Technologies

  Nutrient loss from dairy farms is an issue of serious concern to most dairy farmers around the world. On grazed systems such as those practiced in New Zealand animal excreta has been identified as a major source of nutrient loss, which for nitrogen (N) relates to cattle urine in particular.  A study was commissioned to examine nutrient transfer around dairy farms associated with the cows with a view to developing improved precision nutrient application... I. Draganova, I.J. Yule, K. Betteridge, M.J. Hedley, K.J. Stafford

17. Application Of Algebra Hyper-curve Neural Network In Soil Nutrient Spatial Interpolation

Study on spatial variability of soil nutrient is the basis of soil nutrient management in precision agriculture. For study on application potential and characteristics of algebra hyper-curve neural network(AHNN) in delineating soil properties spatial variability and interpolation, total 956 soil samples were taken for alkaline hydrolytic nitrogen measurement from a 50 hectares field using 20m*20m grid sampling. The test data set consisted of 100 random samples extracti... L. Chen, C. Zhao, W. Huang, T. Chen, J. Wang

18. Canopy Reflectance Sensing As Impacted By Corn Hybrid Growth

  Detection of physical and chemical properties within the growing season could help predict the overall health and yield of a corn crop. Little research has been done to show differences of corn hybrids on canopy reflectance sensing. This study was conducted to examine these potential differences during the early- to mid-vegetative growth stages of corn on three different soil types in Missouri. Canopy sensing (Crop Circle) and SPAD chlorophyll met... A. Sheridan, K.A. Sudduth, N.R. Kitchen

19. Is A Nitrogen-rich Reference Needed For Canopy Sensor-based Corn Nitrogen Applications?

The nitrogen (N) supplying capacity of the soil available to support corn (Zea mays L.) production can be highly variable both among and within fields. In recent years, canopy reflectance sensing has been investigated for in-season assessment of crop N health and fertilization. Typically the procedure followed compares the crop in an area known to be non-limiting in N (called a N-rich area) to the crop in areas inadequately fertilized. Measurements from the two areas are used to ... N.R. Kitchen, K.S. Suddth, S.T. Drummond

20. Innovative Optical Sensors For Diagnosis, Mapping And Real-time Management Of Row Crops: The Use Of Polyphenolics And Fluorescence

Force-A’s Dualex® leaf-clips and Multiplex® proximal optical sensors give rapid and quantitative estimations of chlorophyll and polyphenolics of crops by measuring the fluorescence and absorption properties of these molecules. The in vivo and real-time assessments of these plant compounds allow us to define new indicators of crop nitrogen status, health and quality. The measurements of these indicators allow consultants and farmers to monitor the nitrogen status of row crop... V. Martinon, , C. Duval, J. Fumery

21. Variability In Wheat Crop Production Based On Management Zones In Humid Pampas Region, Argentina

Crop productivity within fields is heterogeneous and it responds to the variation in crop management patterns, and in previous, random, and natural crop management factors. The methodologies for the delimitation of management zones (MZ) within production fields differ based on their application objectives. The ... M. L, M. Diaz-zorita, P. Mercuri

22. Timeliness In Agricultural Credit Delivery: A Precision Tool For Improved Farm Output And Income For Cocoa Farmers In Nigeria

The agricultural sector in Nigeria is still dominated by peasant farmers’ characterized by low level of income and saving capacity. One way to improve their farm capital investment is by providing them with timely and targeted accessible credit to enhance their production outputs and income because of the clear knowledge of the time specific nature of some farm operations. Then, how timely is the agricultural credit in Nigeria? This study determined the time-lag of credit facility disbu... J. Lawal

23. Analysis Of Water Use Efficiency Using On-the-go Soil Sensing And A Wireless Network

An efficient irrigation system should meet the demands of the growing crops. While limited water supply may result in yield reduction, excess irrigation is a waste of resources. To investigate water use efficiency, on-the-go sensing technology was used to reveal soil spatial variability relevant to water holding capacity (in this example, field elevation and apparent electrical conductivity). These high-density data layers were used to identify strategic sites where monitoring water availabil... L. Pan, V.I. Adamchuk, D.L. Martin, M.A. Schroeder, R.B. Fergugson

24. Evaluation Of Different N Management Strategies Using A Tool For Fuzzy Multi Attributive Comparison Of Alternatives

Application of precision agriculture is related with choosing of optimal agrotechnilogy and, in particular, with definition of the best alternative of N management strategy. A potential satisfactory solution of this decision analysis problem could be the uses of multi attribute decision-making analysis based on fuzzy set theory and fuzzy logic (FMADA). This technique provides a means to achieve an optimal decision for real world problems which involve multiple alternatives and criteri... E. Krueger, D. Kurtener, D. Kurtener, R. Khosla

25. Evaluation Of Yield Maps Using Fuzzy Indicators

  The ultimate goal of application of yield maps is profitable crop output in many farming systems. Yield maps are the starting point in the precision farming system, and provide the final record indicating the effectiveness of any management changes. Researches on yield mapping shown, that positions and boundaries of zones with different levels ... E. Krueger shvetsova, D. Kurtener, D. Kurtener, H. Torbert

26. GNSS Tracking Of Livestock: Towards Variable Fertilizer Strategies For The Grazing Industry

This study reveals the potential for GPS tracking in the grazing industry. By monitoring the locations and movement of livestock, times of peak grazing activity can be identified and these can in turn produce maps of preferred grazing areas, and by examining residency times provide an indication of spatial variability in grazing pressure. A comparison of grazing preference can be made to similarly inferred camping areas to understand the potential redistribution of nutrients within a paddock.... M.G. Trotter, D.W. Lamb, G.N. Hinch, C.N. Guppy

27. Ultra Low Level Aircraft (ULLA) As A Platform For Active Optical Sensing Of Crop Biomass

Crop producers requiring crop biomass maps to support timely application of in-season fertilisers, pesticides or growth regulators rely on either on-ground active sensors or airborne/satellite imagery. Active crop sensing (for example using Yara N-SensorTM, GreenseekerTM or CropcircleTM) can only be used when the crop is accessible by person or vehicle, and extensive, high-resolution coverage is time consuming. On the other hand, airborne or satellite imaging ... D.W. Lamb, M.G. Trotter, D. Schneider

28. Investigation Of Crop Varieties At Different Growth Stages Using Optical Sensor Data

Cotton, soybean and sorghum are economically important crops in Texas. Knowing the growing status of crops at different stages of growth is crucial to apply site-specific management and increase crop yield for farmers. Field experiments were initiated to measure cotton, soybean and sorghum plants growth status and spatial variability through the whole growing cycle. A ground-based active optical sensor, Greenseeker®, was used to collect the Normalized Difference Vegetation Index (NDVI) da... H. Zhang, Y. Lan, J. Westbrook, C. Suh, C. Hoffmann, R. Lacey

29. Precision Farm Labour Supply For Effective Cocoa Production In Nigeria

In Nigeria, labour is an essential factor in farming. In view of the importance of labour in agriculture, this study was carried out to investigate the sources of labour used in cocoa production. Multi-stage sampling technique was used to select 100 cocoa farming households. The first stage was a random selection of two Local Government Areas (LGAs), the second stage was the selection of two communities from each of the LGAs while the third stage involved the random selection of twenty five c... J. Lawal

30. Mepiquat Chloride Application On Cotton At Variable Rate

Mepiquat chloride (1,1-dimethylpiperidinium chloride) are used to control excessive vegetative growth in cotton (Gossypium hirsutum L.) broadcast sprayed by ground or air. As proven by previous researches the variability of the cotton plants height in the field is large enough to justify the application of Mepiquat at variable rate. The major advantages of it are: (i) yield increase; (ii) economy of the applied input; (iii) reducing the potential of environmental pollution. The main objective... P.S. Magalhaes, ,

31. Typology Of Farms And Regions In EU States Assessing The Impacts Of Precision Farming-technologies

A typology is developed describing the typical farms and the agricultural regions in Europe which presumably would apply Precision Farming technologies (PFT) and how. The typology focuses on the potential agronomic (cropping practices) benefits of PFT in crop production. Precision Farming covers a wide range of technologies for different sectors in agriculture. They differ in techniques, equipment and procedures and form core elements of information oriented production of various cr... L. Herold, B. Poelling, A. Wurbs, A. Werner

32. Assessment Of Climate Variability On Optimal Nitrogen Fertilizer Rates For Precision Agriculture

 Yield response functions... B. Basso, G. Http://icons.paqinteractive.com/16x16/ac, G. Http://icons.paqinteractive.com/16x16/ac, G. Http://icons.paqinteractive.com/16x16/ac

33. Mapping The Effect Of Food Prices, Productivity And Poverty In The Development Domains Of Nigeria

  Poverty remains the major obstacle to economic emancipation and achievement of development agenda in Nigeria. Worse still, rising food prices pose a major threat to feeding the teeming population in Nigeria. Declining food production, high population growth, and negative food trade balance combine to worsen the food and poverty situations in Nigeria. We stand on the premise that surging and volatile food prices could have a hardest hit on those who could not afford it –... O.E. Olayide, A.E. Ikpi, V.O. Okoruwa, , T. Alabi, T. Omodele

34. Variable-rate Irrigation Management For Peanut Using Irrigator Pro

  Variable-rate irrigation has the potential to save substantial water. These water savings will become more important as urban, industrial, and environmental sectors compete with agriculture for available water. However, methodologies to precision-apply water for maximum agronomic and economic utility are needed.  Information is needed to optimally management variable-rate irrigation systems. In this study, we conducted irrigation experiments on peanut to c... K. Stone, P.J. Bauer, W.J. Busscher, J.A. Millen, D.E. Evans, E.E. Strickland

35. Performance Evaluation Of Off-shelf Range Sensors For In-field Crop Height Measurement

Abstract: In-season plant height is a good predictor of yield potential, which needs to be measured with techniques of high spatial resolution and accuracy. In this study, systematic performance evaluations were conducted on three types of commercial range sensors, an ultrasonic sensor, a laser range finder and a range camera on plant height measurement, under laboratory and field conditions. Results showed that the average errors between the measured heigh... N. Wang, Y. Shi, R.K. Taylor

36. Economic Analysis Of Auto-swath Control For Alabama Crop Production

With the rising costs of fertilizer and pesticides and a push towards increasing environmental stewardship, farmers are seeking means to save money while preserving the environment and wildlife habitat. One technology that aids in remedying these concerns is auto-swath control. This investigation evaluates overlap savings using this technology on different application equipment and resulting in economic savings for those adopting it. Several field boundaries were obtained from across the stat... D. Mullenix, A.M. Troesch, J.P. Fulton, A.T. Winstead, S.H. Norwood

37. A Model For Wheat Yield Prediction Based On Real-time Monitoring Of Environmental Factors

... B. Dumont, F. Vancutsem, J. Destain, B. Bodson, F. Lebeau, M. Destain

38. Early Identification Of Leaf Rust On Wheat Leaves With Robust Fitting Of Hyperspectral Signatures

Early recognition of pathogen infection is of great relevance in precision plant protection. Disease detection before the occurrence of visual symptoms is of particular interest. By use of a laserfluoroscope, UV-light induced fluorescence data were collected from healthy and with leaf rust infected wheat leaves of the susceptible cv. Ritmo 2-4 days after inoculation under controlled conditions. In order to evaluate disease impact on spectral characteristics 215 wavelengths in the range of 370... C. R, T. Rumpf, K. B, M. Hunsche, L. Pl, G. Noga

39. Real-time Calibration Of Active Crop Sensor System For Making In-season N Applications

... K.H. Holland, J.S. Schepers

40. Comparison Of Three Canopy Reflectance Sensors For Variable-rate Nitrogen Application In Corn

In recent years, canopy reflectance sensing has been investigated for in-season assessment of crop nitrogen (N) health and subsequent control of N fertilization. The several sensor systems that are now commercially available have design and operational differences. One difference is the sensed wavelengths, although these typically include wavelengths in both the visible and near-infrared ranges. Another difference is orientation – the sensors most commonly used in the US are designed to... K.A. Sudduth, N.R. Kitchen, S.T. Drummond

41. Changes Of Data Sampling Procedure To Avoid Energy And Data Losses During Microclimates Monitoring With Wireless Sensor Networks

... J.C. Benavente, C.E. Cugnasca, M.F. Barros, H.P. Santos, G. Http://icons.paqinteractive.com/16x16/ac

42. Decision Making And Operational Planning

In order to automatize crop farming and its processes, a number of technological and other problems have to be solved. Agricultural field robots are in our vision to fulfill operations in fields. Robots involve number of technological challenges in order to be functional and reliable, but also systems controlling these robots are to be developed. In this paper automatic crop farming is the vision, and decision making models and operational planning is discussed. Study is carried out with simu... T. Oksanen, ,

43. Site-specific Fertilization Management: Influence Of The Past History Of The Addition Of Fertilizers On The Intra Field Variability Of The Rate Of P And K In The Soil.

