Coconut Palm Tree Counting on Drone Images with Deep Object Detection and Synthetic Training Data

Rohe, Tobias; Böhm, Barbara; Kölle, Michael; Stein, Jonas ; Müller, Robert und Linnhoff-Popien, Claudia (2025): Coconut Palm Tree Counting on Drone Images with Deep Object Detection and Synthetic Training Data. ICAART 2025: 17th International Conference on Agents and Artificial Intelligence, Porto, Portugal, 23.- 25. Februar 2025. Rocha, Ana Paula; Steels, Luc und Herik, H. Jaap van den (Hrsg.): In: Proceedings of the 17th International Conference on Agents and Artificial Intelligence, Bd. 3 Setúbal: SciTePress. S. 662-669

Abstract

Drones have revolutionized various domains, including agriculture. Recent advances in deep learning have propelled among other things object detection in computer vision. This study utilized YOLO, a real-time object detector, to identify and count coconut palm trees in Ghanaian farm drone footage. The farm presented has lost track of its trees due to different planting phases. While manual counting would be very tedious and error-prone, accurately determining the number of trees is crucial for efficient planning and management of agricultural processes, especially for optimizing yields and predicting production. We assessed YOLO for palm detection within a semi-automated framework, evaluated accuracy augmentations, and pondered its potential for farmers. Data was captured in September 2022 via drones. To optimize YOLO with scarce data, synthetic images were created for model training and validation. The YOLOv7 model, pretrained on the COCO dataset (excluding coconut palms), was adapted using tailored data. Trees from footage were repositioned on synthetic images, with testing on distinct authentic images. In our experiments, we adjusted hyperparameters, improving YOLO's mean average precision (mAP). We also tested various altitudes to determine the best drone height. From an initial mAP@.5 of 0.65, we achieved 0.88, highlighting the value of synthetic images in agricultural scenarios.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-989-758-737-5
• Ort: Setúbal
• Sprache: Englisch