An Investigation on Crop Yield Prediction Using Machine Learning

For the existence of humans, agriculture is vitally crucial. For a big population of the globe, agriculture provides a living. It also provides the locals with a large number of work openings. Many farmers desire to use old-fashioned farming techniques, which provide poor income. Critical to the economy's long-term development and advancement are agriculture and the related industries. Decision making, crop selection and supporting systems for increased crop output are the primary problems for agricultural production. The prediction of agriculture depends on parameters such as temperature, soil fertility, amount of water, water quality and seasons, crop price, etc. Machine learning plays an important role in crop yield prediction on the basis of geography, climate details, and season. It helps farmers in growing most appropriate crop for their farm land. This paper presents a machine learning based framework for prediction of crop yield. For experimental set up, a data set is created for crop details. Machine learning algorithms SVM, random forest and ID3 are used for investigation.