The advent of cutting-edge technologies to synthesize and assess large amounts of data using advanced computation has greatly increased our ability to forecast crops. In comparison to simulation crop modelling, recent research has demonstrated that machine learning can provide more accurate predictions faster and with greater flexibility. However, a "committee" of machine learning models that reduce prediction bias, variance, or each and better describe the underlying distribution of the data may beat one machine learning model. This study examines sampling and modeling techniques to predict crop type based on several limitations such as fertilizers, pH value, temperature, humidity, and rainfall. We trained the ensemble network to get the crop as output from its input features, with the ensemble network we are predicting the crops with 99.31% accuracy, and Precision, Recall, and f1-Score

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