A Systematic Review on Data Fusion Techniques for Agri-cultural Yield Prediction: Integrating Satellite Imagery with Climatic Data

An answer to the worldwide need for solutions to food security, data fusion technology that combines climate data with satellite imagery greatly improves the accuracy of agricultural yield predictions; this study intends to examine the advancements, methods, and key contributions of this area. By sifting through 62 papers pulled from Scopus, this research employs the SLR methodology. Document type, data source, open access, subject area, and year of publication (2020–2024) are some of the categories filtered through by Boolean keywords in the selection process. To assess patterns in publications, the efficacy of machine learning models, and key contributions, bibliometric analysis was performed. An upward tendency in publication has been identified by the analysis, particularly beyond the year 2023. Integrating geographical and temporal data has been a great success with machine learning models like Random Forest, Random Forest, and Gradient Boosting. Data resolution, integration of data from several sources, and a real-time framework are still missing pieces to the puzzle when it comes to generalizing research outcomes. More complex data fusion approaches, multiregional datasets, and advanced machine learning models to back more accurate agricultural predictions are all things that this study notes as needing additional investigation in the future. To further innovate agricultural yield prediction, multidisciplinary collaboration is also crucial.