ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-XI-3-2026-757-2026 Multiscale Multispectral–Hyperspectral Data for Estimating Coffee Yield Using Machine Learning Algorithms Martins George Deroco 1 de Carvalho Lucas Henrique Vicentini Viana 1 da Silva Filipe Vieira 1 Barbosa Rayssa Santos 1 Santos Maria Cecília Lemes 1 Federal University of Uberlândia (UFU), Uberlândia, Minas Gerais, Brazil 08 07 2026 XI-3-2026 757 764 Copyright: © 2026 George Deroco Martins et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/XI-3-2026/757/2026/isprs-annals-XI-3-2026-757-2026.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/XI-3-2026/757/2026/isprs-annals-XI-3-2026-757-2026.pdf

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%).