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Uberuaga, D.P
Varner, D.L
Jezek, J
Eivazi, F
Gnip, P
Coates, R
Gebler, L
Ferguson, R.B
Bouchard, M
Viator, B.J
Simard, M
Gore, A.K
Fontaine, D
Ardigueri, M
Hammond, J
Kakarla, S
Roehrdanz, P
Kroulik, M
Olayide, O.E
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Authors
Shiratsuchi, L
Lutz, C.C
Ferguson, R.B
Adamchuk, V.I
Adamchuk, V.I
Pan, L
Ferguson, R.B
Shaver, T
Schmer, M
Irmak, S
Van Donk, S
Wienhold, B
Jin, V
Bereuter, A
Francis, D
Rudnick, D
Ward, N
Hendrickson, L
Ferguson, R.B
Adamchuk, V.I
Pawar, S.N
Gore, A.K
Shinde, G.U
Pendke, M.S
Varner, D.L
Eivazi, F
Adamchuk, V.I
Ferguson, R.B
Longchamps, L
Panneton, B
Simard, M
Theriault, R
Roger, T
Longchamps, L
Panneton, B
Leroux, G.D
Simard, M
Theriault, R
Olayide, O.E
Ikpi, A.E
Okoruwa, V.O
Alabi, T
Omodele, T
Shiratsuchi, L
Ferguson, R.B
Shanahan, J.F
Adamchuk, V.I
Slater, G
Charvat, K
Gnip, P
Charvat, K
Cepicky, J
Gnip, P
Charvat, K
Jezek, J
Musil, M
Krivanek, Z
Gnip, P
Gnip, P
Kemanian, A.R
Huggins, D.R
Uberuaga, D.P
Rojo, F
Roach, J
Coates, R
Upadhyaya, S
Delwiche, M
Han, C
Dhillon, R
Stevens, L.J
Ferguson, R.B
Franzen, D.W
Kitchen, N.R
Viator, B.J
Johnson, R.M
Bean, G
Kitchen, N.R
Franzen, D.W
Miles, R.J
Ransom, C
Scharf, P
Camberato, J
Carter, P
Ferguson, R.B
Fernandez, F.G
Laboski, C
Nafziger, E
Sawyer, J
Shanahan, J
Ransom, C.J
Bean, M
Kitchen, N
Camberato, J
Carter, P
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C
Nafziger, E
Sawyer, J
Shanahan, J
Bastos, L
Ferguson, R.B
Luck, J
Parrish, J
Thompson, L
Krienke, B
Glewen, K
Ferguson, R.B
Kablan, L
Chabot, V
Mailloux, A
Bouchard, M
Fontaine, D
Bruulsema, T
Kroulik, M
Brant, V
Zabransky, P
Chyba, J
Krcek, V
Skerikova, M
Bastos, L
Ferguson, R.B
KC, K
Hannah, L
Roehrdanz, P
Donatti, C
Fraser, E
Berg, A
Saenz, L
Wright, T.M
Hijmans, R.J
Mulligan, M
Ampatzidis, Y
Derival, M
Kakarla, S
Albrecht, U
Zhang, X
Bazzi, C.L
Silva, F.V
Gebler, L
Souza, E.G
Schenatto, K
Sobjak, R
Dos Santos, R.S
Hachisuca, A.M
Franz, F
Bazzi, C.L
Martins, M.R
Gebler, L
Souza, E.G
Schenatto, K
Sobjak, R
Hachisuca, A.
Franz, F
Li, D
Miao, Y
Fernández, .G
Kitchen, N.R
Ransom, C.
Bean, G.M
Sawyer, .E
Camberato, J.J
Carter, .R
Ferguson, R.B
Franzen, D.W
Franzen, D.W
Franzen, D.W
Franzen, D.W
Laboski, C.A
Nafziger, E.D
Shanahan, J.F
Karampoiki, M
Todman, L
Mahmood, S
Murdoch, A
Paraforos, D
Hammond, J
Ranieri, E
Bazzi, C.L
Rauber, L.A
Oliveira, W.K
Sobjak, R
Schenatto, K
Gebler, L
Rabello, L.M
Lacerda, L
Felipe dos Santos, A
Bedwell, E
Jakhar, A
Costa Barboza, T.O
Ardigueri, M
Topics
Proximal Sensing in Precision Agriculture
Precision A to Z for Practitioners
Spatial Variability in Crop, Soil and Natural Resources
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change)
Precision A-Z for Practitioners
Precision Conservation
Education and Training in Precision Agriculture
Precision Weed Management
Modeling and Geo-statistics
Sensor Application in Managing In-season Crop Variability
Profitability, Sustainability, and Adoption
Optimizing Farm-level use of Spatial Technologies
Information Management and Traceability
Precision Carbon Management
Proximal Sensing in Precision Agriculture
Sensor Application in Managing In-season CropVariability
Precision Nutrient Management
Precision Nutrient Management
Sensor Application in Managing In-season Crop Variability
Site-Specific Nutrient, Lime and Seed Management
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
In-Season Nitrogen Management
Geospatial Data
Precision Horticulture
Decision Support Systems
ISPA Community: Nitrogen
Big Data, Data Mining and Deep Learning
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Education of Precision Agriculture Topics and Practices
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
2024
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Filter results34 paper(s) found.

