Analyzing Near-Infrared Hyperspectral Imaging for Protein Content Regression and Grain Variety Classification Using Bulk References and Varying Grain-to-Background Ratios

TL;DR

This study evaluates NIR-HSI imaging for protein content prediction and grain classification, addressing data biases and the influence of grain-to-background ratios to improve model accuracy and robustness.

Contribution

It introduces bias mitigation techniques for NIR-HSI calibration models and analyzes the effects of grain-to-background ratios on prediction performance.

Findings

Bias correction improves protein prediction accuracy.

Higher grain-to-background ratios lead to better predictions.

Including low-ratio images in calibration enhances robustness.

Abstract

Based on previous work, we assess the use of NIR-HSI images for calibrating models on two datasets, focusing on protein content regression and grain variety classification. Limited reference data for protein content is expanded by subsampling and associating it with the bulk sample. However, this method introduces significant biases due to skewed leptokurtic prediction distributions, affecting both PLS-R and deep CNN models. We propose adjustments to mitigate these biases, improving mean protein reference predictions. Additionally, we investigate the impact of grain-to-background ratios on both tasks. Higher ratios yield more accurate predictions, but including lower-ratio images in calibration enhances model robustness for such scenarios.