基于改进YOLO v10s的温室黄瓜病害识别方法

Method for Identifying Cucumber Diseases in Greenhouses Based on Improved YOLO v10s

摘要:

为了进一步提高温室黄瓜病害的识别精度,本文提出一种基于改进YOLO v10s的模型。首先,在主干网络引入ResNet50网络,增强网络的深度,提升模型的表达能力;其次,在颈部添加CSPPC卷积神经网络结构,在减少计算冗余的同时增强模型对不完整或遮挡数据的特征提取能力;同时引入NAM注意力机制,提升模型对关键信息的关注能力,避免传统注意力机制中的复杂计算,实现高效的特征增强,最终形成黄瓜病害检测模型RCN。实验结果表明,RCN模型的精确率、召回率、mAP@0.5、mAP@0.5:0.95分别达到了95.0%、98.1%、98.3%、70.4%,相比于YOLO v10s分别提升了4.3、7.4、2.9、5.3个百分点,改进效果明显。与主流模型相比,RCN模型效果更优,能够满足检测需求,为温室黄瓜病害的识别提供了一种更优解,对于温室黄瓜病害的防治具有重要意义。

Abstract:

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.