Aplicación Web Basada en Aprendizaje Profundo para la Clasificación Multiclase de Enfermedades Foliares en Cultivos Agrícolas

• Autor: Torres Talaverano, Luz Elena
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revistas - Universidad Nacional Mayor de San Marcos
• Lenguaje: español
• OAI Identifier: oai:revistasinvestigacion.unmsm.edu.pe:article/32350
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/rpcsis/article/view/32350
• Nivel de acceso: acceso abierto
• Materia: Deep learning; Convolutional neural networks; Transfer learning; Xception; Web application; Aprendizaje profundo; Redes neuronales convolucionales; Aprendizaje por transferencia; Aplicación web

Foliar diseases are one of the main factors that limit agricultural productivity by reducing crop quality and yield. Traditional identification methods, based on visual inspection, are costly, time-consuming, and prone to human error, which highlights the need for automated solutions. This study aims to develop a web application based on deep learning for the multiclass classification of foliar diseases in apple, maize, pepper, potato, and tomato crops. The approach employed transfer learning with fine-tuning, using pretrained convolutional neural network architectures and training models on a dataset of 35,725 images distributed across 23 classes. Among the evaluated architectures, Xception achieved the best performance, with an accuracy of 98.1% and a macro F1-score of 97.6%. This model was integrated into a web application structured in a layered architecture based on the client-server model, where tests with real images confirmed its practical validity and demonstrated stable inference times below 0.1 seconds per image. The results show that the proposed system constitutes a decision-support tool for crop management with potential application in real agricultural scenarios.