Review on Pest Detection and Classification in Agricultural Environments Using Image-Based Deep Learning Models and Its Challenges
Author:
Publisher
Allerton Press
Subject
Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials
Link
https://link.springer.com/content/pdf/10.3103/S1060992X23040112.pdf
Reference51 articles.
1. Sujatha, R., Jyotir Moy Chatterjee, Jhanjhi, N.Z., and Sarfraz Nawaz Brohi, Performance of deep learning vs. machine learning in plant leaf disease detection, Microprocess. Microsyst., 2021, ISSN 0141-9331. https://doi.org/10.1016/j.micpro.2020.103615
2. Shanwen Zhang, Subing Zhang, Chuanlei Zhang, Xianfeng Wang, amd Yun Shi, Cucumber leaf disease identification with global pooling dilated convolutional neural network, Comput. Electron. Agric. 2019.https://doi.org/10.1016/j.compag.2019.03.012
3. Prem Enkvetchakul and Olarik Surinta, Effective data augmentation and training techniques for improving deep learning in plant leaf disease recognition, Appl. Sci. Eng. Progress, 2022, vol. 15, no. 3.
4. Li, L., Zhang, S., and Wang, B., Plant disease detection and classification by deep learning—A review, IEEE Access., 2021, vol. 9, pp. 56683–56698. https://doi.org/10.1109/ACCESS.2021.3069646
5. Liu, J. and Wang, X., Plant diseases and pests detection based on deep learning: a review, Plant Methods, 2021, vol. 17, pp. 1–18.
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