Revolutionizing Agriculture: A Mobile App for Rapid Plant Disease Prediction and Sustainable Food Security
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61929-8_3
Reference27 articles.
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2. Shewale, M.V., Daruwala, R.D.: High performance deep learning architecture for early detection and classification of plant leaf disease. J. Agric. Food Res. 14, 100675 (2023). https://doi.org/10.1016/j.jafr.2023.100675
3. Anwarul, S., Mohan, M., Agarwal, R.: An unprecedented approach for deep learning assisted web application to diagnose plant disease. Procedia Comput. Sci. 218, 1444–1453 (2023). https://doi.org/10.1016/j.procs.2023.01.123
4. Singh, S.P., Pritamdas, K., Devi, K.J., Devi, S.D.: Custom convolutional neural network for detection and classification of rice plant diseases. Procedia Comput. Sci. 218, 2026–2040 (2023). https://doi.org/10.1016/j.procs.2023.01.179
5. Paul, S.G., Al Biswas, A., Saha, A., Zulfiker, M.S., Ritu, N.A., Zahan, I., Rahman, M., Islam, M.A.: A real-time application-based convolutional neural network approach for tomato leaf disease classification. Array 19, 100313 (2023). https://doi.org/10.1016/j.array.2023.100313
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