Classification of Mango Leaf Diseases Based on Convolutional Neural Network (CNN) Using Images from Smartphone Cameras (Karapan Network Journal : Journal Computer Technology and Mobile Ad Hoc Network, 2025)
Mango leaf disease is one of the main factors that can reduce crop productivity and quality. Conventional detection of mango leaf disease is still largely done through visual observation, which is subjective and depends on the experience of farmers or experts. Therefore, an automated method is needed that can identify diseases quickly and accurately. This study aims to develop a Convolutional Neural Network (CNN)-based mango leaf disease classification system using images obtained from a smartphone camera. The research data consists of mango leaf images of several classes, including healthy leaves and leaves with various types of diseases. The images were obtained by taking direct shots using a smartphone camera under varying lighting conditions. The research stages include image pre-processing, application of data augmentation techniques, CNN architecture design, model training process, and model performance evaluation. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the CNN model was able to classify mango leaf diseases with a high degree of accuracy and had good generalization capabilities against image condition variations. The use of smartphone cameras as a source of image data makes this system practical and easy to implement.  This research is expected to serve as the basis for developing an artificial intelligence-based early detection system for mango diseases to support smart agriculture.
Keywords: Keywords: Mango Leaf Disease Classification, Convolutional Neural Network (CNN), Smartphone Images