Proceedings :: International Society of Precision Agriculture

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Li, Y

Lo, T

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Authors

Li, Y

Jin, C

Zhang, X

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

Year

2026

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Filter results2 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. 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 results2 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. 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...