Enhancing snap bean yield prediction through synergistic integration of UAS-Based LiDAR and multispectral imagery

Abstract

Unmanned aerial system (UAS)-based remote sensing technologies have seen increasing application in precision agriculture and crop management. By collecting and analyzing remote imagery of crops, we can extract valuable information about the nutrient, structure, and growth variation of crops at scales 0.74, rRMSE < 0.13) by the full bloom stage, at least a week before harvest. This early prediction capability is crucial for effective agricultural planning and management. Our findings underscore the potential of combining multiple UAS-based remote sensing technologies for efficient and accurate yield assessment and prediction in agriculture.

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