During the sweet potato breeding process, phenotypes are measured for different quality traits, which differ from one crop to another. Their genetic, nutritional, organoleptic, biological, and morphological characteristics are classified into other groups. Color and appearance are morphological traits typically used by consumers as visual cues to decide underlying quality attributes such as taste, cooking quality, and texture of a particular crop or product. This means that color and appearance are crucial indicators for targeting end-user preferences for breeding nutritious food crops in Africa. The assessment of the organoleptic attributes is especially important for the breeders, including characteristics like taste, texture, and mealiness. These attributes are scored by a human-trained panel who taste different samples of sweet potatoes and also score some of the morphological traits like color. The assessment of this panel is used to inform the selection of samples that are progressed during the breeding and release of new varieties. In order to achieve a high-throughput sensory evaluation chain, the image-based analysis is used to evaluate different quality traits of the sweet potato samples. Images were taken using the DigiEye machine, a non-contact digital imaging system. The color and appearance of raw and cooked products can be instantly and accurately captured and analyzed under a stable light environment. After capturing the images, the attributes are analyzed using computer vision and machine learning techniques, and advanced tools that will increase the throughput of the human sensory panel.
