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Edan, Y
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Authors
Edan, Y
Berenstein, R
Ben-Halevi, I
Levanon, D
Cohen, Y
Gafni, R
Shmuel, L
Edan, Y
Tenenboim, Y
Edan, Y
Ginzberg, I
Paz Kagan, T
Carmon, T
Aflalo , E
Sagi, A
Edan, Y
Topics
Guidance, Robotics, Automation, and GPS Systems
Precision Crop Protection, Pest, and Plant Health
Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture
Type
Poster
Oral
Year
2012
2026
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Filter results4 paper(s) found.

1. A Remote Interface for a Human-Robot Cooperative Vineyard Sprayer

... Y. Edan, R. Berenstein, I. Ben-halevi

2. Spatio-Temporal Sampling-Point Allocation for High-Density Robotic Pest Monitoring and Precision Treatment

Efficient monitoring of pests in crops, such as the two-spotted spider mite (Tetranychus urticae), is essential for optimizing pesticide application and minimizing yield losses. However, conventional manual scouting is labor-intensive and costly, limiting spatial coverage and sampling frequency. Consequently, infestation hotspots are often detected too late, reducing the effectiveness of timely and targeted interventions. This limitation... D. Levanon, Y. Cohen, R. Gafni, L. Shmuel, Y. Edan

3. 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

4. Cross-Season Transfer Learning for Prawn Morphometric Estimation Using YOLOv11-Pose

The problem of maintaining accurate computer vision models in dynamic aquaculture pond environments is increasingly important as real world imaging conditions vary over time. Even in controlled indoor ponds, factors such as water turbidity, lighting angle, background reflections, and camera setup can change between monitoring sessions or seasons. These variations introduce domain shifts that can significantly degrade the performance of deep learning models trained under controlled conditions.... T. Carmon, E. Aflalo , A. Sagi, Y. Edan