Detection of On-Ground Chestnuts Using Artificial Intelligence Toward Automated Picking
Kaixuan Fang, Yuzhen Lu, Xinyang Mu

TL;DR
This study evaluates various state-of-the-art object detection models for accurately identifying chestnuts on orchard floors under complex environmental conditions, aiming to develop cost-effective automated harvesting solutions.
Contribution
It provides a comprehensive comparison of 29 real-time object detectors, identifying YOLOv12m as the most accurate model for chestnut detection in challenging outdoor environments.
Findings
YOLOv12m achieved 95.1% [email protected] accuracy.
RT-DETRv2-R101 achieved 91.1% [email protected].
YOLO models outperform RT-DETR models in accuracy and inference speed.
Abstract
Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11-13) and 15 in the RT-DETR (v1-v4) families at varied model scales, was systematically evaluated through replicated modeling experiments for chestnut detection.…
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Taxonomy
TopicsSmart Agriculture and AI · Plant Disease Management Techniques · Nuts composition and effects
