SBICafé
Biblioteca do Café

Quality assessment of coffee beans through computer vision and machine learning algorithms

Show simple item record

dc.contributor.author Santos, Fernando Ferreira Lima dos
dc.contributor.author Rosas, Jorge Tadeu Fim
dc.contributor.author Martins, Rodrigo Nogueira
dc.contributor.author Araújo, Guilherme de Moura
dc.contributor.author Viana, Lucas de Arruda
dc.contributor.author Gonçalves, Juliano de Paula
dc.date.accessioned 2021-09-15T10:17:30Z
dc.date.available 2021-09-15T10:17:30Z
dc.date.issued 2020
dc.identifier.citation SANTOS, F. F. L. et al. Quality assessment of coffee beans through computer vision and machine learning algorithms. Coffee Science, Lavras, v. 15, p. 1-9, 2020. pt_BR
dc.identifier.issn 1984-3909
dc.identifier.uri Doi: https://doi.org/10.25186/.v15i.1752 pt_BR
dc.identifier.uri http://www.sbicafe.ufv.br/handle/123456789/12800
dc.description.abstract The increasing market interest in coffee beverage, lead coffee growers around the world to adopt more efficient methods to select the best-quality coffee beans. Currently, coffee beans selection is carried out either manually, which is a costly and unreliable process, or using electronic sorting machines, which are often inefficient because some coffee beans defects, such as sour and immature beans, have similar spectral response patterns. In this sense, the present work aimed to analyze the importance of shape and color features for different machine learning techniques, such as Support Vector Machine (SVM), Deep Neural Network (DNN) and Random Forest (RF), to assess coffee beans’ defects. For this purpose, an algorithm written in Python language was used to extract shape and color features from coffee beans images. The dataset obtained was then used as input to the machine learning algorithms, developed using Python and R programing languages. The data reported in this study pointed to the importance of color descriptors for classifying coffee beans defects. Among the variables used, the components Gmean from RGB (Red, Green and Blue) color space and Vmean from HSV (Hue, Saturation and Value) color space were some of the most relevant features for the classification models. The results reported in this study indicate that all the classifier models presented similar performance. In addition, computer vision along with machine learning algorithms can be used to classify coffee beans with a very high accuracy (> 88%). pt_BR
dc.format pdf pt_BR
dc.language.iso en pt_BR
dc.publisher Editora UFLA pt_BR
dc.relation.ispartofseries Coffee Science:v.15;
dc.rights Open Access pt_BR
dc.subject Deep neural network pt_BR
dc.subject Classification pt_BR
dc.subject Artificial intelligence pt_BR
dc.subject Image processing pt_BR
dc.subject Granulometry pt_BR
dc.subject.classification Cafeicultura::Qualidade de bebida pt_BR
dc.title Quality assessment of coffee beans through computer vision and machine learning algorithms pt_BR
dc.type Artigo pt_BR

Files in this item

Files Size Format View
Coffee Science_v. 15_1752_2020.pdf 2.653Mb application/pdf View/Open ou Pre-visualizar

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Sobre o SBICafé

Browse

My Account