Enhancing Crop Yield Prediction Through Time Series Forecasting with XGBOOST Algorithm
Paurav Goel, Bhumika Tiwari, Banisha Sharma, Priyanka Chopra · 2025
Agriculture is the only sector in the country that benefits everyone. It gives a large section of the population work possibilities in addition to food and raw materials. The primary source of income for the vast majority of Indians is agriculture. Although agriculture has been practiced in the nation for thousands of years, outdated agricultural methods have been replaced by modern machinery and technology. The importance of integrating technology into agriculture is growing as it spreads throughout all sectors of the economy. Machine learning facilitates large-scale data processing and has the potential to yield faster, more accurate results. These advantages can be used to recognize risky situations as well as lucrative possibilities. The main goal of this research is to create a model using Machine Learning which implements all the Classification algorithm to predict the crop yields. For Prediction of crop yield, firstly the data is taken and trained by using Machine Learning’s type that is Supervised Machine Learning with different types of Models, which calculates the Mean square value(MSE), Mean absolute Error(MAE), Root square (R2). The resultant provides XGBOOST as a best predicting Algorithm. After that the data is now trained by using XGBOOST algorithm which proves the accuracy of prediction.