The Deep Learning Based Analysis and Prediction of Juniper Apple Rust Disease in Apple Plants using ResNet-50 Hybrid Algorithm

Juniper Apple Rust, also known as Cedar-Apple Rust, is a fungal disease caused by Gymnosporangium juniperi-virginianae. The disease requires two hosts, primarily Eastern red cedars and apple trees, to complete its life cycle. The apple plantations can be affected by this disease as it can result in falling of leafs and the lower-quality fruit. This disorder emerges as yellow-orange spots on the leaves that have black centers and red borders. Managing this disease includes using fungicides during the grave spring infection period, planting the disease-resistant apple varieties, and removing the juniper galls. This study introduces a deep-learning classifier designed to distinguish between two types of image data, one showing apple leaves affected by Cedar-apple rust disease and the other showing healthy apple leaves. The images undergo de-noising to improve quality. The cleaned data is then used to train ResNet-50 networks, achieving high precision. In summary, this research aims to develop a highly accurate image classifier for detecting Cedar-apple rust disease, achieving an accuracy rate of 98.99%.