<![CDATA[This research discusses the implementation of deep learning for leaf disease classification in peanut plants, focusing on Convolutional Neural Network (CNN), Modified K-Nearest Neighbor (MKNN), and Multiclass Support Vector Machine (SVM) models. The main objective is to evaluate the accuracy and efficiency of the models in automatically detecting leaf disease types to support smart agricultural practices. Using a dataset of infected peanut leaf images, the proposed CNN model achieved an accuracy of 95%, superior to the MKNN method which obtained an accuracy of 89% and SVM of 87%. These results demonstrate the potential of CNNs in fast and accurate plant disease classification, while highlighting the need for specific datasets to improve performance in real environments. This study provides guidance for further development in the application of deep learning in agriculture, particularly in peanut plant disease detection systems.]]>
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