Features of pyramid dilation rate with residual connected convolution neural network for pest classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02712-x.pdf
Reference20 articles.
1. Wu, X., Zhan, C., Lai, Y.-K., Cheng, M.-M., Yang, J.: Ip102: A large-scale benchmark dataset for insect pest recognition. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8779–8788 (2019). https://doi.org/10.1109/CVPR.2019.00899
2. Nanni, L., Maguolo, G., Pancino, F.: Insect pest image detection and recognition based on bio-inspired methods. Eco. Inform. 57, 101089 (2020). https://doi.org/10.1016/j.ecoinf.2020.101089
3. Kasinathan T, Singaraju D, Srinivasulu Reddy U. Insect classification and detection in field crops using modern machine learning techniques. Inf. Process. Agric. 8(3), 446–457 (2021) https://doi.org/10.1016/j.inpa.2020.09.006
4. Zhang, Y., Liu, Y.: Identification of navel orange diseases and pests based on the fusion of densenet and self-attention mechanism. Comput. Intell. Neurosci. 2021, 1–12 (2021). https://doi.org/10.1155/2021/5436729
5. Li, W., Zheng, T., Yang, Z., Li, M.: Classification and detection of insects from field images using deep learning for smart pest management: a systematic review. Ecol. Inform. 66, 101460 (2021). https://doi.org/10.1016/j.ecoinf.2021.101460
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