Crop pest detection by three-scale convolutional neural network with attention
Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neur...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237663/?tool=EBI |
| _version_ | 1797810402650750976 |
|---|---|
| author | Xuqi Wang Shanwen Zhang Xianfeng Wang Cong Xu |
| author_facet | Xuqi Wang Shanwen Zhang Xianfeng Wang Cong Xu |
| author_sort | Xuqi Wang |
| collection | DOAJ |
| description | Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field. |
| first_indexed | 2024-03-13T07:08:16Z |
| format | Article |
| id | doaj.art-11b6f1ec3b76483ba10709bc843bd8aa |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T07:08:16Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-11b6f1ec3b76483ba10709bc843bd8aa2023-06-06T05:31:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01186Crop pest detection by three-scale convolutional neural network with attentionXuqi WangShanwen ZhangXianfeng WangCong XuCrop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237663/?tool=EBI |
| spellingShingle | Xuqi Wang Shanwen Zhang Xianfeng Wang Cong Xu Crop pest detection by three-scale convolutional neural network with attention PLoS ONE |
| title | Crop pest detection by three-scale convolutional neural network with attention |
| title_full | Crop pest detection by three-scale convolutional neural network with attention |
| title_fullStr | Crop pest detection by three-scale convolutional neural network with attention |
| title_full_unstemmed | Crop pest detection by three-scale convolutional neural network with attention |
| title_short | Crop pest detection by three-scale convolutional neural network with attention |
| title_sort | crop pest detection by three scale convolutional neural network with attention |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237663/?tool=EBI |
| work_keys_str_mv | AT xuqiwang croppestdetectionbythreescaleconvolutionalneuralnetworkwithattention AT shanwenzhang croppestdetectionbythreescaleconvolutionalneuralnetworkwithattention AT xianfengwang croppestdetectionbythreescaleconvolutionalneuralnetworkwithattention AT congxu croppestdetectionbythreescaleconvolutionalneuralnetworkwithattention |