A Machine Learning Approach for Predicting Crop Yield in Precision Agriculture

Agriculture is the main earnings-producing field as well as a cause of livelihood in India. Different biological variables and seasonal and financial factors affect yield growth, but unexpected variations in these variables result in a major loss of crops. When adequate mathematical or statistical techniques are applied to information related to soil, climate, and previous yield, these hazards can be quantified. With the advancement of machine learning, crop yields may be anticipated by extracting helpful information from crop fields that assist the government in deciding import/ export in advance. This study provides a machine-learning approach based on Random Forest Regression to predict crop yield with an R-square of 0.95. This agricultural yield prediction helps farmers to make plans for shortage/surplus of production in well advance to get significant benefits.