基于改进YOLO v10s的温室黄瓜病害识别方法OA

Aiming to further improve the speed and accuracy of cucumber disease recognition in greenhouses,a model based on an improved YOLO v10s was proposed.Firstly,the ResNet50 network was integrated into the backbone network to enhance the network depth,through which the model's expressive capability was significantly improved.Subsequently,a CSPPC convolutional neural network structure was added to the neck,where computational redundancy was reduced while the feature extraction ability for incomplete or occluded data was strengthened.Simultaneously,the NAM attention mechanism was incorporated to amplify attention to critical information,avoiding complex computations in traditional attention mechanisms and achieving efficient feature enhancement,ultimately forming the RCN model for cucumber disease detection.Experimental results demonstrated that the RCN model achieved precision,recall,mAP@0.5,and mAP@0.5:0.95 rates of 95.0%,98.1%,98.3%,and 70.4%,respectively,representing improvements of 4.3,7.4,2.9,and 5.3 percentage points compared with the baseline YOLO v10s,with significant enhancements observed.Ablation studies revealed that the integration of the ResNet50 network contributed most significantly to accuracy improvement,with all proposed modifications collectively enhancing the recognition precision of the YOLO v10s model.Comparative evaluations revealed that the RCN model exhibited superior performance relative to mainstream models,meeting detection requirements and providing an optimized solution for cucumber disease recognition in greenhouse environments.This approach was validated as holding substantial significance for the prevention and control of cucumber diseases in agricultural systems.