OPTIMIZATION OF TOMATO LEAF DISEASE DETECTION USING DEEP MACHINE LEARNING WITH ADVANCED NEURAL NETWORKS
The article discusses the application of deep machine learning methods for detecting tomato leaf diseases. The goal of the study is to improve the accuracy of classifying images of diseased plants through modifications to convolutional neural networks (CNN), combining the Inception module, the Mish activation function, and batch normalization. The proposed approach outperforms basic CNN models and the support vector machine method. The PlantVillage dataset, containing images of both diseased and healthy plants, was used for model evaluation.