Qiu Zhaomei, Wang Fei, Wang Weili, Li Tingting, Jin Xin, Qing Shunhao, Shi Yi
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, China.
Science and Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang, Henan, China.
Front Plant Sci. 2024 Nov 19;15:1495222. doi: 10.3389/fpls.2024.1495222. eCollection 2024.
Wheat, being a crucial global food crop, holds immense significance for food safety and agricultural economic stability, as the quality and condition of its grains are critical factors. Traditional methods of wheat grain detection are inefficient, and the advancements in deep learning offer a novel solution for fast and accurate grain recognition. This study proposes an improved deep learning model based on YOLOv8n, referred to as YOLO-SDL, aiming to achieve efficient wheat grain detection. A high-quality wheat grain dataset was first constructed, including images of perfect, germinated, diseased, and damaged grains. Multiple data augmentation techniques were employed to enhance the dataset's complexity and diversity. The YOLO-SDL model incorporates the ShuffleNetV2 architecture in its backbone and combines depthwise separable convolutions (DWConv) with the large separable kernel attention (LSKA) mechanism in its neck structure, significantly improving detection speed and accuracy while ensuring the model remains lightweight. The results indicate that YOLO-SDL achieves superior performance in wheat grain detection, balancing lightweight design and performance optimization. The model achieved a P of 0.942, R of 0.903, mAP50 of 0.965, and mAP50-95 of 0.859, with low computational complexity, making it suitable for resource-constrained environments. These findings demonstrate the efficiency of the ShuffleNetV2, DWConv, and LSKA structures. The proposed YOLO-SDL model provides a new technical solution for agricultural automation and serves as a reliable reference for detecting other crops.
小麦作为全球重要的粮食作物，对食品安全和农业经济稳定具有重大意义，因为其籽粒的质量和状况是关键因素。传统的小麦籽粒检测方法效率低下，而深度学习的发展为快速准确的籽粒识别提供了新的解决方案。本研究提出了一种基于YOLOv8n的改进深度学习模型，称为YOLO-SDL，旨在实现高效的小麦籽粒检测。首先构建了一个高质量的小麦籽粒数据集，包括完美、发芽、患病和受损籽粒的图像。采用了多种数据增强技术来提高数据集的复杂性和多样性。YOLO-SDL模型在其主干中采用了ShuffleNetV2架构，并在其颈部结构中将深度可分离卷积（DWConv）与大内核可分离注意力（LSKA）机制相结合，在确保模型保持轻量级的同时，显著提高了检测速度和准确性。结果表明，YOLO-SDL在小麦籽粒检测中表现优异，在轻量级设计和性能优化之间取得了平衡。该模型的P值为0.942，R值为0.903，mAP50为0.965，mAP50-95为0.859，计算复杂度低，适用于资源受限的环境。这些发现证明了ShuffleNetV2、DWConv和LSKA结构的有效性。所提出的YOLO-SDL模型为农业自动化提供了一种新 的技术解决方案，并为检测其他作物提供了可靠的参考。
