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Bai, F
Wilson, G.L
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Authors
Wilson, G.L
Mulla, D.J
Galzki, J
Laacouri, A
Vetsch, J
Chamara, N
Ge, Y
Bai, F
Topics
In-Season Nitrogen Management
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2018
2024
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1. 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

2. Estimating Real-time Soil Water Content (SWC) in Corn and Soybean Fields Using Machine Learning Models, Proximal Remote Sensing, and Weather Data

Soil Water Content (SWC) is crucial for precise irrigation management, especially in center-pivot systems. Real-time estimation of SWC is vital for scheduling irrigation to prevent overwatering or underwatering. Proper irrigation yields benefits such as improved water efficiency, enhanced crop yield and quality, minimized environmental impact, optimized labor and energy costs, and improved soil health. Various in-situ techniques, such as Time-domain reflectometry (TDR), frequency-domain... N. Chamara, Y. Ge, F. Bai