Real-time classification of coffee fruits using FPGA

The goal in this work was to design a circuit that could classify objects by color in real-time that can be used for quality improvement. A circuit that performs color analysis of an image and, according to that analysis, classifies the object was designed. A histogram of the Spherical Coordinate Tr...

Full description

Autores:
Montes Castrillón, Nubia Liliana
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2015
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/55273
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/55273
http://bdigital.unal.edu.co/50617/
Palabra clave:
0 Generalidades / Computer science, information and general works
51 Matemáticas / Mathematics
62 Ingeniería y operaciones afines / Engineering
Real-time
Image processing - digital techniques
Color analysis
Coffee grain - classification
Tiempo real
Procesamiento digital de imágenes
Análisis de color
Granos de café - clasificación
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:The goal in this work was to design a circuit that could classify objects by color in real-time that can be used for quality improvement. A circuit that performs color analysis of an image and, according to that analysis, classifies the object was designed. A histogram of the Spherical Coordinate Transform of the image is computed and compared to histogram patterns to make a classification decision. The circuit was tested on the classification of coffee fruits in four maturity stages: immature, under-mature, mature and over-mature. The results showed that it is possible to build a system for color object classification that works in real-time and that can be affordable and portable. The designed circuit is implemented on a Field Programmable Gate Array (FPGA), acquires video at 64 frames per second, classifies the coffee fruits at a rate of 25 fruits per second and achieved an average efficacy of 75.7%