TY - GEN
T1 - Sistema de Visualización Artificial de Clasificación de Espárragos en una Empresa Procesadora y Exportadora de Espárragos Frescos
AU - León León, Ryan A.
AU - Castillo Alva, Nathaly D.
AU - Soto Lozada, Xiomara A.
N1 - Publisher Copyright:
© 2023 IMCIC.All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - This research project applies computer vision to the detection of specific characteristics of asparagus destined for the food industry. Using computer vision supported by a camera and the Python programming language, this research aims to simulate the classification of asparagus based on two criteria: color and tip type, with a focus on achieving precision in the analysis of each. Python is chosen as the programming language for its developer-friendly features, making it an ideal choice for this project. Additionally, computer vision is considered efficient, working with image analysis of asparagus from the reception area, gathered data, and a programming language, which relies heavily on continuous research for the application to function. The goal of this research is to be implemented as a solution for classifying asparagus according to the aforementioned criteria, achieving an efficiency rate of 95%.
AB - This research project applies computer vision to the detection of specific characteristics of asparagus destined for the food industry. Using computer vision supported by a camera and the Python programming language, this research aims to simulate the classification of asparagus based on two criteria: color and tip type, with a focus on achieving precision in the analysis of each. Python is chosen as the programming language for its developer-friendly features, making it an ideal choice for this project. Additionally, computer vision is considered efficient, working with image analysis of asparagus from the reception area, gathered data, and a programming language, which relies heavily on continuous research for the application to function. The goal of this research is to be implemented as a solution for classifying asparagus according to the aforementioned criteria, achieving an efficiency rate of 95%.
UR - https://www.scopus.com/pages/publications/85171303764
U2 - 10.54808/CICIC2023.01.192
DO - 10.54808/CICIC2023.01.192
M3 - Contribución a la conferencia
AN - SCOPUS:85171303764
T3 - CICIC 2023 - Decima Tercera Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica en el contexto de the 14th International Multi-Conference on Complexity, Informatics, and Cybernetics, IMCIC 2023 - Memorias
SP - 192
EP - 197
BT - CICIC 2023 - Decima Tercera Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica en el contexto de the 14th International Multi-Conference on Complexity, Informatics, and Cybernetics, IMCIC 2023 - Memorias
A2 - Callaos, Nagib C.
A2 - Horne, Jeremy
A2 - Ruiz-Ledesma, Elena Fabiola
A2 - Sanchez, Belkis
A2 - Tremante, Andres
T2 - Decima Tercera Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica, CICIC 2023 en el contexto de the 14th International Multi-Conference on Complexity, Informatics, and Cybernetics, IMCIC 2023 - 13th Ibero-American Conference on Complexity, Informatics and Cybernetics, CICIC 2023 in the context of the 14th International Multi-Conference on Complexity, Informatics, and Cybernetics, IMCIC 2023
Y2 - 28 March 2023 through 31 March 2023
ER -