Researchers at University of Illinois Urbana-Champaign are exploring the use of hyperspectral imaging and explainable artificial intelligence to assess sweetpotato attributes.
“Traditionally, quality assessment is done using laboratory analytical methods. You need different instruments to measure different attributes in the lab, and you need to wait for the results,” Mohammed Kamruzzaman, assistant professor in the university’s Department of Agricultural and Biological Engineering, said in a news release. “With hyperspectral imaging, you can measure several parameters simultaneously. You can assess every potato in a batch, not just a few samples. Spectral imaging is non-invasive, fast, accurate and cost-effective.”
The study is part of a multistate collaboration funded by the USDA that includes researchers from Mississippi, North Carolina, Michigan, Louisiana and Illinois. Each university addresses different aspects of the project; Kamruzzaman’s team will focus on the assessment of three chemical attributes — dry matter, firmness, and soluble sugar content or brix — which affect the market price and whether a potato is suitable for the consumer or for processing, according to the release.
The researchers use a visible near-infrared hyperspectral imaging camera to take images of sweetpotatoes from two different angles, the university said. Analyzing the images produces spectral data, which identifies key wavelengths and develops color maps that display the distribution of desired attributes.
“We combine hyperspectral imaging with explainable AI, allowing us to understand the processes behind the results. It is a way to visualize how the machine learning algorithms work, how input data are processed, and how features are connected to predict the output,” Md Toukir Ahmed, a doctoral student in the university’s Department of Agricultural and Biological Engineering and lead author of the paper, said in the release. “We believe this is a novel application of this method for sweetpotato assessment. This pioneering work has the potential to pave the way for usage in a wide range of other agricultural and biological research fields as well.”
The research team said the results can help industry professionals and researchers understand the significance of different features in predicting quality attributes, which leads to more informed decision-making and ensures supplies of higher-quality products to consumers.
Kamruzzaman said one goal of the project is to develop a tool that processors can use to quickly and easily scan batches of sweetpotatoes to determine features and attributes. Eventually, researchers could create a mobile app consumers can use in the grocery store to scan the quality of sweetpotatoes at the point of purchase, according to the release.
