Hyperspectral imaging is a useful technique for analyzing the chemical composition of food and agricultural products. However, it is a costly and complicated procedure, which limits its practical application. A team of University of Illinois Urbana-Champaign researchers has developed a method to reconstruct hyperspectral images from standard RGB images using deep machine learning. This technique can greatly simplify the analytical process and potentially revolutionize product assessment in the agricultural industry.

“Hyperspectral imaging uses expensive equipment. If we can use RGB images captured with a regular camera or smartphone, we can use a low-cost, handheld device to predict product quality,” said lead author Md Toukir Ahmed, a doctoral student in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at Illinois.  

The researchers tested their method by analyzing the chemical composition of sweet potatoes. They focused on soluble solid content in one study and dry matter in a second study — important features that influence the taste, nutritional value, marketability, and processing suitability of sweet potatoes. Using deep learning models, they converted the information from RGB images into hyperspectral images.

“With RGB images, you can only detect visible attributes like color, shape, size, and external defects; you can’t detect any chemical parameters. In RGB images you have wavelengths from 400 to 700 nanometers, and three channels — red, green, and blue. But with hyperspectral images you have many channels and wavelengths from 700 to 1000 nm. With deep learning methods, we can map and reconstruct that range so we now can detect the chemical attributes from RGB images,” said Mohammed Kamruzzaman, assistant professor in ABE and corresponding author on both papers.

Hyperspectral imaging captures a detailed spectral signature at spatial locations across hundreds of narrow bands, combining to form hypercubes. Applying cutting-edge deep learning-based algorithms, Kamruzzaman and Ahmed were able to create a model to reconstruct the hypercubes from RGB images to provide the relevant information for product analysis.

They calibrated the spectral model with reconstructed hyperspectral images of sweet potatoes, achieving over 70% accuracy in predicting soluble solid content and 88% accuracy in dry matter content, marking a significant improvement over previous studies.

In a third paper, the research team applied deep learning methods to reconstruct hyperspectral images for predicting chick embryo mortality, which has applications for the egg and hatchery industry. They explored different techniques and made recommendations for the most accurate approach.

"Our results show great promise for revolutionizing agricultural product quality assessment. By reconstructing detailed chemical information from simple RGB images, we're opening new possibilities for affordable, accessible analysis. While challenges remain in scaling this technology for industrial use, the potential to transform quality control across the agricultural sector makes this a truly exciting endeavor,” Kamruzzaman concluded.

The first paper, “Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product,” is published in the Journal of Food Engineering [DOI: 10.1016j.jfoodeng.2024.112223]. Authors are Md. Toukir Ahmed, Ocean Monjur, and Mohammed Kamruzzaman.

The second paper, “Comparative analysis of hyperspectral Image reconstruction using deep learning for agricultural and biological applications,” is published in Results in Engineering [DOI: 10.1016/j.rineng.2024.102623]. Authors are Md. Toukir Ahmed, Arthur Villordon, and Mohammed Kamruzzaman.

Both studies were funded by the USDA Agricultural Marketing Service through the Specialty Crop Multistate Program grant AM21SCMPMS1010.

The third paper, “Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry,” is published in Smart Agricultural Technology [DOI: 10.1016/j.atech.2024.100533]. Authors are Md. Toukir Ahmed, Md. Wadud Ahmed, Ocean Monjur, Jason Emmert, Grirish Chowdhary, and Mohammed Kamruzzaman. Funding was provided by the USDA National Institute of Food and Agriculture, Award # 2023–67015–39154.