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Agriculture is India's economic backbone. Crop cultivation used to be done by farmers with on-the-ground experience. However, crop yields have begun to suffer as a result of climate change. As a result, farmers are unable to select the appropriate crops based on soil and environmental parameters, and the process of manually predicting the appropriate crop/s of land has frequently failed. Crop prediction accuracy leads to higher crop production. Agriculture is entirely dependent on soil fertility, climatic conditions, irrigation, seed quality, harvesting, and other factors.It is really important all around the world. Only a seasoned farmer can recognize the type of soil and select the appropriate crop for it. Predicting the soil type and its surrounding environment for a specific field is crucial for future crop yields. This research focuses on applying a variety of data mining approaches to forecast soil conditions and improve crop yields in the future. The study employs data mining techniques such as clustering, OneR, and J48.These data mining approaches are systematically utilized to anticipate and analyze the bearing of the soil in order to improve crop yield. This technique would be more useful to farmers in identifying the type of the soil and its riches, which, in turn, would help them choose the crop that is best suited to their soil and produces the highest yield.