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Information Management and Traceability
Precision Crop Protection
Decision Support Systems
Applications of UAVs (unmanned aircraft vehicle systems) in precision agriculture
Standards & Data Stewardship
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
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Authors
Abbasi, E
Abdala, M
Adamchuk, V
Adedeji, O
Aikes Junior, J
Alahe, M
Albarenque, S.M
Alchanatis, V
Ali, U
Aliloo, J
Anderson, V
Applegate, D.B
Archer, J.K
Archontoulis, S
Avemegah, E
Bajwa, S
Balboa, G
Balla, I
Bazzi, C
Bazzi, C
Bazzi, C
Bazzi, C.L
Bazzi, C.L
Behrendt, K
Bekkerman, A
Belec, C
Berger, A.W
Berne, D.T
Betzek, N
Betzek, N
Bierman, D
Boejer, O
Bonfil, D.J
Bonke, V
Bouroubi, M.Y
Bradacova, K
Bradacova, K
Bruce, A.E
Brungardt, J.J
Bullock, R.J
Butts, C
Bückmann, H
Cambouris, A.N
Canavari, M
Carter, A
Chang, Y
Chang, Y
Charvat jr., K
Charvat, K
Chen, L
Cho, J
Ciampitti, I
Ciampitti, I.A
Clark, J
Clay, D.E
Cohen, Y
Coonen, J
Craker, B.E
Craker, B.E
Cummings, T
Daggett, D.G
Delgadillo, C.A
Dhal, S
Dorado, J
Dos Santos, R.S
Duchemin, M
Duff, H.D
Ellingson, J.L
Ellixson, A
Ellsworth, J.W
Erickson, B
Esau, T.J
Eshel, G
Fallon, E
Farooque, A
Ferreyra, R
Franz, F
Franz, F
Gadler, D.J
Gallios, I
Ganascini, D
Gavioli, A
Gavioli, A
Gebler, L
Gebler, L
Ghanbari Parmehr, E
Ghimire, B.P
Gnip, P
Goel, R
Goeringer, P
Gowler, A
Griffin, T
Guinness, J
Gumero, J
Gummi, S
Guo, W
Haapala, H.E
Hachisuca, A
Hachisuca, A
Hachisuca, A
Hachisuca, A.M
Hachisuca, A.M
Hamm, P.B
Harari, A
Haringx, S.C
Harnisch, W
Harris, W.E
Hegedus, P
Hegedus, P.D
Hillyer, C
Holub, B.K
Horakova, S
Horneck, D.A
Howatt, T
Hunt, A
Hunt, E
Jarolimek, J
Ji, Z
Johnson, D
Kalafatis, S
Karam, A
Karamidehkordi, E
Kemerer, A.C
Kemeshi, J.O
Kemeshi, J.O
Kepka, M
Ketterings, Q
Khosla, R
Khot, L
Klapp, I
Kotlyarov, D
Kotlyarov, V
Kovacs, P
Kukal, S
Kyveryga, P
Kyveryga, P.M
López-Granados, F
Lange, A
Laor, Y
Leduc, M
Li, M
Lin, Z
Linker, R
Loewen, S.D
Lopez-Granados, F
Louis, J
Lowenberg-DeBoer, J
Lowenberg-DeBoer, J
Ludewig, U
Lukas, V
Lusher, J
Magyar, F
Mahanta, S
Mangus, D
Marcaida, M
Martin, D.E
Martins, M.R
Maxwell, B
Maxwell, B.D
Mayer, W
McArthor, B
McFadden, J
Melchiori, R.J
Mendes, I
Mercante, E
Mercante, E
Mercante, E
Michels, M
Michels, M
Mieno, T
Milics, G
Milics, G
Montull, J.M
Morad-Talab, N
Morales Luna, G.L
Morales, G.L
Moreira, W
Moreira, W
Morgan, S.E
Morris, T
Mueller, D
Murrell, S
Musacchi, S
Mußhoff, O
Mußhoff, O
Müller, T
Nef, B.K
Nerpel, D
Neumann, G
Nieman, S.T
Nkebiwe, M
Norquest, S
Nowatzki, J
Nze Memiaghe, J
O'Sullivan, N
Paccioretti, P
Pacher, B
Pauly, K
Paz-Kagan, T
Peña, J
Peña, J.M
Peerlinck, A
Peerlinck, A.D
Peets, S
Prasad, V
Prestholt, A
Price, K
Puntel, L
Puntel, L
Puntel, L
Qian, J
Quaderer, J
Ramos-Tanchez, J
Raupp, M
Reddy, L
Reznik, T
Rhea, S.T
Rodrigues, M
Rodrigues, M
Rodrigues, M
Rozenstein, O
Russo, J.M
Rutter, M.S
Rydahl, P
Saifuzzaman, M
Salzer, Y
Sanders, P
Sankaran, S
Schad, J
Schatz, B
Schenatto, K
Schenatto, K
Schenatto, K
Schenatto, K
Schultz, E.D
Sedinina, N
Seger, J
Serra, S
Shajahan, S
Sharda, A
Shearouse, T.W
Shen, F
Sheppard, J
Sheppard, J.W
Shroyer, K
Siegfried, J
Silva, F.V
Sobjak, R
Sobjak, R
Sobjak, R
Sobjak, R
Sobjak, R
Soetan, M
Souza, E.G
Souza, E.G
Souza, E.G
Souza, E.G
Souza, E.G
Spinelli, C.B
Srinivasagan, S
Stamm, M.J
Stelford, M.W
Stočes, M
Sun, C
Taberner, A
Tang, Q
Tevis, J.W
Thompson, L
Thompson, L
Thompson, L
Thompson, L
Thompson, L
Torres-Sánchez, J
Torres-Sanchez, J
Torresen, K
Tremblay, N
Tremblay, N
Turner, R.W
Ulman, M
Vaněk, J
Varga, P.M
Vellidis, G
Vellidis, G
Verschwele, A
Vigneault, P
Vitali, G.-
Wang, H
Weber, N
Weinmann, M
Werkmeister, B.K
Wever, H
Wever, H
Wilson, J.A
Wilson, J.W
Wulfsohn, D
Xu, G
Xu, M
Yadav, P.K
Yang, C
Yang, X
Yi, T
Yilma, W.A
Zaman, Q
Zamora, I
Zhang, R
Ziadi, N
de Castro, A
de Castro, A.I
Topics
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Precision Crop Protection
Decision Support Systems
Applications of UAVs (unmanned aircraft vehicle systems) in precision agriculture
Standards & Data Stewardship
Information Management and Traceability
Type
Oral
Poster
Year
2024
2016
2022
2014
2010
Home » Topics » Results

