Published Paper Details:

Machine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this work, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved relevant studies from six electronic databases, of which we have selected five studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is machine learning in these models. After this observation based on the analysis of machine learning-based algorithm, to recognize machine learning, we conducted additional researches in databases on crop yields. To find studies that used machine learning, we also searched crop yield datasets. This further study reveals that the Decision Tree Method is the most frequently employed machine learning algorithm in these studies.