YOLO-Ginseng: a detection method for ginseng fruit in natural agricultural environment
Zhedong Xie1, Zhuang Yang1, Chao Li1
1College of Engineering and Technology, Jilin Agricultural University, Changchun, China.
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 model reaches 7.4ms. The compressed model exhibits reductions of 76.4%, 79.3%, and 74.2% in terms of model weight size, parameter count, and computational load, respectively.
Discussion:
Compared to other models, YOLO-Ginseng demonstrates superior overall detection performance. During the model deployment experiments, YOLO-Ginseng successfully performs real-time detection of ginseng fruits on the Jetson Orin Nano computing device, exhibiting good detection results. The average detection speed reaches 24.9 fps. The above results verify the effectiveness and practicability of YOLO-Ginseng, which creates primary conditions for the development of intelligent ginseng fruit picking equipment.
