Using machine learning for image-based analysis of sweetpotato root sensory attributes

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cg.authorship.typesCGIAR and developing country instituteen_US
cg.authorship.typesCGIAR and advanced research instituteen_US
cg.contributor.affiliationMakerere Universityen_US
cg.contributor.affiliationInternational Potato Centeren_US
cg.contributor.donorBill & Melinda Gates Foundationen_US
cg.creator.identifierJoyce Nakatumba-Nabende: 0000-0002-0108-3798en_US
cg.creator.identifierJudith Nantongo: 0000-0001-7914-9139en_US
cg.creator.identifierMariam Nakitto: 0000-0002-4140-7216en_US
cg.creator.identifierReuben SSALI Tendo: 0000-0002-8143-6564en_US
cg.creator.identifierGodwill Makunde: 0000-0002-9003-7266en_US
cg.creator.identifierMukani Moyo: 0000-0001-5658-2669en_US
cg.creator.identifierHugo Campos: 0000-0003-0070-1336en_US
cg.howPublishedFormally Publisheden_US
cg.identifier.doihttps://doi.org/10.1016/j.atech.2023.100291en_US
cg.isijournalISI Journalen_US
cg.issn2772-3755en_US
cg.journalSmart Agricultural Technologyen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.cipANDEAN ROOTS AND TUBERSen_US
cg.subject.cipSWEETPOTATOESen_US
cg.subject.cipSWEETPOTATO AGRI-FOOD SYSTEMSen_US
cg.volume5en_US
dc.contributor.authorNakatumba-Nabende, J.en_US
dc.contributor.authorBabirye, C.en_US
dc.contributor.authorTusubira, J.en_US
dc.contributor.authorMutegeki, H.en_US
dc.contributor.authorNabiryo, A.en_US
dc.contributor.authorMurindanyi, S.en_US
dc.contributor.authorKatumba, A.en_US
dc.contributor.authorNantongo, J.S.en_US
dc.contributor.authorSserunkuma, E.en_US
dc.contributor.authorNakitto, M.en_US
dc.contributor.authorSsali, R.T.en_US
dc.contributor.authorMakunde, G.S.en_US
dc.contributor.authorMoyo, M.en_US
dc.contributor.authorCampos, Hugoen_US
dc.date.accessioned2023-08-04T17:48:06Zen_US
dc.date.available2023-08-04T17:48:06Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/131399en_US
dc.titleUsing machine learning for image-based analysis of sweetpotato root sensory attributesen_US
dcterms.abstractThe sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders. In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as highthroughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained 𝑅2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained 𝑅2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable 𝑅2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceAcademicsen_US
dcterms.audienceCGIARen_US
dcterms.audienceDevelopment Practitionersen_US
dcterms.audienceDonorsen_US
dcterms.audienceExtensionen_US
dcterms.audienceFarmersen_US
dcterms.audienceGeneral Publicen_US
dcterms.audienceNGOsen_US
dcterms.audiencePolicy Makersen_US
dcterms.audienceScientistsen_US
dcterms.available2023-07-25en_US
dcterms.bibliographicCitationNakatumba-Nabende, J.; Babirye, C.; Tusubira, J.; Mutegeki, H.; Nabiryo, A.; Murindanyi, S.; Katumba, A.; Nantongo, J.; Sserunkuma, E.; Nakitto, M.; Ssali, R.T.; Makunde, G.S.; Moyo, M.; Campos, H. 2023. Using machine learning for image-based analysis of sweetpotato root sensory attributes. Smart Agricultural Technology. ISSN 2772-3755. 5. DOI: https://doi.org/10.1016/j.atech.2023.100291en_US
dcterms.extent14 p.en_US
dcterms.issued2023-10en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherElsevieren_US
dcterms.subjectsweet potatoesen_US
dcterms.subjectrootsen_US
dcterms.typeJournal Articleen_US

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