Topics

Filter results57 paper(s) found.

1. Traceability And Management Information System Of Agricultural Product Quality Safety In China

Agricultural product quality safety is the hot topic in the world. From the technical view, the agricultural production management and traceability are the key measurement for insuring the quality safety. From 2005 until now, we have been investig... X. Yang, M. Li, C. Sun, J. Qian, Z. Ji

2. Integrated Land Management – ICT Solutions & Business Models

  PROGIS and Adcon have developed a comprehensive solution to address the major challenges of our time: improve daily agricultural practice on all levels, increase and secure food supplies, take care of the environment and manage ever increasing risks, while last not least assist in fighting global warming.   In all of the above agriculture is playing a key role, but the methods of the past will no longer be adequate. Information technology is the n... W. Mayer, B. Pacher

3. Analysis Of Principles For Adaptive Knowledge Management On Pilot Farms

Collected data, which are used in this research, are coming from several different data sources and time periods (soil test, satellite images, airborne pictures, soil type’s maps, yield predictions maps and other agronomist data).  According to above mentioned data was calculate also variable rate for application of Nitrogen, Potash, Phosphor and Calcium and applied time table during the 10 years period. Main goal of this... P. Gnip

4. Development Of An Enterprise Level Precision Agriculture System

Development of an Enterprise Level Precision Agriculture System   James Ellingson, Chih Lai University of St. Thomas, School of Engineering 2115 Summit Ave, St. Paul, MN USA elli4729@stthomas.edu;   Abstract – In this paper, a plan for the development of an Enterprise Level system for Precision Agriculture (PA) is described. The ... J.L. Ellingson, B.K. Holub, S.E. Morgan, B.K. Werkmeister

5. Detection Of Nitrogen Deficiency In Potatoes Using Small Unmanned Aircraft Systems

  Small Unmanned Aircraft Systems (sUAS) are recognized as potentially important remote-sensing platforms for precision agriculture. A nitrogen rate experiment was established in 2013 with ‘Ranger Russet’ potatoes by applying four rates of nitrogen fertilizer (112, 224, 337, and 449 kg N/ha) in a randomized block design with 3 replicates. A Tetracam Hawkeye sUAS and Agricultural Digital Camera Lite sensor were used to collect imagery with near-infra... D.A. Horneck, D.J. Gadler, A.E. Bruce, R.W. Turner, C.B. Spinelli, J.J. Brungardt, P.B. Hamm, E. Hunt

6. The TOAS Project: UAV Technology For Optimizing Herbicide Applications In Weed-Crop Systems

Site-specific weed management refers to the application of customised control treatments, mainly herbicide, only where weeds are located within the crop-field. In this context, the TOAS project is being developed under the financial support of the European Commission with the main objective of generating georeferenced weed infestation maps of certain herbaceous (corn and sunflower) and permanent woody crops (poplar and olive orchards) by using aerial images collected by an unmanned aeria... J.M. Peña, J. Torres-sanchez, A.I. De castro, J. Dorado, F. Lopez-granados

