Scientific Reports (May 2024)

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

  • Yuxin Xia,
  • Wenxia Yuan,
  • Shihao Zhang,
  • Qiaomei Wang,
  • Xiaohui Liu,
  • Houqiao Wang,
  • Yamin Wu,
  • Chunhua Yang,
  • Jiayi Xu,
  • Lei Li,
  • Junjie He,
  • Zhiyong Cao,
  • Zejun Wang,
  • Zihua Zhao,
  • Baijuan Wang

DOI
https://doi.org/10.1038/s41598-024-62451-y
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 12

Abstract

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Abstract 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.

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