 Site specific crop management adapts the fertilizer amount applied in relation to the intra field crop needs. In this context, tries were carried out under field conditions. The aim of the trials was to develop technico-economic baseline data and methodology of soil sampling for precision agriculture in Upper-Normandy. ... C. Duval, J. Llorens, C. Duval, C. Duval, S. Ta

44. Development Of A Nitrogen Requirement Algorithm Using Ground-based Active Remote Sensors In Irrigated Maize

Studies have shown that normalized difference vegetation index (NDVI) from ground-based active remote sensors is highly related with leaf N content in maize (Zea mays). Remotely sensed NDVI imagery can provide valuable information about in-field N variability in maize and significant linear relationships between sensor NDVI and maize grain yield have been found suggesting that an N recommendation algorithm based on NDVI could optimize N application. Therefore, a study was conducted using the ... T. Shaver, R. Khosla, D. Westfall

45. Wheat Growth Stages Discrimination Using Generalized Fourier Descriptors In Pattern Recognition Context

... F. Cointault, A. Marin, L. Journaux, J. Miteran, R. Martin

46. Cotton NDVIResponse To Applied N At Different Soil EC Levels

  Spatial variability for crop productivity in the southeastern US Coastal Plain is often due to differences in soil water holding capacity. An experiment was conducted to investigate the use of soil EC as an aid in the site-specific application of sidedress N to cotton. Treatments in the study consisted of three N rates (0, 34, and 112 kg N ha-1). Each treatment was replicated four times in plots that were 4 m wide (four cotton rows) and 350 m long. Soil EC was meas... P.J. Bauer

47. Spatial Variability Of Crop And Soil Properties In A Crop-livestock Integrated System

The knowledge of spatial variability soil properties is useful in the rational use of inputs, as in the site specific application of lime and fertilizer. The objective of this work was to map and evaluate the spatial variability of the crop, soil chemical and physical properties. The study was conducted in 2 areas of 6.9 and 11.7 ha of a Typic Haplustox in Sao Carlos, SP, Brazil. The summer crops corn and sorghum were sowed together to the forage crop Brachiaria brizantha in the system of cro... A.C. Bernardi, C.R. Grego, R.G. Andrade, C.M. Vaz, L.M. Rabello, R.Y. Inamasu

48. Comparison Of Spectral Indices Derived From Active Crop Canopy Sensors For Assessing Nitrogen And Water Status

... L. Shiratsuchi, R.B. Ferguson, J.F. Shanahan, V.I. Adamchuk, G. Slater

49. HLB Detection Using Hyperspectral Radiometry

The need for sustainable agriculture requires the adoption of low input, long-term and cost-effective strategies to overcome the adverse impact of disease and nutritional deficiencies on citrus groves. In this context, early detection of diseased trees has become an important topic in the citrus industry. Multiple factors make field assessment of disease conditions a challenging task: the non-specific nature of many symptoms, the possibility of having localized affections in only certain area... J. Gonzalez-mora, C. Vallespi gonzalez, R. Ehsani, C.S. Dima, G. Duhachek

50. Embedded Sensing System To Control Variable Rate Agricultural Inputs

 This paper presents an embedded sensing system for agricultural machines to collect information about plants and also to control the application of fertilizer with variable rate in corn crop. The Crop Circle reflectance sensor was used with the aim to explore the spe... G.T. Tangerino, R.V. Sousa, A.J. Porto, R. . Inamasu, P. Pinkston

51. Development Of Ground-based Sensor System For Automated Agricultural Vehicle To Detect Diseases In Citrus Plantations

An integrated USDA-funded project involving Carnegie Mellon University, University of Florida, Cornell University and John Deere is ongoing, to develop an autonomous tractors for sustainable specialty crop farming. The research teams have come together to develop an automated system for detecting plant stress, estimating yields, and reducing chemical usage through precision spraying for specialty crops. The goals of the automation process are to reduce the tractor-related labor costs, r... S. Sankaran, R. Ehsani, A. Mishra, C. Dima

52. Normalized Difference Vegetative Index For Evaluating Turfgrass Color: A Comparison Of Two Handheld Devices

The normalized difference vegetative index (NDVI) is a commonly used light reflectance index in agriculture. For turfgrass research, color and herbicide phytotoxicity have historically been subjectively rated by human evaluators. Prior research has related NDVI to creeping bentgrass (Agrostis stolonifera L.) (R2 = 0.50) and tall fescue (Festuca arundinacea Schreb) (R2 = 0.80) color, and bermudagrass [Cynodon ... J.Q. Moss, X. Pan, Y. Tian, A. Hutchinson

53. Economics Of Precision Agriculture For Wheat And Barley Cultivation In Hamedan, Western Iran

    Precision agriculture can influence agricultural operation economics. In this study, minimum economical farm sizes for producing irrigated/dry wheat and barley in... M. Lak, F. Khosro anjom, J. Tatge

54. Design And Experiment On Target Spraying Robot For Greenhouse

In greenhouse, the robot sprayers give rise to concern as they  reduce the labor intensity and improve the accuracy of  the spraying. This paper details the progress to date in the development of a precision robot sprayer. The precision robot sprayer is able to adjust both liquid and air volume to match, the branches contour and location of the greenhouse crops with two ultrasonic sensors  which ensures the position of the plants in the greenhouse. The spraying robot with ... W. Ma, C. Zhao, Q.U. Zaman, D. Zach

55. The Effect Of Variable-Rate Fertilizer Nitrogen Decision-Making On Winter Wheat

... J. Guo, L. Chen, X. Wang, R. Zhang, L. Zotarelli

56. Matching Nitrogen To Plant Available Water For Malting Barley On Highly Constrained Vertosol Soil

Crop yield monitoring, high resolution aerial imagery and electromagnetic induction (EMI) soil sensing are three widely used techniques in precision agriculture (PA). Yield maps provide an indication of the crop’s response to a particular management regime in light of spatially-variable constraints. Aerial imagery provides timely and accurate information about photosynthetically-active biomass during crop growth and EMI indicates spatial variability in soil texture, salinity and/o... B. Sauer, C.N. Guppy, M.G. Trotter, D.W. Lamb, J.A. Delgado

57. Development Of Batch Type Yield Monitor For Small Fields

 Abstract The yield monitor is intended to give the user an accurate assessment of yield variations y within a field. A yield monitor can assist grain producers in many aspects of crop management. A yield monitor by itself can provide useful information and enhance on-farm research. Yield data c... M. Singh, A. Sharma, G. Singh, P. Fixen

58. Development Of A Decision Support System For Precision Areawide Pest Management In Cotton Production

  Crop models simulate growth and development, and provide relevant information for the routine management of the crop.  The use of crop models on large areas for diagnosing crop growing conditions or predicting crop production is hampered by the lack of sufficient spatial information about model inputs. Integrating crop models with other information technologies such as geographic information systems (GIS), variable rate technology, remote sensing, and global p... Y. Lan, W.C. Hoffmann, J. Westbrook, M. Zaller

59. Mapping Soil Salinity Using Cokriging Method In Arsanjan Plain, Southern Iran

  Salt-affected landscapes are highly sensitive to changes in climatic, edaphic and hydrological conditions in time and space in semi-arid regions such as Arsanjan plain, southern Iran. The objective of this study was to combine digital satellite data with ground based measurements of ECe by cokriging method to possibility improve the soil salinity maps of study area. Soil samples in the 85 sampling site (10187 ha)were collected from 0-30 cm depths, georefrenced using GPS recei... M.P. Baghernejad, M.M. Emadi

60. Precision Livestock Management: An Example Of Pasture Monitoring In Eastern Australian Pastures Using Proximal And Remote Sensing Tools

  Pasture monitoring Australian rangelands by Remote Sensing   G.E.Donald.  CSIRO Livestock Industries, Locked Bag 1, Armidale NSW, 2350 Australia     A series of spatial models and datasets were jointly developed to estimate pasture biomass as feed on offer (FOO®) and pasture growth rate (PGR®) in the so... G.E. Donald, M.G. Trotter, D.W. Lamb, G. Levow, H.M. Van es

61. Assessment Of Physiological Effects Of Fungicides In Wheat

The use of fungicides is one of the most widespread methods implemented in intensive crop production focused in solving phytosanitary problems. The use of fungicides belonging to groups such as strobilurins has been associated with positive physiological effects such as increased tolerance against abiotic stresses, changes in plant growth regulator activities and delayed leaf senescence. The use of thermography is a non- destructive method which permits to distinguish physiological changes ca... C. Berdugo, U. Steiner, E. Oerke, H. Dehne

62. Development Of A Sensor Suite To Determine Plant Water Potential

The goal of this research was to develop a mobile sensor suite to determine plant water status in almonds and walnuts. The sensor suite consisted of an infrared thermometer to measure leaf temperature and additional sensors to measure relevant ambient conditions such as light intensity, air temperature, air humidity, and wind speed. In the Summer of 2009, the system was used to study the relationship between leaf temperature, plant water status, and relevant microclimatic information in an al... V. Udompetaikul, S. Upadhyaya, B. Lampinen, D. Slaughter

63. Accounting For Spatial Correlation Using Radial Smoothers In Statistical Models Used For Developing Variable-rate Treatment Prescriptions

Variable-rate treatment prescriptions for use on commercial farms can be developed from embedded field trials on those farms. Such embedded trials typically involve non-random, high-density sampling schemes that result in large datasets and response variables exhibiting spatial correlation. In order to accurately evaluate the significance of the effects of the applied treatments and the measured field characteristics on the response of interest, this spatial correlation must be accounted for ... K.S. Mccarter, E. Burris

64. Sensor And System Technology For Individual Plant Crop Scouting

Sensor and system technologies are key components for automatic treatment of individual plants as well as for plant phenotyping in field trials. Based on experiences in research and application of sensors in agriculture the authors have developed phenotyping platforms for field applications including sensors, system and software development and application-specific mountings.   Sensor and data fusion have a high potential by compensating varying s... A. Ruckelshausen, K.V. Alheit, L. Busemeyer, R. Klose, A. Linz, K. Moeller, F. Rahe, M. Thiel, D. Trautz, U. Weiss

65. Vision Of Farm Of Tomorrow

... K. Charvat, P. Gnip

66. Adoption Of N-application Rates In Different Broccoli Cultivars By Reflectance Measurements

 To date many sensors have been solely developed and tested for arable crops. This project aims to develop the means to rapidly map N-demand in broccoli plants on a site-specific, plant-by-plant basis using reflectance measurements. The aim of this specific study was to monitor nitrogen status in six different broccoli cultivars using reflectance measurements and to derive suitable N-fertilization strategies based on the sensor measurements.... S. Graeff, J. Pfenning, W. Claupein

67. Vlite Node – New Sensor Technology For Precision Farming

... K. Charvat, J. Jezek, M. Musil, Z. Krivanek, P. Gnip

68. Spatial And Vertical Distribution Of Soil P, K, And Mg Content In A Vineyard Of The Do Ca Rioja Using Grid And Target Sampling Methods

  Knowledge of spatial variability of soil nutrient contents is very important to design a fertilization strategy based on the needs of the vine. Matching fertilization and nutritional plant needs is very important due to the influence of nutritional status of vineyards on productive and qualitative factors. The aim of this work was to study the spatial and vertical variability of P, K and Mg in a vineyard soil by two methods: (i) the grid sampling at three depth ranges (... O. Unamunzaga, A. Castell, G. Besga, R. Perez-parmo, A. Aizpurua