7. Applying Conventional Vegetation Vigor Indices To UAS-Derived Orthomosaics: Issues And Considerations

In recent years, unmanned airborne systems (UAS) have gained a lot of interest for their potential use in precision agriculture. While the imagery from near-infrared (NIR) enabled off-the-shelf cameras included in UAS can be directly used to facilitate crop scouting, the application in quantitative analyses remains cumbersome. The ultimate goal is to calculate (nitrogen) prescription maps from vegetation indices obtained from UAS imagery, but two main issues hamper this workflow: (1) the... J. Quaderer, J. Coonen, A. Lange, K. Pauly

8. Verify The Effectiveness Of UAS-Mounted Sensors In Field Crop And Livestock Production Management Issues

This research project is a “proof-of-concept” demonstrating specific UAS applications in production agriculture. Project personnel will use UAS-mounted sensors to collect data of ongoing crop and livestock research projects during the 2014 crop season at the North Dakota State University (NDSU) Carrington Research Extension Center (CREC). Project personnel will collaborate with NDSU research scientists conducting research at the CREC. During the first year of the pro... S. Bajwa, J. Nowatzki, W. Harnisch, B. Schatz, V. Anderson

9. Unmanned Aerial System Applications In Washington State Agriculture

Three applications of unmanned aerial systems (UAS) based imaging were explored in row, field, and horticultural crops at Washington State University (WSU). The applications were: to evaluate the necrosis rate in potato field crop rotation trials, to quantify the emergence rates of three winter wheat advanced yield trials, and detecting canker disease-infection in pear. The UAS equipped with green-NDVI imaging was used to acquire field aerial images. In the first appli... L. Khot, S. Sankaran, D. Johnson, A. Carter, S. Serra, S. Musacchi, T. Cummings

10. Weed Seedlings Detection In Winter Cereals For Site-Specific Control: Use Of UAV Imagery To Overcome The Challenge

Weed management is an important part of the investments in crop production. Cost of herbicides accounts for approximately 40% of the cost of all the chemicals applied to agricultural land in Europe. In order to increase the profitability of crop production and to reduce the environmental concerns related to chemicals application, it is needed to develop site-specific weed management strategies in which herbicides are only applied in the crop zones were weeds spread. Moreover, th... J. Peña, A. De castro, F. López-granados, J. Torres-sánchez

11. Unmanned Aerial System To Determine Nitrogen Status In Maize

Maize field production shows spatial variability during vegetative crop growth that could be used to prescribe nitrogen variable rates. The use of portable sensors mounted on high-clearance applicators is well documented, however new UAS vehicle equipped with high resolution digital cameras could be used to determine crop spatial variability with the advantage of survey extensive field areas. To our knowledge, comparisons between vegetation indices obtained by a modified digital camera a... A.C. Kemerer, S.M. Albarenque, R.J. Melchiori

12. sUAVS Technology For Better Monitoring Crop Status For Winter Canola

The small-unmanned aircraft vehicles (sUAVS) are currently gaining more popularity in agriculture with uses including identification of weeds and crop production issues, diagnosing nutrient deficiencies, detection of chemical drift, scouting for pests, identification of biotic or abiotic stresses, and prediction of biomass and yield. Research information on the use of sUAVS have been published and conducted in crops such as rice, wheat, and corn, but the development of... I.A. Ciampitti, K. Shroyer, V. Prasad, A. Sharda, M.J. Stamm, H. Wang, K. Price, D. Mangus

13. A Comparison Of Performance Between UAV And Satellite Imagery For N Status Assessment In Corn

A number of platforms are available for the sensing of crop conditions. They vary from proximal (tractor-mounted) to satellites orbiting the Earth. A lot of interest has recently emerged from the access to unmanned aerial vehicles (UAVs) or drones that are able to carry sensors payloads providing data at very high spatial resolution. This study aims at comparing the performance of a UAV and satellite imagery acquired over a corn nitrogen response trial set-up. The nitrogen (N) r... P. Vigneault, N. Tremblay, M.Y. Bouroubi, C. Bélec, E. Fallon

14. The Use Of A Multirotor And High-Resolution Imaging For Precision Horticulture In Chile: An Industry Perspective

As part of the prototype development of a yield forecasting and precision agriculture service for Chilean horticulture, we evaluated the use of an eight-rotor Mikrokopter for high-resolution aerial imaging to support ground-based surveys. Specific considerations for UAV and communications performance under Chilean conditions are windy conditions, limited space for take-off and landing in orchards, tree height and plantation density, and the presence of high metal contents in soils. We di... I. Zamora, D. Wulfsohn

