<![CDATA[Automated detection of plant diseases, particularly in crops like potatoes, is essential for maintaining agricultural productivity. The use of convolutional neural networks (CNNs), especially employing architectures like DenseNet, offers promising avenues for accurate disease classification. Your study's exploration of three different optimizers – Adam, SGD, and RMSprop – provides insights into their effectiveness in training CNN models for potato disease classification. The Adam optimizer stands out with its exceptionally high average accuracy of 97%, alongside impressive precision, recall, and F1-score metrics, all reaching 98%. This indicates its robustness in optimizing models for superior results. On the other hand, the SGD optimizer, although slightly less accurate at 83%, still performs commendably, considering its simplicity and widespread usage. Its precision, recall, and F1-score metrics around 82% underscore its reliability in disease classification tasks. Additionally, the RMSprop optimizer, while not as effective as Adam, demonstrates good performance with an accuracy of around 94% and stable precision, recall, and F1-score metrics, each approximately 94%. Overall, the findings suggest that all three optimizers can effectively train CNN models for potato disease classification. However, the Adam optimizer tends to yield the best results in this context, emphasizing its potential for optimizing models in similar agricultural applications. This comprehensive analysis provides valuable insights for researchers and practitioners aiming to deploy automated disease detection systems in potato cultivation and potentially other agricultural domains.]]>
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