1. Networking Advances Emerging Agricultural Technologies

  Innovative Nebraska farmers and agribusinesses partnered with University of Nebraska-Lincoln (UNL) extension in 2001 to form the Nebraska Agricultural Technologies Association (NeATA). UNL Extension faculty and NeATA members have collaborated for nearly a decade to further agriculturists' understanding and adoption of emerging agricultural technologies via machinery/technology field days, hands-on GIS/GPS computer workshops, aerial imagery experiential learning,... D.L. Varner

2. Minimizing On-farm Point Source Contamination Of Pesticides Using The “Biobed” Method

  Microorganisms play a vital role in maintaining the environmental quality through their metabolic activities. The highly versatile metabolic capabilities of fungi and bacteria can be used to reclaim polluted ecosystems. A variety of biological, chemical, and physical methods have been used for degradation and detoxification of pesticides. However, conventional clean- up methods are costly and not always effective. Agrochemical storage and handling practices have been targeted... F. Eivazi

3. Precision Agriculture Education Program In Nebraska

With the cost of agricultural inputs and the instability of commodity prices increasing, demand is growing for training in the essential skills needed to successfully implement site-specific crop management. This set of skills is uniquely interdisciplinary in nature. Thus, it is essential for potential users of precision agriculture to understand the basics of geodetic and electronic control equipment, principles of geographic information systems, fundamentals... V.I. Adamchuk, R.B. Ferguson

4. Sensing The Inter-row For Real-time Weed Spot Spraying In Conventionally Tilled Corn Fields

The spatial distribution of weeds is aggregated most of the time in crop fields. Site-specific management of weeds could result in economical and environmental benefits due to herbicide... L. Longchamps, B. Panneton, M. Simard, R. Theriault, T. Roger

5. Partial Weed Scouting For Exhaustive Real-time Spot Spraying Of Herbicides In Corn

Real-time spot spraying of weeds implies the use of plant detectors ahead of a sprayer. The range of weed spatial autocorrelation perpendicularly to crop rows is often greater than the space between the corn rows. To assess the possibility of using less than one plant detector scouting each inter-row, a one hectare field was entirely sampled with ground pictures at the appropriate timing for weed spraying. Different ways of disposing the detectors ahead of the sprayer were virtually tested. Scouting... L. Longchamps, B. Panneton, G.D. Leroux, M. Simard, R. Theriault

