Periódicos
URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
Navegar
2 resultados
Resultados da Pesquisa
Item Quality assessment of coffee beans through computer vision and machine learning algorithms(Editora UFLA, 2020) Santos, Fernando Ferreira Lima dos; Rosas, Jorge Tadeu Fim; Martins, Rodrigo Nogueira; Araújo, Guilherme de Moura; Viana, Lucas de Arruda; Gonçalves, Juliano de PaulaThe 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%).Item Spatial correlation between the chlorophyll index and foliar npk levels in coffee crop(Editora UFLA, 2020) Zanella, Marco Antonio; Rodrigues Junior, Francelino Augusto; Sousa, Emanoel Di Tarso dos Santos; Martins, Rodrigo Nogueira; Calijuri, Maria LúciaDetection of spatial variability of data that can improve crop management is a key factor for precision agriculture. In agriculture, there is a need for tools to assist farmers in decision-making about proper nutrient management, aiming to achieve their full productive potential. Based on that, this study aimed to (1) determine the spatial correlations between the chlorophyll index (CI) and the foliar levels of nitrogen, phosphorus and potassium (NPK) in the coffee crop using geostatistical tools; and (2) to evaluate the potential use of this index as a tool for site-specific nutrient management in an irrigated coffee field. For that, a study was carried out in a 2.1 ha area under arabica coffee cultivation in Paula Cândido, Minas Gerais State, Brazil. Samplings of the CI were performed in 1141 plants using a portable chlorophyll meter (SPAD-502). Regarding the NPK analysis, leaf samples from one of each 10 plants used to measure the CI were taken for chemical analysis (114 plants). Then, the data were submitted to descriptive and geostatistical analysis. For the spatial correlation analysis, the Moran Bivariate Global (I) and the Local index (Ixy) were used. The results showed a moderate correlation between the CI and N (0.500), showing the potential of the chlorophyll meter as a tool for site-specific nitrogen management in the coffee crop. Differently, the CI is not recommended for P and K management since they were not well correlated. Lastly, as a tool that performs indirect measurements, the results from the chlorophyll meter should be validated by field measurements to local calibrations.