Tree crop yield estimation and prediction using remote sensing and machine learning: A systematic review

Abstract

Yield prediction has long been a valuable tool for farmers seeking to enhance crop production. Among the many ways to predict yield, the integration of machine learning (ML) techniques is becoming more common for refining prediction methodologies. This study highlights the current landscape of remote sensing and ML techniques employed in predicting tree crop yield while also identifying critical gaps and areas for further exploration. Studies with limited datasets for training often use simpler models such as linear regression, while studies with larger datasets use more complex models, including techniques such as deep learning, ensemble methods, and hyperparameter tuning; in these cases, the performance evaluation tends to be more sophisticated. Yield prediction using ML has demonstrated accuracy levels ranging from 50 % to 99 %. Studies using smaller datasets consistently demonstrate higher accuracy rates. While ML techniques can enhance yield prediction, their effectiveness depends on strategic data collection and a multi-factor and multi-method approach. Integration of various data sources, including weather, soil, and plant data, could enhance model resilience and applicability. Enhancing research in this field could be achieved through overcoming challenges in accurate data collection and fostering the development of open datasets. This comprehensive analysis lays the groundwork for future research endeavors aimed at refining and advancing the application of remote sensing and ML techniques in accurately predicting tree crop yield.