Analyzing the best machine learning algorithm for plant disease classification

Plants make up more than 80 percent of the human diet. As a result, they are critical for food security and ensuring that we all have access to enough, cheap, clean, and nutritional food to lead healthy and active living. This research focuses on plant diseases as they create a major threat to food security. Expert people are hired in traditional farming to physically evaluate line by line for host disease plants, which would be labor-intensive, time taking and potentially error-prone activity because it is done by people manually. This work focus on, supervised machine learning algorithms like RF, SVM, DT, KNN, NB, and KNN with image processing methods and also analysis algorithm results with each other and finds the best algorithm for plant disease classification. RF algorithm achieved 89 percent accuracy compared with other algorithms.