Aplicación Web Basada en Aprendizaje Profundo para la Clasificación Multiclase de Enfermedades Foliares en Cultivos Agrícolas - Torres Talaverano, Luz Elena (2025)
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.