Prediction of Crop Diseases to Improve Crop Yield Using Ensemble Methods

Research related to agriculture is growing rapidly, the challenges that lie ahead is solved with the help of advancement in technology. It is found that it is very beneficial for the economical growth and development of any nation. Especially in India, it requires the need for a good research in order to improve agricultural productivity. In order to enhance the growth and solve the problems in the agricultural sector, the scientists use a variety of data mining methods. Different data mining techniques, like classification or prediction can be used to predict diseases in crops, and losses incurred as a result of these diseases. The diseases can be bacterial, fungal or viral. Some of the common plant diseases are bacterial wilt, black knot, curly top, etc. These diseases are caused by a variety of insects and micro- organisms. Our main focus in this research is on early identification of the diseases and helps the farmers in taking the decision to use the fertilizers that helps to protect the crops so that the diseases are eliminated in the early stage of production and so the farmers can get maximum yield. Ensemble method combines several classifiers to produce one finest predictive model and it is a very important technique in Machine Learning. In this paper, ensemble methods are used to predict the crop disease and an analyze has been done with the help of different classifiers such as Decision Trees, Neural Networks, Naive Bayes Classifier, Random Forests, , Support Vector Machine and K- Nearest Neighbor. Ensemble models, improves the performance of the classifiers that are weak. The accuracy can be measured in terms of precision, recall and F1 Score. An ensemble method improves the results of KNN and SVM to 36 and 33.