Abstract
Abstract: Machine learning is a useful decision-making tool for predicting crop yields, as well as for deciding what crops to plant and what to do during the crop's growth season. To aid agricultural yield prediction studies, a number of machine learning techniques have been used. We employed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features used in crop production prediction research in this investigation This paper provides a comprehensive overview of the most recent machine learning applications in agriculture, with a focus on pre-harvesting, harvesting, and post-harvesting issues The papers have been studied in depth, analysed the methodology and features employed, and made recommendations for future study. Temperature, rainfall, and soil type are the most commonly utilised features, according to our data, while Artificial Neural Networks are the most commonly employed method in these models.
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