ABSTRACT
Machine learning (ML) is a fundamental aspect of finding a practical and workable solution to the crop yield problem. Using supervised learning, machine learning (ML) may predict a goal or result from a collection of predictors. To get the required results, an appropriate function must be created using a group of variables that will map the input parameter to the desired result. Crop yield prediction comprises predicting crop yield based on historical data. It provides us with a general concept of the best crop that can be grown under the current field weather circumstances. The Enhanced Additive Binomial Linear Regression Data Mining technique is capable of making these predictions (EA-BLR). It will be able to anticipate crops with the highest degree of accuracy. The EA-BLR algorithm generates the best crop yield model by considering the lowest amount of models. Predicting crop yield is extremely useful in the agriculture sector.
