Detection of On-Ground Chestnuts Using Artificial Intelligence Toward Automated Picking

• URL: http://arxiv.org/abs/2602.14140v1
• Date: Sun, 15 Feb 2026 13:28:23 GMT
• Title: Detection of On-Ground Chestnuts Using Artificial Intelligence Toward Automated Picking
• Authors: Kaixuan Fang, Yuzhen Lu, Xinyang Mu,
• Abstract summary: Traditional mechanized chestnut harvesting is too costly for small producers.<n> Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology.<n>This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts.
• Score: 0.09176056742068812
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. Experimental results show that the YOLOv12m model achieves the best mAP@0.5 of 95.1% among all the evaluated models, while the RT-DETRv2-R101 was the most accurate variant among RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrate significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. Both the dataset and software programs in this study have been made publicly available at https://github.com/AgFood-Sensing-and-Intelligence-Lab/ChestnutDetection.

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