###### Abstract  
Hyperspectral imaging (HSI) is one of the most often used techniques for rapid quality evaluation for various applications. It is a non-destructive technique that effectively evaluates the quality attributes of root and tuber crops, including yam and cassava, and their food products. Hyperspectral imaging technology, which combines spectroscopy and imaging principles, has an advantage over conventional spectroscopy due to its ability to simultaneously evaluate the physical characteristics and chemical components of various food products and specify their spatial distributions. HSI has demonstrated significant potential for obtaining quick information regarding the chemical composition of the root and tuber, such as starch, protein, dry matter, amylose, and soluble sugars, as well as physical characteristics such as textural properties and water binding capacity. This review highlights the principles of near-infrared hyperspectral imaging (NIR-HSI) techniques combined with relevant image processing tools. It then provides cases of its application in determining crucial biochemical quality traits and textural attributes of roots and tuber crops, focusing on cassava and yam. The need for more information on using NIR-HSI in the quality evaluation of yam and cassava was underscored. It also presents the challenges and prospects of this technology. hyperspectral imaging; spectroscopy; quality evaluation; yam; cassava; image processing +
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is costly and time-consuming, especially when using conventional approaches. Therefore, this brings to the fore the necessity to provide a cost-effective, time-saving, and accurate prediction of those important traits to make informed decisions in the selection process, especially in large breeding populations. Near-infrared spectroscopy (NIRS) has proven to be a reliable tool for predicting various quality parameters in many breeding programs, such as cassava, yam, potato, and sweet potato [1; 6; 7; 8; 9; 10; 11]. Its application in breeding programs has improved, as it enhances the adoption of modern NIRS and has been used to accurately predict key quality traits, as reported by many authors on genetic technologies that require the phenotyping of many clones for complex features within the shortest time possible and at minimal cost, especially at the early breeding stages [8]. NIRS has been used to evaluate many quality traits in crops, and their flour, with a good to a medium coefficient of prediction, as reported in [12; 13; 14; 15; 16; 17]. To highlight the potential of NIRS for the investigation of numerous chemical constituents, [PERSON] et al. [18] wrote a review which shows that NIRS has potential as a high-throughput phenotyping tool for root and tuber crops. Additionally, their research showed that most published studies supported the ability of NIRS to accurately predict biochemical parameters such as starch, soluble sugar, and many others. However, there are only a few studies confirming the possibility of NIRS predicting other quality attributes related to end-product quality, which inform consumers' preferences. These are seemingly complex traits because the quality of the product has been impacted by processing factors [18]. However, the emergence of near-infrared hyperspectral imaging (NIR-HSI) represents a new development in the application of spectroscopy. By combining the spatial and spectral data of the target sample, this method merges imaging and spectroscopic concepts with the ability to capture additional inherent information about the product. It can accurately predict the biochemical properties, physical (internal and external) features, and spatial information of the chemical components in the products [19]. It has gained broad interest in the noninvasive quality monitoring of many food crops [19]. NIR-HSI was originally developed for remote sensing applications, but it is now used to facilitate complete and reliable analyses of the inherent physical and chemical properties of food products [20].