Apple leaf scab recognition using CNN and transfer learning

Apple scab, caused by the pathogen Venturia inaequalis, is one of the most prevalent diseases affecting apple trees, significantly reducing both yield and fruit quality. Early detection and diagnosis are critical for the effective management of this disease, which is typically characterized by the appearance of black lesions on the leaves and fruit. In this study, we developed a deep learning-based system for classifying apple scab using Convolutional Neural Networks (CNN) and transfer learning. The system was trained on a dataset of apple leaf images, employing various data augmentation techniques, such as rotation, flipping, and scaling, to improve the model's robustness. Our proposed model achieved high accuracy in distinguishing between healthy leaves and those affected by apple scab, making it a promising tool for precision agriculture and automated disease monitoring. This research offers a potential solution for reducing dependence on manual labor and enhancing early intervention practices in apple orchards.