Yield Prediction and Recommendation of Crops in the Northeastern Region Using Machine Learning Regression Models

Abstract

Agriculture has a big impact on society because it is essential for a large percentage of our food. The issue of hunger is getting worse by a growing population in many nations, resulting in food shortages or insufficiencies. To meet the world's food needs, it is ever more crucial to provide crop protection, conduct detailed land surveys, and predict crop yields. To calculate the estimated number of crops that are produced in a year, this research focuses on the use of machine learning techniques to predict crop yield and recommend crops with the highest yield in the Northeast region of India. The crop market's fluctuations in prices may be controlled with the aid of this information. To estimate agricultural crop yields, this study accurately evaluates a range of machine learning regression models, such as Linear Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost (eXtreme Gradient Boosting), and AdaBoost. With a 0.98 R2 score for the XGBoost and 0.96 for the Random Forest, they performed better than the other models.

Keywords

• Crop yield prediction
• Machine Learning
• Ensemble Learning
• Recommender system
• Regression

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