Open Access
| Issue |
ITM Web Conf.
Volume 77, 2025
2025 International Conference on Education, Management and Information Technology (EMIT 2025)
|
|
|---|---|---|
| Article Number | 01038 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/itmconf/20257701038 | |
| Published online | 02 July 2025 | |
- Sun Daming, Xing Lulu, Zhao Wei, et al. Target Detection Algorithm of YOLO Series in Agricultural Production[J]. Information and Computer (Theoretical Edition), 2024, 36(12): 23–26. [Google Scholar]
- Zhang Yong, Zhai Jincheng, Wang Lixiao, et al. Agricultural pest and disease recognition algorithm based on PP-YOLO[J]. China Fruit and Vegetable, 2024, 44(05): 80–87. [Google Scholar]
- Nannan Zhang, Xiao Zhang, Tiecheng Bai, et al. Recognition of cotton leaf pests and diseases in natural environment based on CBAM-YOLO v7 [J]. Journal of Agricultural Machinery, 2023, 54(S1): 239–244. [Google Scholar]
- Li Fengmei, Gong Qingsong, Li Benben, et al. Rice pest and disease monitoring system based on improved YOLO v5s model[J]. Agricultural Engineering Technology, 2024, 44(20): 17–20. [Google Scholar]
- Pang Chao, Wang Chuanan, Su Yu, et al. Rice disease detection method based on improved YOLOv8n[J]. Journal of Inner Mongolia Agricultural University (Natural Science Edition), 2024, 45(02): 62–68. [Google Scholar]
- Liu Guang, Hu Guoyu, Gulibahar Tohti, et al. An improved YOLOv3-based method for detecting leaf pests and diseases in grapes [J]. Microelectronics and Computers, 2023, 40(02): 110–119. [Google Scholar]
- Dai Jiujun, Ma Xiaheng, Wu Jian, et al. Identification of grape leaf diseases based on improved residual network[J]. Jiangsu Agricultural Science, 2023, 51(05): 208–215. [Google Scholar]
- Zhao Hui, Huang Dart, Wang Hongjun, et al. Research on pest identification algorithm based on improved YOLO v7 in complex environment of farmland[J]. Journal of Agricultural Machinery, 2023, 54(10): 246–254. [Google Scholar]
- Zhang Huili, Dai Chenlong, Ren Jinglong, et al. Research on grapevine disease identification method based on GhostNetV2 improved YOLOv8 model[J/OL]. Journal of Agricultural Machinery, 1–11[2024-11-20]. [Google Scholar]
- Akindele O., Atolagbe J. YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection[J]. arXiv preprint arXiv:2410.11727, 2024. [Google Scholar]
- Liu H., Chen T., Guo P., et al. Non-local attention optimized deep image compression[J]. arXiv preprint arXiv:1904.09757, 2019. [Google Scholar]
- Yang G., Lei J., Zhu Z., et al. AFPN: Asymptotic feature pyramid network for object detection[C]//2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023: 2184–2189. [CrossRef] [Google Scholar]
- Ma S., Xu Y. Mpdiou: a loss for efficient and accurate bounding box regression[J]. arXiv preprint arXiv:2307.07662, 2023. [Google Scholar]
- Zheng Z., Wang P., Liu W., et al. Distance-IoU loss: Faster and better learning for bounding box regression[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 12993–13000. [Google Scholar]
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