69. A Computer Decision Aid For The Cotton Precision Agriculture Investment Decision

This article introduces the Cotton Precision Agriculture Investment Decision Aid (CPAIDA), a software decision tool for analyzing the precision agriculture investment decision. CPAIDA was developed to provide improved educational information about precision farming equipment ownership costs, and the required returns to pay for their investment. The partial budgeting and breakeven analysis framework is documented along with use of the decision aid. With care in specifying values, program users... J.A. Larson, D.F. Mooney, R.K. Roberts, B.C. English

70. Indirect Measurement Of Creeping Bentgrass N, Chlorophyll, And Color For Precision Golf Green Management

Indirect measurement of turfgrass tissue through optical sensing may provide golf course managers with non-destructive and relatively simple real-time measurements of golf green N requirements. The objective of this study was to determine the effect of N rate on ‘Crenshaw’ creeping bentgrass (Agrostis stolonifera L.) tissue N, chlorophyll concentration, and color using the GreenSeeker (NTech Industries, Ukiah, CA) handheld sensor... J.Q. Moss, G.E. Bell

71. Cotton Precision Farming Adoption In The Southern United States: Findings From A 2009 Survey

The objectives of this study were 1) to determine the status of precision farming technology adoption by cotton producers in 12 states and 2) to evaluate changes in cotton precision farming technology adoption between 2000 and 2008. A mail survey of cotton producers located in Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, Tennessee, Texas and Virginia was conducted in February and March of 2009 to establish the use of precision farming tec... M. Velandia, D.F. Mooney, R.K. Roberts, B.C. English, J.A. Larson, D.M. Lambert, S.L. Larkin, M.C. Marra, R. Rejesus, S.W. Martin, K.W. Paxton, A. Mishra, C. Wang, E. Segarra, J.M. Reeves

72. Citrus Greening Disease Detection Using Airborne Multispectral And Hyperspectral Imaging

Citrus greening disease (Huanglongbing or HLB) has become a major catastrophic disease in Florida’s $9 billion citrus industry since 2005, and continued to be spread to other parts of the U.S. There is no known cure for this disease. As of October 2009, citrus trees in 2,702 different sections (square mile) in 34 counties were infected in Florida. A set of hyperspectral imageries were used to develop disease detection algorithms using image-derived spectral library, the mixture tu... W. Lee, A. Kumar, R. Ehsani, C. Yang, L.G. Albrigo,

73. Development Of A Precision Sensing Sprayer For The Application Of Nitrogen Fertilizer To Turfgrass

  Normalized difference vegetation index (NDVI) may be very useful for turfgrass managers to measure turf quality and obtain an indirect measurement of turf N status. The objective of this research was to develop a Nitrogen Fertilization Optimization Algorithm (NFOA) for use in a turfgrass variable rate N applicator on bermudagrass [Cynodon dactylon (L.) Pers] fairways and creeping bentgrass (Agrostis stolonifera L.) greens in Oklahoma. Plots (0.9 X 1.5 ... J.Q. Moss, G.E. Bell, J.B. Solie, M.L. Stone, D.L. Martin, M.E. Payton

74. Adoption And Perceived Usefulness Of Precision Soil Sampling Information In Cotton Production

  Soil testing assists farmers in identifying nutrient variability to optimize input placement and timing. Anecdotal evidence suggests that soil test information has a useful life of 3–4 years. However, perceived usefulness may depend on a variety of factors, including field variability, farmer experience and education, farm size, Extension, and factors indirectly related to farming. In 2009, a survey of cotton farmers in 12 Southeastern states collected information... D.C. Harper, D.M. Lambert, B.C. English, J.A. Larson, R.K. Roberts, M. Velandia, D.F. Mooney, S.L. Larkin

75. Crop Rotation Impacts ‘Temporal Sampling’ Needed For Landscape-defined Management Zones

Yield and landscape position are used to delineate management zones, but this approach is confounded by yield’s weather dependence, causing yield to evidence temporal variability/lack of yield stability. Management options (e.g. crop rotation) also influence yield stability. Our objective was to build a model that would describe the influence of crop rotation on the temporal yield stability of landscape defined management zones. Corn (Zea mays L.) yield data for two rotat... E.M. Pena-yewtukhiw, J. Grove

76. Spatial Livestock Research In Australia And New Zealand: Towards A Cooperative Research Model

  A number of researchers in Australia and New Zealand are working in the area of animal tracking as an important technological  step to gaining a deeper  understanding of animal behavior in various farmed and natural environments. The ultimate goals of the research vary from simply trying to understand how animals can be farmed more effectively to how animals could be controlled without fences. There are a number of parallels with the development of c... I.J. Yule

77. Evaluation Of A Controlled Release N-P Fertilizer Using A Modified Drill For Variable Rate Fertilization

Base NP or NPK fertilization is a common practice in cereal production in Chile. Usually, a physical NPK blend is band applied with the seed at planting with the drill. Normal fertilizer rates vary from 400 to 500 kg ha-1; however, there is a tendency in the market to move from physical blend towards chemical blends (monogranule) and, more recently, to controlled release fertilizers (CRF). The CRF are usually recommended at very low rates, varying from 70 to 120 kg ha-1, however this rates ar... R.A. Ortega, J.F. Reyes, W. Esquivel, J. Orellana

78. Yield Limiting Factors In The Conditions Of Southern Alberta

The main goal of our experiment was to determine the main factors determining yield of green biomass of spring barley in the conditions of Southern Alberta. To analyze soil properties in the field, grid sampling was conducted at 1-ha grid. Soil samples were collected from the depths of 0…15 and 15…60 cm and analyzed for over 20 different characteristics including soil organic matter content, pH, cation exchange capacity (CEC), and the concentrations of macro- and micronutrients.... A. Melnitchouck

79. A Preliminary Evaluation Of Proximity Loggers To Detect Oestrus Behaviour In Grazing Dairy Cows

... D. Mcneill, G.J. Bishop-hurley, L. Irvine, M. Freeman, R. Bellenguez

80. Cognitive Radio In Precision Agriculture

 This is an attempt to design a precision agriculture (PA) model, to control the required parameters in greenhouse with wireless sensor network (WSN). This proto type model of wireless sensor and actuators network is designed as per required parameters of available crops in a greenhouse. The design of the sensor node consists of sensors, a micro-controller and a low-powered radio module. Real-time data, enable the operators to characterise the operating parameters of the greenhouse and a... S.P. Nayse, D.D. Choudhari, V.M. Wadhai

81. Study Of Nitrogen Fixation And Nodulation In Annual Medic(medicago Rigidula) In Inoculation With Foreign And Inside Root Symbiotic Bacteria

  Annual species of Medicago are important pasture legumes in western parts of iran. Their productions are affected by suitable soil Rhizobium meliloti strains and environmental conditions. The principle objective of this study was to find a suitable Rhizobium meliloti strain(s) for Medicago rigidula. Two experiments: one in the greenhouse and the other one on the field were conducted in 2006 to determine nodulation, and ni... E. Nabizadeh

82. Site Specific Management Of An Oxisol Cultivated With Corn For Application Of Lime And Gypsum

Due to the necessity to improve soil fertility diagnostic, the researchers have been searched for more efficient technologies on agronomic, economic and environmental aspects. One of these technologies is the use of the concept of site-specific for soil fertility management. This research was conducted in a farm field (100 ha) located in Corinto, Minas Gerais state. The soil is classified as clayey Oxisol, cropped with corn (Zea mays L.) and irrigated with a center-pivot sprinkler irrigation ... A. Coelho, T.F. Cunha, T.F. Cunha

83. Laboratory Evaluation Of Ion-selective Electrodes For Simultaneous Analysis Of Macronutrients In Hydroponic Solution

... H. Kim, , , , K.A. Sudduth

84. Optimizing Vineyard Irrigation Through The Automatic Resistivity Profiling (arp) Technology. The Proposal Of A Methodological Approach

 In Tuscany, central Italy, grape cultivation and wine production (i.e., Chianti DOCG, Brunello di Montalcino) are farming activities appreciated worldwide. Differently from the past, irrigation is allowed to meet the intense physiological stress that may occur during seasons affected by the increasing climate variability, in order to guarantee quality product and hence high market profitability in many vines areas. Most ... P. Pagni, G.P. Ghinassi, M.P. Vieri

85. Pa Adoption By A Korean Rice Farming Group: Case Study Of Pyeongtaek City

Research on precision agriculture (PA) has been conducted in Korea for about 10 years since 1999. Most of the research was focused on rice paddy fields that were flooded, flat, and small sized (e.g., 30 m x 100 m). Accomplishment during the period includes investigation on spatial variability in soil, crop growth, and yield properties, application of imported sensors and variable rate applicators, and development of Korean version of these ... S. Chung, H. Yoo, S. Hong

86. Effect Of Nitrogen Application Rate On Soil Residual N And Cotton Yield

A long-term study was conducted on nitrogen application rate and its impact on soil residual nitrogen and cotton (FM960B2RF) lint yield under a drip irrigation production system near Plainview, Texas. The experiment was a randomized complete block design with five nitrogen application rates (0, 56, 112, 168 and 224 kg per ha) and five replications. The soil nitrogen treatment was applied as side dressing. Cotton yield, leaf N, seed N, soil residual nitrate, amount of irrigation, and rainfall ... M. Parajulee, D. Neupane, C. Wang, S. Carroll, R. Shrestha

87. Gps Tracking Of Sheep To Investigate Shelter And Shade Use In Relation To Climatic Conditions

In Australia inclement weather contributes to losses of new-born lambs and recently-shorn sheep. Provision of forced shelter has been observed to reduce lamb losses by up to 10 percent and when given a choice, ewes preferentially seek shelter on offer for a period of approximately two weeks post shearing (Alexander et al. 1980). Given significant sheep losses can occur during adverse weather conditions a better understanding of sheep use of shelter and/or alternative ways of attracting sheep ... D. Taylor, , , , , ,

88. Canopy Reflectance-based Nitrogen Management Strategies For Subsurface Drip Irrigated Cotton

Nitrogen (N) fertilizer management in subsurface drip irrigation (SDI) systems for cotton (Gossypium hirsutum L.) can be very efficient when N is fertigated on a near daily time step.  Determining the amounts and timing of the N fertigation, however are questions that weekly canopy reflectance measurements may answer.   The main objective of this 3-yr. study was to test two canopy reflectance strategies for adjusting urea ammonium nitrate (UAN) fertilizer in-season injections... K. Bronson

89. Precision Agriculture Development In Canada

This poster provides an overview of precision agriculture development in Canada.  It focuses on the specific practices of auto steer tracking and variable rate nutrient application in the prairie region.  The development of these practices has been largely driven by technology innovation and private sector crop consultants and equipment providers.  Nevertheless, academia and government have supported this development through research since the 1990’s and funding incentive... D. Haak

90. Soil Quality Improvement Through Proper Combination Of Tillage, Nitrogen Fertilization And Cover Cropping Systems

No-tillage, N fertilization and cover cropping affect physical, chemical and biological qualities of soil. We investigated the effect of 15-yr of tillage systems, N fertilization and cover crops on soil organic matter, aggregation, bulk density and on microbial community in the sandy loam soil of central Italy. The soil in no-tillage (NT) system had 50% more organic matter and 3 folds higher aggregate stability than the soil in conventional tillage (CT) system. The NT system significantly inc... T.B. Sapkota

91. Research On Nutrition Detection Technology Of Soil And Leaf Of Citrus Based On Spectroscopic Techniques

The diagnosis technique of real-time lossless crop nutrition is the foundation and conditions for the precise and effective fertilization. Currently, the diagnosis of crop nutrition mainly relies on the routine chemical analysis of laboratory. Due to the complicated procedure, time-consuming, high cost and high professional technique requirement, it can hardly meet the need of precise variable fertilization technology. Spectrum technology is the technology of real-time and non-destructive tes... S. Yi, L. Deng

92. Edxrfs-based Sensing Of Phosphorus And Other Mineral Macronutrient Distribution In Field Soils

Phosphorus (P) requirements for major agronomic crops have been currently based on a pre-plant mass balance method.  Fertilizer needs are estimated from crop needs, available soil P and other external nutrient inputs that include animal manure, crop residues, etc...  Thus, this approach uses f... T.H. Dao

93. Application of Information Technologies in Precision Apiculture

Apiculture, widely known as beekeeping, is one of the agriculture’s sub directions, where Precision Agriculture (PA) methods can be successfully applied. Adaptation of PA methods and technics into Apiculture, as well as integrating information technologies into beekeeping process can change and improve the beekeepers understanding of bee... E. Stalidzans, A. Zacepins, J. Meitalovs

