CLASSIFICATION OF DISEASES ON MANGGO LEAVES USING ARCHITECTURE VGG16 AND SUPPORT VECTOR MACHINE (SVM)

Indonesia is one of the countries that has a diversity of mango plants. Mango fruit is in great demand throughout the world, but pest control of mango diseases is still not too optimal, one of which is on the leaves. So the automatic recognition of diseases on mango leaves will have a very important role in achieving satisfactory yields. With these problems, this study evaluates models using Convolutional Neural Network (CNN) architecture VGG16 + SVM and VGG alone. The dataset consists of 4000 digital images with seven (7) disease classes and one (1) healthy class. This study shows that the VGG16 + SVM model has a fairly good performance in disease detection on mango leaves with accuracy, precision, recall, and F1-Score values of 99.50%, 99.50%, 99.50%, and 99.50%, respectively.