Crop yield prediction using machine learning: A systematic literature review

TLDR

Machine learning serves as a key decision‑support tool for crop yield prediction, guiding crop selection and in‑season management, and numerous algorithms have been applied in this domain. This systematic literature review aimed to extract and synthesize the algorithms and features employed in crop yield prediction studies. The authors searched six electronic databases, identified 567 studies, selected 50 for detailed analysis, examined their methods and features, and then conducted a focused search that yielded 30 deep‑learning papers to catalog the applied deep‑learning algorithms. The analysis revealed that temperature, rainfall, and soil type are the most common features, with Artificial Neural Networks the most used algorithm, while deep‑learning studies predominantly use Convolutional Neural Networks, followed by LSTM and DNN.

Abstract

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 study, 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 567 relevant studies from six electronic databases, of which we have selected 50 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 Artificial Neural Networks in these models. After this observation based on the analysis of machine learning-based 50 papers, we performed an additional search in electronic databases to identify deep learning-based studies, reached 30 deep learning-based papers, and extracted the applied deep learning algorithms. According to this additional analysis, Convolutional Neural Networks (CNN) is the most widely used deep learning algorithm in these studies, and the other widely used deep learning algorithms are Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN).