Non-Destructive Determination of Moisture Content in Husk-On Fresh Corn Using Multichannel Visible–Near-Infrared Spectroscopy Combined with Deep Learning
) of 0.897 and a root mean square error of prediction (RMSEP) of 4.13%. Furthermore, multichannel data relatively enhanced model performance, with the four-channel combination achieving the best performance. This study demonstrates the potential of deep learning and multichannel spectral data fusion in improving MC prediction accuracy, offering a practical solution for non-destructive moisture measurement in fresh corn.
Authors
- Ya Chen (ORCID: https://orcid.org/0009-0009-5076-1522)
- Min Huang
- Xin Zhao
- Qibing Zhu
- Min Xu
Institutions
- Jiangnan University (CN)
Publication Details
- Journal
- Applied Spectroscopy
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1177/00037028261434615
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Jiangsu Agricultural Science and Technology Independent Innovation Fund