Navegando por Autor "Cunha, João P. B."

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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. de
Coffee 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%.
Modeling of operational performance parameters applied in mechanized harvest of coffee
(Departamento de Engenharia Agrícola - UFCG, 2016-10) Cunha, João P. B.; Silva, Fabio M. da; Andrade, Ednilton T. de; Carvalho, Luis C. C.
In super-mechanized coffee harvesting system, all operations are performed mechanically. In order to improve the logistics of mechanized agricultural operations, the knowledge on the variables that affect the operational performance can generate models to accurately estimate these parameters. The use of response surface methodology (RSM) allows to verify the influence of different independent variables and the generated response to allow for a great value. This study aimed to verify, using RSM, the influence of speed, mean length of rows and the slope of the areas on the operational performance parameters in different mechanized operations in coffee production, such as: harvest, sweeping and gathering. The results show that the slope directly influences the operational performance of the mechanical harvesting of coffee. The RSM proved to be an important tool to verify the effect of variables on performance parameters, and the generated models showed high significance.
Parameters of operational performance of soil preparation and semi-mechanized transplantation of coffee seedling
(Associação Brasileira de Engenharia Agrícola, 2018-11) Cunha, João P. B.; Silva, Fabio M. da; Andrade, Ednilton T.; Barros, Murilo M. de
In recent years, the coffee has undergone major changes, and in case of transplanting operation, the use of machinery has proved to be a viable alternative to producers. Prior knowledge of the influence of the variables that influence the operational capability of these machines can generate models to estimate precisely these parameters, thus enabling the optimization and management of mechanized operations. One of this tool is the use of the response surface methodology, which allows checking the influence of different independent variables and the response generated to allow a great value. This study aims to verify the use of the response surface method to determine parameters of mechanized operations in coffee plant implantation. The results show that the number of seedlings deposited increases with the increase in operating speed. In contrast, the adoption of higher speeds decreases the efficiency of the evaluated field operations. The response surface methodology was an important tool to check the effect of variables on performance parameters, and the generated models showed high significance allowing the identification of the effects of the operational speed and the average length of the cultivation line.