Wang Jun, Qi Zhengyuan, Wang Yanlong, Liu Yanyang
College of Information Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Sci Rep. 2025 Jan 2;15(1):457. doi: 10.1038/s41598-024-84748-8.
In modern agriculture, the proliferation of weeds in cotton fields poses a significant threat to the healthy growth and yield of crops. Therefore, efficient detection and control of cotton field weeds are of paramount importance. In recent years, deep learning models have shown great potential in the detection of cotton field weeds, achieving high-precision weed recognition. However, existing deep learning models, despite their high accuracy, often have complex computations and high resource consumption, making them difficult to apply in practical scenarios. To address this issue, developing efficient and lightweight detection methods for weed recognition in cotton fields is crucial for effective weed control. This study proposes the YOLO-Weed Nano algorithm based on the improved YOLOv8n model. First, the Depthwise Separable Convolution (DSC) structure is used to improve the HGNetV2 network, creating the DS_HGNetV2 network to replace the backbone of the YOLOv8n model. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the feature fusion layer, further optimizing the model's ability to recognize weed features in complex backgrounds. Finally, a lightweight detection head, LiteDetect, suitable for the BiFPN structure, is designed to streamline the model structure and reduce computational load. Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively.
在现代农业中，棉田杂草的大量繁殖对作物的健康生长和产量构成了重大威胁。因此，高效检测和控制棉田杂草至关重要。近年来，深度学习模型在棉田杂草检测中显示出巨大潜力，实现了高精度的杂草识别。然而，现有的深度学习模型尽管准确率高，但往往计算复杂且资源消耗大，难以应用于实际场景。为解决这一问题，开发高效轻量级的棉田杂草识别检测方法对于有效控制杂草至关重要。本研究提出了基于改进的YOLOv8n模型的YOLO-Weed Nano算法。首先，使用深度可分离卷积（DSC）结构改进HGNetV2网络，创建DS_HGNetV2网络来替换YOLOv8n模型的主干。其次，引入双向特征金字塔网络（BiFPN）来增强特征融合层，进一步优化模型在复杂背景下识别杂草特征的能力。最后，设计了一个适用于BiFPN结构的轻量级检测头LiteDetect，以简化模型结构并减少计算量。实验结果表明，与原始YOLOv8n模型相比，YOLO-Weed Nano的平均精度均值（mAP）提高了1%，同时参数数量、计算量和权重分别减少了63.8%、42%和60.7%。
