A systematic review on crop yield prediction with deep learning and remote sensing

Deep learning has emerged as a potential tool for predicting crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. In this study, we have performed a systematic literature review to extract and synthesize the deep learning approaches, remote sensing technologies, and features that influence crop yield prediction. Based on the analysis, we have summarized (a) the advantages of using deep learning in crop yield prediction; (b) the suitable remote sensing technology based on the data acquisition requirements; and (c) the various features that influence crop yield. A total of 44 articles are selected for the systematic review after using the inclusion and exclusion criteria. Based on the analysis, we have summarized (a) the advantages of using deep learning in crop yield prediction; (b) the suitable remote sensing technology based on the data acquisition requirements; and (c) the various features that influence crop yield. The study shows that Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012-2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study found that Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology was satellite remote sensing technology, in particular the use of Moderate Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are on how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property.