An empirical study on machine learning algorithm for plant disease prediction

Abstract

In this study, a neuroevolution algorithm has been developed for predicting various diseases in plants. The machine learning algorithms support different classification techniques that can be demonstrated for the performance improvement in plant disease prediction. Prediction of the disease depends on the weather factors which have a relationship with climate change data, the soil of that area. Here we have illustrated an approach of implementing the neuroevolution model based on ANN for predicting various plant diseases. Multiple causes and the type of illness that can affect different plants during the different seasons are predicted. Therefore the result of the proposed model assists in decision making in advance in precaution taking in a disease that may affect the plant. The results are utilized for making an advanced decision for disease avoidance in plants as well as various farm activities throughout multiple-stage it also uses the same model that can be utilized for predicting various agricultural data such as yield prediction and weather prediction.