Proceedings
Authors
| Filter results5 paper(s) found. |
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1. A Remote Interface for a Human-Robot Cooperative Vineyard Sprayer... Y. Edan, R. Berenstein, I. Ben-halevi |
2. Development of a Multiband Sensor for Citrus Black Spot Disease DetectionCitrus black spot (CBS), or Guignardia citricarpa, is known as the most destroying citrus fungal disease worldwide. CBS causes yield loss as a result of early fruit drop, and it leaves severely blemished and unmarketable fruit. While leaves usually remain symptomless, CBS generates various forms of lesions on citrus fruits including hard spot, cracked spot, and virulent spot. CBS lesions often appear on maturing fruit, starting two months before maturity. Warm temperature and sunlight exposure... A. Pourreza, W. Lee, J. Lu, P. Roberts |
3. Spatio-Temporal Sampling-Point Allocation for High-Density Robotic Pest Monitoring and Precision TreatmentEfficient 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 |
4. Comparative Evaluation of Combined and Task Specific Detectors for Pomegranate Yield and Fruit Loss DetectionFruit 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 |
5. Cross-Season Transfer Learning for Prawn Morphometric Estimation Using YOLOv11-PoseThe 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 |