94. Factors Influencing the Timing of Precision Agriculture Technology Adoption in Southern U.S. Cotton Production

Technology innovators in cotton production adopted precision agriculture (PA) technologies soon after they became commercially available, while others adopted these technologies in later years after evaluating the success of the innovators. The timing ... D.M. Lambert, J.A. Larson, B.C. English, R.M. Rejesus, M.C. Marra, A.K. Mishra, C. Wang, P. Watcharaanantapong, R.K. Roberts, M. Velandia

95. Evaluation of Photovoltaic Modules at Different Installation Angles and Times of the Day

Several electricity-consuming components for cooling and heating, illumination, ventilation, and irrigation are used to maintain proper environments of protected crop cultivation facilities. Photovoltaic system is considered as one of the most promising alternative power source for protected cultivation. Effects of environm... S. Chung, J. Kong, Y. Huh, K. Bae, S. Hur, D. Lee, Y. Chae

96. Brazilian Precision Agriculture Research Network

The adoption of adequate technologies for food, biomass and fiber production can increase yield and quality and also reduce environmental impact through an efficient input application. Precision agriculture is the way to decisively contribute with efficient production with environment protection in Brazil. Based on this, recently Embrapa established the Brazilian P... J.D. Naime, L.R. Queiros, A.V. Resende, M.D. Vilela, L.H. Bassoi, N.B. Perez, A.C. Bernardi, R.Y. Inamasu

97. Climatological Diagnostic Analysis: A Case Study for Parbhani District in Marathwada Region of India

... S.N. Pawar, A.K. Gore, G.U. Shinde, M.S. Pendke

98. The Opportunities to Implement Precision Agriculture Technology in Indonesia: A Review

... S. Virgawati

99. System Approach to Implementing Precision Agriculture in Ukraine

As Ukrainian agricultural production undergoes major changes, a better understanding of the diversity of land resources is needed to optimize management.  Dealing with large fields (over 100 ha in size) with non-uniform growing conditions presents an opportunity for site-specific management of agricultural inputs. This presentation highlights the most satisfactory practices implemented during the past three years and provides an outlook for the continued on adoption of precision agr... I. Boiko

100. Development of a PWM Precision Spraying System for Unmanned Helicopter

Application of protection materials is a crucial component in the high productivity of agriculture. Motivated by the needs of aerial precision application, in this paper we present a pulse width modulation (PWM) based precision spraying system for unmanned helicopter. The system is composed of the tank, pipelines, pump, nozzles and the automatic control unit. The system can spray with a constant rate automatically when the speed of the UAV fluctuates between 1 m/s to 8 m/s. The application ra... R. Zhang, L. Chen, T. Yi, Y. Guo, H. Zhang

101. Use of Unmanned Aerial Vehicles to Inform Herbicide Drift Analysis

A primary advantage of unmanned aerial vehicle-based imaging systems is responsiveness.  Herbicide drift events require prompt attention from a flexible collection system, making unmanned aerial vehicles a good option for drift analysis.  In April 2015, a drift event was documented on a Mississippi farm.  A combination of corn and rice fields exhibited symptomology consist with non-target injury from a tank mix of glyphosate and clethodim.  An interesting observation was t... J.M. Prince czarnecki, D.B. Reynolds, R.J. Moorhead

102. Plant Stand Count and Corn Crop Density Assessment Using Texture Analysis on Visible Imagery Collected Using Unmanned Aerial Vehicles

Ensuring successful corn farming requires an effective monitoring program to collect information about stand counts at an early stage of growth and plant damages due to natural calamities, farming equipment, hogs, deer and other animals. These monitoring programs not only provide a yield estimate but also help farmers and insurance companies in assessing the causes of damages. Current field-based assessment methods are labor intensive, costly, and provide very limited information. Manual asse... S. Samiappan, B. Henry, R.J. Moorhead, M.W. Hock

103. Use of Satellite Data to Improve Damage Assessment Process for Agricultural Insurance Scheme in Indonesia

Goal is to develop new method utilizing satellite data for assessment of damage in paddy field which can contribute toward substantial reduction of the damage assessment time and costs in framework of agricultural insurance in Indonesia. For the damage assessment, estimation of yield in each paddy plot is a key, so the research on the estimation of rice yield was carried out using satellite data which was acquired in harvesting season. Multiple linear regression analysis was conducted for the... C. Hongo, C. Ogasawara, E. Tamura, G. Sigit

104. Site Specific Costs Concerning Machine Path Orientation

Computer algorithms have been created to simulate in advance the orientation/pattern of a machine operation on a field. Undesired impacts were obtained and quantified for these simulations, like: maneuvering and overlap of inputs in headlands; servicing of secondary units; and soil loss by water erosion. While the efforts could minimize the overall costs, they disregard the fact that these costs aren’t uniformly distributed over irregular fields. The cost of a non-productive machine pro... M. Spekken, J.P. Molin, T.L. Romanelli, M.N. Ferraz

105. Considering Farmers' Situated Expertise in AgriDSS Development to Fostering Sustainable Farming Practices in Precision Agriculture

Agriculture is facing immense challenges and sustainable intensification has been presented as a way forward where precision agriculture (PA) plays an important role. More sustainable agriculture needs farmers who embrace situated expertise and can handle changing farming systems. Many agricultural decision support systems (AgriDSS) have been developed to support farm management, but the traditional approach to AgriDSS development is mostly based on knowledge transfer. This has resulted in te... C. Lundström, J. Lindblom

106. Privacy Issues and the Use of UASs/Drones in Maryland

 According to the Federal Aviation Administration (FAA), the lawful use of Unmanned Aerial Vehicles (UAV), also known as Unmanned Aircraft Systems (UAS), or more commonly as drones, are currently limited to military, research, and recreational applications. Under the FAA’s view, commercial uses of drones are illegal unless approved by the Federal government.  This will change in the future.  Congress authorized the FAA to develop regulations for the use of drones by priva... P. Goeringer, A. Ellixson, J. Moyle

107. Comparing Adapt-N to Static N Recommendation Approaches for US Maize Production

Large temporal and spatial variability in soil N availability leads many farmers across the US to over apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses.  Static N recommendation tools are typically promoted in the US, but new dynamic model-based tools allow for more precise and adaptive N recommendations that account for specific production environments and conditions. This study compares two static N recommendation tools... H. Van es, S. Sela, R. Marjerison, B. Moebiu-clune, R. Schindelbeck, D. Moebius-clune

108. Data Normalization Methods for Definition of Management Zones

The use of management zones is considered a viable economic alternative for the management of crops due to low cost of adoption as well as economic and environmental benefits. The decision whether or not to normalize the attributes before the grouping process (independent of use) is a problem of methodology, because the attributes have different metric size units, and may influence the result of the clustering process. Thus, the aim of this study was to use a Fuzzy C-Means algorithm to evalua... K. Schenatto, E.G. De souza, C.L. Bazzi, A. Gavioli, N.M. Betzek, H.M. Beneduzzi

109. EZZone - An Online Tool for Delineating Management Zones

Management zones are a pillar of Precision Agriculture research.  Spatial variability is apparent in all fields, and assessing this variability through measurement devices can lead to better management decisions.  The use of Geographic Information Systems for agricultural management is common, especially with management zones.  Although many algorithms have been produced in research settings, no online software for management zone delineation exists.  This research used a ... G. Vellidis, C. Lowrance, S. Fountas, V. Liakos

110. Multispectral Imaging and Elevation Mapping from an Unmanned Aerial System for Precision Agriculture Applications

As the world population continues to grow, the need for efficient agricultural production becomes more pressing.  The majority of farmers still use manual techniques (e.g. visual inspection) to assess the status of their crops, which is tedious and subjective.  This paper examines an operational and analytical workflow to incorporate unmanned aerial systems (UAS) into the process of surveying and assessing crop health.  The proposed system has the potential to significantly red... C. Lum, M. Dunbabin, C. Shaw-feather, M. Mackenzie, E. Luker

111. Weather Impacts on UAV Flight Availability for Agricultural Purposes in Oklahoma

This research project analyzed 21 years of historical weather data from the Oklahoma Mesonet system.  The data examined the practicality of flying unmanned aircraft for various agricultural purposes in Oklahoma.  Fixed-wing and rotary wing (quad copter, octocopter) flight parameters were determined and their performance envelope was verified as a function of weather conditions.  The project explored Oklahoma’s Mesonet data in order to find days that are acceptable for fly... P. Weckler, C. Morris, B. Arnall, P. Alderman, J. Kidd, A. Sutherland

112. Safety and Certification Considerations for Expanding the Use of UAS in Precision Agriculture

The agricultural community is actively engaged in adopting new technologies such as unmanned aircraft systems (UAS) to help assess the condition of crops and develop appropriate treatment plans.  In the United States, agricultural use of UAS has largely been limited to small UAS, generally weighing less than 55 lb and operating within the line of sight of a remote pilot.  A variety of small UAS are being used to monitor and map crops, while only a few are being used to apply agricul... H. Verstynen, K. Hayhurst, J. Maddalon, N. Neogi

113. Early Detection of Nitrogen Deficiency in Corn Using High Resolution Remote Sensing and Computer Vision

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which of... D. Mulla, D. Zermas, D. Kaiser, M. Bazakos, N. Papanikolopoulos, P. Stanitsas, V. Morellas

114. Smart Agriculture: A Futuristic Vision of Application of the Internet of Things (IoT) in Brazilian Agriculture

With the economy based on agribusiness, Brazil is an important representative on the world stage in agricultural production, either in terms of quantity or cultivated diversity due to a scenario with vast arable land and favorable climate. There are many crops that are adapteble to soils of the country. Despite the global representation, it is known that the Brazilian agricultural production does not yet have a modern agriculture by restricting the use of new technologies to farmers with bett... C.L. Bazzi, R. Araujo, E.G. Souza, K. Schenatto, A. Gavioli, N.M. Betzek

115. Towards Data-intensive, More Sustainable Farming: Advances in Predicting Crop Growth and Use of Variable Rate Technology in Arable Crops in the Netherlands

Precision farming (PF) will contribute to more sustainable agriculture and the global challenge of producing ‘More with less’. It is based on the farm management concept of observing, measuring and responding to inter- and intra-field variability in crops. Computers enabled the use of Farm Management Information Systems (FMIS) and farm and field specific Decision Support Systems (DSS) since mid-1980s. GIS and GNSS allowed since ca. 2000 geo-referencing of data and controlled traff... C. Kempenaar, F. Van evert, T. Been, C. Kocks, K. Westerdijk, S. Nysten

116. Quo Vadis Precision Farming

The agriculture sector is a unique sector due to its strategic importance for both citizens and economy which, ideally, should make the whole sector a network of interacting organizations. There is an increasing tension, the like of which is not experienced in any other sector, between the requirements to assure full safety and keep costs under control, but also assure the long-term strategic interests of Europe and worldwide. In that sense, agricultural production influences, and is influenc... K. Charvat, T. Reznik, V. Lukas, K. Charvat jr., S. Horakova, M. Splichal, M. Kepka

117. Climate Smart Precision Nitrogen Management

Climate Smart Agriculture (CSA) aims at improving farm productivity and profitability in a sustainable way while building resilience to climate change and mitigating the impacts of agriculture on greenhouse gas emissions. The idea behind this concept is that informed management decision can help achieve these goals. In that matter, Precision Agriculture goes hand-in-hand with CSA. The Colorado State University Laboratory of Precision Agriculture (CSU-PA) is conducting research on CSA practice... L. Longchamps, R. Khosla, R. Reich

118. SMARTfarm Learning Hub: Next Generation Precision Agriculture Technologies for Agricultural Education

The industry demands on higher education agricultural students are rapidly changing. New precision agriculture technologies are revolutionizing the farming industry but the education sector is failing to keep pace. This paper reports on the development of a key resource, the SMARTfarm Learning Hub (www.smartfarmhub.com) that will increase the skill base of higher education students using a range of new agricultural technologies and innovations. The Hub is a world first; it links real industry... M. Trotter, S. Gregory, T. Trotter, T. Acuna, D. Swain, W. Fasso, J. Roberts, A. Zikan, A. Cosby

