Crop Yield Prediction Using Machine Learning Approaches

Crop yield prediction plays an essential role in agricultural planning and decision-making. The present research offers a comprehensive study into the use of machine learning algorithms for agricultural production prediction based on a variety of fertilizers consumption and area production of various crops. The study’s essential process includes data gathering which is gathered in two parts and then combined into one, preprocessing, and model selection. Random Forest Regressor, Decision Tree Regressor, Linear Regression, LSTM, and more such regression models were among the algorithms tested and compared. According to the data, Random Forest Regressor and linear Regression had the greatest accuracy in crop yield prediction. By utilizing machine learning methods and bridging the gap between technology and the agriculture sector, this study contributes to the progress of agricultural practices. The results may help farmers choose the best crops for future years, therefore optimizing agricultural operations and increasing production and yield.