Proceedings :: International Society of Precision Agriculture

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Khan, A

Khot, L.R

Kabenge, R

Karran, D

Khalid, H

Kusnierek, K

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Authors

Dong, L

Miao, Y

Wang, X

Berry, P

Hatley, D

Kusnierek, K

Zavala, E

, G

Oliveira, M.D

Khalid, H

, P

Nketia, K

Lotfi, A

Neudorf, S

Ha, T

Khan, A

Otterson, J

Loganathan Girija, D

Usama Bin Sabir, S

Rathore, D

Khot, L.R

Mattupalli, C

Karkee, M

Loganathan Girija, D

Gorthi, S

Bhalekar, D

Khot, L.R

Keller, M

Karran, D

Hoffos, B

Karp, F.H

Rudnick, D

Tumwesige, K

Kabenge, R

Lacasa, J

Njuki Nakabuye, H

Katimbo, A

Lo, T

Proctor, C

Tuttle, R

Stremel, K

Topics

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Remote and Proximal Sensing of Soils and Crops

UAV-Based Scouting, Imaging, and Targeted Applications

Agricultural Robotics, Automation, and Mechanization

Precision Horticulture and Specialty Crop Management

Education, Training, and Extension for Precision Agriculture

Type

Oral

Poster

Year

2026

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Filter results7 paper(s) found.
1. Early Forecasting of Maize Lodging Risk Through Multi-period and Multi-source Data Integration Lodging is a critical constraint on global maize (Zea mays L.) productivity, primarily through detrimental effects on both grain yield and quality. However, reliable methods to predict maize lodging risk early in the growing season are lacking, which hinders timely implementation of effective agronomic management interventions to increase crop lodging resistance and reduce corresponding yield losses. This work aimed to develop a feasible early season maize lodging risk prediction method... L. Dong, Y. Miao, X. Wang, P. Berry, D. Hatley, K. Kusnierek Download paper
2. Comparative Assessment of Proximal Sensing and UAV Multispectral Data for Coffee Vigor Analysis The assessment of vegetative vigor through spectral indices, particularly the Normalized Difference Vegetation Index (NDVI), is widely adopted in precision agriculture as a rapid, indirect, and non-destructive method for evaluating plant physiological status. A broad range of sensing technologies has been employed for this purpose, including proximal sensors and multispectral imaging systems mounted on remotely piloted aircraft (RPAs). In this context, the objective of this study was to evaluate... E. Zavala, G. , M.D. Oliveira, H. Khalid, P.
3. Autonomous Edge-AI–Enabled Drone Systems for Real-Time Agricultural Inference and Decision-Making High-throughput, low-latency phenotyping and field surveillance remain critical bottlenecks in precision agriculture and environmental monitoring due to delayed data turnaround, large data volumes, computationally intensive preprocessing, and expertise-heavy analysis workflows. These constraints hinder timely crop improvement, pest and disease management, and informed agronomic decision-making. To address these challenges, we present an integrated, end-to-end autonomous drone system that enables...
4. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee
5. Enhanced Deep Learning Framework Driven Grape Berry Temperature Estimation and 72-h Forecasting for Precision Heat Stress Management The increasing frequency of extreme summer heat events poses a significant threat to grape production in the Pacific Northwest (PNW), U.S., and globally. Elevated temperatures can induce sunburn, accelerate organic acid degradation, and cause anthocyanin loss, ultimately reducing berry quality. Berry surface temperature (BST), which can exceed ambient air temperature by up to 15 °C, is a primary indicator of heat stress severity. However, BST dynamics are governed by complex, nonlinear thermodynamic...
6. Who is the Agricultural Practitioner of the Future? An agricultural industry that continues to adopt new technologies and rely on data-driven decisions demands a unique skillset from its practitioners that goes beyond traditional agricultural training. Despite this demand, relatively few technology-focused agriculture programs are available at the post-secondary level worldwide. In 2020, Olds College of Agriculture & Technology (Alberta, Canada) created a 2-year diploma in Precision Agriculture (launched in 2020) and a 4-year Bachelor of Digital... D. Karran, B. Hoffos, F.H. Karp
7. Growth-Stage and Hourly Modeling of Non-Stressed Soybean Canopy Temperature Using High-Frequency Proximal Thermal Sensing Canopy temperature (Tc) sensing provides a proximal, non-destructive approach for monitoring crop water status. It supports irrigation scheduling through thermal indices such as the Crop Water Stress Index (CWSI) and Degrees Above Non-Stressed (DANS), both of which require accurate estimation of non-stressed canopy temperature (Tcns) (Nakabuye et al., 2022). Maintaining a continuously non-stressed reference treatment to determine Tcns is operationally difficult, motivating development of weather-based...

Filter results7 paper(s) found.

1. Early Forecasting of Maize Lodging Risk Through Multi-period and Multi-source Data Integration

Lodging is a critical constraint on global maize (Zea mays L.) productivity, primarily through detrimental effects on both grain yield and quality. However, reliable methods to predict maize lodging risk early in the growing season are lacking, which hinders timely implementation of effective agronomic management interventions to increase crop lodging resistance and reduce corresponding yield losses. This work aimed to develop a feasible early season maize lodging risk prediction method... L. Dong, Y. Miao, X. Wang, P. Berry, D. Hatley, K. Kusnierek

Download paper

2. Comparative Assessment of Proximal Sensing and UAV Multispectral Data for Coffee Vigor Analysis

The assessment of vegetative vigor through spectral indices, particularly the Normalized Difference Vegetation Index (NDVI), is widely adopted in precision agriculture as a rapid, indirect, and non-destructive method for evaluating plant physiological status. A broad range of sensing technologies has been employed for this purpose, including proximal sensors and multispectral imaging systems mounted on remotely piloted aircraft (RPAs). In this context, the objective of this study was to evaluate... E. Zavala, G. , M.D. Oliveira, H. Khalid, P.

3. Autonomous Edge-AI–Enabled Drone Systems for Real-Time Agricultural Inference and Decision-Making

High-throughput, low-latency phenotyping and field surveillance remain critical bottlenecks in precision agriculture and environmental monitoring due to delayed data turnaround, large data volumes, computationally intensive preprocessing, and expertise-heavy analysis workflows. These constraints hinder timely crop improvement, pest and disease management, and informed agronomic decision-making. To address these challenges, we present an integrated, end-to-end autonomous drone system that enables...

4. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers

High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee

5. Enhanced Deep Learning Framework Driven Grape Berry Temperature Estimation and 72-h Forecasting for Precision Heat Stress Management

The increasing frequency of extreme summer heat events poses a significant threat to grape production in the Pacific Northwest (PNW), U.S., and globally. Elevated temperatures can induce sunburn, accelerate organic acid degradation, and cause anthocyanin loss, ultimately reducing berry quality. Berry surface temperature (BST), which can exceed ambient air temperature by up to 15 °C, is a primary indicator of heat stress severity. However, BST dynamics are governed by complex, nonlinear thermodynamic...

6. Who is the Agricultural Practitioner of the Future?

An agricultural industry that continues to adopt new technologies and rely on data-driven decisions demands a unique skillset from its practitioners that goes beyond traditional agricultural training. Despite this demand, relatively few technology-focused agriculture programs are available at the post-secondary level worldwide. In 2020, Olds College of Agriculture & Technology (Alberta, Canada) created a 2-year diploma in Precision Agriculture (launched in 2020) and a 4-year Bachelor of Digital... D. Karran, B. Hoffos, F.H. Karp

7. Growth-Stage and Hourly Modeling of Non-Stressed Soybean Canopy Temperature Using High-Frequency Proximal Thermal Sensing

Canopy temperature (Tc) sensing provides a proximal, non-destructive approach for monitoring crop water status. It supports irrigation scheduling through thermal indices such as the Crop Water Stress Index (CWSI) and Degrees Above Non-Stressed (DANS), both of which require accurate estimation of non-stressed canopy temperature (Tcns) (Nakabuye et al., 2022). Maintaining a continuously non-stressed reference treatment to determine Tcns is operationally difficult, motivating development of weather-based...