Analysis and Prediction of Soybean Plant Disease

Disease detection of the plant is one of the absorbing research areas in the farming field. This field needs a dependable prediction methodology to understand elements influencing disease. Plant diseases become major threats for us because it harms our body by reducing our immune system and also degrade the quality of crops. As the production of soybean is decreased due to natural hazard, overuse of pesticides, soil problem, and lack of agricultural knowledge and mainly due to diseases. Analyzing data from different aspects and summarizing it into valuable information is the operation done in machine learning. It permits users to analyses data from different dimensions, categorize and the relationships are identified. WEKA is a data analysis tool for machine learning classification. Machine learning classification is a vital technique with more applications in various fields. It is used to classify each item in a set of data into one predefined set of classes. This paper presents the analysis of soybean plant diseases based on the dataset of plant growth, seed germination, damaged area, external decay, leafspot size, seed discolor, etc. The proposed work focuses on machine learning techniques and using the WEKA tool