Improving Crop Productivity Through A Crop Recommendation System Using Ensembling Technique

Agriculture plays a predominant role in the economic growth and development of the country. The major and serious setback in the crop productivity is that the farmers do not choose the right crop for cultivation. In order to improve the crop productivity, a crop recommendation system is to be developed that uses the ensembling technique of machine learning. The ensembling technique is used to build a model that combines the predictions of multiple machine learning models together to recommend the right crop based on the soil specific type and characteristics with high accuracy. The independent base learners used in the ensemble model are Random Forest, Naive Bayes, and Linear SVM. Each classifier provides its own set of class labels with an acceptable accuracy. The class labels of individual base learners are combined using the majority voting technique. The crop recommendation system classifies the input soil dataset into the recommendable crop type, Kharif and Rabi. The dataset comprises of the soil specific physical and chemical characteristics in addition to the climatic conditions such as average rainfall and the surface temperature samples. The average classification accuracy obtained by combining the independent base learners is 99.91%.