Yuanzhao Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yuanzhao Wang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Chaoxue Wang
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
1 shared publication - Chun-Sen Ma
Climate Change Biology Research Group, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China; Climate Change Biology Research Group, School of Life Science, Hebei University, Baoding, China
1 shared publication - Gang Ma
Climate Change Biology Research Group, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
1 shared publication - Genqing Bian
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
1 shared publication
Latest Publications
- Open Access
- 12 May 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (10) , 5978
Identification of Grape Diseases Based on Improved YOLOXS
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.
- 12 May 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (10) , 5978
Open AccessIdentification of Grape Diseases Based on Improved YOLOXS
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.