The application of visual programming technology to classify mango leaves using a convolutional neural network

Silfia Andini (Faculty of Information Technology, Putra Indonesia University YPTK Padang, Padang 25145, Indonesia)
Teuku Radillah (Faculty of Information Technology, Mitra Gama Institute of Technology, Duri 28784, Indonesia)
Sumijan Sumijan (Faculty of Information Technology, Putra Indonesia University YPTK Padang, Padang 25145, Indonesia)

Science, Technology, and Communication Journal
Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)

Leaf images are representations of leaves captured using digital cameras, which visually display the morphology and structure of the leaves. The classification of mango fruit can be identified from the type of leaves by using a convolutional neural network (CNN). The aim of this research is to obtain information on the type of mango fruit based on leaf type using image processing. The experimental setup involved the application of the test data set and the training data set to the classification of mango leaves, utilising iterations on both the training and testing data sets. This process involved the use of images exhibiting slight variations in shape, both in terms of image position and leaf type, to facilitate a comparison with images of the intended leaf type and leaves not included in the training classification. The experimental results yielded an accuracy level of 80.76%, validating the efficacy of the approach.