119. Field Phenotyping Infrastructure in a Future World - Quantifying Information on Plant Structure and Function for Precision Agriculture and Climate Change

Phenotyping in the field is an essential step in the phenotyping chain. Phenotyping begins in the well-defined, controlled conditions in laboratories and greenhouses and extends to heterogeneous, fluctuating environments in the field. Field measurements represent a significant reference point for the relevance of the laboratory and greenhouse approaches and an important source of information on potential mechanisms and constraints for plant performance tested at controlled conditions. In this... O. Muller, M.P. Cendrero mateo, H. Albrecht, F. Pinto, M. Mueller-linow, R. Pieruschka, U. Schurr, U. Rascher, A. Schickling, B. Keller

120. Ear Deployed Accelerometer Behaviour Detection in Sheep

An animal’s behaviour can be a clear indicator of their physiological and physical state. Therefore as resting, eating, walking and ruminating are the predominant daily activities of ruminant animals, monitoring these behaviours could provide valuable information for management decisions and individual animal health status. Traditional animal monitoring methods have relied on human labor to visually observe animals. Accelerometer technology offers the possibility of remotely monitoring ... J.D. Barwick, M. Trotter, D.W. Lamb, R. Dobos, M. Welch

121. In-season Diagnosis of Rice Nitrogen Status Using Crop Circle Active Canopy Sensor and UAV Remote Sensing

Active crop canopy sensors have been used to non-destructively estimate nitrogen (N) nutrition index (NNI) for in-season site-specific N management. However, it is time-consuming and challenging to carry the hand-held active crop sensors and walk across large paddy fields. Unmanned aerial vehicle (UAV)-based remote sensing is a promising approach to overcoming the limitations of proximal sensing. The objective of this study was to combine unmanned aerial vehicle (UAV)-based remote sensing sys... J. Lu, Y. Miao, Y. Huang, W. Shi

122. Open Data for Food Quality and Food Security Control: a Case Study of the Czech Republic

Food quality and food security is of a high public interest in the European Union. In the Czech Republic, food quality and food security is under control of three different public authorities: the Czech Trade Inspection Authority (CTIA) that is affiliated with the Ministry of Industry and Trade of the Czech Republic, the Czech Agriculture and Food Inspection Authority (CAFIA) that is affiliated with the Ministry of Agriculture of the Czech Republic and the regional network of hygienic station... M. Ulman, M. Stoces, J. Jarolimek, P. Simek

123. Agronomic Characteristics of Green Corn and Correlations with Productivity for the Establishment of Management Zones in Vale Do Ribeira, SP, Brazil

In Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphi... W.J. Souza, V.S. Akune, S.H. Benez, L.C. Citon, P.H. Nakazawa, A.J. Santana neto

124. Developing UAV Image Acquisition System and Processing Steps for Quantitative Use of the Data in Precision Agriculture

Mapping natural variability of crops and land is first step of the management cycle in terms of crop production. Several methods have been developed and engaged for data recording and analyzing that generate prescription maps such as yield monitoring, soil mapping, remote sensing etc. Although conventional remote sensing by capturing images via satellites has been very popular tool to monitor the earth surface, it has several drawbacks such as orbital period, unattended capture, investment co... A. Tekin, M. Fornale

125. Towards Calibrated Vegetation Indices from UAS-derived Orthomosaics

Crop advisors and farmers increasingly use drone data as part of their decision making. However, the vast majority of UAS-based vegetation mapping services support only the calculation of a relative NDVI derived from compressed JPEG pixel values and do not include the possibility to include more complex aspects like soil correction. In our ICPA12 contribution, we demonstrated the effects and consequences of the above shortcomings. Here, we present the stepwise development of a solution to ens... K. Pauly

126. Large-scale UAS Data Collection, Processing and Management for Field Crop Management

North Dakota State University research and Extension personnel are collaborating with Elbit Systems of America to compare the usefulness and economics of imagery collected from a large unmanned aircraft systems (UAS), small UAS and satellite imagery. Project personnel are using a large UAS powered with an internal combustion engine to collect high-resolution imagery over 100,000 acres twice each month during the crop growing season. Four-band multispectral Imagery is also being collected twic... J. Nowatzki, S. Bajwa, D. Roberts, M. Ossowski, A. Scheve, A. Johnson, Y. Chaplin

127. On Farm Studies to Determine Seeding Rate in Corn

Seeding rate (SDR) is one of the most critical production practices impacting productivity and economic return for corn (Zea mays L.) By changing SDRs in different zones within a field, herein termed as site-specific management, better economic results can be produced as the outcome of reducing SDRs in low productivity areas and increasing SDRs under high-yielding environments, relative to the uniform SDR management performed by the producer. The aim of this study was to analyze yield respons... G. Balboa, S. Varela, I. Ciampitti, S. Duncan, T. Maxwell, D. Shoups, A. Sharda

128. Closing Yield Gaps with GxExM and Precision Agriculture

There are many challenges to be faced by agriculture if the global population of nine billion people projected for 2050 is to be fed and clothed, especially given the effects of changing climate.  A focus on the interactions of genetics x environment x management (GxExM) offers potential for meeting the yield, and environment and economic sustainability goals that are integral to these challenges.  The yield gap –defined as the difference between current farmer yields and pote... C. Walthall, J. Hatfield, S. Schneider, M. Vigil

129. Small UAS Integrated Sensing Tools for Abiotic Stress Monitoring in Irrigated Pinto Beans

Precision agriculture is a practical approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of specific and high resolution crop data at critical growth stages is a key for real-time data driven decision support for precision agriculture management during the production season. The goal of this study was to evaluate the feasibility of using small unmanned aerial system (UAS) integrated remote sensing tools to monitor the abiotic stress of eight i... L. Khot, J. Zhou, R. Boydston, P.N. Miklas, L. Porter

130. High Resolution Vegetation Mapping with a Novel Compact Hyperspectral Camera System

The COSI-system is a novel compact hyperspectral imaging solution designed for small remotely piloted aircraft systems (RPAS). It is designed to supply accurate action and information maps related to the crop status and health for precision agricultural applications. The COSI-Cam makes use of a thin film hyperspectral filter technology which is deposited onto an image sensor chip resulting in a compact and lightweight instrument design. This paper reports on the agricultural monitor... B. Delauré, P. Baeck, J. Blommaert, S. Delalieux, S. Livens, A. Sima, M. Boonen, J. Goffart, G. Jacquemin, D. Nuyttens

131. Sensor-based Variable-rate N on Corn Reduced Nitrous Oxide Emissions

More nitrogen fertilizer is applied to corn than to all other U.S. crops combined, contributing to atmospheric heat trapping when nitrous oxide is produced.  Higher nitrogen rate is well known to increase nitrous oxide emissions, and earlier N application time may increase the window during which nitrous oxide can form.  An experiment was initiated in 2012 comparing nitrogen management and drainage effects on corn yield and nitrous oxide emissions.  Two nitrogen treatments... P. Scharf

132. Comparative Benefits of Drone Imagery for Nitrogen Status Determination in Corn

Remotely sensed vegetation data provide an effective means of measuring the spatial variability of nitrogen and therefore of managing applications by taking intrafield variations into account. Satellites, drones and sensors mounted on agricultural machinery are all technologies that can be used for this purpose. Although a drone (or unmanned aerial vehicle [UAV]) can produce very high-resolution images, the comparative advantages of this type of imagery have not been demonstrated. The goal of... N. Tremblay, K. Khun, P. Vigneault, M.Y. Bouroubi, F. Cavayas, C. Codjia

133. Precision Farming Basics Manual - a Comprehensive Updated Textbook for Teaching and Extension Efforts

Today precision agricultural technologies are limited by the lack of a workforce that is technology literate, creative, innovative, fully trained in their discipline, able to utilize and interpret information gained from information-age technologies to make smart management decisions, and have the capacity to convert locally collected information into practical solutions. As part of a grant entitled Precision Farming Workforce Development:  Standards, Working Groups, and Experimental Lea... K. Shannon

134. A Content Review of Precision Agriculture Courses Across the US

Knowledge of what precision agriculture (PA) content is currently taught across the United States will help build a better understanding for what PA instructors should incorporate into their classes in the future. The University of Missouri partnered with several universities throughout the nation on a USDA challenge grant. Precision Agriculture faculty from 24 colleges/universities from across the U.S. shared their PA content by sharing their syllabi from 43 different courses. The syllabi we... D. Skouby, L. Schumacher, M. Yost, N.R. Kitchen

135. Knowledge, Skills and Abilities Needed in the Precision Ag Workforce: an Industry Survey

Precision agriculture encompasses a set of related technologies aimed at better utilization of crop inputs, increasing yield and quality, reducing risks, and enabling information flow throughout the crop supply and end-use chains.  The most widely adopted precision practices have been automated systems related to equipment steering and precise input application, such as autoguidance and section controllers.  Once installed, these systems are relatively easy for farmers and their sup... B. Erickson, D.E. Clay, S.A. Clay, S. Fausti

136. Climate Sensitivity Analysis on Maize Yield on the Basis of Precision Crop Production

In this paper by prediction we have defined maize yield in precision plant production technologies according to five different climate change scenarios (Ensembles Project) until 2100 and in one scenario until 2075 using DSSAT v. 4.5.0. CERES-Maize decision support model. Sensitivity analyses were carried out. The novelty of the method presented here is that precision, variable rate technologies from relatively small areas (in our case 2500 m2) enable a large amount of data to be co... A. Nyeki, G. Milics, A.J. Kovacs, M. Neményi, J. Kalmar

137. Nitrogen Management in Lowland Rice

Rice is staple diet for more than fifty percent of the world population and nitrogen (N) deficiency is one of the major yields limiting constraints in most of the rice producing soils around the world. The lowland rice N recovery efficiency is <50% of applied fertilizers in most agro-ecological regions. The low N efficiency is associated with losses caused by leaching, volatilization, surface runoff, and denitrification. Hence, improving N use efficiency is crucial for higher yields, low c... N.K. Fageria, A.B. Santos

138. Prediction of Nitrogen Needs with Nitrogen-rich Strips and Ramped Nitrogen Strips

Both nitrogen rich strips and ramped nitrogen strips have been used to estimate topdress nitrogen needs for winter wheat based on in-season optical reflectance data. The ramped strip system places a series of small plots in each field with increasing levels of nitrogen to determine the application rate at which predicted yield response to nitrogen reaches a plateau. The nitrogen-rich strip system uses a nitrogen fertilizer optimization algorithm based on optical reflectance measures from the ... D.C. Roberts, B.W. Brorsen, W.R. Raun, J.B. Solie

139. Spatial Patterns of Nitrogen Response Within Corn Production Fields

Corn (Zea mays L.) yield response to nitrogen (N) application is critical to being able to develop management practices that reduce N application or improve N use efficiency. Nitrogen rate studies have been conducted within small plots; however, there have been few field scale evaluations. This study was designed to evaluate N response across 14 corn fields in central Iowa using different rates of N applied in strips across fields. These fields ranged in size from 15 to 130 ha with N... J.L. Hatfield

140. Developing Nitrogen Algorithms for Corn Production Using Optical Sensors

Remote sensing for nitrogen management in cereal crops has been an intensive research area due to environmental concerns and economic realities of today’s agronomic system. In the search for improved nitrogen rate decisions, what approach is most often taken and are those approaches justified through scientific investigation? The objective of this presentation is to educate decision makers on how these algorithms are developed and evaluate how well they work in the field on a small-plot... R.W. Mullen, S.B. Phillips, W.R. Raun, W.E. Thomason

141. Variability in Observed and Sensor Based Estimated Optimum N Rates in Corn

Recent research showed that active sensors such as Crop Circle can be used to estimate in-season N requirements for corn. The objective of this research was to identify sources of variability in the observed and Crop Circle-estimated optimum N rates. Field experiments were conducted at two locations for a total of five sites during the 2007 growing season using a randomized complete block design with increasing N rates applied at V6-V8 (NV6) as the treatment factor. Field sites were selected ... R.P. Sripada, J.P. Schmidt

