Open access
Multiscale Multispectral–Hyperspectral Data for Estimating Coffee Yield Using Machine Learning Algorithms
G. D. Martins
Lucas Henrique Vicentini Viana de Carvalho
Filipe Vieira da Silva
Rayssa Santos Barbosa
Maria Cecília Lemes Santos
Jul 2026 · ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences · 0 citations
· 2 references
Abstract
Abstract. This study evaluated the performance of multispectral (Mavic 3M) and hyperspectral (Blue Wave) data in estimating coffee crop productivity using linear regression, SVM, and neural networks. Forty plots with different varieties were analyzed. Multispectral data showed high correlation with productivity, especially the Red Edge (r = 0.704) and Green (r = 0.644) bands. For hyperspectral data, PRI (r = 0.535), GNDVI (r = -0.394), NDVI (r = -0.33), and CIRE (r = -0.328) were significant, highlighting the negative correlation pattern typically observed in perennial crops. Neural network models applied to hyperspectral data achieved the best performance (r = 0.92; RMSE = 6.6%), surpassing multispectral models (r = 0.84; RMSE = 9.4%).