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| Content Provider | frontiers |
|---|---|
| Author | Jiao, Lin Li, Gaoqiang Chen, Peng Wang, Rujing Du, Jianming Liu, Haiyun Dong, Shifeng |
| Abstract | An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, the precise detection of multi-classes pest faces great challenges. In this study, an end-to-end pest detection algorithm has been proposed on the basis of deep convolutional neural networks. The detection method adopts a deformable residual network to extract pest features and a global context-aware module for obtaining region-of-interests of agricultural pests. The detection results of the proposed method are compared with the detection results of other state-of-the-art methods, for example, RetinaNet, YOLO, SSD, FPN, and Cascade RCNN modules. The experimental results show that our method can achieve an average accuracy of 77.8% on 21 categories of agricultural pests. The proposed detection algorithm can achieve 20.9 frames per second, which can satisfy real-time pest detection. |
| ISSN | 1664462X |
| DOI | 10.3389/fpls.2022.895944 |
| Volume Number | 13 |
| Journal | Frontiers in Plant Science |
| Language | English |
| Publisher Date | 2022-06-02 |
| Access Restriction | Open |
| Subject Keyword | Deep learning Convolutional Neural Network Agricultural pest Deformable residual network Target detection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Plant Science |
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