Abstract
In the presented work, the authors intend to detect and classify disease in cherry plants at a premature stage by analyzing its leaves. For experimental purposes, PlantVillage dataset has been used. Several machine learning models and a pre-trained CNN model have also been implemented for performance analysis. The performance analysis uses various metrics for evaluation like the number of epochs, AUC-ROC curve, recall, precision, and several other parameters. The proposed model when applied, the experimental results gave a better accuracy than the conventional ML algorithms. The implemented pre-trained model has achieved an approximate accuracy of about 99.89%.
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Tiwari, A. et al. (2021). CDID: Cherry Disease Identification Using Deep Convolutional Neural Network. In: Garg, L., Sharma, H., Goyal, S.B., Singh, A. (eds) Proceedings of International Conference on Innovations in Information and Communication Technologies. ICI2CT 2020. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0873-5_11
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