YOLO-Ginseng: a detection method for ginseng fruit in natural agricultural environment

作者：Zhedong Xie, Zhuang Yang, Chao Li, Zhen Zhang, Jiazhuo Jiang, Hongyu Guo · 发表于：Frontiers in Plant Science · 年份：2024 · DOI：10.3389/fpls.2024.1422460 · 被引用次数：9 · 研究领域：Smart Agriculture and AI、Biosensors and Analytical Detection、Advanced Chemical Sensor Technologies

Introduction: The accurate and rapid detection of ginseng fruits in natural environments is crucial for the development of intelligent harvesting equipment for ginseng fruits. Due to the complexity and density of the growth environment of ginseng fruits, some newer visual detection methods currently fail to meet the requirements for accurate and rapid detection of ginseng fruits. Therefore, this study proposes the YOLO-Ginseng detection method. Methods: Firstly, this detection method innovatively proposes a plug-and-play deep hierarchical perception feature extraction module called C3f-RN, which incorporates a sliding window mechanism. Its unique structure enables the interactive processing of cross-window feature information, expanding the deep perception field of the network while effectively preserving important weight information. This addresses the detection challenges caused by occlusion or overlapping of ginseng fruits, significantly reducing the overall missed detection rate and improving the long-distance detection performance of ginseng fruits; Secondly, in order to maintain the balance between YOLO-Ginseng detection precision and speed, this study employs a mature channel pruning algorithm to compress the model. Results: The experimental results demonstrate that the compressed YOLO-Ginseng achieves an average precision of 95.6%, which is a 2.4% improvement compared to YOLOv5s and only a 0.2% decrease compared to the uncompressed version. The inference time of the m...