1. Yield Prediction and Recommendation of Crops in India’s Northeastern Region Using Machine Learning Regression Models.
- Author
-
SHARMA, Nisha and DUTTA, Mala
- Subjects
MACHINE learning ,CROPS ,CROP yields ,RANDOM forest algorithms ,FOOD shortages ,PLANT protection - 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 because of 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 and profitability 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. By evaluating crop yields along with their corresponding market prices and costs, we have determined their profitability. As a result, we have provided recommendations for the top five profitable crops in India’s Northeastern region. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF