Coffee Science
URI permanente desta seção${dspace.url}/handle/123456789/3355
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Resultados da Pesquisa
Item Roasted coffee beans characterization through optoelectronic color sensing(Universidade Federal de Lavras, 2023-12-19) Vanegas-Ayala, Sebastian-Camilo; Leal-Lara, Daniel-David; Barón-Velandia, JulioThe degree of roasting of the coffee determines the physical properties of the bean which are directly represented in the quality of the coffee, to classify the coffee bean efficiently represents a challenge that has been addressed from different technological approaches with colorimeters. This research aims to simplify the identification of the roast level of ground coffee on the Agtron scale by characterizing the degree of roast using an optoelectronic color sensor and establishing a correlation between the Red, Green, and Blue (RGB) scales. This allows for the assurance of quality levels of the beans right from the roasting process. This research comprehends the collection and preparation of samples, the definition of RGB and CIE L*a*b* values, and their interpretation in the Agtron scale using the red component of the RGB scale. The results showed an efficient and accurate estimation for the roast degree of ground coffee beans (0.1371 MSE) that uses minimum processing requirements and a function to assess the intermediate values in the Agtron scale. The characterization of the roast degree of ground coffee beans using data collected from an optoelectronic color sensor through a high-precision function with a linear structure enables the description of intermediate values not fully represented on the Agtron scale. This enhances the process of identifying the roast degree, facilitating subsequent quality assurance processes by maintaining the beans at the desired roast level.Item Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee(Editora UFLA, 2020) Parreiras, Taya Cristo; Lense, Guilherme Henrique Expedito; Moreira, Rodrigo Santos; Santana, Derielsen Brandão; Mincato, Ronaldo LuizNitrogen is an essential element for coffee production. However, when fertilization do not consider the spatial variability of the agricultural parameters, it can generate economic losses, and environmental impacts. Thus, the monitoring of nitrogen is essential to the fertilizing management, and remote sensing based on unmanned aerial vehicles imagery has been evaluated for this task. This work aimed to analyze the potential of vegetation indices of the visible range, obtained with such vehicles, to monitor the nitrogen content of coffee plants in southern Minas Gerais, Brazil. Therefore, we performed leaf analysis using the Kjeldahl method, and we processed the images to produce the vegetation indices using Geographic Information Systems and photogrammetry software. Moreover, the images were classified using the Color Index of Vegetation and the Maximum Likelihood Classifier. As estimator tool, we created Random Forest models of classification and regression. We also evaluated the Pearson correlation coefficient between the nitrogen and the vegetation indices, and we performed the analysis of variance and the Tukey-Kramer test to assess whether there is a significant difference between the averages of these indices in relation to nitrogen levels. However, the models were not able to predict the nitrogen. The regression model obtained a R2 = 0.01. The classification model achieved an overall accuracy of 0.33 (33%), but it did not distinguish between the different levels of nitrogen. The correlation tests revealed that the vegetation indices are not correlated with the nitrogen, since the best index was the Green Leaf Index (R = 0.21). However, the image classification achieved a Kappa coefficient of 0.92, indicating that the tested index is efficient. Therefore, visible indices were not able to monitor the nitrogen in this case, but they should continue to be explored, since they could represent a less expensive alternative.