6. 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

7. 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

8. Vision Of Farm Of Tomorrow

... K. Charvat, P. Gnip

9. New Geospatial Technologies For Precision Farming

... K. Charvat, J. Cepicky, P. Gnip

10. Vlite Node – New Sensor Technology For Precision Farming

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

11. 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

12. Modeling Soil Carbon Spatial Variation: Case Study In The Palouse Region

Soil organic carbon (Cs) levels in the soil profile reflect the transient state or equilibrium conditions determined by organic carbon inputs and outputs. In areas with strong topography, erosion, transport and deposition control de soil carbon balance and determine strong within-field differences in soil carbon. Carbon gains or losses are therefore difficult to predict for the average field. Total Cs ranged from 54 to 272 Mg C ha-1, with 42% (range 25 to 78%) of Cs in the top 0.3-m of the soil... A.R. Kemanian, D.R. Huggins, D.P. Uberuaga

13. Integrated Crop Canopy Sensing System for Spatial Analysis of In-Season Crop Performance

Over the past decade, the relationships between leaf color, chlorophyll content, nitrogen supply, biomass and grain yield of agronomic crops have been studied widely.... L. Shiratsuchi, C.C. Lutz, R.B. Ferguson, V.I. Adamchuk

14. An Approach to Selection of Soil Water Content Monitoring Locations within Fields

Increased input efficiency is one of the main challenges for a modern agricultural enterprise. One way to optimize production cycles is to rationalize crop residue utilization. In conditions where there is limited use of mineral fertilizers and without applying manure, plant residues may be used as an organic fertilizer as... V.I. Adamchuk, L. Pan, R.B. Ferguson

15. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn

... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk

16. 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

17. Development And Evaluation Of A Leaf Monitoring System For Continuous Measurement Of Plant Water Status In Almond And Walnut Crops

Abstract: Leaf temperature measurements using handheld infrared thermometers have been used to predict plant water stress by calculating crop water stress index (CWSI). However, for CWSI calculations it is recommended to measure canopy temperature of trees under saturated, stressed and current conditions simultaneously, which is not very practical while using handheld units. An inexpensive, easy to use sensing system was developed to predict plant water status for tree crops by measuring... F. Rojo, J. Roach, R. Coates, S. Upadhyaya, M. Delwiche, C. Han, R. Dhillon

18. In-Season Nitrogen Requirement For Maize Using Model And Sensor-Based Recommendation Approaches

Nitrogen (N), an essential element, is often limiting to plant growth.  There is great value in determining the optimum quantity and timing of N application to meet crop needs while minimizing losses.  Low nitrogen use efficiency (NUE) has been attributed to several factors including poor synchrony between N fertilizer and crop demand, unaccounted for spatial variability resulting in varying crop N needs, and temporal variances in crop N needs.  Applying a portion... L.J. Stevens, R.B. Ferguson, D.W. Franzen, N.R. Kitchen

19. Variable-Rate Application Of Nitrogen And Potassium Fertilizers In Louisiana Sugarcane Production Systems.

If sugar and cane yields are to be optimized and profitability improved, it is critical that a sugarcane crop receive the proper levels of plant nutrients.  Under-fertilization can result in reduced cane yields, while over-fertilization can reduce sugar recovery.  In addition, improper fertilization may increase crop susceptibility to environmental stresses and disease and insect pests. Nitrogen (N) continues to be one of the most important and costly... B.J. Viator, R.M. Johnson

20. Modifying the University of Missouri Corn Canopy Sensor Algorithm Using Soil and Weather Information

Corn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N recommendations... G. Bean, N.R. Kitchen, D.W. Franzen, R.J. Miles, C. Ransom, P. Scharf, J. Camberato, P. Carter, R.B. Ferguson, F. Fernandez, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

21. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor Algorithm

Nitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

22. Active and Passive Crop Canopy Sensors As Tools for Nitrogen Management in Corn

The objectives of this research were to (i) assess the correlation between active and passive crop canopy sensors’ vegetation indices at different corn growth stages and (ii) assess sidedress variable rate nitrogen (N) recommendation accuracy of active and passive sensors compared to the agronomic optimum N rate (AONR). The experiment was conducted near Central City, Nebraska on a Novina sandy loam planted to corn on 15 April 2015. The experiment was a randomized complete-block design with... L. Bastos, R. Ferguson

