Deep Transfer Learning for Enhanced Blackgram Disease Detection: A Transfer Learning - Driven Approach
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64070-4_12
Reference26 articles.
1. Aggarwal, M., et al.: Pre-trained deep neural network-based features selection supported machine learning for rice leaf disease classification. Agriculture 13(5), 936 (2023). https://doi.org/10.3390/agriculture13050936
2. Ahmad, A., Saraswat, D., El Gamal, A.: A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Adv. Technol. (2022). https://doi.org/10.1016/j.atech.2022.100083
3. Bansal, K., Singh, A.: Development of VGG-16 transfer learning framework for geographical landmark recognition. Intell. Decis. Technol., 1–12 (2023)
4. Binnar, V., Sharma, S.: Plant leaf diseases detection using deep learning algorithms. In: Doriya, R., Soni, B., Shukla, A., Gao, X.Z. (eds.) Machine Learning, Image Processing, Network Security and Data Sciences. LNEE, vol. 946, pp. 217–228. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-5868-7_17
5. Chaturvedi, A., Sharma, S., Janghel, R.R.: Detection of external defects in tomatoes using deep learning. J. Ambient Intell. Hum. Comput., 2709–2721 (2023). https://doi.org/10.1007/s12652-023-04514-y
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