Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence

Abstract

The quality evaluation of sweetpotatoes is of utmost importance during postharvest handling as it significantly impacts consumer satisfaction, nutritional value, and market competitiveness. This study presents an innovative approach that integrates explainable artificial intelligence (AI) with hyperspectral imaging to enhance the assessment of three important quality attributes in sweetpotatoes, i.e., dry matter content, soluble solid content, and firmness. Sweetpotato samples of three different varieties, including “Bayou Belle”, “Murasaki”, and “Orleans”, were imaged using a portable visible near-infrared hyperspectral imaging (VNIR-HSI) camera, with a 400–1000 nm spectral range. The extracted spectral data were used to select key wavelengths, develop multivariate regression models, and utilize SHapley Additive exPlanations (SHAP) values to ascertain model effectiveness and interpretability. The regression models (dry matter: R2p = 0.92, RMSEP = 1.50 % and RPD = 5.58; soluble solid content: R2p = 0.66, RMSEP = 0.85obrix, and RPD = 1.72; firmness: R2p = 0.85; RMSEP = 1.66 N and RPD = 2.63) developed with key wavelengths were used to generate prediction maps to visualize the spatial distribution of response attributes, facilitating an improved evaluation of sweetpotato quality. The study demonstrated that the combination of HSI, variable selection, and explainable AI has the potential to enhance the quality assessment of sweetpotatoes, ensuring supplies of higher quality products to consumers.