Crop Yield Prediction based on Feature Selection and Machine Learners: A Review

India is mostly an agricultural nation. In terms of both the Indian economy and the future of mankind, agriculture is a key topic to focus on. In addition, agriculture accounts for a significant share of total employment. Agricultural activity supports the livelihoods of 70% of the rural people in the India. For any country, crop production forecast is one of the most desired and hard activities they may do. Any farmer is interested in learning how much crop output he or she might anticipate in the near future. Yield prediction used to be done by taking into account the farmer's knowledge of the land and crop. Precision and previously new patterns or information are extracted from massive amounts of data utilizing Machine Learning techniques, which use algorithms that are trained using training data. As a result, forecasts for future crop output will assist farmers in making the most suitable choice for their crops. In addition, they may make a greater profit. In this paper, multiple attribute selection approaches, as well as Machine Learning approaches, for crop prediction are discussed in detail. Towards the conclusion of the session, the future direction of agricultural production prediction systems has been suggested.