15. Airspeed and Pressure Affect Spray Droplet Spectrum from an Aerial Nozzle for Fixed-wing Applications

The atomization of the droplets generated by a flat fan nozzle has been studied in the IEA-I high speed wind tunnel at NERCIEA with Marvern Spraytec Laser Diffraction system. The measurement point is set at 0.15m, 0.25m and 0.35m away from the orifice of the nozzle. The wind speed range is from 150km/h to 305km/h, and the tube pressure is set about 0.3MPa, 0.4MPa and 0.5MPa. The measuring distance from the orifice of the nozzle is found important to the diameter and relative span of the dropl... Q. Tang, L. Chen, R. Zhang, M. Xu, G. Xu, T. Yi

16. Field Evaluation of a Variable-rate Aerial Application System

Variable rate aerial application systems are becoming more readily available; however, aerial applicators typically only use the systems for constant rate application of materials, allowing the systems to compensate for upwind and downwind ground speed variations. Much of the resistance to variable rate application system adoption pertains to applicator’s trust in the systems to turn on and off automatically as desired.  If an application system operating in an automatic mode ... D.E. Martin, C. Yang

17. Technology Support for Game Monitoring As a Tool for Damages Reduction of Field Crops

Wild boars (Sus scrofa) are increasingly becoming the main cause of field crops damage in Czech Republic and central Europe area. There are many reasons why wild boars population is growing. The major reason is most likely change in the composition of field crops. In some areas in particular there is focus on oilseed rape and maize, for which there are also recorded the biggest losses. One of the key discussion topics is the issue of estimation of animal quantities and its traceabil... J. Jarolimek, M. Stočes, M. Ulman, J. Vaněk

18. New Technologies in Biological Plant Protection and Its Localization

The sharp increase in the use of pesticides in agrobiocenosis in the background of no-till and minimum tillage called: the growth of costs, the decline of soil fertility, the occurrence of resistance in harmful organisms and change in species composition, a number of other pressing environmental problems. In this regard, the most preferred and safe bipolarization of plant protection. The use of microorganisms in plant protection can reduce the number of harmful organisms in anthropogenic ecos... N. Sedinina, D. Kotlyarov , V. kotlyarov

19. FOODIE Data Model for Precision Agriculture

The agriculture sector is a unique sector due to its strategic importance for both citizens (consumers) and economy (regional and global), which ideally should make the whole sector a network of interacting organizations. The FOODIE project aims at building an open and interoperable agricultural specialized platform hub on the cloud for the management of spatial and non-spatial data relevant for farming production. The FOODIE service platform deals with including their thematic, spatial, and ... K. Charvat, T. Reznik, K. Charvat jr., V. Lukas, S. Horakova, M. Kepka

20. Modus: a Standard for Big Data

Modus Standard is a system of defined terminology, agreed metadata and file transfer format that has grown from a need to exchange, merge and trend agricultural testing data. The three presenters will discuss steps taken to develop the system, benefits to data exchange, current user base and additions being made to the standard. ... D. Nerpel, J.W. Ellsworth, A. Hunt

21. Key Data Ownership, Privacy and Protection Issues and Strategies for the International Precision Agriculture Industry

Precision agriculture companies seek to leverage technology to process greater volumes of data, greater varieties of data, and at a velocity unfathomable to most. The promises of boundless benefits are coupled with risks associated with data ownership, stewardship and privacy. This paper presents some risks related to the management of farm data, in general, as well as those unique to operating in the international arena.  Examples of U.S. and international laws related to data protectio... J.K. Archer, C.A. Delgadillo, F. Shen

22. Ownership and Protections of Farm Data

Farm data has been a contentious point of debate with respect to ownership rights and impacts when access rights are misappropriated. One of the leading questions farmers ask deals with the protections provided to farm data. Although no specific laws or precedence exists, the possibility of trade secret is examined and ramifications for damages discussed. Farm management examples are provided to emphasize the potential outcomes of each possible recourse for misappropriating farm data. ... A. Ellixson, P. Goeringer, T. Griffin

23. Toward Geopolitical-Context-Enabled Interoperability in Precision Agriculture: AgGateway's SPADE, PAIL, WAVE, CART and ADAPT

AgGateway is a nonprofit consortium of 240+ businesses working to promote, enable and expand eAgriculture. It provides a non-competitive collaborative environment, transparent funding and governance models, and anti-trust and intellectual property policies that guide and protect members’ contributions and implementations. AgGateway primarily focuses on implementing existing standards and collaborating with other organizations to extend them when necessary. In 2010 AgGateway id... R. Ferreyra, D.B. Applegate, A.W. Berger, D.T. Berne, B.E. Craker, D.G. Daggett, A. Gowler, R.J. Bullock, S.C. Haringx, C. Hillyer, T. Howatt, B.K. Nef, S.T. Rhea, J.M. Russo, S.T. Nieman, P. Sanders, J.A. Wilson, J.W. Wilson, J.W. Tevis, M.W. Stelford, T.W. Shearouse, E.D. Schultz, L. Reddy

24. Rationale for and Benefits of a Community for On-Farm Data Sharing

Most data sets for evaluating crop production practices have too few locations and years to create reliable probabilities from predictive analytical analyses for the success of the practices. Yield monitors on combines have the potential to enable networks of farmers in collaboration with scientists and farm advisors to collect sufficient data for calculation of more reliable guidelines for crop production showing the probabilities that new or existing practices will improve the efficiency of... T. Morris, N. Tremblay, P.M. Kyveryga, D.E. Clay, S. Murrell, I. Ciampitti, L. Thompson, D. Mueller, J. Seger

25. Fruit Fly Electronic Monitoring System

Insects are a constant threat to agriculture, especially the cultivation of various types of fruits such as apples, pears, guava, etc. In this sense, it is worth mentioning the Anastrepha genus flies (known as fruit fly), responsible for billionaire losses in the fruit growing sector around the world, due to the severity of their attack on orchards. In Brazil, this type of pests has been controlled in most product areas by spraying insecticides, which due to the need for prior knowledge regar... C.L. Bazzi, F.V. Silva, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, R.S. Dos santos, A.M. Hachisuca, F. Franz

26. Yield Mapping in Fruit Farming

Due to the importance of increasing the quantity and quality of world agricultural production, the use of technologies to assist in production processes is essential. Despite this, a timid adoption by precision agriculture (PA) technologies is verified by the Brazilian fruit producers, even though it is one of the segments that had been stood out in recent years in the country's economy. In the PA context, yield maps are rich sources of information, especially by species harvested through... C.L. Bazzi, M.R. Martins, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, A. . Hachisuca, F. Franz

27. AgDataBox: Web Platform of Data Integration, Software, and Methodologies for Digital Agriculture

Agriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. Digital agriculture enables the flow of informatio... E.G. Souza, C. Bazzi, A. Hachisuca, R. Sobjak, A. Gavioli, N. Betzek, K. Schenatto, E. Mercante, M. Rodrigues, W. Moreira

28. Economic Potential of IPMwise – a Generic Decision Support System for Integrated Weed Management in 4 Countries

Reducing use and dependency on pesticides in Denmark has been driven by political action plans since the 1980ies, and a series of nationally funded accompanying R&D programs were completed in the period 1989-2006. One result of these programs was a decision support system (DSS) for integrated weed management. The 4th generation (2016) of the agro-biological models and IT-tools in this DSS, named IPMwise. The concept of IPMwise is to systematically exploit that: ... P. Rydahl, O. Boejer, K. Torresen, J.M. Montull, A. Taberner, H. Bückmann, A. Verschwele

29. Web Application for Automatic Creation of Thematic Maps and Management Zones - AgDataBox-Fast Track

Agriculture is challenging to produce more profitably, with the world population expected to reach some 10 billion people by 2050. Such a challenge can be achieved by adopting precision agriculture and digital agriculture (Agriculture 4.0). Digital agriculture (DA) has become a reality with the availability of cheaper and more powerful sensors, actuators and microprocessors, high-bandwidth cellular communication, cloud communication, and Big Data. DA enables information to flow from used agri... J. Aikes junior, E.G. Souza, C. Bazzi, R. Sobjak, A. Hachisuca, A. Gavioli, N. Betzek, K. Schenatto, W. Moreira, E. Mercante, M. Rodrigues

30. Delineation of Site-specific Management Zones with Proximal Data and Multi-spectral Imagery

Many findings suggested that it’s possible to improve the accuracy of delineating site-specific management zones (SSMZs) through a combination of proximal data with remote sensing imagery. The objective of this study is to assess the feasibility of delineating SSMZs with a wide range of ancillary data (proximal survey and multi-spectral data). The study area is a 22.1acre located 10 miles north of Fort Collins, CO and is known for having a high spatial and temporal variability of soil p... W.A. Yilma, J. Siegfried, R. Khosla

31. AgDataBox-IoT Application Development for Agrometeorogical Stations in Smart Farm

Currently, Brazil is one of the world’s largest grain producers and exporters. Brazil produced 125 million tons of soybean in the 2019/2020 growing season, becoming the world’s largest soybean producer in 2020. Brazil’s economic dependence on agribusiness makes investments and research necessary to increase yield and profitability. Agriculture has already entered its 4.0 version, also known as digital agriculture, when the industry has entered the 4.0 era. This new paradigm ... A. Hachisuca, E.G. Souza, E. Mercante, R. Sobjak, D. Ganascini, M. Abdala, I. Mendes, C. Bazzi, M. Rodrigues

