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Modern agriculture has aided in the reduction of food costs as a percentage of profits. Estimating crop yield based on environment, soil, water, and crop parameters has been proposed as a potential research topic in modern agriculture. The prediction of crop yield is one of the most difficult issues in precision farming, and several models have already been suggested and tested. This is because a variety of factors such as climate, weather, soil, fertilizer and seed diversity affect crop yields. This issue requires the application of multiple datasets.Researchers have been unable to create a clear non-linear or linear relationship between raw data and crop yield values, and the performance of the derived characteristics is highly dependent on the production of those models. This means that it is not an easy task to predict yield, but rather a number of complex steps. Crop output models can now accurately estimate the actual output, but better output prediction results remain desirable. Data mining is an important tool for forecasting the yield of crops, with a focus on plants and what to do in the growth period. A number of algorithms for data mining were used for prediction studies on crop yields. For better prediction accuracy, an efficient data mining approach based on linear regression is proposed in this study.
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