App2: software solution for apple leaf disease detection based on deep learning (CNN+SVM) - Aronés, Erick; Espinal, Jefferson; Salas, Cesar (Frontiers in Artificial Intelligence, 2025)
Early detection of crop diseases is essential to reduce yield losses and improve management efficiency in agricultural production. This work presents the development of a mobile application, called App2, designed to detect diseases in apple tree leaves from images taken or uploaded by the user. The solution integrates a hybrid model based on a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM), developed for computer vision tasks focused on recognizing diseases in apple leaves. The system architecture includes a user interface built with React Native, an API developed using FastAPI and deployed on Azure, and a pre-filter implemented through the OpenAI API to validate that the uploaded images correspond to crop leaves. The model was trained to classify images into six categories: Scab, Black Rot, Rust, Healthy, Powdery Mildew, and Spider Mite. Experimental results showed a 95% success rate in test cases and 80% performance in detecting clear images of affected leaves. User evaluations indicated high usability and satisfaction, demonstrating that the mobile application has strong potential as an accessible and effective technological tool for disease monitoring in apple crops.