32. Integration of High Resolution Multitemporal Satellite Imagery for Improving Agricultural Crop Classification: a Case Study

Timely and accurate agriculture information is vital for ensuring global food security. Satellite imagery has already been proved as a reliable tool for remote crop mapping. Planet satellite imagery provides high cadence, global satellite coverage with higher temporal and spatial resolution than the Landsat-8 and Sentinel-2. This study examined the potential of utilizing high-resolution multitemporal imagery along with and normalized difference vegetation index (NDVI) to map the agricultural ... U. Ali, T. Esau, A. Farooque, Q. Zaman

33. Data Sources and Risk Management in Precision Agriculture

The digitalisation of the agricultural economy provides more data about the biological processes and technological solutions used for producing agricultural products than ever before. Paralell to the data collection – aiming to provide information for agricultural decision-making and operations – the data informs the farmers, public administration officers and other players in agriculture about the state of the environment. The strategic planning on operation of farms and data han... G. Milics, P.M. Varga, F. Magyar, I. Balla

34. Modeling Spatial and Temporal Variability of Cotton Yield Using DSSAT for Decision Support in Precision Agriculture

The quantification of spatial and temporal variability of cotton yield provides critical information for optimizing resources, especially water. The Southern High Plains (SHP) of Texas is a major cotton (Gossypium hirsutum L.) production region with diminishing water supply. The objective of this study was to predict cotton yield variability using soil properties and topographic attributes. The DSSAT CROPGRO-Cotton model was used to simulate cotton growth, development and yield ... B.P. Ghimire, O. Adedeji, Z. Lin, W. Guo

35. Decision Support from On-field Precision Experiments

Empirically driven adaptive management in large-scale commodity crop production has become possible with spatially controlled application and sub-field scale crop monitoring technology. Site-specific experimentation is fundamental to an agroecosystem adaptive management (AAM) framework that results in information for growers to make informed decisions about their practices. Crop production and quality response data from combine harvester mounted sensors and internet available remote sensing d... B.D. Maxwell, P.D. Hegedus, S.D. Loewen, H.D. Duff, J.W. Sheppard, A.D. Peerlinck, G.L. Morales, A. Bekkerman

36. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat Production

Field-specific fertilizer rate optimization is known to be beneficial for improving farming profit, and profits can be further improved by dividing the field into smaller plots and applying site-specific rates across the field. Finding optimal rates for these plots is often based on data gathered from said plots, which is used to determine a yield response curve, telling us how much fertilizer needs to be applied to maximize yield. In related work, we use a Convolutional Neural Network, known... A. Peerlinck, J. Sheppard, G.L. Morales luna, P. Hegedus, B. Maxwell

37. Evaluating APSIM Model for Site-Specific N Management in Nebraska

Many approaches have been developed to estimate the optimal N application rates and increase nitrogen use efficiency (NUE). In particular, in-season and variable-rate fertilizer applications have the potential to apply N during the time of rapid plant N uptake and at the rate needed, thereby reducing the potential for nitrogen fertilizer losses. However, there remains great challenges in determining the optimal N rate to apply in site-specific locations within a field in a given year.&nb... L. Thompson, L. Puntel, S. Archontoulis

38. Impacts of Interpolating Methods on Soil Agri-environmental Phosphorus Maps Under Corn Production

Phosphorus (P) is an essential nutrient for crops production including corn. However, the excessive P application, tends to P accumulation at the soil surface under crops systems. This may contribute to increase water and groundwater pollution by surface runoff. To prevent this, an agri-environmental P index, (P/Al)M3, was developed in Eastern Canada and USA. This index aims to estimate soil P saturation for accurate P fertilizer recommendations, while integrating agronomical aspec... J. Nze memiaghe, A.N. Cambouris, N. Ziadi, M. Duchemin, A. Karam

39. Soybean Variable Rate Planting Simulator Using Economic Scenarios

Soybean seed costs have increased considerably over the past 15 years, causing a growing interest in variable rate planting (VRP) to optimize seeding rates within soybean fields. We developed a publicly available online Soybean Variable Rate Planting Simulator (http://analytics.iasoybeans.com/cool-apps/SoybeanVRPsimulator/) tool to help farmers, agronomists, and other agriculturalists to understand the essential prerequisite agronomic or economic conditions necessary for profitable VRP implem... B. Mcarthor , A. Prestholt, P. Kyveryga

40. Stem Characteristics and Local Environmental Variables for Assessment of Alfalfa Winter Survival

Alfalfa (Medicago sativa L.) is considered the queen of forage due to its high yield, nutritional qualities, and capacity to sequester carbon. However, there are issues with its relatively low persistency and winter survival as compared to grass. Winter survival in alfalfa is affected by diverse factors, including the environment (e.g., snow cover, hardiness period, etc.) and management (e.g., cutting timing, manure application, etc.). Alfalfa's poor winter survival reduces the number of ... M. Saifuzzaman, V. Adamchuk, M. Leduc

