A Comprehensive Early Detection of Seasonal Plant Diseases Using a Convolutional Neural Network (CNN)

Plant diseases arise as a consequence of shifts in the environment and climate, resembling natural disasters that disrupt plant growth and can even lead to the demise of plants during their development, starting from the seed stage. Detecting these disorders can be challenging without adequate knowledge and expertise, and many farmers and plantation growers lack the necessary skills, professional knowledge, and resources for accurate identification and treatment of plant issues. To address this problem, a Convolutional Neural Network (CNN) model is employed, leveraging the power of deep learning technology to appropriately recognize plant disorders and their potential remedies. The CNN model will be trained using a vast dataset from various sources, encompassing information about plant diseases. Moreover, it will be fine-tuned to identify diseases based on pictures supplied by farmers, taken through their cameras or retrieved from their galleries. Specifically, this research concentrates on the early detection of seasonal diseases in distinct plant leaves, including Apples, Corn, Tomatoes, and Potatoes. From our experiments, we obtained an accuracy of 98.89 %. Outperforming related works in the field.