Evaluation and Optimization of an AI Model for European Canker Detection - Arruda, Camile Coelho; Corrêa, Jonatam Sturcio; Castello Branco Neto, Wilson; Costa, Robson (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16618, 2025)
European canker, caused by Neonectria ditissima, is one of the most destructive diseases affecting apple orchards worldwide. Early and accurate diagnosis is essential to reduce economic losses, but traditional methods rely on expert visual inspection, which is often slow and subjective. Advances in Convolutional Neural Networks (CNNs) have shown great potential for plant disease detection by enabling automated image-based diagnosis. This work investigates the application of pre-trained CNN architectures to the identification of European canker symptoms in apple branches. The study demonstrates that CNNs can achieve promising results, although challenges remain due to the complexity of branch-based symptoms compared to leaf diseases.