Enhanced conditional self-attention generative adversarial network for detecting cotton plant disease in IoT-enabled crop management
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11276-024-03762-w.pdf
Reference37 articles.
1. Kalaiselvi, T., & Narmatha, V. (2023). Cotton crop disease detection using FRCM segmentation and convolution neural network classifier. In S. Smys, M. R. João, S. Tavares, & F. Shi (Eds.), Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022 (pp. 557–577). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-9819-5_40
2. Rodda, J., Hema, R., & Devi Durga, C. H. (2023). A comparative analysis of CNN models in deep learning for leaf disease detection. International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2023.v05i05.6041
3. Tao, Y., et al. (2022). Cotton disease detection based on context and attention mechanisms. IEEE Journal of Radio Frequency Identification, 6, 805–809.
4. Kumar, S., Musharaf, D., & Sagar, A. K. (2022). Comparative study of pre-trained models on cotton plant disease detection using transfer learning. In R. N. Shaw, S. Das, V. Piuri, & M. Bianchini (Eds.), Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2022 (pp. 147–155). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2980-9_12
5. Noon, S. K., Amjad, M., Qureshi, M. A., & Mannan, A. (2022). Handling severity levels of multiple co-occurring cotton plant diseases using improved Yolox model. IEEE Access, 10, 134811–134825.
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