142. Controller Performance Criteria for Sensor Based Variable Rate Application

Sensor based variable rate application of crop inputs provides unique challenges for traditional rate controllers when compared to map based applications. The controller set point is typically changing every second whereas with a map based systems the set point changes much less frequently. As applied data files for a sensor based variable rate nitrogen applicator were obtained from a wheat field in north central Oklahoma. These data were analyzed to determine the magnitude and frequency of r... R.K. Taylor, P. Bennur, J.B. Solie, N. Wang, P. Weckler, W.R. Raun

143. Experimental Study Using Wind Tunnel for Measuring Variability of Spray Drift Sedimentation

Spray drift is defined as physical movement of pesticides by air action as a particle droplet and is not deposited on the intended target. Evaluation of the parameters affecting on spray drift is difficult. The accurate studies are expensive, as well as, the variability is high under field conditions due to instability in wind speed and turbulence. Wind tunnel experiments are adequate to simulate the results of field measurements for spray drift. A laboratory experiments were carried out to s... M.H. Alheidary, J. Douzals, C. Sinfort

144. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the Tree

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for s... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli

145. Using Drone Based Sensors to Direct Variable-Rate, In-Season, Aerial Nitrogen Application on Corn

Improving nutrient management on farms is a critical issue nationwide. Applying a portion of N fertilizer during the growing season, alongside the growing corn crop is one way to improve nitrogen management. Sidedress N applications allow the availability of N fertilizer to more closely match the time when the crop is rapidly uptaking N. Additionally, waiting to apply a portion of the N during the growing season allows for management which is responsive to current growing season conditions.... L.J. Thompson

146. Digital Transformation of Canadian Agri-Food

Agriculture in Canada is on the cusp of a dramatic revolution as a result of the digital transformation of the industry driven by the emergence of tools such as Precision Agri-Food Technologies and the Internet of Things (IoT, a network of interconnected physical devices capable of connecting to the internet). With the expected exponential growth of data from the application of innovative technologies such as IoT by the Canadian Agri-Food industry, Canada has the potential to gain valuable in... K.J. Hand

147. Automated Segmentation and Classification of Land Use from Overhead Imagery

Reliable land cover or habitat maps are an important component of any long-term landscape planning initiatives relying on current and past land use. Particularly in regions where sustainable management of natural resources is a goal, high spatial resolution habitat maps over large areas will give guidance in land-use management. We propose a computational approach to identify habitats based on the automated analysis of overhead imagery. Ultimately, this approach could be used to assist expert... C. Pradalier, A. Richard, V. Perez, R. Van couwenberghe, A. Benbihi, P. Durand

148. Identifying and Filtering Out Outliers in Spatial Datasets

Outliers present in the dataset is harmful to the information quality contained in the map and may lead to wrong interpretations, even if the number of outliers to the total data collected is small. Thus, before any analysis, it is extremely important to remove these errors. This work proposes a sequential process model capable of identifying outlier data when compared their neighbors using statistical parameters. First, limits are determined based on the median range of the values of all the... L. Maldaner, J. Molin, T. Tavares, L. Mendez, L. Corrêdo, C. Duarte

149. Effective Use of a Debris Cleaning Brush for Mechanical Wild Blueberry Harvesting

Wild blueberries are an important horticultural crop native to northeastern North America. Management of wild blueberry fields has improved over the past decade causing increased plant density and leaf foliage. The majority of wild blueberry fields are picked mechanically using tractor mounted harvesters with 16 rotating rakes that gently comb through the plants. The extra foliage has made it more difficult for the cleaning brush to remove unwanted debris (leaf, stems, weeds, etc.) from the p... K. Esau, Q. Zaman, A. Farooque, A. Schumann

150. Three Years of On-Farm Evaluation of Dynamic Variable Rate Irrigation: What Have We Learned?

This paper will present a dynamic Variable Rate Irrigation System developed by the University of Georgia. The system consists of the EZZone management zone delineation tool, the UGA Smart Sensor Array (UGA SSA) and an irrigation scheduling decision support tool. An experiment was conducted in 2015, 2016 and 2017 in two different peanut fields to evaluate the performance of using the UGA SSA to dynamically schedule Variable Rate Irrigation (VRI). For comparison reasons strips were designed wit... V. Liakos, W. Porter, X. Liang, M. Tucker, A. Mclendon, C. Perry, G. Vellidis

151. Rapid Identification of Mulberry Leaf Pests Based on Near Infrared Hyperspectral Imaging

As one of the most common mulberry pests, Diaphania pyloalis Walker (Lepidoptera: Pyralididae) has occurred and damaged in the main sericulture areas of China. Naked eye observation, the most dominating method identifying the damage of Diaphania pyloalis, is time-wasting and labor consuming. In order to improve the identification and diagnosis efficiency and avoid the massive outbreak of Diaphania pyloalis, near infrared (NIR) hyperspectral imaging technology combined with partial least discr... L. Yang, L. Huang, L. Meng, J. Wang, D. Wu, X. Fu, S. Li

152. Development of a High Resolution Soil Moisture for Precision Agriculture in India

Soil moisture and temperature are key inputs to several precision agricultural applications such as irrigation scheduling, identifying crop health, pest and disease prediction, yield and acreage estimation, etc.  The existing remote sensing satellites based soil moisture products such as SMAP are of coarse resolution and physics based land surface model such as NLDAS, GLDAS are of coarse resolution as well as not available for real time applications.  Keeping this in focus, we are d... K. Das, J. Singh, J. Hazra

153. Agricultural Remote Sensing Information for Farmers in Germany

The European Copernicus program delivers optical and radar satellite imagery at a high temporal frequency and at a ground resolution of 10m worldwide with an open data policy. Since July 2017 the satellite constellation of the Sentinel-1 and -2 satellites is fully operational, allowing e.g. coverage of Germany every 1-2 days by radar and every 2-3 days with optical sensors. This huge data source contains a variety of valuable input information for farmers to monitor the in-field variability a... H. Lilienthal, H. Gerighausen, E. Schnug

154. Optimum Spatial Resolution for Precision Weed Management

The occurrence and number of herbicide-resistant weeds in the world has increased in recent years. Controlling these weeds becomes more difficult and raises production costs. Precision spraying technologies have been developed to overcome this challenge. However, these systems still have relatively high acquisition cost, requiring studies of the relation between the spatial distribution of weeds and the economically optimum spatial resolution of the control method. In this context, the object... R.G. Trevisan, M.T. Eitelwein, M.N. Ferraz, T.R. Tavares, J.P. Molin, D.C. Neves

155. Optimal Sensor Placement for Field-Wide Estimation of Soil Moisture

Soil moisture is one of the most important parameters in precision agriculture. While techniques such as remote sensing seems appropriate for moisture monitoring over large areas, they generally do not offer sufficiently fine resolution for precision work, and there are time restrictions on when the data is available. Moreover, while it is possible to get high resolution-on demand data, but the costs are often prohibitive for most developing countries. Direct ground level measuremen... H. Pourshamsaei, A. Nobakhti

156. Utilization of Spatially Precise Measurements to Autocalibrate the EPIC Agroecosystem Model

Corn nitrogen recommendations for individual fields must improve to minimize the negative influence that agriculture has on the environment and society. Two adaptive N management approaches for making in-season N fertilizer recommendations are remote sensing and crop systems modeling. Remote sensing has the advantage of characterizing the spatial variability at a high spatial resolution, and crop models are prognostic and can assess expected additions and losses that are not yet reflected by ... T. Nigon, D. Mulla, C. Yang

157. Corn Nitrogen Fertilizer Recommendation Models Based on Soil Hydrologic Groups Aid in Predicting Economically Optimal Nitrogen Rates

Nitrogen (N) fertilizer recommendations that match corn (Zea mays L.) N needs maximize grower profits and minimize water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement is consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account fo... G.M. Bean, N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf

158. Active Canopy Sensors for the Detection of Non-Responsive Areas to Nitrogen Application in Wheat

Active canopy sensors offer accurate measurements of crop growth status that have been used in real time to estimate nitrogen (N) requirements. NDVI can be used to determine the absolute amount of fertilizer requirement, or simply to distribute within the field an average rate defined by decision models using other diagnostics. The objective of this work was to evaluate the capacity of active canopy sensors to determine yield and N application requirements within a site at jointing stage (Fee... A.G. Berger, E. Hoffman, N. Fassana, F. Alfonso

159. A Case Study Comparing Machine Learning and Vegetation Indices for Assessing Corn Nitrogen Status in an Agricultural Field in Minnesota

Compact hyperspectral sensors compatible with UAV platforms are becoming more readily available. These sensors provide reflectance in narrow spectral bands while covering a wide range of the electromagnetic spectrum. However, because of the narrow spectral bands and wide spectral range, hyperspectral data analysis can benefit greatly from data mining and machine learning techniques to leverage its power. In this study, rainfed corn was grown during the 2017 growing season using four nitrogen ... A. Laacouri, T. Nigon, D. Mulla, C. Yang

160. Weed Detection Among Crops by Convolutional Neural Networks with Sliding Windows

One of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neu... K. Kantipudi, C. Lai, C. Min, R.C. Chiang

161. Changing the Cost of Farming: New Tools for Precision Farming

Accurate prescription maps are essential for effective variable rate fertilizer application.  Grid soil sampling has most frequently been used to develop these prescription maps.  Past research has indicated several technical and economic limitations associated with this approach.  There is a need to keep the number of samples to a minimum while still allowing a reasonable level of map quality.  As can be seen, precision agriculture managemen... P. Nagel, K. Fleming

162. On-Farm Digital Solutions and Their Associated Value to North American Farmers

Digital tools and data collection have become standard in a wide variety of present day agricultural operations. An array of digital tools, such as high resolution operational mapping, remote sensing, and farm management software offer solutions to many of the problems in modern agriculture. These technologies and services can, if implemented correctly, provide both immediate and long term agronomic value. A growing number of producers in Ohio and around North America question the proper meth... R. Colley iii, J. Fulton, N. Douridas, K. Port

163. An Efficient Data Warehouse for Crop Yield Prediction

Nowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-pr... V.M. Ngo, N. Le-khac, M. Kechadi

164. Real-Time Control of Spray Drop Application

Electrostatic application of spray drops provides unique opportunities to precisely control the application of pesticides due to the additional electrostatic force on the spray drops, in addition to the normally seen forces of aerodynamic drag, gravity, and inertia. In this work, we develop a computational model to predict the spray drop trajectories. The model is validated through experiments with high speed photography of spray drop trajectories, and quantification of which trajectories lea... S. Post, M. Jermy, P. Gaynor, N. Kabaliuk, A. Werner

165. Reverse Modelling of Yield-Influencing Soil Variables in Case of Few Soil Data

Our hypothesis was that simple models can be applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models can be used to test different sampling and interpolation approaches commonly applied in precision agriculture and to better predict soil variables at not observed locations. Three strategies for composite sample collection were compared in our study. Point samples were taken 1.) along lines within homogenou... I. Sisák, A. Benő, K. Szabó, M. Kocsis, J. Abonyi

166. AgDataBox – API (Application Programming Interface)

E-agricultural is an emerging field focusing in the enhancement of agriculture and rural development through improve in information and data processing. The data-intensive characteristic of these domains is evidenced by the great variety of data to be processed and analyzed. Countrywide estimates rely on maps, spectral images from satellites, and tables with rows for states, regions, municipalities, or farmers. Precision agriculture (PA) relies on maps of within field variability of soil and ... C.L. Bazzi, E.P. Jasse, E.G. Souza, P.S. Magalhães, G.K. Michelon, K. Schenatto, A. Gavioli

167. Accelerating Precision Agriculture to Decision Agriculture: Enabling Digital Agriculture in Australia

For more than two decades, the success of Australia’s agricultural and rural sectors has been supported by the work of the Rural Research and Development Corporations (RDCs). The RDCs are funded by industry and government. For the first time, all fifteen of Australia’s RDC’s have joined forces with the Australian government to design a solution for the use of big data in Australian agriculture. This is the first known example of a nationwide approach for the digital transfor... J. Trindall, R. Rainbow

168. Spatial Variability of Optimized Herbicide Mixtures and Dosages

Driven by 25 years of Danish, political 'pesticide action plans', aiming at reducing the use of pesticides, a Danish Decision Support System (DSS) for Integrated Weed Management (IWM) has been constructed. This online tool, called ‘IPMwise’ is now in its 4th generation. It integrates the 8 general IPM-principles as defined by the EU. In Denmark, this DSS includes 30 crops, 105 weeds and full assortments of herbicides. Due to generic qualities in both the integrat... P. Rydahl, R.N. Jorgensen, M. Dyrmann, N. Jensen, M.D. Sorensen, O.M. Bojer, P. Andersen

