Agritrop
Accueil

Predicting sweetpotato sensory attributes using image analysis. DigiEye and image analysis as a breeding tool

Nakatumba-Nabende Joyce, Nabiryo Ann Lisa, Babirye Claire, Tusubira Jeremy Francis, Katumba Andrew, Murindanyi Sudi, Mutegeki Henry, Nantongo Judith Ssali, Sserunkuma Edwin, Nakitto Mariam, Ssali Reuben, Davrieux Fabrice. 2023. Predicting sweetpotato sensory attributes using image analysis. DigiEye and image analysis as a breeding tool. Kampala : RTBfoods Project-CIRAD, 15 p.

Document technique et de recherche
[img]
Prévisualisation
Version publiée - Anglais
Sous licence Licence Creative Commons.
RTBfoods_Image tentative calibration_Sensory_Raw & cooked sweetpotato.pdf

Télécharger (575kB) | Prévisualisation

Résumé : The 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 respectively.

Mots-clés libres : DigiEye, Cooked sweetpotato, Sensorial profiles, Textural properties, Calibrations, Chemometrics

Agences de financement hors UE : Bill and Melinda Gates Foundation, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Centro Internacional de Agricultura Tropical, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, James Hutton Institute

Projets sur financement : (FRA) Breeding RTB Products for End User Preferences

Auteurs et affiliations

  • Nakatumba-Nabende Joyce, Makerere University (UGA)
  • Nabiryo Ann Lisa, Makerere University (UGA)
  • Babirye Claire, Makerere University (UGA)
  • Tusubira Jeremy Francis, Makerere University (UGA)
  • Katumba Andrew, Makerere University (UGA)
  • Murindanyi Sudi, Makerere University (UGA)
  • Mutegeki Henry, Makerere University (UGA)
  • Nantongo Judith Ssali, CIP (UGA)
  • Sserunkuma Edwin, CIP (UGA)
  • Nakitto Mariam, CIP (UGA)
  • Ssali Reuben, CIP (UGA)

Contributeurs et affiliations

  • Davrieux Fabrice, CIRAD-PERSYST-UMR Qualisud (REU) - collaborateur

Source : Cirad-Agritrop (https://agritrop.cirad.fr/603453/)

Voir la notice (accès réservé à la Dist) Voir la notice (accès réservé à la Dist)

[ Page générée et mise en cache le 2023-09-20 ]