23. Liquid Flow Control Requirements for Crop Canopy Sensor-Based N Management in Corn: A Project SENSE Case Study

While on-farm adoption of crop canopy sensors for directing in-season nitrogen (N) application has been slow, research focused on these systems has been significant for decades. Much emphasis has been placed on developing and testing algorithms based on sensor output to predict N needs, but little information has been published regarding liquid flow control requirements on equipment used in conjunction with these sensing systems. Addition of a sensor-based system to a standard spray rate controller... J. Luck, J. Parrish, L. Thompson, B. Krienke, K. Glewen, R.B. Ferguson

24. Variability in Corn Yield Response to Nitrogen Fertilizer in Quebec

Optimizing nitrogen (N) fertilization is important to improve corn yield and to reduce N losses to the environment. The economic optimum nitrogen rate  (EONR) is variable and depends on many factors, including weather conditions and crop management.  The main objective of this study was to examine how grain corn yield response to N varies with planting date, soil texture and spring weather across sites and years in Monteregie, which is the most important with 64% of total area and 69%... L. Kablan, V. Chabot, A. Mailloux, M. Bouchard, D. Fontaine, T. Bruulsema

25. Machine Monitoring As a Smartfarming Concept Tool

Current development trends are associated with the digitization of production processes and the interconnection of individual information layers from multiple sources into common databases, contexts and functionalities. In order to automatic data collection  of machine operating data, the farm tractors were equipped with monitoring units ITineris for continuous collection and transmission of information from tractors CAN Bus. All data sets are completed with GPS location data. Acreage... M. Kroulik, V. Brant, P. Zabransky, J. Chyba, V. Krcek, M. Skerikova

26. 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-applied... L. Bastos, R.B. Ferguson

27. 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 thermal... K. Kc, L. Hannah, P. Roehrdanz, C. Donatti, E. Fraser, A. Berg, L. Saenz, T.M. Wright, R.J. Hijmans, M. Mulligan

28. Evaluation of HLB-Infected Citrus Rootstocks Using Ground Penetrating Radar

Citrus production in Florida continues to decline steadily, since the arrival of Huanglongbing (HLB or citrus greening). HLB does not kill the tree, but HLB-infected trees become less productive. Since now, there is no cure for this disease. However, several strategies have been developed to manage and control HLB-infected citrus trees. We have developed and evaluated a heat thermotherapy system (short-term solution) for sustaining productivity of HLB-affected trees. This system heats the canopy... Y. Ampatzidis, M. Derival, S. Kakarla, U. Albrecht, X. Zhang

29. 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 regarding... C.L. Bazzi, F.V. Silva, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, R.S. Dos santos, A.M. Hachisuca, F. Franz

30. 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

31. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest

Effective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly across... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan

32. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri

33. Portable Soil EC - Development of an Electronic Device for Determining Soil Electrical Conductivity

Decision-making in agriculture demands continuous monitoring, a factor that propels the advancement of tools within Agriculture 4.0. In this context, understanding soil characteristics is essential. Electrical conductivity (EC) sensors play a pivotal role in this comprehension. Given this backdrop, the core motivation of this research was developing an accessible and effective electronic device to measure the apparent EC of the soil. It provides features like geolocation, recording of the date... C.L. Bazzi, L.A. Rauber, W.K. Oliveira, R. Sobjak, K. Schenatto, L. Gebler, L.M. Rabello

34. Fostering Student Engagement and Leadership Development in Integrative Precision Agriculture Across Borders

Efforts to advance integrative precision agriculture technologies are growing exponentially across the globe with the common interest of upholding food security and developing more sustainable food and fiber production systems. Countries such as the United States and Brazil are among the biggest crop producers in the world and will play an even bigger role in food security in the next decades. It is of utmost importance that countries can advance together to overcome future food production challenges... L. Lacerda, A. Felipe dos santos, E. Bedwell, A. Jakhar, T.O. Costa barboza, M. Ardigueri