ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1648867
This article is part of the Research TopicAdvances and Challenges in AI-Driven Visual Intelligence: Bridging Theory and PracticeView all 6 articles
App2: Software solution for apple leaf disease detection based on Deep Learning (CNN+SVM)
Provisionally accepted
- Peruvian University of Applied Sciences, Lima, Peru
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This work presents the development of a mobile application, called App2, capable of detecting 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), designed for computer vision tasks focused on the recognition of diseases in apple leaves. The architecture of App2 includes an interface developed in React Native, an API built with FastAPI and deployed in Azure, and a pre-filter using the OpenAI API to validate that the images effectively correspond to crop leaves. The model was trained to classify images into six categories: Scab, Black rot, Rust, Healthy, Powdery mildew and Spider mite. During experimental testing, the application achieved a 95% success rate in test cases, and 80% performance in detecting clear images showing diseases attacking apple trees. User evaluation revealed high acceptance in terms of ease of use and practical utility, which reinforces the potential of the tool as a technological support for farmers.
Keywords: Computer Vision, deep learning, CNN, SVM, Apple leaf disease detection, Tensorflow, Scikit Learn
Received: 17 Jun 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Aronés, Espinal and Salas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Cesar Salas, cesar.salas@upc.edu.pe
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