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URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
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Resultados da Pesquisa
Item Estimation of percentage of impurities in coffee using a computer vision system(Departamento de Engenharia Agrícola - UFCG, 2022-01-14) Costa, Anderson G.; Silva, Eudócio R. O. da; Barros, Murilo M. de; Fagundes, Jonatthan A.The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.Item Classification of robusta coffee fruits at different maturation stages using colorimetric characteristics(Associação Brasileira de Engenharia Agrícola, 2020) Costa, Anderson G.; Sousa, Daniela A. G. de; Paes, Juliana L.; Cunha, João P. B.; Oliveira, Marcus V. M. deCoffee growers who produce the robusta species (Conilon) have sought to increase productivity and drink quality by improving production techniques. Artificial vision systems can assist in increasing the efficiency of operations associated with crop management. This study aimed to obtain colorimetric characteristics of robusta coffee fruits at different stages of maturity and use them for classifying fruits from digital images. A digital camera with spectral resolution in the visible was used to acquire images from 60 samples of coffee fruits at the green, cherry, and over-ripe stages of maturity. Colorimetric variables were extracted from the RGB, HIS, and L*a*b* color models and correlated with the physicochemical attributes of the fruits. The principal componente analysis associated with the k-means technique was applied to the colorimetric variables that showed a significant correlation with the physical-chemical attributes. The colorimetric variables were reduced to a principal component, which presented na explanatory percentage of the variance of 82.33%. The clustering obtained by the application of the k-means technique showed the feasibility of using images for the automatic classification of robusta coffee fruits, with an overall accuracy of 100%.Item Use of classifier to determine coffee harvest time by detachment force(Departamento de Engenharia Agrícola - UFCG, 2018-09) Barros, Murilo M. de; Silva, Fábio M. da; Costa, Anderson G.; Ferraz, Gabriel A. e S.; Silva, Flávio C. daCoffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.