Authors - Allezandra A. Adriano, Joshua Basile Mhar L. Austria, Benjamin L. Carnate, Xamantha Angelique E. Ruiz, Wilben Christie R. Pagtaconan Abstract - Plant diseases due to various pathogens can cause significant loss in yield and productivity. The classification of these diseases is necessary to prevent damage to crops. For classification, a large number of Machine learning and deep learning algorithms have been developed. In this research, five classes of plant leaves and a further fifteen different diseases of these plants (three subcategories for each class) are used for classification. In the proposed methodology, we have used three pre-trained models, namely, ResNet 152v2, InceptionResNetV2, and mGoogleNet, and a custom-built model. This research has used three basic steps to classify the disease categories, namely image preprocessing, image segmentation, and feature extraction. Fifteen thousand plant leaf images have been collect-ed from the online available Kaggle PlantVillage dataset. This data is present in a JPG file format. After the class label distribution of the dataset, the dataset is first trained and then tested on these deep learning models. The label distribution is done in such a way that each of these fifteen categories has 80% training images and 20% validation images. We have used different performance measures, namely, precision, recall, F1-score, and support, to calculate the accuracy. The obtained validation accuracy of ResNet152V2 is 97%, GoogleNet is 96%, Incep-tionResNetV2 is 93%, and a custom-built model is 99%. These results show that the custom-built model has attained the highest accuracy. These models can also be used to build a recommender system framework for the recommendation of fertilizers in the future.
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