Fecha de publicación: 06/05/2022 Fuente: WIPO "processing tomato" The present invention is a system and a method to detect diseases in tomato leaves. The method includes the steps of collecting a dataset of the tomatoes and pre-processing the dataset, using pre-trained models as a convolutional base for feature extraction of the tomato dataset, feeding a stack of fully connected layers as a classifier by the features extracted from the pre-trained models, hyperparameter tuning of the models, analyzing and comparing the pre-trained models, and obtaining the results based on the compared data. Through empirical analysis it is observed that the MobileNet model performs better than the remaining models, thus the hyperparameter tuning of the model with MobileNet as feature extractor is also done for different optimizers Adam, SGD, Adagrad, Adadelta and RMSprop and the results have been analysed. Also, the experimentation of the MobileNet model for the various batch sizes 32, 64 and 128 has also been done. The results obtained from the deep learning architectures are then compared in terms of precision, recall, F1 score. Comparative analysis and the experimental results verify the efficiency of the method with existing systems for tomato leaf disease detection.