Deep Learning-Based Image Recognition of Agricultural Pests
Source abstract
Abstract
Pests and diseases are an inevitable problem in agricultural production, causing substantial economic losses yearly. The application of convolutional neural networks to the intelligent recognition of crop pest images has become increasingly popular due to advances in deep learning methods and the rise of large-scale datasets. However, the diversity and complexity of pest samples, the size of sample images, and the number of examples all directly affect the performance of convolutional neural networks. Therefore, we designed a new target-detection framework based on Cascade RCNN (Regions with CNN features), aiming to solve the problems of large image size, many pest types, and small and unbalanced numbers of samples in pest sample datasets. Specifically, this study performed data enhancement on the original samples to solve the problem of a small and unbalanced number of examples in the dataset and developed a sliding window cropping method, which could increase the perceptual field to learn sample features more accurately and in more detail without changing the original image size. Secondly, combining the attention mechanism with the FPN (Feature Pyramid Networks) layer enabled the model to learn sample features that were more important for the current task from both channel and space aspects. Compared with the current popular target-detection frameworks, the average precision value of our model (mAP@0.5) was 84.16%, the value of (mAP@0.5:0.95) was 65.23%, the precision was 67.79%, and the F1 score was 82.34%. The experiments showed that our model solved the problem of convolutional neural networks being challenging to use because of the wide variety of pest types, the large size of sample images, and the difficulty of identifying tiny pests.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Date Palm Research Studies · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, North University of China, Taiyuan 030051, China
School of Life Science, Southwest Forestry University, Kunming 650233, China
School of Social Development, Yangzhou University, Yangzhou 225002, China
School of Computer Science and Engineering, UNSW Sydney, Sydney 2052, Australia
Enmotech Data AU, Sydney 2113, Australia
References (43)
Show source-provided references
1. Taigman, Y., Yang, M., Ranzato, M.A., and Wolf, L. (2014, January 23–28). Deepface: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.220 ↗
2. Huang, W., Qiao, Y., and Tang, X. (2014, January 6–12). Robust scene text detection with convolution neural network induced mser trees. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.10.1007/978-3-319-10593-2_33 ↗
3. Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., and Bai, X. (2016, January 27–30). Multi-oriented text detection with fully convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.451 ↗
4. Ouyang, W., and Wang, X. (2013, January 1–8). Joint deep learning for pedestrian detection. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.10.1109/ICCV.2013.257 ↗
5. Zhang, L., Lin, L., Liang, X., and He, K. (2016, January 27–30). Is faster R-CNN doing well for pedestrian detection. Proceedings of the European Conference on Computer Vision, Las Vegas, NV, USA.10.1007/978-3-319-46475-6_28 ↗
6. Hoi, S.C., Wu, X., Liu, H., Wu, Y., Wang, H., Xue, H., and Wu, Q. (2015). Logo-net: Large-scale deep logo detection and brand recognition with deep region-based convolutional networks. arXiv.
7. Kleban, J., Xie, X., and Ma, W.Y. (June, January 26). Spatial pyramid mining for logo detection in natural scenes. Proceedings of the 2008 IEEE International Conference on Multimedia and Expo, Hannover, Germany.10.1109/ICME.2008.4607625 ↗
8. T-cnn: Tubelets with convolutional neural networks for object detection from videos10.1109/TCSVT.2017.2736553 ↗
9. Kang, K., Ouyang, W., Li, H., and Wang, X. (2016, January 27–30). Object detection from video tubelets with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.95 ↗
10. Chen, X., Xiang, S., Liu, C.L., and Pan, C.H. (2013, January 5–8). Vehicle detection in satellite images by parallel deep convolutional neural networks. Proceedings of the 2013 2nd IAPR Asian Conference on Pattern Recognition, Washington, DC, USA.10.1109/ACPR.2013.33 ↗
11. Fan, Q., Brown, L., and Smith, J. (2016, January 19-22). A closer look at Faster R-CNN for vehicle detection. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.10.1109/IVS.2016.7535375 ↗
12. A survey on deep learning in medical image analysis10.1016/j.media.2017.07.005 ↗
13. Ahmad, I., Yang, Y., Yue, Y., Ye, C., Hassan, M., Cheng, X., Wu, Y., and Zhang, Y. (2022). Deep Learning Based Detector YOLOv5 for Identifying Insect Pests. Appl. Sci., 12.10.3390/app121910167 ↗
14. Boedeker, W., Watts, M., Clausing, P., and Marquez, E. (2020). The global distribution of acute unintentional pesticide poisoning: Estimations based on a systematic reviez. BMC Public Health, 20.10.1186/s12889-020-09939-0 ↗
15. Application of non-orthogonal multiple access in wireless sensor networks for smart agriculture10.1109/ACCESS.2019.2924917 ↗
16. Localization and identification of pests in tea plantations based on image saliency analysis and convolutional neural network
17. Image recognition of farmland pests based on sparse coding pyramid model
18. Image recognition of soybean pests based on attentional convolutional neural network
19. Pest identification via deep residual learning in complex background10.1016/j.compag.2017.08.005 ↗
20. Fuentes, A., Yoon, S., Kim, S.C., and Park, D.S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17.10.3390/s17092022 ↗
21. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection10.1016/j.compag.2020.105522 ↗
22. A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest–Damaged Wheat Grain Detection10.1007/s12161-022-02251-0 ↗
23. Gambhir, J., Patel, N., Patil, S., Takale, P., Chougule, A., Prabhakar, C.S., Managanvi, K., Raghavan, A.S., and Sohane, R.K. (2022). Deep Learning for Real-Time Diagnosis of Pest and Diseases on Crops, Springer. Intelligent Data Engineering and Analytics.10.1007/978-981-16-6624-7_19 ↗
24. Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., Wang, F., Zhou, M., and Liu, W. (2020). A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network. Sensors, 20.10.3390/s20030578 ↗
25. Representation learning: A review and new perspectives10.1109/TPAMI.2013.50 ↗
26. Learning convolutional feature hierarchies for visual recognition
27. Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014, January 6–12). Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Zurich, Switzerland.10.1109/CVPR.2014.222 ↗
28. Learning representations by back-propagating errors10.1038/323533a0 ↗
29. Imagenet classification with deep convolutional neural networks10.1145/3065386 ↗
30. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 6–12). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Zurich, Switzerland.10.1109/CVPR.2014.81 ↗
31. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 19–22). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Gothenburg, Sweden.10.1109/CVPR.2016.91 ↗
32. Girshick, R. (2015, January 7–12). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
33. Cai, Z., and Vasconcelos, N. (2018, January 18–23). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00644 ↗
34. Hu, J., Shen, L., and Sun, G. (2018, January 18–23). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
35. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
36. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13–19). Supplementary material for ‘ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
37. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 19–22). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Gothenburg, Sweden.10.1109/CVPR.2016.90 ↗
38. Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017, January 21–26). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.634 ↗
39. Two-stage grasp strategy combining CNN-based classification and adaptive detection on a flexible hand10.1016/j.asoc.2020.106729 ↗
40. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
41. Van Etten, A. (2018). You only look twice: Rapid multi-scale object detection in satellite imagery. arXiv.
42. Efficient non-maximum suppression10.1109/ICPR.2006.479 ↗
43. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv.