detection and classification of some diseases of tomato crops using transfer learning

In the context of plant diseases, the selection of appropriate preventive measures, such as correct pesticide application, is only possible when plant diseases have been diagnosed quickly and accurately. in this study, a transfer learning model based on the pre-trained efficientnet model was implemented to detect and classify some diseases in tomato crops, using an augmented training dataset of 2340 images of tomato plants. the study’s findings indicate that during the model’s validation phase, the rate of image categorization was roughly 5 fps (frames per second), which makes sense for a deep learning model operating on a laptop computer equipped with a standard cpu. furthermore, the model was learned well because increasing the number of epochs no longer improved its accuracy. after all, the curves of the train and test accuracies, as well as the losses versus epoch numbers, remained largely horizontal for epoch numbers greater than 20. notably, the highest coefficient of variation across these four cases was only 7%. furthermore, the cells of the primary diagonal of the confusion matrix were filled with larger numbers in comparison with the values of the other cells; precisely, 88.8%, 7.7%, and 3.3% of the remaining cells of the matrix (cells of the primary diagonal excluded) were filled with 0, 1, and 2, respectively. the model’s performance metrics are: sensitivity 85%, specificity 98%, precision 86%, f1-score 84%, and accuracy 85%.

detection and classification of some diseases of tomato crops using transfer learning

in the context of plant diseases, the selection of appropriate preventive measures, such as correct pesticide application, is only possible when plant diseases have been diagnosed quickly and accurately. in this study, a transfer learning model based on the pre-trained efficientnet model was implemented to detect and classify some diseases in tomato crops, using an augmented training dataset of 2340 images of tomato plants. the study’s findings indicate that during the model’s validation phase, the rate of image categorization was roughly 5 fps (frames per second), which makes sense for a deep learning model operating on a laptop computer equipped with a standard cpu. furthermore, the model was learned well because increasing the number of epochs no longer improved its accuracy. after all, the curves of the train and test accuracies, as well as the losses versus epoch numbers, remained largely horizontal for epoch numbers greater than 20. notably, the highest coefficient of variation across these four cases was only 7%. furthermore, the cells of the primary diagonal of the confusion matrix were filled with larger numbers in comparison with the values of the other cells; precisely, 88.8%, 7.7%, and 3.3% of the remaining cells of the matrix (cells of the primary diagonal excluded) were filled with 0, 1, and 2, respectively. the model’s performance metrics are: sensitivity 85%, specificity 98%, precision 86%, f1-score 84%, and accuracy 85%.