Proceedings :: International Society of Precision Agriculture

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Luiz Panini, R

Tenenboim, Y

Tumwesige, K

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Authors

Tenenboim, Y

Edan, Y

Ginzberg, I

Paz Kagan, T

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. Comparative Evaluation of Combined and Task Specific Detectors for Pomegranate Yield and Fruit Loss Detection Fruit cracking and drop represent major sources of yield loss in pomegranate orchards; however, existing vision-based yield estimation methods focus on counting healthy fruit and do not usually capture losses occurring on-tree and on the orchard floor, thereby constraining their operational relevance. This study evaluates detection strategies for simultaneous yield and loss quantification, with a specific comparison between combined multi class models and task specific single class models. Detection... Y. Tenenboim, Y. Edan, I. Ginzberg, T. Paz Kagan 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. Comparative Evaluation of Combined and Task Specific Detectors for Pomegranate Yield and Fruit Loss Detection

Fruit cracking and drop represent major sources of yield loss in pomegranate orchards; however, existing vision-based yield estimation methods focus on counting healthy fruit and do not usually capture losses occurring on-tree and on the orchard floor, thereby constraining their operational relevance. This study evaluates detection strategies for simultaneous yield and loss quantification, with a specific comparison between combined multi class models and task specific single class models. Detection... Y. Tenenboim, Y. Edan, I. Ginzberg, T. Paz Kagan

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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...