Authors

Abstract

Stripe rust, leaf rust, and powdery mildew are important leaf diseases in wheat, which significantly affect the yield and quality of wheat. Their timely identification and diagnosis are of great significance for disease management. To achieve convenient identification of wheat leaf diseases based on mobile devices, an improved YOLOv8 method for wheat leaf disease detection is proposed. This method incorporates the CBAM(Convolutional Block Attention Module) attention mechanism module into the feature fusion network to enhance the network's feature expression ability. Experimental results show that the improved YOLOv8 model has an accuracy, recall rate, and mean average precision (mAP) of 95%, 98.3%, and 98.8% respectively for wheat leaf disease detection, with a model memory usage of 5.92MB. Compared with the Faster R-CNN, YOLOv5, YOLOv7, and YOLOv8 models, the mAP has been improved by 66.76, 48, 13.2, and 1.9 percentage points respectively, and it also has the lowest model memory usage. The research demonstrates that the improved YOLOv8 model can provide an effective exploration for automated detection of wheat leaf diseases.

Abstract in Chinese

小麦条锈病、叶锈病和白粉病是小麦叶部重要病害，严重影响小麦的产量和品质，其及时识别和诊断对于病害管理具有重要意义。为实现基于移动端的小麦叶部病害图像便捷识别，提出一种改进的yolov8小麦叶部病害检测方法。该方法在特征融合网络中加入CBAM注意力机制模块以提高网络的特征表达能力。实验结果表明，改进的yolov8模型对小麦叶片病害检测的精确率、召回率和平均精度均值（Mean average precision，mAP）分别为 95%、98.3% 和 98.8%，模型内存占用量为5.92MB。对比Faster R-CNN、YOLOv5、YOLOv7 和 YOLOv8 模型，平均精度均值mAP分别提升了66.76、48、13.2、1.9个百分点，模型内存占用量方面也最优。研究表明改进的 YOLOv8 模型能够为小麦叶片病害实现自动化检测提供有效探索。