Proceedings :: International Society of Precision Agriculture

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Zhang, X

Dhaliwal, A

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Authors

Li, Y

Jin, C

Zhang, X

Jakhar, A

Bastos, L

Bhattarai, A

Poudel, K

Dhaliwal, A

Jakhar, A

Bastos, L

Roth, R

Virk, S

Bhattarai, A

Poudel, K

Dhaliwal, A

Poudel, K

Bhattarai, A

Jakhar, A

Bastos, L

Dhaliwal, A

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Site-Specific Nutrient, Lime and Seed Management

Remote and Proximal Sensing of Soils and Crops

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Poster

Oral

Year

2026

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Authors

Filter results4 paper(s) found.
1. Plot2Phenome: A UAV-Based Deep Learning Framework for Automated Micro-Plot Segmentation and Plot Level Phenotyping Automated micro-plot segmentation is a foundational requirement for plot-level phenotyping from UAV orthomosaics in field breeding trials. However, reliable delineation of individual plots remains difficult in realistic agronomic settings, particularly under canopy closure that erodes inter-plot gaps and under irregular or degraded plot boundaries caused by lodging, variable emergence, and field operations. These conditions reduce boundary contrast, increase instance adjacency, and introduce shape... Y. Li, C. Jin, X. Zhang Download paper
2. Sensor-based Variable Rate Nitrogen Recommendations: Comparing Proximal, Drone, and Satellite Sensors in Corn Nitrogen (N) represents 20–25% of corn (Zea mays L.) production costs, yet 15–65% is lost through volatilization and leaching. Conventional uniform-rate application ignores spatial variability and seasonal demand. Sensor-based variable rate nitrogen (VRN) addresses this by using real-time reflectance data, but the influence of sensing platform proximal, drone, or satellite on economic outcomes under varying N stress remains under-researched. The objective of this study at Iron Horse,... A. Jakhar, L. Bastos, A. Bhattarai, K. Poudel, A. Dhaliwal
3. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated Corn Sensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal sensor... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal
4. Predicting Yield Stability Classes Using Satellite Imagery in the Absence of Yield Monitor Data Site-specific management is essential for improving agricultural productivity while reducing input costs and minimizing environmental impacts. Although yield monitor data are commonly used to characterize within-field yield variability, their availability is often limited by technological and economic constraints. The primary objective of this study was to compare spatial–temporal stability classes derived from yield monitor data and satellite imagery in cotton production... K. Poudel, A. Bhattarai, A. Jakhar, L. Bastos, A. Dhaliwal

Filter results4 paper(s) found.

1. Plot2Phenome: A UAV-Based Deep Learning Framework for Automated Micro-Plot Segmentation and Plot Level Phenotyping

Automated micro-plot segmentation is a foundational requirement for plot-level phenotyping from UAV orthomosaics in field breeding trials. However, reliable delineation of individual plots remains difficult in realistic agronomic settings, particularly under canopy closure that erodes inter-plot gaps and under irregular or degraded plot boundaries caused by lodging, variable emergence, and field operations. These conditions reduce boundary contrast, increase instance adjacency, and introduce shape... Y. Li, C. Jin, X. Zhang

Download paper

2. Sensor-based Variable Rate Nitrogen Recommendations: Comparing Proximal, Drone, and Satellite Sensors in Corn

Nitrogen (N) represents 20–25% of corn (Zea mays L.) production costs, yet 15–65% is lost through volatilization and leaching. Conventional uniform-rate application ignores spatial variability and seasonal demand. Sensor-based variable rate nitrogen (VRN) addresses this by using real-time reflectance data, but the influence of sensing platform proximal, drone, or satellite on economic outcomes under varying N stress remains under-researched. The objective of this study at Iron Horse,... A. Jakhar, L. Bastos, A. Bhattarai, K. Poudel, A. Dhaliwal

3. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated Corn

Sensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal sensor... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal

4. Predicting Yield Stability Classes Using Satellite Imagery in the Absence of Yield Monitor Data

Site-specific management is essential for improving agricultural productivity while reducing input costs and minimizing environmental impacts. Although yield monitor data are commonly used to characterize within-field yield variability, their availability is often limited by technological and economic constraints. The primary objective of this study was to compare spatial–temporal stability classes derived from yield monitor data and satellite imagery in cotton production... K. Poudel, A. Bhattarai, A. Jakhar, L. Bastos, A. Dhaliwal