41. Evaluation of Crop Model Based Tools for Corn Site-specific N Management in Nebraska

There is a critical need to reduce the nitrogen (N) footprint from corn-based cropping systems while maintaining or increasing yields and profits. Digital agriculture technologies for site-specific N management have been demonstrated to improve nitrogen use efficiency (NUE). However, adoption of these technologies remains low. Factors such as cost, complexity, unknown impact and large data inputs are associated with low adoption. Grower’s hands-on experience coupled with targeted resear... L. Puntel, L. Thompson , T. Mieno, S. Norquest

42. Making Irrigator Pro an Adaptive Irrigation Decision Support System

Irrigator Pro is a public domain irrigation scheduling model developed by the USDA-ARS National Peanut Research Laboratory. The latest version of the model uses either matric potential sensors to estimate the plant’s available soil water or manual data input. In this project, a new algorithm is developed, which will provide growers and consultants with much more flexibility in how they can feed data to the model. The new version will also run with Volumetric Water Content sensors, givin... I. Gallios, G. Vellidis, C. Butts

43. An IoT-based Smart Real Time Sensing and Control of Heavy Metals to Ensure Optimal Growth of Plants in an Aquaponic Set-up

The concentration of heavy metals that needs to be maintained in aquaponic environments for habitable growth of plants has been a cause of concern for many decades now as it is not possible to eliminate them completely in a commercial set-up. Our goal is to design a cost-effective real-time smart sensing and actuation system in order to control the concentration of heavy metals in aquaponic solutions. Our solution consists of sensing the nutrient concentrations in the aquaponic solution, name... S. Dhal, J. Louis, N. O'sullivan, J. Gumero, M. Soetan, S. Kalafatis, J. Lusher, S. Mahanta

44. Developing a neural-network model for detecting Aflatoxin hotspots in peanut fields

Aflatoxin is a carcinogenic toxin produced by a soilborne fungi, called Aspergillus flavus, causing a difficult struggle for the peanut industry in terms of produce quality, price and the range of selling market. This study aims to develop a successful U-Net CNN (Convolutional Neural Network) model, a reliable image segmentation method, that will help in distinguishing high probability zones of occurrence of Aflatoxin in peanut fields using remotely sensed hyperspectral imagery. The research ... S. Kukal, G. Vellidis

45. Content Analysis of the Challenges of Using Drones in Paddy Fields in the Haraz Plain Watershed, Iran

Drone technology has gained popularity in recent years as a sustainable solution to changing agricultural conditions. Using drones in agriculture provides many advantages in farm management. However, the use of drones in paddy fields in Iran is a new phenomenon facing numerous challenges. This study aims to explore the challenges for using drones in paddy fields and provide practical guidelines to solve the challenges facing the their application. This research was conducted with a qualitativ... J. Aliloo, E. Abbasi, E. Karamidehkordi , E. Ghanbari parmehr, M. Canavari, G.-. Vitali

46. Treetop Tech: Uplifting German Foresters' Drone Perspectives Through the Technology Acceptance Model

Forests play a key role in nature as they purify water, stabilize soil, cycle nutrients, store carbon and also provide habitats for wildlife. Economically, forest product industries provide jobs and economic wealth. Sustainable forest management and planning requires foresters’ understanding of the forests dynamics for which the collection of field data is necessary, which can be time consuming and expensive. Unmanned aerial vehicles or drones can improve the efficiency of tradition acq... M. Michels, H. Wever, O. Mußhoff

47. Farming for a Greener Future: the Behavioural Drive Behind German Farmers’ Alternative Fuel Machinery Purchase Intentions

Climate change due to greenhouse gas emissions, e.g. anthropogenic carbon dioxide (CO2), in the atmosphere will lead to damages caused by global warming, increases in heavy rainfall, flooding as well as permafrost melt. One of the main issues for reducing greenhouse gas emissions is the dependence on oil for fueling transportation and other sectors. Accordingly, policy makers aim to reduce dependency on fossil fuels with the accelerated roll-out of renewable energy. Among others, t... M. Michels, V. Bonke, H. Wever, O. Mußhoff

48. Finnish Future Farm Speeding Up the Uptake of Precision Agriculture

The Finnish Future Farm (FFF) is an innovative concept that seamlessly integrates a physical Smart Farm with a Digital Twin, complemented by educational programs and business development opportunities. This holistic approach aims to propel the evolution of Smart Agriculture in Finland. At its core, FFF is a platform for co-creation with a strong emphasis on User-Centered Design. It employs a Multi-Actor Approach, bringing together companies, experts, researchers, and end users to co... H.E. Haapala

