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.