CROP YIELD PREDICTION BASED ON MACHINE LEARNING

In order to find relevant information and draw recommendations, the data analysis process involves analyzing, cleaning, and modeling the data. India has a large population, hence it is important to protect the world’s food supply despite climate fluctuations.Framers face serious problems in drought conditions.The agricultural output is significantly influenced by the kind of soil. Advising farmers to utilize fertilizers might assist them in making the best choice possible for their farming circumstances. There are several studies Information and Communication Technology (ICT) may be used for agricultural yield prediction. We can also anticipate the agricultural production by using data mining. We can advise the farmer to plant a better crop for a higher yield by thoroughly analyzing the prior data. Information from traditional farmers is transferred to educated farmers through smart agriculture. to calculate estimates of overall physical production functions for the yields of various crops in a given condition, taking into account a newly designed weather index as an input.To adequately compare our actual result, also known as the target, with the prediction model, which provides farmers with the analysis of rice production based on available data, regression and coefficient of determination analysis as well as average error rate were conducted. To increase crop production, several data mining approaches were employed to estimate the crop output. For operational projects, it is crucial to accurately and promptly evaluate the state of agricultural crops and predict possible crop yields. The goal of this study is to employ a variety of forecasting techniques to assess agricultural yield estimations in Ghana due to the significance of crop yield prediction. Crop yield prediction, which offers information to decision-makers.