ABSTRACT– When pests attack plants and crops, it impacts the country’s agricultural output. Farmers usually detect and identify illness by looking at the plants with their eyes. This is how it’s been done for ages. However, this procedure can be time-consuming, unaffordable, and imprecise. The findings of automatic detection by using image processing techniques are quick and accurate. This project presents a novel strategy to develop a leaf disease detection model based on leaf image classification and deep convolutional networks. Advances in computer vision provide the potential to expand and improve the practice of special plant protection while also expanding the market for computer vision applications in agriculture. The methodology and the novel training technique allow for a quick and effortless system set up in practice. All of the necessary steps for implementing this disease recognition model are detailed throughout the report, beginning with the collection of images to a database and using a deep learning CNN model for training. This presents a method for identifying plant diseases using a convolutional neural network/CNN that has been trained and fine-tuned to fit accurately to a database of a plant’s leaves gathered independently for a variety of plant diseases