沈阳农业大学学报2023，Vol.54Issue(6)：712-721,10.DOI:10.3969/j.issn.1000-1700.2023.06.008
基于改进YOLOv5s的番茄叶片病害检测方法
Tomato Leaf Diseases Detection Method Based on Improved YOLOv5s
摘要
Abstract
In order to solve the problem of low accuracy and poor effect of existing crop disease detection methods for different tomato leaf diseases,a tomato leaf disease detection model YOLOv5s-TLD based on YOLOv5 network model was proposed.Firstly,the DCAM attention mechanism module was constructed in the Backbone of the original YOLOv5s model.Dual-channel attention and spatial attention mechanisms were developed to strengthen the model's ability to extract pathological features of tomato leaves and weaken the model's influence on complex background features,to improve the model's detection accuracy and classification accuracy for different kinds of diseases.Secondly,the C3STR module of integrated Swin Transformer was used to replace the C3 module at the sixth layer of the original network to strengthen the model's multi-scale modeling ability and realize the model's high-precision learning of small-size tomato leaf disease residual features.Then the BiFPN weighted bidirectional feature pyramid network was used to replace the PANet path aggregation network of the Head of the original YOLOv5 model.The network used cross-scale feature fusion and learnable weights to integrate features of different levels of the model,which enhanced the feature fusion capability of the network and enabled the network to obtain more feature information to improve the model's receptive field and feature expression ability.Finally,different models were tested and compared,and tomato leaf disease test was carried out in the actual complex scene.The experimental results showed that the average accuracy and recall rate of the YOLOv5s-TLD model were 97.7%and 96.3%,respectively,which were 1.9 percentage points and 2.5 percentage points higher than the original YOLOv5s model.The model has good detection accuracy and detection effect,and the model can accurately detect and identify different types of tomato leaf diseases under the actual growing environment with complex background.The research results can provide references for the practical application of agricultural intelligent management and tomato leaf disease detection technology.关键词
深度学习/YOLOv5s/卷积神经网络/病害检测/番茄叶片病害Key words
deep learning/YOLOv5s/convolutional neural network/disease detection/tomato leaf diseases分类
信息技术与安全科学引用本文复制引用
陶兆胜,石鑫宇,王勇,伍毅,吴浩..基于改进YOLOv5s的番茄叶片病害检测方法[J].沈阳农业大学学报,2023,54(6):712-721,10.基金项目
安徽省自然科学基金面上项目(2108085ME166) （2108085ME166）
安徽高校自然科学研究项目重点项目(KJ2021A0408) （KJ2021A0408）
