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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.

Details

Title
Plant Disease Recognition Model Based on Improved YOLOv5
Author
Chen, Zhaoyi 1 ; Wu, Ruhui 2 ; Lin, Yiyan 1 ; Li, Chuyu 1 ; Chen, Siyu 1 ; Yuan, Zhineng 2 ; Chen, Shiwei 2 ; Zou, Xiangjun 3   VIAFID ORCID Logo 

 College of Engineering, South China Agricultural University, Guangzhou 510642, China; chenzhaoyi@stu.scau.edu.cn (Z.C.); linyiyan@stu.scau.edu.cn (Y.L.); lichuyu@stu.scau.edu.cn (C.L.); darkdrgonflycsy@gmail.com (S.C.) 
 Guangdong Agribusiness Tropical Agricultrue Institute Co., Ltd., Guangzhou 511365, China; 15595625042@163.com (R.W.); yuanzhin7@gmail.com (Z.Y.); hzchenmeng@163.com (S.C.) 
 College of Engineering, South China Agricultural University, Guangzhou 510642, China; chenzhaoyi@stu.scau.edu.cn (Z.C.); linyiyan@stu.scau.edu.cn (Y.L.); lichuyu@stu.scau.edu.cn (C.L.); darkdrgonflycsy@gmail.com (S.C.); Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture, Foshan 528010, China 
First page
365
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20734395
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2632188376
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.