Using Computer Vision Deep Learning Models to Benefit NIR Hyperspectral Image Processing

This study demonstrates the application of open-source deep learning models from the computer vision domain to streamline tasks in near-infrared (NIR) hyperspectral imaging (HSI) data processing. Specifically, it demonstrates a challenging case of dry matter prediction in mango fruits under conditions typical for fruit importers and exporters, who often need to assess the quality parameters of fruit packed in boxes. NIR HSI offers a non-destructive alternative to traditional hot air oven drying for dry matter determination. However, processing HSI images of fruit boxes presents challenges due to objects touching, overlapping, or being partially hidden, which complicates traditional HSI analysis. Modern artificial intelligence (AI) approaches can simplify such HSI data analysis, and integrating AI with chemometric modeling represents a promising future direction for NIR HSI data processing.