Revista Brasileira de Engenharia Agrícola e Ambiental
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/10362
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Item Performance of a variable-rate distribution system for simultaneous fertilizer application(Departamento de Engenharia Agrícola - UFCG, 2016) Barros, Murilo M. de; Volpato, Carlos E. S.; Silva, Fabio M. da; Conceição, Fagner G. da; Corrêa Júnior, Delorme; Ribeiro, Luiz F.The objective of this study was to evaluate the performance of a variable-rate fertilizer distribution system for coffee crop, simultaneously applying two products. Two types of tests were performed: transversal deposition and longitudinal deposition. The transversal deposition test, with tarps, aimed to quantify the variations between programmed and applied doses, using a completely randomized design (CRD), in a factorial scheme, and the Scott-Knott test at p < 0.05. The longitudinal deposition test aimed to determine the distribution characteristics of the equipment along the displacement line, based on relative frequency values. In addition, the application rates on both sides of the distribution system were analysed using a CRD and the Scott-Knott test at p < 0.05. The application variation in the transversal deposition test with tarps was 1.59%. The variable-rate distribution system remained stable regarding the longitudinal deposition, regardless of any interaction.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 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.