A Comparative Analysis of Machine Learning Prediction Techniques for Crop Yield Prediction in India (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
Nowadays, crop yield prediction is one of the most recent, interesting and challenging tasksdue to its dependence on various variable parameters like environmental, weather, soil andclimate factors. Machine learning has become one of the important tools for predicting cropyield. This paper presents a machine learning framework for crop yield prediction using cropand weather data. It also compares the performance of potential machine learning methodslike regression, decision trees, random forest, support vector machine and gradient boostingto forecast the yield of 80 crops in India for the year 2001 to 2016 using historical data.Furthermore, it has been observed from the results that the root mean square (RMSE) of therandom forest method is 9433.7 for the dataset.