ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases
Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight mode...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1204569/full |
| _version_ | 1797741144198610944 |
|---|---|
| author | Weishi Xu Runjie Wang |
| author_facet | Weishi Xu Runjie Wang |
| author_sort | Weishi Xu |
| collection | DOAJ |
| description | Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight model for apple leaf disease detection based on YOLO-V5s (ALAD-YOLO) is proposed in this paper. An apple leaf disease detection dataset is collected, containing 2,748 images of diseased apple leaves under a complex environment, such as from different shooting angles, during different spans of the day, and under different weather conditions. Moreover, various data augmentation algorithms are applied to improve the model generalization. The model size is compressed by introducing the Mobilenet-V3s basic block, which integrates the coordinate attention (CA) mechanism in the backbone network and replacing the ordinary convolution with group convolution in the Spatial Pyramid Pooling Cross Stage Partial Conv (SPPCSPC) module, depth-wise convolution, and Ghost module in the C3 module in the neck network, while maintaining a high detection accuracy. Experimental results show that ALAD-YOLO balances detection speed and accuracy well, achieving an accuracy of 90.2% (an improvement of 7.9% compared with yolov5s) on the test set and reducing the floating point of operations (FLOPs) to 6.1 G (a decrease of 9.7 G compared with yolov5s). In summary, this paper provides an accurate and efficient detection method for apple leaf disease detection and other related fields. |
| first_indexed | 2024-03-12T14:22:39Z |
| format | Article |
| id | doaj.art-a9c8ff3cfc0d4af59f189b3a122438fa |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-12T14:22:39Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-a9c8ff3cfc0d4af59f189b3a122438fa2023-08-18T14:25:29ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-08-011410.3389/fpls.2023.12045691204569ALAD-YOLO:an lightweight and accurate detector for apple leaf diseasesWeishi Xu0Runjie Wang1School of Intelligent Science and Technology, East China University of Science and Technology, Shanghai, ChinaSchool of Geological Engineering, Tongji University, Shanghai, ChinaSuffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight model for apple leaf disease detection based on YOLO-V5s (ALAD-YOLO) is proposed in this paper. An apple leaf disease detection dataset is collected, containing 2,748 images of diseased apple leaves under a complex environment, such as from different shooting angles, during different spans of the day, and under different weather conditions. Moreover, various data augmentation algorithms are applied to improve the model generalization. The model size is compressed by introducing the Mobilenet-V3s basic block, which integrates the coordinate attention (CA) mechanism in the backbone network and replacing the ordinary convolution with group convolution in the Spatial Pyramid Pooling Cross Stage Partial Conv (SPPCSPC) module, depth-wise convolution, and Ghost module in the C3 module in the neck network, while maintaining a high detection accuracy. Experimental results show that ALAD-YOLO balances detection speed and accuracy well, achieving an accuracy of 90.2% (an improvement of 7.9% compared with yolov5s) on the test set and reducing the floating point of operations (FLOPs) to 6.1 G (a decrease of 9.7 G compared with yolov5s). In summary, this paper provides an accurate and efficient detection method for apple leaf disease detection and other related fields.https://www.frontiersin.org/articles/10.3389/fpls.2023.1204569/fullapple leaf diseaselightweight object detectionALAD-YOLOcoordinate attentiongroup convolution |
| spellingShingle | Weishi Xu Runjie Wang ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases Frontiers in Plant Science apple leaf disease lightweight object detection ALAD-YOLO coordinate attention group convolution |
| title | ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases |
| title_full | ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases |
| title_fullStr | ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases |
| title_full_unstemmed | ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases |
| title_short | ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases |
| title_sort | alad yolo an lightweight and accurate detector for apple leaf diseases |
| topic | apple leaf disease lightweight object detection ALAD-YOLO coordinate attention group convolution |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1204569/full |
| work_keys_str_mv | AT weishixu aladyoloanlightweightandaccuratedetectorforappleleafdiseases AT runjiewang aladyoloanlightweightandaccuratedetectorforappleleafdiseases |