Nakatumba-Nabende, JoyceNabiryo, Ann LisaBabirye, ClaireTusubira, Jeremy FrancisKatumba, AndrewMurindanyi, SudiMutegeki, HenryNantongo, JudithSserunkuma, EdwinNakitto, MariamSsali, ReubenDavrieux, Fabrice2023-04-052023-04-052022Nakatumba-Nabende, J., Nabiryo, A. L., Babirye, C., Tusubira, J. F., Katumba, A., Murindanyi, S., ... & Ssali, R. (2022). Predicting sweetpotato sensory attributes using image analysis. DigiEye and image analysis as a breeding tool.https://doi.org/10.18167/agritrop/00733https://nru.uncst.go.ug/handle/123456789/8431The objective of the work was to develop, test and evaluate a color and mealiness classification model based on images of sweetpotato roots. A total of 3018 images were collected from 950 samples from October 2021 to November 2022. The captured image data samples were harvested from several sites, including Namulonge, Arua, Bulindi, Nassari, Serere, Rwebitaba, Iganga, Kabarole, Mbale, Mpigi, Busia, Kamuli, Hoima, Kabale and Kenya. Calibrations were done using reference data collected by a sensory panel. Up to twelve cooked roots per genotype were used for sensory evaluation of traits per session. Calibrations used various linear and non-linear models. Using linear regression, high performances were observed of the calibration for orange color intensity (R2 = 0.92, Mean Squared Error (MSE) =0.58), suggesting that the model is sufficient for field application. For mealiness-by-hand and positive area, the best model has a Mean Absolute Error (MAE) of 2.16 and 9.01 respectivelyenDigiEyeCooked sweetpotatoChemometricsTechnical Report