169. Optimized Soil Sampling Location in Management Zones Based on Apparent Electrical Conductivity and Landscape Attributes

One of the limiting factors to characterize the soil spatial variability is the need for a dense soil sampling, which prevents the mapping due to the high demand of time and costs. A technique that minimizes the number of samples needed is the use of maps that have prior information on the spatial variability of the soil, allowing the identification of representative sampling points in the field. Management Zones (MZs), a sub-area delineated in the field, where there is relative homogeneity i... G.K. Michelon, G.M. Sanches, I.Q. Valente, C.L. Bazzi, P.L. De menezes, L.R. Amaral, P.G. Magalhaes

170. Detecting Basal Stem Rot (BSR) Disease at Oil Palm Tree Using Thermal Imaging Technique

Basal stem rot (BSR), caused by Ganoderma boninense is known as the most damaging disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoderma could prevent production losses and reduce the cost of plantation management. This study focuses on identifying the thermal properties of healthy and BSR-infected tree using a thermal ima... S. Bejo, G. Abdol lajis, S. Abd aziz, I. Abu seman, T. Ahamed

171. Using a UAV-Based Active Canopy Sensor to Estimate Rice Nitrogen Status

Active canopy sensors have been widely used in the studies of crop nitrogen (N) estimation as its suitability for different environmental conditions. Unmanned aerial vehicle (UAV) is a low-cost remote sensing platform for its great flexibility compared to traditional ways of remote sensing. UAV-based active canopy sensor is expected to take the advantages of both sides. The objective of this study is to determine whether UAV-based active canopy sensor has potential for monitoring rice N statu... S. Li, Q. Cao, X. Liu, Y. Tian, Y. Zhu

172. Deriving Fertiliser VRA Calibration Based on Ground Sensing Data from Specific Field Experiments

Nitrogen (N) fertilisation affects both rice yield and quality. In order to improve grain yield while limiting N losses, providing N fertilisers during the critical growth stages is essential. NDRE is considered a reliable crop N status indicator, suitable to drive topdressing N fertilisation in rice. A multi-year experiment on different rice varieties (Gladio, Centauro, and Carnaroli) was conducted between 2011 and 2017 in Castello d’Agogna (PV), northwest Italy, with the aim of i) est... E. Cordero, D. Sacco, B. Moretti, E.F. Miniotti, D. Tenni, G. Beltarre, M. Romani, C. Grignani

173. Optimal Placement of Proximal Sensors for Precision Irrigation in Tree Crops

In agriculture, use of sensors and controllers to apply only the quantity of water required, where and when it is needed (i.e., precision irrigation), is growing in importance. The goal of this study was to generate relatively homogeneous management zones and determine optimal placement of just a few sensors within each management zone so that reliable estimation of plant water status could be obtained to implement precision irrigation in a 2.0 ha almond orchard located in California, USA. Fi... C.L. Bazzi, K. Schenatto, S. Upadhyaya, F. Rojo

174. Active and Passive Sensor Comparison for Variable Rate Nitrogen Determination and Accuracy in Irrigated Corn

The objectives of this research were to (i) compare active and passive crop canopy sensors’ sidedress variable rate nitrogen (VRN) derived from different vegetation indices (VI) and (ii) assess VRN recommendation accuracy of active and passive sensors as compared to the agronomic optimum N rate (AONR) in irrigated corn. This study is comprised of six site-years (SY), conducted in 2015, 2016 and 2017 on different soil types (silt loam, loam and sandy loam) and with a range of preplant-ap... L. Bastos, R.B. Ferguson

175. Use of Field Diagnostic Tools for Top Dressing Nitrogen Recommendation When Organic Manures Are Applied in Humid Mediterranean Conditions

Nitrogen is often applied in excessive quantities, causing nitrogen losses. In recent years, the management of large quantities of manure and slurry compounds has become a challenge. The aim of this study was to assess the usefulness of the proxy tools Yara N-testerTMand RapidScan CS-45 for diagnosing the N nutritional status of wheat crops when farmyard manures were applied. Our second objective was to start designing a N fertilization strategy based on these measurements. To achieve these o... A. Castellón, A. Aizpurua, M. Aranguren

176. Prediction of Corn Economic Optimum Nitrogen Rate in Argentina

Static (i.e. texture and soil depth) and dynamic (i.e. soil water, temperature) factors play a role in determining field or subfield economically optimal N rates (EONR). We used 50 nitrogen (N) trials from Argentina at contrasting landscape positions and soil types, various soil-crop measurements from 2012 to 2017, and statistical techniques to address the following objectives: a) characterize corn yield and EONR variability across a multi-landscape-year study in central west Buenos Aire... L. Puntel, A. Pagani, S. Archontoulis

177. Field Test of a Satellite-Based Model for Irrigation Scheduling in Cotton

Cotton irrigation in Israel began in the mid-1950s. It is based on an irrigation protocol developed over dozens of years of cotton farming in Israel, and proved to provide among the world's best cotton yield results. In this experiment, we examined the use of an irrigation recommendation system that is based on satellite imagery and hyper-local meteorological data, "Manna treatment", compared to the common irrigation protocols in Israel, which use a crop coefficient (Kc) table a... O. Beeri, S. May-tal, J. Raz, R. Rud

178. Predicted Nitrate-N Loads for Fall, Spring, and VRN Fertilizer Application in Southern Minnesota

Nitrate-N from agricultural fields is a source of pollution to fresh and marine waters via subsurface tile drainage.  Sensor-based technologies that allow for in-season monitoring of crop nitrogen requirements may represent a way to reduce nitrate-N loadings to surface waters by allowing for fertilizer application on a more precise spatial and temporal resolution.  However, little research has been done to determine its effectiveness in reducing nitrate-N losses.  In this study... G.L. Wilson, D.J. Mulla, J. Galzki, A. Laacouri, J. Vetsch

179. Variable Selection and Data Clustering Methods for Agricultural Management Zones Delineation

Delineation of agricultural management zones (MZs) is the delimitation, within a field, of a number of sub-areas with high internal similarity in the topographic, soil and/or crop characteristics. This approach can contribute significantly to enable precision agriculture (PA) benefits for a larger number of producers, mainly due to the possibility of reducing costs related to the field management. Two fundamental tasks for the delineation of MZs are the variable selection and the cluster anal... A. Gavioli, E.G. Souza, C.L. Bazzi, N.M. Betzek, K. Schenatto

180. Improving Corn Nitrogen Rate Recommendations Through Tool Fusion

 Improving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation ... C.J. Ransom, N.R. Kitchen, J.J. Camberato, P.R. Carter, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J. Shanahan, J.E. Sawyer

181. Utilizing Weather, Soil, and Plant Condition for Predicting Corn Yield and Nitrogen Fertilizer Response

Improving corn (Zea mays L.) nitrogen (N) fertilizer rate recommendation tools should increase farmer’s profits and help mitigate N pollution. Weather and soil properties have repeatedly been shown to influence crop N need. The objective of this research was to improve publicly-available N recommendation tools by adjusting them with additional soil and weather information. Four N recommendation tools were evaluated across 49 N response trials conducted in eight U.S. states over three gr... N.R. Kitchen, M.A. Yost, C.J. Ransom, G. Bean, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer

182. Using Geospatial Data to Assess How Climate Change May Affect Land Suitability for Agriculture Production

Finding solutions to the challenge of sustainably feeding the world’s growing population is a pressing research need that cuts across many disciplines including using geospatial data. One possible area could be developing agricultural frontiers. Frontiers are defined as land that is currently not cultivated but that may become suitable for agriculture under climate change. Climate change may drive large-scale geographic shifts in agriculture, including expansion in cultivation at the th... K. Kc, L. Hannah, P. Roehrdanz, C. Donatti, E. Fraser, A. Berg, L. Saenz, T.M. Wright, R.J. Hijmans, M. Mulligan

183. Pest Detection on UAV Imagery Using a Deep Convolutional Neural Network

Presently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin

184. Forecasting Crop Yield Using Multi-Layered, Whole-Farm Data Sets and Machine Learning

The ultimate goal of Precision Agriculture is to improve decision making in the business of farming. Many broadacre farmers now have a number of years of crop yield data for their fields which are often augmented with additional spatial data, such as apparent soil electrical conductivity (ECa), soil gamma radiometrics, terrain attributes and soil sample information. In addition there are now freely available public datasets, such as rainfall, digital soil maps and archives of satellite remote... P. Filippi, E.J. Jones, M. Fajardo, B.M. Whelan, T.F. Bishop

185. Field Grown Apple Nursery Tree Plant Counting Based on Small UAS Imagery Derived Elevation Maps

In recent years, growers in the state are transitioning to new high yielding, pest and disease resistant cultivars. Such transition has created high demand for new tree fruit cultivars. Nursery growers have committed their incoming production of the next few years to meet such high demands. Though an opportunity, tree fruit nursery growers must grow and keep the pre-sold quantity of plants to supply the amount promised to the customers. Moreover, to keep the production economical amidst risin... M. Martello, J.J. Quirós, L. Khot

186. Development of an Overhead Optical Yield Monitor for a Sugarcane Harvester in Louisiana

A yield monitor is a device used to measure harvested crop weight per unit area for a specific location within a field.  The device documents yield variability in harvested fields and ultimately can be used to create a geographical-referenced yield map. Yield maps can be used to identify low yielding areas where poor soil fertility, disease, or pests may adversely affect yield.  Management practices can then be adjusted to correct these issues, resulting in an increase in yields and... R.R. Price, R.M. Johnson, R.P. Viator

187. Optimising Nitrogen Use in Cereal Crops Using Site-Specific Management Classes and Crop Reflectance Sensors

The relative cost of Nitrogen (N) fertilisers in a cropping input budget, the 33% Nitrogen use efficiency (NUE) seen in global cereal grain production and the potential environmental costs of over-application are leading to changes in the application rates and timing of N fertiliser. Precision agriculture (PA) provides tools for producers to achieve greater synchrony between N supply and crop N demand. To help achieve these goals this research has explored the use of management classes derive... B. Whelan, M. Fajardo

188. AgronomoBot: A Smart Answering Chatbot Applied to Agricultural Sensor Networks

Mobile devices advanced adoption has fostered the creation of various messaging applications providing convenience and practicality in general communication. In this sense, new technologies arise bringing automatic, continuous and intelligent features for communication through messaging applications by using web robots, also called Chatbots. Those are computer programs that simulate a real conversation between humans to answer questions or do tasks, giving the impression that the person is ta... G.M. Mostaço, L.B. Campos, C.E. Cugnasca, I.R. Souza

189. Levels of Inclusion of Crambe Meal (Crambe Abyssinica Hochst) in Sheep Diet on the Balance of Nitrogen and Ureic Nitrogen in the Blood Serum

Crambe meal, which is a co-product of biodiesel production, is a potential substitute for conventional protein sources in ruminant diets. The objective of this study was to evaluate the effect of the substitution of crude protein of the concentrate by crude protein of crambe meal with increasing levels (0, 25, 50, and 75%) on nitrogen balance and blood plasma urea nitrogen concentration in sheep. Four male sheep, rumen fistulated, were placed in metabolic crates and distributed in a 4 x 4 Lat... K.K. De azevedo, D.M. Figueiredo, M.G. De sousa, G.M. Dallago, R.R. Silveira, L.D. Da silva, L.N. Rennó, R.A. Santos

190. Evaluating Remote Sensing Based Adaptive Nitrogen Management for Potato Production

Conventional nitrogen (N) management for potato production in the Upper Midwest, USA relies on using split-applications of N fertilizer or a controlled release N product. Using remote sensing to adaptively manage N applications has the potential to improve N use efficiency and reduce losses of nitrate to groundwater, which are important regional concerns. A two-year plot-scale experiment was established to evaluate adaptive N-management using remote sensing compared to conventional practices ... B. Bohman, D. Mulla, C. Rosen

