Wheat Crop Yield Prediction Using Machine Learning

Abstract

With the substantial increment in population, wheat production is the major consent to fulfill human requirements. In this study, the wheat production yield has been predicted using the SVR, Decision Tree, Random Forest, and Gradient Boosted regressor techniques. SVR is built based on the concept of a Support Vector Machine (SVM). A Decision Tree is a supervised learning technique that can be used for both classification and Regression problems and it follows a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules, and also each leaf node represents the conclusion. Also, Random Forest Regression uses an ensemble learning method for regression. Gradient boosting Regression calculates the difference between the current prediction and the known correct target value. With the help of regressor techniques, the effectiveness of yield parameters on wheat such as average rainfall, pesticides, and the average temperature on the yearly basis was calculated. The experimental results show that the random forest regressor has high Mean Absolute error minimum of 2351.86 than SVM and SVR for yield prediction. Also, three different types of error analyzing mechanisms were used to calculate the best accuracy for our prediction algorithm. Our study provides a gateway for the betterment of farming practices and showcases potential growth in decision-making by providing data-driven analysis for prediction.