Proceedings :: International Society of Precision Agriculture

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Alves, M.N

Aflalo , E

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Authors

Carmon, T

Aflalo , E

Sagi, A

Edan, Y

Cifuentes Arenas, J

Lunewski, C

Keil, F

Alves, M.N

Wulff, N.A

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Type

Poster

Oral

Year

2026

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Filter results2 paper(s) found.
1. 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 Download paper
2. Advanced 2D and 3D Image-Based Plant Phenotyping of Citrus Morphological Responses to Candidatus Liberibacter Asiaticus Infection Huanglongbing (HLB), associated with Candidatus Liberibacter asiaticus (CLas), is the most destructive citrus disease worldwide and threatens the long-term sustainability of production because all commercially cultivated varieties are susceptible. Identifying tolerant or resistant genotypes has therefore become a central priority for modern citrus breeding programs. Conventional phenotyping based on visual scoring and manual measurements is time-consuming, labor intensive, subjective,... J. Cifuentes Arenas, C. Lunewski, F. Keil, M.N. Alves, N.A. Wulff Download paper

Filter results2 paper(s) found.

1. 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

Download paper

2. Advanced 2D and 3D Image-Based Plant Phenotyping of Citrus Morphological Responses to Candidatus Liberibacter Asiaticus Infection

Huanglongbing (HLB), associated with Candidatus Liberibacter asiaticus (CLas), is the most destructive citrus disease worldwide and threatens the long-term sustainability of production because all commercially cultivated varieties are susceptible. Identifying tolerant or resistant genotypes has therefore become a central priority for modern citrus breeding programs. Conventional phenotyping based on visual scoring and manual measurements is time-consuming, labor intensive, subjective,... J. Cifuentes Arenas, C. Lunewski, F. Keil, M.N. Alves, N.A. Wulff

Download paper