191. Improving the Precision of Maize Nitrogen Management Using Crop Growth Model in Northeast China

The objective of this project was to evaluate the ability of the CERES-Maize crop growth model to simulate grain yield response to plant density and N rate for two soil types in Northeast China, with the long-term goal of using the model to identify the optimum plant density and N fertilizer rate forspecific site-years. Nitrogen experiments with six N rates, three plant densities and two soil types were conducted from 2015 to 2017 in Lishu county, Jilin Province in Northeast China. The CERES-... X. Wang, Y. Miao, W.D. Batchelor, R. Dong, D.J. Mulla

192. Improving Active Canopy Sensor-Based In-Season N Recommendation Using Plant Height Information for Rain-Fed Maize in Northeast China

The inefficient utilization of nitrogen (N) fertilizer due to leaching, volatilization and denitrification has resulted in environmental pollution in rain-fed maize production in Northeast China. Active canopy sensor-based in-season N application has been proven effective to meet maize N requirement in space and time. The objective of this research was to evaluate the feasibility of using active canopy sensor for guiding in in-season N fertilizer recommendation for rain-fed maize in Northeast... X. Wang, Y. Miao, T. Xia, R. Dong, G. Mi, D.J. Mulla

193. Spatial Decision Support System: Controlled Tile Drainage – Calculate Your Benefits

Climate projection studies suggest that extreme heat waves and floods will become more frequent, affecting future crop yields by 20%-30%, globally. Managing vulnerability and risk begins at the farm level where best management practices can reduce the impacts associated with extreme weather events. A practice that can assist in mitigating the impact of some extreme events is controlled tile drainage (CTD). With CTD, producers use water flow control structures to manage the drainage of water f... A. Kross, G. Kaur, D. Callegari, D. Lapen, M. Sunohara, H. Mcnairn, H. Rudy, L. Van vliet

194. Application of Routines for Automation of Geostatistical Analysis Procedures and Interpolation of Data by Ordinary Kriging

Ordinary kriging (OK) is one of the most suitable interpolation methods for the construction of thematic maps used in precision agriculture. However, the use of OK is complex. Farmers/agronomists are generally not highly trained to use geostatistical methods to produce soil and plant attribute maps for precision agriculture and thus ensure that best management approaches are used. Therefore, the objective of this work was to develop and apply computational routines using procedures and geosta... N.M. Betzek, E.G. Souza, C.L. Bazzi, P.G. Magalhães, A. Gavioli, K. Schenatto, R.W. Dall'agnol

195. Precision Irrigation Management Through Conjunctive Use of Treated Wastewater and Groundwater in Oman

Agriculture under arid environment is always become a challenge due to water scarcity and salinity problems.  With average rainfall of 100 mm, agriculture in Oman is limited due to the arid climate and limited arable lands. More than 50 percent of the arable lands are located in the 300 km northern coastal belt of Al-Batinah region. In addition, country is facing severe problem of sea water intrusion into the groundwater aquifers due to undisciplined excessive groundwater (GW) abstractio... H. Jayasuriya, A. Al-busaidi, M. Ahmed

196. Shared Protocols and Data Template in Agronomic Trials

Due to the overlap of many disciplines and the availability of novel technologies, modern agriculture has become a wide, interdisciplinary endeavor, especially in Precision Agriculture. The adoption of a standard format for reporting field experiments can help researchers to focus on the data rather than on re-formatting and understanding the structure of the data. This paper describes how a European consortium plans to: i) create a “handbook” of protocols for reporting definition... D. Cammarano, D. Drexler, P. Hinsinger, P. Martre, X. Draye, A. Sessitsch, N. Pecchioni, J. Cooper, W. Helga, A. Voicu

197. Improving the Use of Artificial Neural Networks for 
Site-Specific Nitrogen Fertilization

For the planning of site-specific nitrogen fertilization, adequate decision rules are needed. Prerequisite for site specific nitrogen fertilization is the site specific forecast of yield. For this the use of artificial neural networks (ANN) has proven particularly interesting. Therefore, ANN based small-scale yield forecasts are realized in order to deviate the economic optimum of fertilization. The basis of yield forecasts with ANN are different site-specific input variables that have presum... J.S. Hauser, P. Wagner

198. Data Clustering Tools for Understanding Spatial Heterogeneity in Crop Production by Integrating Proximal Soil Sensing and Remote Sensing Data

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively hom... M. Saifuzzaman, V.I. Adamchuk, H. Huang, W. Ji, N. Rabe, A. Biswas

199. Overview and Value of Digital Technologies for North American Soybean Producers

In the current state of digital agriculture, many digital technologies and services are offered to assist North American soybean producers.  Opportunities for capturing and analyzing information related to soybean production methods are made available through the adoption of these technologies.  However, often it is difficult for producers to know which digital tools and services are available to them or understand the value they can provide.  The objective of th... J. Lee, J. Fulton, K. Port, R. Colley iii

200. Precision Nitrogen and Water Management for Enhancing Efficiency and Productivity in Irrigated Maize

Nitrogen and water continue to be the most limiting factors for profitable maize production in the western Great Plains. The objective of this research was to determine the most productive and efficient nitrogen and water management strategies for irrigated maize.  This study was conducted in 2016 at Colorado State University’s Agricultural Research Development and Educational Center, in Fort Collins, Colorado. The experiment included a completely randomized block design with ... E. Phillippi, R. Khosla, L. Longchamps, P. Turk

201. Data-Driven Agricultural Machinery Activity Anomaly Detection and Classification

In modern agriculture, machinery has become the one of the necessities in providing safe, effective and economical farming operations and logistics. In a typical farming operation, different machines perform different tasks, and sometimes are used together for collaborative work. In such cases, different machines are associated with representative activity patterns, for example, in a harvest scenario, combines move through a field following regular swaths while grain carts follow irregular pa... Y. Wang, A. Balmos, J. Krogmeier, D. Buckmaster

202. ADAPT: A Rosetta Stone for Agricultural Data

Modern farming requires increasing amounts of data exchange among hardware and software systems. Precision agriculture technologies were meant to enable growers to have information at their fingertips to keep accurate farm records (and calculate production costs), improve decision-making and promote effi­cien­cies in crop management, enable greater traceability, and so forth. The attainment of these goals has been limited by the plethora of proprietary, incompatible data formats among... D.D. Danford, K.J. Nelson, S.T. Rhea, M.W. Stelford, R. Ferreyra, J.A. Wilson, B.E. Craker

203. Development of an Online Decision-Support Infrastructure for Optimized Fertilizer Management

Determination of an optimum fertilizer application rate involves various influential factors, such as past management, soil characteristics, weather, commodity prices, cost of input materials and risk preference. Spatial and temporal variations in these factors constitute sources of uncertainties in selecting the most profitableapplication rate. Therefore, a decision support system (DSS) that could help to minimize production risks in the context of uncertain crop performance is needed. ... S. Shinde, V. Adamchuk, R. Lacroix, N. Tremblay, Y. Bouroubi

204. Analysis of Soil Properties Predictability Using Different On-the-Go Soil Mapping Systems

Understanding the spatial variability of soil chemical and physical attributes allows for the optimization of the profitability of nutrient and water management for crop development. Considering the advantages and accessibility of various types of multi-sensor platforms capable of acquiring large sensing data pertaining to soil information across a landscape, this study compares data obtained using four common soil mapping systems: 1) topography obtained using a real-time kinematic (RTK) glob... H. Huang, V. Adamchuk, A. Biswas, W. Ji, S. Lauzon

205. Analyzing Trends for Agricultural Decision Support System Using Twitter Data

The trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmers’ opinions and a method ... S. Jha, D. Saraswat, M.D. Ward

206. GIS Web and Mobile Development with Interfaces in QGIS for Variable Rate Fertilization

In this paper we described the implementation of a GIS for Precision Agriculture for sugarcane crop in Colombia. An spatial equation for Variable Rate Fertilization Model was defined using as inputs estimated harvest data, nutrients in soil and fertilizer efficiently. Models for soil and harvest variability are also defined. A personalized plugin for precision agriculture was developed into QGIS software, there is the option of upload maps to a Web and mobile app using the Desktop software an... R. Cuitiva baracaldo, O. Munar vivas, G. Carrillo romero

207. Practical Prescription of Variable Rate Fertilization Maps Using Remote Sensing Based Yield Potential

This paper describes a practical approach for the prescription of variable rate fertilization maps using remote sensing data (RS) based on satellite platforms, Landsat 8 and Sentinel-2 constellation. The methodology has been developed and evaluated in Albacete, Spain, in the framework of the project FATIMA (http://fatima-h2020.eu/). The global approach considers the prescription of N management prior to the growing season, based on a spatially distributed N balance. Although the diagnosis of ... A. Osann, I. Campos, M. Calera, C. Plaza, V. Bodas, A. Calera, J. Villodre, J. Campoy, S. Sanchez, N. Jimenez, H. Lopez

208. Experiences in the Development of Commercial Web-Based Data Engines to Support UK Growers Within an Industry-Academic Partnership

The lifecycle of Precision Agriculture data begins the moment that the measurement is taken, after which it may pass through each multiple data processes until finally arriving as an output employed back in the production system. This flow can be hindered by the fact that many farm datasets have different spatial resolutions. This makes the process to aggregate or analyse multiple Precision Agriculture layers arduous and time consuming.  Precision Decisions Ltd located in Yorks... J. Taylor, Y. Shahar, P. James, C. Blacker, S. Leese, R. Sanderson, R. Kavanagh

209. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing Data

This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the succ... D. Li, H. Jiang, S. Chen, C. Wang

210. Evaluation of Indwelling Rumen Temperature Monitoring System for Dairy Calf Illness Detection and Management

Precision Dairy Farming technology has mostly focused on tools to improve cow care, but new tools are available to improve the care of pre-wean calves and heifers. These technologies apply real-time monitoring to measure individual animal data and detect a deviation from normal. On-farm validation of new technologies remains important for successful deployment of new technologies within commercial farms to understand how the technology can improve dairy calf welfare, performance, and health. ... J.M. Hartschuh, J.P. Fulton, S.A. Shearer, B.D. Enger, G.M. Schuenemann

211. Transforming Precision Agriculture Education, Research and Outreach in Sub-saharan Africa Through Intra-africa Cooperation

Productivity and profitability of sub-Saharan (SSA) agriculture can be enhanced greatly through the adoption of precision agriculture technologies and tools. However, until 2020 when the African Plant Nutrition Institute (APNI) established the African Association for Precision Agriculture (AAPA), most SSA PA enthusiast worked in isolation.  The AAPA was formed to innovate Africa’s agricultural industry by connecting PA science to its practice and disseminate PA tailored to the need... K.A. Frimpong, S. Phillips, V. Aduramigba-modupe, N. Fassinou hotegni, M. Mechri, M. Mishamo, J.M. Sogbedji, Z. hazzoumi, R. Chikowo, M. Fodjo kamdem

212. A Case for Increased Precision Pesticide Application Adoption in California Perennial Specialty Crop Production

Maintaining high and quality crop yields in California’s diverse agriculture requires both good crop care through nutrient management and water management and effective crop protection through integrated pest management (IPM). Despite the promotion and adoption of non-chemical IPM practices in California such as sanitation and biological control, pesticide use remains inevitable in many cases. According to the 2021 California Pesticide Use Report, 37,444,331 kg of pesticide was used in ... P.A. Larbi

213. Pesticide Application Management Toolset for Improved Worker Protection

The practice of pesticide use has been widely adopted by production agriculture to maximize yields since the 1950s. Even though it provides beneficial economic returns to the farmers, it also enhances the risk of environmental pollution and is directly associated with the risk of poisoning to agricultural workers. While adhering to United States Federal Environmental Protection Agency (EPA) Worker Protection Standard (WPS) guidelines, the current systems need considerable time to provide cruc... C. Narayana, N. Thorson, J.D. Luck

214. Participatory Irrigation Extension Programs to Increasing Adoption of Best Irrigation Strategies

Farmers in Alabama, Tennessee, and other US southeastern states lack experience in irrigation water management and adoption of the state-of-the-art technologies and practices to increase irrigation water use efficiency. Several federal and state-funded projects are being implemented to demonstrate and train farmers and consultants on irrigation scheduling strategies and variable rate irritation. Half a dozen on-farm demonstration sites are selected every year to evaluate, demonstrate, and tra... L. Nunes, E. Francisco, R. Prasad, B.V. Ortiz, E. Abban-baidoo , M. Worosz, M. Robinette , C. O'connor, A. Gamble