Many states in India have taken up sericulture as an important agro-industry with good results. The mulberry leaf is the most important economic component in sericulture since the quality and quantity of the leaf have a direct impact on the cocoon bearing. Leaf disease diagnosis and classification in mulberry plants can be useful to framers and researchers to identify and classify diseases. It is an interesting technique that aids in managing the pathogens within the fields automatically and effectively at a minimal cost. Many mulberry diseases usually have symptoms on the leaf during the early stages of infections. These can be easily analyzed and classified using images. This paper proposes a model for implementing mulberry infection detection using Convolution Neural Networks (CNN) and You Look Only Once (YOLO). The proposed model identifies and classifies mulberry leaf diseases effectively. The image is divided into several grids before the image processing. The speed and accuracy of detection and classification are relatively high.