Identification of Grape Diseases Based on Improved YOLOXS

文献类型： 外文期刊

第一作者： Wang, Chaoxue

作者： Wang, Chaoxue;Wang, Yuanzhao;Bian, Genqing;Ma, Gang;Ma, Chunsen;Ma, Chunsen

作者机构：

关键词： grape disease; YOLOXS; FOCUS; Convolutional Block Attention Module; double residual edge

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.7； 五年影响因子：2.9 ）

ISSN：

年卷期： 2023 年 13 卷 10 期

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收录情况： SCI

摘要： Here we proposed a grape disease identification model based on improved YOLOXS (GFCD-YOLOXS) to achieve real-time detection of grape diseases in field conditions. We build a dataset of 11,056 grape disease images in 15 categories, based on 2566 original grape disease images provided by the State Key Laboratory of Plant Pest Biology data center after pre-processing. To improve the YOLOXS algorithm, first, the FOCUS module was added to the backbone network to reduce the lack of information related to grape diseases in the convolution process so that the different depth features in the backbone network are fused. Then, the CBAM (Convolutional Block Attention Module) was introduced at the prediction end to make the model focus on the key features of grape diseases and mitigate the influence of the natural environment. Finally, the double residual edge was introduced at the prediction end to prevent degradation in the deep network and to make full use of the non-key features. Compared with the experimental results of relevant authoritative literature, GFCD-YOLOXS had the highest identification accuracy of 99.10%, indicating the superiority of the algorithm in this paper.

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