Open AccessImage analysis of real-time classification of cherry fruit from colour features
Author(s) -
José F. Reyes,
Elías Contreras,
Christian Correa,
Pedro Melín
Publication year - 2021
Publication title -
journal of agricultural engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.3
H-Index - 18
eISSN - 2239-6268
pISSN - 1974-7071
DOI - 10.4081/jae.2021.1160
Subject(s) - rgb color model , artificial intelligence , hsl and hsv , pixel , set (abstract data type) , segmentation , histogram , mathematics , computer vision , hue , software , image processing , computer science , pattern recognition (psychology) , sample (material) , image (mathematics) , virus , chemistry , chromatography , virology , biology , programming language
An image analysis algorithm for the classification of cherries in real time by processing their digitalized colour images was developed, and tested. A set of five digitalized images of colour pattern, corresponding to five colour classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the coloured area of the fruit. A histogram analysis was carried out for the RGB and HSV colour spaces, where the Red and Hue components showed differences between each of the specified colour patterns of the exporting reference system. This information led to the development of a hybrid Bayesian classification algorithm based on the components R and H. Its accuracy was tested with a set of cherry samples within the colour range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm showed a 100% effectiveness in classifying a sample set of cherries into the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, thus ensuring an affordable commercial deployment.