Study on Machine Learning Techniques used for Agricultural Yield Estimation

This paper presents the various crop yield prediction algorithms using machine learning techniques. In the Indian economy, agriculture plays an important role and it is the most generally practiced occupation in India. The majority of the land is utilized for agriculture exercises to meet the food necessities of the Indian population. The focus of this paper is on the application of machine learning algorithms in the agricultural domain for crop yield prediction. Various Data Mining and machine learning techniques, such as K Means, Decision Tree, Random Forest classification, K Nearest neighbor, XGboost, artificial neural network, and SVM have been used for recent data applications. Techniques used in agriculture with the rapid development of data mining technology, it has made significant progress. Artificial intelligence, and computer science Data Mining is a new research area in crop yield analysis in agriculture. Yield prediction is critical, based on the available data, there is an agricultural problem that needs to be solved. The yield prediction problem can be solved by using data mining and machine learning techniques This paper helps the farmers which are the advancements that assist with forecasting the yield dependent on various parameters.