Plant Disease Detection using Machine Learning Algorithm

In various applications of the agricultural industry, computer methodologies have been used for automation. Timely farming decisions and disease management are taken using image analysis and machinery of learning techniques in planning and creating a method for the diagnosis of diseases. As human beings are still tracking crop disease, human visual vision is used for diagnosing plant disease. Machine learning and image processing methods are also ideally suited to this end. In this study, the processing of diseased pictures of plant leaf is taken into account. The work focuses on the identification and diagnosis of plant leaf diseases based on visual symptoms, anthracnose, and powdery mildew. Machine learning and image processing require many steps to identify and distinguish disease signs. Step one is the acquisition of images. In the second step, photographs from the UCI repository are pre- processed and segmented. After this method is segmented, the best functions dependent on mathematical data are derived further. Finally, separate classifiers are used for the full classification