Corn yield prediction in site-specific management zones using proximal soil sensing, remote sensing, and machine learning approach

•This study evaluated the feasibility of accurately predicting corn (Zea mays) yield at the management zone level.•Data fusion of visible and near-infrared spectroscopy-derived soil properties, remote sensing-derived crop spectral indices, and machine learning algorithms was used.•A clustering analysis was used to develop MZs in order to implement variable-rate nitrogen fertilization in a drip-irrigated corn field.•The developed models demonstrated moderate accuracy for corn yield prediction.•Further research should include supplementary spectral crop canopy indices and alternative deep and machine learning approaches. The integration of advanced technologies, such as soil proximal sensing, remote sensing, and machine learning, has revolutionized agricultural practices, particularly for corn yield prediction. This interdisciplinary approach harnesses the power of cutting-edge sensors to gather high-resolution data on soil conditions coupled with remote sensing technologies that provide a comprehensive view of crop health and environmental factors. This study aimed to evaluate the feasibility of accurately predicting corn (Zea mays) yield at the management zones (MZs) level using the fusion of visible and near-infrared spectroscopy (Vis-NIRS)-derived soil properties, remote sensing-derived crop spectral indices, and machine learning algorithms. Clustering analysis was used to develop MZs to implement variable-rate nitrogen fertilization (VRNF) in a drip-irrigated corn field. Site-specific models to forecast corn yield at the MZs level were developed using Sentinel 2A-derived spectral indices and machine learning regression algorithms. Partial least squares Vis-NIR spectral regression modelling for MZs development achieved high accuracy in terms of the coefficient of determination (R2) which was ranged from 0.60 to 0.99 in cross-validation and from 0.52 to 0.78 in online validation. The developed corn yield prediction models demonstrated moderate efficacy, as evidenced by the R2values ranging from 0.50 to 0.71. Further research should include supplementary spectral crop canopy indices and the application of alternative deep and machine learning approaches to improve the accuracy of the prediction models.