Abstract: The agricultural industry is increasingly in peril from climate change and other environmental issues. Machine learning is a key tactic for identifying practical and effective solutions to this problem (ML). The technique of predicting crop involves making projections about the crop's output based on historical information such as weather, soil, and prior crop yields. Due to the agriculture industry's rapid innovation and liberalised market economy, accuracy in crop prediction is necessary (CP). For accurate prediction, machine learning (ML) techniques and the selected attributes are crucial. The performance of any ML algorithm may be improved by employing a special set of features from the same training dataset. This study evaluates the crucial elements of a precise CP.
Keywords: Agriculture, Machine learning, technique, prediction.