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

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.

Plant phenotyping relevance

茶葉の病害画像から病状を推定するYOLOv7改良モデルの開発・性能評価が中心であり、植物の病害状態を対象とする画像ベース表現型計測に該当する。

Code and data availability

The paper's data availability statement says all data and code are available on GitHub and provides an authors' public URL for the improved YOLOv7 code, which matches an allowed URL. The tea disease image dataset itself is referenced but no explicit dataset URL is supplied, so only the code asset qualifies.