Aplicação de redes neurais convolucionais para detecção de doenças em folhas de macieiras

Abstract

Apple cultivation holds significant economic importance in the Brazilian agricultural sector, especially in the southern region of the country. However, orchard productivity is often compromised by foliar diseases which, if not identified and treated early, can lead to substantial losses. In this context, advances in Machine Learning techniques have enabled the development of computational solutions capable of supporting agricultural diagnostics with greater accuracy and agility. This work proposes an approach based on Convolutional Neural Networks, using transfer learning for the automatic detection of disease symptoms in apple leaves. The developed methodology includes segmentation and analysis of symptomatic regions to reduce noise from healthy areas and focus the model’s learning on relevant signals. A total of 32,382 symptom patches were extracted from 1,995 original images, covering five foliar disorder classes: glomerella, scab, herbicide damage, magnesium deficiency, and potassium deficiency. The MobileNetV2, trained via transfer learning, achieved a F1-score of 0.926 and 93.8% accuracy on the held-out test set. The results indicate good performance in the evaluated setting, suggesting the approach’s potential as a tool to support plant-health diagnosis and field decision-making.