High-performance prediction of protein content in brown rice via multi-spectral fusion and deep learning: a comparative study of visible, near infrared, mid infrared spectroscopy and hyperspectral imaging.

Abstract

This original work explored the potential of near-infrared (NIR), mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI) in visible-near-infrared (Vis-NIR-HSI) and short-wave infrared (SWIR-HSI) range for non-destructive prediction and visualization of protein content in brown rice from 138 rice varieties. Feature selection and predictive modeling were integrated to identify protein-related wavelengths and enable pixel-level protein mapping. Using full-spectrum data, the MIR-based convolutional neural network (CNN) model achieved the highest predictive accuracy (R p 2 = 0.96, RPD = 4.84), confirming the superior chemical sensitivity of MIR spectroscopy. Following feature band selection, the NIR-based uninformative variable elimination-support vector machine (UVE-SVM) model exhibited optimal performance among wavelength-reduced models (R p 2 = 0.90, RPD = 3.16), surpassing Vis-NIR-HSI and SWIR-HSI-based approaches. For multi-spectral feature fusion integrating selected wavelengths from all four spectral modes, the competitive adaptive reweighted sampling-CNN (CARS-CNN) model yielded the best overall performance, demonstrating synergistic advantages of combining complementary spectral information. Furthermore, protein distribution maps from SWIR hyperspectral images displayed superior spatial resolution compared to Vis-NIR-HSI images. Overall, this study establishes a high-performance and transferable protein prediction framework based on multi-spectral fusion and deep learning, and provides a systematic comparison of visible, near-infrared, mid-infrared spectroscopy, and hyperspectral imaging for brown rice quality assessment.

Plant phenotyping relevance

褐玄米のタンパク質含量という植物器官形質を対象に、分光・ハイパースペクトル画像、特徴選択、深層学習による予測を開発・比較・検証しており、表現型取得法が研究の中心である。

Code and data availability

公開本文の所在を確認できませんでした。非公開または購読が必要な可能性があります。

No evidence-backed public reproduction asset is currently recorded.