数字农科院2.0

Estimation of the orientation of potatoes and detection bud eye position using potato orientation detection you only look once with fast and accurate features for the movement strategy of intelligent cutting robots

文献类型： 外文期刊

作者： Jie Huang;Xiangyou Wang;Chengqian Jin;Fernando Auat Cheein;Xinyu Yang

作者机构：

关键词： Cutting robot;Deep learning;Lightweight;Potato;Rotated object detection;You-only-look-once-v8

期刊名称： Engineering Applications of Artificial Intelligence

ISSN： 0952-1976

年卷期： 2025 年 142 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： The accurate detection of potato orientation and bud eye positions is critical for guiding the end-effector of intelligent cutting robots. This study introduced Potato Orientation Detection You Only Look Once (POD-YOLO), a novel lightweight model based on YOLOv8n, designed for fast and precise detection of potato orientation and bud eye locations. Key innovations include replacing the Cross Stage Partial Dark Network (CSPDarkNet) with the Cross Stage Partial and Dual Partial Network (CSPDPNet) to reduce parameter count and improve detection accuracy. Additionally, the “no more strided convolutions or pooling” approach replaced downsampling modules in the backbone and neck, enhancing detection of small targets and low-resolution images. The regression loss function was further optimized by substituting Kalman Filtering Intersection over Union (KFIoU) for improved rotated bounding box performance. Experimental results showed that POD-YOLO achieved a mean Average Precision (mAP) of 97.2%, with a precision of 95.2%, recall of 94.0%, and detection time of 9.01 ms. With only 1.75 million parameters, POD-YOLO was lightweight and efficient, meeting real-time requirements. This research offers a robust and effective solution for automated potato orientation and bud eye detection, laying the groundwork for advanced agricultural automation.

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