Machine Learning Techniques Based Prediction for Crops in Agriculture

Abstract

The bulk of people in India depend on agriculture. Recent years have seen a considerable transition in agricultural practises as a result of globalisation. Several cutting-edge technologies have been introduced in the agricultural industry in an effort to improve the health of the crops. One such technique is precision agriculture. Crop yield forecasting is a crucial aspect of precision agriculture. Crop yield forecasts are required for thorough planning, policy development, and execution for choices regarding, among other things, the procurement, distribution, price fixing, and import-export of crops. These can be used by farmers to make future plans, choose their course of action, and assess their chances. Pre-harvest agricultural yield projections must be precise and timely as a result. The major goal of this research is to recommend to farmers the best crop based on site-specific information such as soil PH level, temperature, humidity, etc. using machine learning algorithms. This improves productivity and lowers crop selection errors.