CROP YIELD PREDICTION USING SUPERVISED LEARNING TECHNIQUES

Among worldwide, agriculture has the major responsibility for improving the economic contribution of the nation. However, still the most agricultural fields are under developed due to the lack of deployment of ecosystem control technologies. Due to these problems, the crop production is not improved which affects the agriculture economy. Hence a development of agricultural productivity is enhanced based on the plant yield prediction [2]. To prevent this problem, Agricultural sectors have to predict the crop from given dataset using machine learning techniques. The analysis of dataset by supervised machine learning technique (SMLT) to capture several information's. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score. Agriculture - improving the economic contribution of the nation. Agricultural fields are not developed due to the lack of deployment of ecosystem control technologies. Agricultural productivity is improvised based on the yield prediction of plants. Agricultural sectors have to predict the crop from given dataset using machine learning techniques [3]. A comparative study between machine learning algorithms