<![CDATA[The CNN model developed in this study demonstrated remarkable performance, achieving an outstanding validation accuracy of 99.96%. Through experimentation, it was found that employing the RMSprop optimizer with a learning rate of 0.001 yielded superior results compared to the Adam optimizer utilized in previous iterations. Additionally, increasing the number of epochs from 10 to 20 resulted in a significant enhancement in accuracy, highlighting the importance of iterative training for model refinement. Moreover, the implementation of Early Stopping proved to be a valuable technique, effectively conserving training time by halting the training process once optimal accuracy levels were reached. These findings underscore the efficacy of various optimization strategies in bolstering the performance of CNN models for rose leaf disease detection. The achieved accuracy rates signify a substantial advancement in disease detection technology, holding promise for enhancing agricultural productivity and ensuring plant quality. This research contributes valuable insights into the optimization of CNN parameters, paving the way for further advancements in automated disease detection systems in the field of agriculture.]]>
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