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Schepers, J.S
Bolten, A
Chau, M
Callegari, D
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Authors
Holland, K.H
Schepers, J.S
Schepers, J.S
Holland, K.H
Schepers, J.S
Schepers, A.R
Schepers, J.S
Mclure, B
Swanson, G
Gnyp, M.L
Panitzki, M
Reusch, S
Jasper, J
Bolten, A
Bareth, G
Trotter, M
Andersson, K
Welch, M
Chau, M
Frizzel, L
Schneider, D
Kross, A
Kaur, G
Callegari, D
Lapen, D
Sunohara, M
McNairn, H
Rudy, H
van Vliet, L
Kross, A
Kaur, G
Znoj, E
Callegari, D
Sunohara, M
McNairn, H
Lapen, D
Rudy, H
van Vliet, L
Topics
Sensor Application in Managing In-season Crop Variability
Proximal Sensing in Precision Agriculture
Sensor Application in Managing In-season CropVariability
Precision Nutrient Management
Remote Sensing Applications in Precision Agriculture
Proximal Sensing in Precision Agriculture
Decision Support Systems
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Poster
Oral
Year
2010
2014
2016
2018
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Authors

Filter results8 paper(s) found.

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

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

2. Hand-Held Sensor For Measuring Crop Reflectance And Assessing Crop Biophysical Characteristics

Crop vigor is difficult enough to define, let alone characterize and conveniently quantify. The human eye is particularly sensitive to green light, but quantifying subtle differences in plant greenness is subjective and therefore problematic in terms of making definitive management decisions. Plant greenness is one component of crop vigor and leaf area index or the relative ability of... J.S. Schepers, K.H. Holland

3. Using Imagery As A Proxy Yield Map And Scouting Tool

Combine yield maps represent a post-mortem quantification of the spatial variability in crop vigor that occurred during the growing season. The spatial resolution of yield maps is defined by the width of the combine header but the length of the cell depends on the ground-speed of the implement and how long it takes for the grain to... J.S. Schepers, A.R. Schepers

4. Beyond The 4-Rs Of Nutrient Management In Conjunction With A Major Reduction In Tillage

Agribusiness and government agencies have embraced the 4-R concept (right form, rate, time, and place) to improve nutrient management and environmental quality. No-tillage... J.S. Schepers, B. Mclure, G. Swanson

5. Comparison Between Tractor-based and UAV-based Spectrometer Measurements in Winter Wheat

In-season variable rate nitrogen fertilizer application needs a fast and efficient determination of nitrogen status in crops. Common sensor-based monitoring of nitrogen status mainly relies on tractor mounted active or passive sensors. Over the last few years, researchers tested different sensors and indicated the potential of in-season monitoring of nitrogen status by unmanned aerial vehicles (UAVs) in various crops. However, the UAV-platforms and the available sensors are not yet accepted to... M. Gnyp, M. Panitzki, S. Reusch, J. Jasper, A. Bolten, G. Bareth

6. Evaluating low-cost Lidar and Active Optical Sensors for pasture and forage biomass assessment

Accurate and reliable assessment of pasture or forage biomass remains one of the key challenges for grazing industries. Livestock managers require accurate estimates of the grassland biomass available over their farm to enable optimal stocking rate decisions. This paper reports on our investigations into the potential application of affordable Lidar (Light Detection and Ranging) systems and Active Optical (reflectance) Sensors (AOS) to estimate pasture biomass. We evaluated the calibration accuracy... M. Trotter, K. Andersson, M. Welch, M. Chau, L. Frizzel, D. Schneider

7. 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 from... A. Kross, G. Kaur, D. Callegari, D. Lapen, M. Sunohara, H. Mcnairn, H. Rudy, L. Van vliet

8. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean Yield

The ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet