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DC poleHodnotaJazyk
dc.contributor.authorAko, Joel E.
dc.contributor.authorNzi, Camille E.
dc.contributor.authorKpalma, Kidiyo
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-29T18:16:59Z-
dc.date.available2024-07-29T18:16:59Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 237-246.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57395
dc.description.sponsorshipThis work is financed in part by Ministère des Affaires Etrangères - France from Service de Coopération et d’Action Culturelle (SCAC) of Embassy of France in Ivory Coast.cs_CZ
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectkakaové bobycs
dc.subjectF-testcs
dc.subjectobsah vlhkostics
dc.subjectbarevné vlastnostics
dc.subjectRReliefFcs
dc.subjectregresecs
dc.subjectstrojové učenícs
dc.titleCocoa beans moisture content prediction using Machine Learning Model, based on the color image featuresen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe moisture content of cocoa beans is an essential factor in their quality. Modeling it during drying is still problematic due to the wide variation in drying conditions and the wide variation in cocoa bean varieties. This article aims to investigate the possibility of modeling the moisture content of cocoa beans as a function of RGB images features of unshelled cocoa beans. The approach is to extract features, analyze them and then use the most relevant ones to study Machine Learning models. Features are extracted by calculating mean, standard deviation, energy, entropy, kurtosis and skewness of the components of the rgb (RGB normalized), HSV, L*a*b*, YCbCr color spaces without the brightness components. These features are extracted from 4 types of samples, namely 10, 30, 50 and 70 bean samples per image. Features analysis using the F-test and RReliefF methods shows that the features based on the energy and entropy of the components rg, yb, Cr, Cb, a*, b* and h* are fairly relevant for predicting the water content of cocoa beans. However, they are highly correlated. The selected predictors allow the analysis of linear models, such as Ridge Regression (RR), PLS Regression (PLSR) and non-linear models, such as polynomial, Support Vector Regression (SVR) with rbf kernel, and Decision Trees Regression (DTR). Except RR and PLSR, the other models were preceded by a principal component analysis (PCA) to handle the collinearity problem. The non-linear models give good predictions for the training dataset, with coefficients of determination R 2 ranging from 0.94 to 0.96 and RMSE from 3.85 to 4.81. However, there is a significant difference between these results and the predictions of the new datasets. RR and PLSR are stable models, but their predictions are less than non-linear ones. It is therefore possible to predict the moisture content of cocoa beans from the features of RGB imagesen
dc.subject.translatedcocoa beansen
dc.subject.translatedF-testen
dc.subject.translatedmoisture contenten
dc.subject.translatedcolor featuresen
dc.subject.translatedRReliefFen
dc.subject.translatedregressionen
dc.subject.translatedmachine learningen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.24
dc.type.statusPeer revieweden
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