AN EMPIRICAL STUDY ON MACHINE LEARNING ALGORITHM FOR PLANT DISEASE PREDICTION

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 affectthe 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 yieldprediction and weather prediction.