Proceedings :: International Society of Precision Agriculture

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Sanches , J

Sagi, A

Santos, T

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Authors

Speranza, E.A

Grego, C.R

Santos, T

Rodrigues, G.C

Inamasu, R.Y

Carmon, T

Aflalo , E

Sagi, A

Edan, Y

Santos, T

Bharti, D

Gebler, L

De Rossi, A

Schilling, K

Soares, F

Small, I

Krco, S

Eduardo Pereira, C

Santos, T

Topics

Remote and Proximal Sensing of Soils and Crops

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Agricultural Robotics, Automation, and Mechanization

Invited Presentations

Type

Poster

Oral

Year

2026

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Filter results4 paper(s) found.
1. Delineation of Management Zones for the Adoption of Precision and Digital Agriculture in Steep-Sloped Arabica Coffee Production Areas Coffea arabica production in Brazil, particularly in regions of São Paulo and Minas Gerais, occurs in environments with a high diversity of climates, altitudes, and soils. The municipality of Caconde (SP) stands out with approximately 11,000 hectares of coffee, predominantly on small properties with altitudes above 800 meters and steep slopes. These characteristics are conducive to the production of high-quality, value-added coffees. Optimizing the use of natural resources and agricultural... E.A. Speranza, C.R. Grego, T. Santos, G.C. Rodrigues, R.Y. Inamasu Download paper
2. 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
3. Proprietary vs. Open-Source Visual-Inertial Fusion Under GNSS Degradation for Orchard-Scale 3D Fruit Mapping A recent pipeline combining GNSS-visual-inertial odometry with factor-graph refinement of fruit landmarks has reported orchard-level apple counting errors below three percent against harvest totals — a result with few precedents in the agricultural SLAM literature, where GNSS-VIO fusion, landmark-level optimization, and harvest-validated yield estimation have until now appeared only in isolation. However, trajectory quality in that pipeline was assessed... T. Santos, D. Bharti, L. Gebler, A. De Rossi Download paper
4. Panel: Developing Enabling Technologies for Digital Agriculture – International Perspective ... K. Schilling, F. Soares, I. Small, S. Krco, C. Eduardo Pereira, T. Santos

Filter results4 paper(s) found.

1. Delineation of Management Zones for the Adoption of Precision and Digital Agriculture in Steep-Sloped Arabica Coffee Production Areas

Coffea arabica production in Brazil, particularly in regions of São Paulo and Minas Gerais, occurs in environments with a high diversity of climates, altitudes, and soils. The municipality of Caconde (SP) stands out with approximately 11,000 hectares of coffee, predominantly on small properties with altitudes above 800 meters and steep slopes. These characteristics are conducive to the production of high-quality, value-added coffees. Optimizing the use of natural resources and agricultural... E.A. Speranza, C.R. Grego, T. Santos, G.C. Rodrigues, R.Y. Inamasu

Download paper

2. 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

3. Proprietary vs. Open-Source Visual-Inertial Fusion Under GNSS Degradation for Orchard-Scale 3D Fruit Mapping

A recent pipeline combining GNSS-visual-inertial odometry with factor-graph refinement of fruit landmarks has reported orchard-level apple counting errors below three percent against harvest totals — a result with few precedents in the agricultural SLAM literature, where GNSS-VIO fusion, landmark-level optimization, and harvest-validated yield estimation have until now appeared only in isolation. However, trajectory quality in that pipeline was assessed... T. Santos, D. Bharti, L. Gebler, A. De Rossi

Download paper

4. Panel: Developing Enabling Technologies for Digital Agriculture – International Perspective

... K. Schilling, F. Soares, I. Small, S. Krco, C. Eduardo Pereira, T. Santos