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Rice leaf disease detection based on enhanced feature fusion and target adaptation

文献类型： 外文期刊

作者： Li, Zhaoxing 1 ; Yang, Kai 2 ; Ye, Wei 1 ; Wang, Jiaoyu 4 ; Qiu, Haiping 4 ; Wang, Hongkai 5 ; Xu, Zhengguo 1 ; Xie, Dejin 3 ;

作者机构： 1.Zhejiang Univ, Huzhou Inst, Huzhou, Peoples R China

2.China Jiliang Univ, Coll Opt & Elect Technol, Hangzhou, Peoples R China

3.Zhejiang Univ, Coll Control Sci & Engn, Hangzhou, Peoples R China

4.Zhejiang Acad Agr Sci, Hangzhou, Peoples R China

5.Zhejiang Univ, Inst Biotechnol, Hangzhou, Peoples R China

关键词： attention mechanism; deep-learning; enhanced feature fusion; rice leaf disease; target adaptation

期刊名称：PLANT PATHOLOGY （ 影响因子：2.7； 五年影响因子：2.8 ）

ISSN： 0032-0862

年卷期： 2024 年 73 卷 4 期

页码：

收录情况： SCI

摘要： Intelligent rice disease recognition methods based on deep neural networks can predict the degree of disease on the basis of, for example, the number of disease spots on an image, so that preventive measures can be taken. Currently, intelligent recognition methods for rice diseases suffer from the disadvantages of poor versatility and low accuracy. This paper uses eight common image classification networks to classify and identify four rice diseases. ResNet50 was selected as the feature extraction network and an enhanced feature fusion and target adaptive network (EFFTAN), referred to as EFFTAN, is proposed. The EFFTAN was used to detect four rice spot diseases in the rice leaf disease image samples dataset; the mean average precision of the final detection was 95.3%, and effective detection was also achieved for the dense spot features. The proposed EFFTAN model was validated for robustness and generalization of four rice leaf spot data plus maize data samples, and the experimental data demonstrated the significance and validity of the research work.image

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