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Remote Sensing
Volume 17
Issue 20
10.3390/rs17203409
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

High-Throughput Identification and Prediction of Early Stress Markers in Soybean Under Progressive Water Regimes via Hyperspectral Spectroscopy and Machine Learning

by
Caio Almeida de Oliveira
1,
Nicole Ghinzelli Vedana
1,
Weslei Augusto Mendonça
1,
João Vitor Ferreira Gonçalves
1,
Dheynne Heyre Silva de Matos
1,
Renato Herrig Furlanetto
2,
Luis Guilherme Teixeira Crusiol
3,
Amanda Silveira Reis
1,
Werner Camargos Antunes
4,
Roney Berti de Oliveira
1,
Marcelo Luiz Chicati
1,
José Alexandre M. Demattê
5,
Marcos Rafael Nanni
1 and
Renan Falcioni
1,4,*
1
Graduate Program in Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
2
Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
3
Embrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), Rodovia Carlos João Strass, s/n°, Distrito de Warta, Londrina 86001-970, Paraná, Brazil
4
Department of Biology, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
5
Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, São Paulo, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(20), 3409; https://doi.org/10.3390/rs17203409 (registering DOI)
Submission received: 17 July 2025 / Revised: 8 October 2025 / Accepted: 9 October 2025 / Published: 11 October 2025
(This article belongs to the Special Issue Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods)
Versions Notes

Abstract

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of soybean plants to various water regimes via hyperspectral reflectance (350–2500 nm) and machine learning (ML) models. The plants were subjected to eleven distinct water regimes, ranging from 100% to 0% field capacity, over 14 days. Seventeen key physiological parameters, including chlorophyll, carotenoids, flavonoids, proline, stress markers and water content, and hyperspectral data were measured to capture changes induced by water deficit. Principal component analysis (PCA) revealed significant spectral differences between the water treatments, with the first two principal components explaining 88% of the variance. Hyperspectral indices and reflectance patterns in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) regions are linked to specific stress markers, such as pigment degradation and osmotic adjustment. Machine learning classifiers, including random forest and gradient boosting, achieved over 95% accuracy in predicting drought-induced stress. Notably, a minimal set of 12 spectral bands (including red-edge and SWIR features) was used to predict both stress levels and biochemical changes with comparable accuracy to traditional laboratory assays. These findings demonstrate that spectroscopy by hyperspectral sensors, when combined with ML techniques, provides a nondestructive, field-deployable solution for early drought detection and precision irrigation in soybean cultivation.
Keywords: agricultural management; biochemical prediction; computational intelligence; phenotyping in plants; precision agriculture; spectroscopy in plants; UV–VIS–NIR–SWIR sensors agricultural management; biochemical prediction; computational intelligence; phenotyping in plants; precision agriculture; spectroscopy in plants; UV–VIS–NIR–SWIR sensors

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MDPI and ACS Style

Oliveira, C.A.d.; Vedana, N.G.; Mendonça, W.A.; Gonçalves, J.V.F.; Matos, D.H.S.d.; Furlanetto, R.H.; Crusiol, L.G.T.; Reis, A.S.; Antunes, W.C.; Oliveira, R.B.d.; et al. High-Throughput Identification and Prediction of Early Stress Markers in Soybean Under Progressive Water Regimes via Hyperspectral Spectroscopy and Machine Learning. Remote Sens. 2025, 17, 3409. https://doi.org/10.3390/rs17203409

AMA Style

Oliveira CAd, Vedana NG, Mendonça WA, Gonçalves JVF, Matos DHSd, Furlanetto RH, Crusiol LGT, Reis AS, Antunes WC, Oliveira RBd, et al. High-Throughput Identification and Prediction of Early Stress Markers in Soybean Under Progressive Water Regimes via Hyperspectral Spectroscopy and Machine Learning. Remote Sensing. 2025; 17(20):3409. https://doi.org/10.3390/rs17203409

Chicago/Turabian Style

Oliveira, Caio Almeida de, Nicole Ghinzelli Vedana, Weslei Augusto Mendonça, João Vitor Ferreira Gonçalves, Dheynne Heyre Silva de Matos, Renato Herrig Furlanetto, Luis Guilherme Teixeira Crusiol, Amanda Silveira Reis, Werner Camargos Antunes, Roney Berti de Oliveira, and et al. 2025. "High-Throughput Identification and Prediction of Early Stress Markers in Soybean Under Progressive Water Regimes via Hyperspectral Spectroscopy and Machine Learning" Remote Sensing 17, no. 20: 3409. https://doi.org/10.3390/rs17203409

APA Style

Oliveira, C. A. d., Vedana, N. G., Mendonça, W. A., Gonçalves, J. V. F., Matos, D. H. S. d., Furlanetto, R. H., Crusiol, L. G. T., Reis, A. S., Antunes, W. C., Oliveira, R. B. d., Chicati, M. L., Demattê, J. A. M., Nanni, M. R., & Falcioni, R. (2025). High-Throughput Identification and Prediction of Early Stress Markers in Soybean Under Progressive Water Regimes via Hyperspectral Spectroscopy and Machine Learning. Remote Sensing, 17(20), 3409. https://doi.org/10.3390/rs17203409

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