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March 3, 2026
Open Access
Integrated hyperspectral–RGB modeling for the estimation of wheat protein and dough rheological properties using machine and deep transfer learning
SZ
Shaohua Zhang
Henan Agricultural University
QC
Qianya Cheng
Henan Agricultural University
TW
Tiantian Wang
Heilongjiang University of Chinese Medicine
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Puntos clave
Wheat protein and dough rheological properties are effectively estimated using integrated hyperspectral and RGB modeling.
The study demonstrates a high predictive accuracy of 92% for protein content and 88% for dough properties based on machine learning algorithms.
Analysis employed deep transfer learning methods to enhance model performance and reliability across diverse datasets.
This innovative approach highlights the potential for advanced agricultural monitoring and precision farming strategies.
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Zhang et al. (Thu,) studied this question.
synapsesocial.com/papers/69a75bebc6e9836116a241ed
https://doi.org/https://doi.org/10.1016/j.crfs.2026.101329
Integrated hyperspectral–RGB modeling for the estimation of wheat protein and dough rheological properties using machine and deep transfer learning | Synapse