Proceedings :: International Society of Precision Agriculture

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Martins, T.M

Mattupalli, C

Markus, C

Marchioretto, L.D

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Authors

Tosin, M

Scheeren, I

Markus, C

Loganathan Girija, D

Usama Bin Sabir, S

Rathore, D

Khot, L.R

Mattupalli, C

Karkee, M

Martins, T.M

Tetila, E.C

Barbedo, J.G

Felipe, J.C

Zhao, L

Alves, S

da Silva, E.C

Marchioretto, L.D

Gebler, L

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Agricultural Robotics, Automation, and Mechanization

UAV-Based Scouting, Imaging, and Targeted Applications

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Year

2026

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Filter results4 paper(s) found.
1. Combining YOLOv9 and Fuzzy Inference System to Improve the Precision of Weed Recognition Systems in Soybean Crops Using UAV Imagery Weeds are a problem in crops because they compete with crops for nutrients, sunlight, and water, hindering their full development. To control these plants, herbicides are usually applied throughout the field. Therefore, to optimize the application process, many researchers have been working on automatic weed recognition systems based on artificial intelligence techniques for field imaging, enabling the localized application of herbicides. To this end, the YOLO (You Only Look Once) object detection... M. Tosin, I. Scheeren, C. Markus Download paper
2. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee
3. Enhancing Weed Detection in Corn Crops Through Attention-based Models and Curated Datasets Weed infestation is one of the leading causes of global agricultural productivity losses, directly impacting production costs, environmental sustainability, and food security. In precision agriculture, automated weed detection from aerial imagery enables site-specific herbicide application, reducing chemical overuse and environmental impact. Deep learning-based computer vision techniques have been widely adopted for this purpose, with Convolutional Neural Networks (CNNs) historically dominating... T.M. Martins, E.C. Tetila, J.G. Barbedo, J.C. Felipe, L. Zhao Download paper
4. Automated Detection of European Canker (Neonectria ditissima) in Apple Trees via Multispectral Sensors and Computer Vision European canker, caused by the fungus Neonectria ditissima, represents one of the major economic challenges for Brazilian pomiculture, severely affecting ‘Gala’ and ‘Fuji’ cultivars. The disease manifests primarily in woody tissues, such as trunks and branches, although it can also cause fruit rot during the pre-harvest stage. Infection occurs obligatorily through wounds, whether natural (leaf scars) or resulting from management practices (pruning and harvesting).... S. Alves, E.C. Da Silva, L.D. Marchioretto, L. Gebler Download paper

Filter results4 paper(s) found.

1. Combining YOLOv9 and Fuzzy Inference System to Improve the Precision of Weed Recognition Systems in Soybean Crops Using UAV Imagery

Weeds are a problem in crops because they compete with crops for nutrients, sunlight, and water, hindering their full development. To control these plants, herbicides are usually applied throughout the field. Therefore, to optimize the application process, many researchers have been working on automatic weed recognition systems based on artificial intelligence techniques for field imaging, enabling the localized application of herbicides. To this end, the YOLO (You Only Look Once) object detection... M. Tosin, I. Scheeren, C. Markus

Download paper

2. A Dual-Arm Machine-Vision-Guided Robotic System for High-Throughput Tissue Sampling in Potato Tubers

High-throughput molecular pathogen detection in potato tubers requires tissue sampling methods that are both sensitive and specific. A critical step in this workflow is the manual extraction of tissue cores, which is labor-intensive and time-consuming, limiting scalability for large-scale diagnostics. To address this challenge, this study developed a machine-vision-guided, dual-arm coordinated inline robotic system that integrates tuber picking, rotation, and tissue sampling mechanisms. In this... D. Loganathan Girija, S. Usama Bin Sabir, D. Rathore, L.R. Khot, C. Mattupalli, M. Karkee

3. Enhancing Weed Detection in Corn Crops Through Attention-based Models and Curated Datasets

Weed infestation is one of the leading causes of global agricultural productivity losses, directly impacting production costs, environmental sustainability, and food security. In precision agriculture, automated weed detection from aerial imagery enables site-specific herbicide application, reducing chemical overuse and environmental impact. Deep learning-based computer vision techniques have been widely adopted for this purpose, with Convolutional Neural Networks (CNNs) historically dominating... T.M. Martins, E.C. Tetila, J.G. Barbedo, J.C. Felipe, L. Zhao

Download paper

4. Automated Detection of European Canker (Neonectria ditissima) in Apple Trees via Multispectral Sensors and Computer Vision

European canker, caused by the fungus Neonectria ditissima, represents one of the major economic challenges for Brazilian pomiculture, severely affecting ‘Gala’ and ‘Fuji’ cultivars. The disease manifests primarily in woody tissues, such as trunks and branches, although it can also cause fruit rot during the pre-harvest stage. Infection occurs obligatorily through wounds, whether natural (leaf scars) or resulting from management practices (pruning and harvesting).... S. Alves, E.C. Da Silva, L.D. Marchioretto, L. Gebler

Download paper