Résumé
The global coffee industry faces significant challenges from crop diseases, of which coffee leaf rust (CLR) caused by the fungus Hemileia vastatrix, stands out as one of the most damaging. Accurate assessment of disease severity is essential for applying effective control strategies. In response to this need, this study introduces a modern approach using deep learning and image processing techniques to identify and quantify CLR injury automatically. We developed thirteen models using convolutional neural networks, to classify lesions into different degrees of severity. It offers a promising alternative to conventional methods, especially under data-limited conditions, although some limitations remain in robustness across datasets. Manual rust detection requires close visual inspection of leaves, a laborious and error-prone process, especially in large cultivation areas. This challenge makes it harder to apply timely and effective disease management strategies.