An Intelligent Deep-Learning Model CNN Used for Multi-Class Classification of Apple Leaf Disease

This article provides a comprehensive study on the detection and classification of three significant diseases that affect apple fruits and leaves. These diseases include black rot, scab, and rust. The research was conducted with the use of Convolutional Neural Networks (CNNs). The illnesses are referred to by the symptoms that they cause, which are known as black rot, scab, and rust. This Paper features a curated dataset that contains a wide variety of high-resolution photographs of apple leaves in both healthy and diseased states. By using the capabilities of CNNs, we can achieve remarkable accuracy in differentiating between the many types of disease, which paves the way for earlier diagnosis and more individualized treatment of disease. Our findings not only contribute to the advancement of precision agriculture but also give insightful information on the use of deep learning in the diagnosis of agricultural illnesses. Our model classified the disease with 92.5% accuracy.