Integrating remote sensing data and efficiently weighted ensemble method for simplified crop yield prediction

Although crop yield prediction remains challenging due to various influencing factors, remote sensing data offer a comprehensive view of crop growth dynamics, enabling the capture of spatial and temporal variations in crop development across large areas. Current researchers in this study domain are actively exploring how remotely sensed data and advanced machine learning (ML) methods can be used to improve yield predictions. We contribute to the ongoing exploration but differ in two main ways. First, unlike the current works that usually focus on developing complex deep learning (DL) models for mining the spatial and temporal information inherent in remotely sensed data, our approach rather focuses on simplifying yield prediction through an efficiently weighted ensemble method. The incorporation of effective weighting strategies into ensemble methods is currently lacking in the existing literature. Second, we streamline the integration of remote sensing data into ML methods by extracting the pixel-recorded values from the various remotely sensed images as opposed to conventionally feeding these numerous bands into a DL algorithm. Our experimental results indicate that the proposed approach is capable of outperforming current state-of-the-art yield prediction algorithms with an R2 of 0.872, mean squared error of 120.211 bu/acre, mean absolute error of 8.355, and root mean squared error of 10.637. In addition, our feature importance analysis confirmed that remotely sensed data are able to provide deep information related to crop yield over other climatic variables.