Rice Leaf Disease Diagnosis Using Dense EfficientNet Model
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64847-2_18
Reference10 articles.
1. Bari, B.S., et al.: A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Comput. Sci. 7, e432 (2021)
2. Bashir, K., Rehman, M., Bari, M.: Detection and classification of rice diseases: an automated approach using textural features (2019)
3. Rahman, C.R., et al.: Identification and recognition of rice diseases and pests using convolutional neural networks (2020)
4. Ramakrishnan, M., Nisha, A.S.A.: Groundnut leaf disease detection and classification by using back probagation algorithm. In: 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India, p. 09640968 (2015)
5. Zhang, G.-F., Cao, H.-X.: Rice blast disease recognition using a deep convolutional neural network. Sci. Rep. 9, 1–10 (2019). https://doi.org/10.1038/s41598-019-38966-0
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