Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53036-4_4
Reference12 articles.
1. Ahmad, I., Yang, Y., Yue, Y., Ye, C., Hassan, M., Cheng, X., Yunzhi, W., Zhang, Y.: Deep learning based detector YOLOv5 for identifying insect pests. Appl. Sci. 12(19), 10167 (2022)
2. Min Dai, Md., Dorjoy, M.H., Miao, H., Zhang, S.: A new pest detection method based on improved YOLOv5m. Insects 14(1), 54 (2023)
3. Zhu, D., et al.: Knowledge graph and deep learning based pest detection and identification system for fruit quality. Internet Things 21, 100649 (2023)
4. Nithin Kumar, N., Flammini, F.: YOLO-based light-weight deep learning models for insect detection system with field adaption. Agriculture 13(3), 741 (2023)
5. Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2020
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