Proceedings :: International Society of Precision Agriculture

Proceedings

Find matching any: Reset

Fiegenbaum, A.S

Shovic, J.C

Stremel, K

» Add more authors

Add filter to result:

Authors

Gabriel, D

Fiegenbaum, A.S

Griebeler, S.R

Franchi, M

dos Santos, N.S

Rocha, K.F

Vian, A.L

Wing, K

Onyeoguzoro, D

Everett, M

Shovic, J.C

Rudnick, D

Tumwesige, K

Kabenge, R

Lacasa, J

Njuki Nakabuye, H

Katimbo, A

Lo, T

Proctor, C

Tuttle, R

Stremel, K

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Oral

Year

2026

Home » Authors » Results

Authors

Filter results3 paper(s) found.
1. Herbicide Savings and Weed Control Performance Using Green-on-Green Spot Spraying in Soybean The conventional approach to weed control in large-scale soybean production relies on full-area herbicide spraying, resulting in high chemical input and operational costs. In this context, artificial intelligence-based spot spraying has emerged as a promising alternative to increase efficiency and reduce environmental impact. This study evaluated the performance of a green-on-green spot spraying system based on deep learning algorithms, CORTEX AI (Soybean Model v08), for post-emergence weed control... D. Gabriel, A.S. Fiegenbaum, S.R. Griebeler, M. Franchi, N.S. Dos Santos, K.F. Rocha, A.L. Vian
2. Adding Plant Water Uptake Monitoring to a Low-Cost Wireless Multi-Sensor Data Aggregation Platform for Precision Agriculture Most low-cost precision agriculture monitoring systems only measure soil conditions such as moisture and pH, leaving plant response to water availability unmonitored. Soil moisture alone does not indicate whether a crop is taking up water or experiencing stress. Commercial plant-sensing systems that could fill this gap are expensive and operate as standalone instruments that are difficult to incorporate into existing multi-sensor field networks. A... K. Wing, D. Onyeoguzoro, M. Everett, J.C. Shovic Download paper
3. 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 results3 paper(s) found.

1. Herbicide Savings and Weed Control Performance Using Green-on-Green Spot Spraying in Soybean

The conventional approach to weed control in large-scale soybean production relies on full-area herbicide spraying, resulting in high chemical input and operational costs. In this context, artificial intelligence-based spot spraying has emerged as a promising alternative to increase efficiency and reduce environmental impact. This study evaluated the performance of a green-on-green spot spraying system based on deep learning algorithms, CORTEX AI (Soybean Model v08), for post-emergence weed control... D. Gabriel, A.S. Fiegenbaum, S.R. Griebeler, M. Franchi, N.S. Dos Santos, K.F. Rocha, A.L. Vian

2. Adding Plant Water Uptake Monitoring to a Low-Cost Wireless Multi-Sensor Data Aggregation Platform for Precision Agriculture

Most low-cost precision agriculture monitoring systems only measure soil conditions such as moisture and pH, leaving plant response to water availability unmonitored. Soil moisture alone does not indicate whether a crop is taking up water or experiencing stress. Commercial plant-sensing systems that could fill this gap are expensive and operate as standalone instruments that are difficult to incorporate into existing multi-sensor field networks. A... K. Wing, D. Onyeoguzoro, M. Everett, J.C. Shovic

Download paper

3. 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...