Date of Publication :28th November 2022

Abstract: There are a number of factors that may be used to forecast the yield of crops, including rainfall and temperature as well as the use of fertilizers and pesticides. Agribusiness is a hotbed for the use of data mining methods. In order to predict crop yields for next year, data mining methods are put to use in agriculture. Farmers and agribusinesses in the agricultural industry face a dizzying array of choices every day, and these decisions are influenced by a wide range of circumstances. It is critical for agricultural planning to accurately estimate the yields of the several crops that will be considered. In order to come up with realistic and efficient answers to this challenge, data mining methods are essential. Prior studies have highlighted that Big Data has been a natural fit for the agriculture sector which can increase the productivity rate of the crop. On the other hand, Farmers are increasingly relying on information and assistance to make crucial agricultural choices because of environmental circumstances, soil variability, input quantities, combinations, and commodity pricing. In addition, this research study has utilized data mining methods such as PAM, CLARA, DBSCAN, and Multiple Linear Regression for maximizing harvest yields. A collaborative system of agricultural output prediction, forecasting, and fertilizer recommendation is suggested in the research study. K Nearest Neighbour is used to the agricultural dataset in this study in order to recommend the most acceptable crops. Predicting and projecting crop yields will lead to increased agricultural productivity. Farmer-friendly fertilization decisions are supported by crop rotation, which improves soil fertility

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