Predictive Modeling for Crop Yield Estimation using Machine Learning - Priteshkumar B. Vasava (International Journal of Intelligent Systems and Applications in Engineering, 2024)
For agricultural planning, resource allocation, and risk management to be successful, crop yields must be accurately predicted. In this study, we provide a thorough method for utilizing machine learning techniques to forecast agricultural yields. By utilizing a dataset that includes several agricultural criteria such as the amount of rainfall that occurs annually, the use of pesticides, the crop year, the state, and the season, we create prediction models with the goal of improving the accuracy of yield estimation. Important processes like data pretreatment, feature engineering, exploration data analysis (EDA), model training, and assessment are all included in our technique.