49. Global Adoption of Precision Agriculture: an Update on Trends and Emerging Technologies

The adoption of precision agriculture (PA) has been mixed. Some technologies (e.g., Global Navigation Satellite System (GNSS) guidance) have been adopted rapidly worldwide wherever there is mechanized agriculture. Adoption of some of the original PA technologies introduced in the 1990s has been modest almost everywhere (e.g., variable rate fertilizer). New and more advanced technologies based on robotics, uncrewed aerial vehicles (UAVs), machine vision, co-robotic automation, and artificial i... J. Mcfadden, B. Erickson, J. Lowenberg-deboer, G. Milics

50. R2B2 Project: Design and Construction of a Low-cost and Efficient Autonomous UGV For Row Crop Monitoring

Driving the adoption of agricultural technological advancements like Unmanned Ground Vehicles (UGVs) by small-scale farmers (SSFs) is a major concern for researchers and agricultural organizations. They aim for the adoption of precision farming (PF) by SSFs to increase crop yield to meet the increasing demand for food due to population growth. In the United States, the cost of purchasing and maintaining rugged UGVs capable of precision agricultural operations stands as a barrier to the a... J.O. Kemeshi, S. Gummi, Y. Chang

51. Barriers and Adoption of Precision Ag Tehcnologies for Nitrogen Management Nebraska

A statewide survey of Nebraska farmers shows that they determine the N rate based on soil lab recommendations (82%),  intuition, traditional rate, and own experience (67%). The adoption of dynamic site-specific models (23%), and sensor-based algorithms (11%) remains low. The survey identified the main barriers to the adoption of these N management technologies.  ... G. Balboa, L. Puntel, L. Thompson, P. Paccioretti

52. Bio-Effectors As a Promising Tool for Precision Agriculture and Integrated Plant Nutrition

Bio-effectors, such as microorganisms and active natural compounds, are of increasing interest as promising alternatives or substitutes to precarious agrochemicals. European and global markets (valued at 14.6 billion US$ in 2023) for agricultural biologicals (bio-pesticides, bio-fertilizers, and bio-stimulants) are predicted to grow at rates of more than 13.5 % per year. Improved availability and use efficiency of mineral nutrients, tolerance to abiotic stresses, yield and quality traits, as ... M. Weinmann, M. Nkebiwe, N. Weber, K. Bradacova, N. Morad-talab, U. Ludewig, T. Müller, G. Neumann, M. Raupp, K. Bradacova

53. Who Are the Data Stewards: Moving Data Driven Agriculture Forward

Nearly a decade ago agricultural equipment manufacturers, service providers, retailers, land grant universities, and grower organizations came together to begin discussing the growing needs for producers to manage their farm data. This discussion was partly fueled by the industry shifting from moving data via physical media to cloud API connections. Several initiatives including the Agricultural Data Coalition (ADC) were subsequently launched focusing on addressing data privacy and security c... B.E. Craker, D. Bierman

54. Comparing Global Shutter and Rolling Shutter Cameras for Image Data Collection in Motion on a UGV

In a bid to drive the adoption of precision farming (PF) technology by reducing the cost of developing an Unmanned Ground Vehicle (UGV), during the Reduction-To-Below-Two grand (R2B2) project we compared Arducam’s AR0234, a global shutter camera (GSC) to their IMX462, a rolling shutter camera (RSC). Since the cost of the AR0234 is approximately three times the price of the IMX462, the comparison was done to determine the possibility of using the latter for image data collection in place... J.O. Kemeshi, Y. Chang, P.K. Yadav, M. Alahe

55. Data-driven Agriculture and Sustainable Farming: Friends or Foes?

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive.  It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience.  Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about syne... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer

56. Single-strip Spatial Evaluation Approach: a Simplified Method for Enhanced Sustainable Farm Management

On-farm experimentation (OFE) plays a pivotal role in evaluating and validating the effectiveness of agricultural practices and products. The results of OFE enable farmers to act and make changes that can enhance the farm’s economic and environmental sustainability. Experimental designs can be a barrier to the adoption of OFE. The conventional approach often involves randomized complete block designs with 3 to 5 replications in the field, which can be space-intensive and disrupt workflo... S. Srinivasagan, Q. Ketterings, M. Marcaida, S. Shajahan, J. Ramos-tanchez, J. Cho, , L. Thompson, J. Guinness, R. Goel

57. Decision Making Factors of Precision Agricultural Practices in South Dakota

A survey among South Dakota Farmers was conducted to document current nutrient management practices. The survey included questions regarding adoption and use of precision ag technologies in addition to information considered to create prescription maps for variable fertilizer and seeding rates. The survey collected demographic information from the producers. The presentation will also highlight how farm size, farm location, farmer/decision maker’s age and/or education level in... P. Kovacs, J. Clark, J. Schad, E. Avemegah