Data Analysis of Indian Agricultural Crop Yield Using Season, Area, and Production Through Machine Learning Models

Abstract

Crop yield in Indian agriculture varies significantly based on several factors such as season, area, production techniques, and yield per hectare. General insights into crop yields in India like Seasonal Variations. India has three major cropping seasons namely Kharif Season, Rabi Season, and Zaid Season. Machine learning provides a powerful tool to predict and analyze crop yields based on seasonal, regional, and production factors. The choice of model depends on the complexity of the data and the intended application. With accurate forecasts, farmers, policymakers and agricultural planners can better manage resources, increase productivity and minimize the risks associated with unpredictable climate conditions. This paper considers Indian crop production dataset like state, district, crop, crop_year, season, area, production, and yield. The machine learning approaches are used to analyze and predict the dataset using linear regression, multilayer perceptron, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.