Classification and identification of tea diseases based on improved YOLOv7 model of MobileNeXt

• 全文链接

基于MobileNeXt改进的YOLOv7模型的茶树病害分类与识别

关键词：

来源：

Scientific Reports 期刊

全文链接：

//agri.nais.net.cn/topic/downloadFile/f71c6cd4-5809-4caa-8a4c-d95f278d2fb2

来源地址：

https://www.nature.com/articles/s41598-024-62451-y

资源所属：

茶学研究专题

类型：

学术文献

语种：

英语

原文发布日期：

2024-05-23

摘要：

To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to reduce computational load and enhance efficiency. Additionally, a dual-layer routing attention mechanism was introduced to enhance the model’s ability to capture crucial details and textures in disease images, thereby improving accuracy. The SIoU loss function was employed to mitigate missed and erroneous judgments, resulting in improved recognition amidst complex image backgrounds.The revised model achieved precision, recall, and average precision of 93.5%, 89.9%, and 92.1%, respectively, representing increases of 4.5%, 1.9%, and 2.6% over the original model. Furthermore, the model’s volum was reduced by 24.69M, the total param was reduced by 12.88M, while detection speed was increased by 24.41 frames per second. This enhanced model efficiently and accurately identifies tea disease types, offering the benefits of lower parameter count and faster detection, thereby establishing a robust foundation for tea disease monitoring and prevention efforts.