title: Colour-Based Binary Discrimination of Scarified Quercus robur Acorns under Varying Illumination.
abstract: Efforts to predict the germination ability of acorns using their shape, length, diameter and density are reported in the literature. These methods, however, are not efficient enough. As such, a visual assessment of the viability of seeds based on the appearance of cross-sections of seeds following their scarification is used. This procedure is more robust but demands significant effort from experienced employees over a short period of time. In this article an automated method of acorn scarification and assessment has been announced. This type of automation requires the specific setup of a machine vision system and application of image processing algorithms for evaluation of sections of seeds in order to predict their viability. In the stage of the analysis of pathological changes, it is important to point out image features that enable efficient classification of seeds in respect of viability. The article shows the results of the binary separation of seeds into two fractions (healthy or spoiled) using average components of regular red-green-blue and perception-based hue-saturation-value colour space. Analysis of accuracy of discrimination was performed on sections of 400 scarified acorns acquired using two various setups: machine vision camera under uncontrolled varying illumination and commodity high-resolution camera under controlled illumination. The accuracy of automatic classification has been compared with predictions completed by experienced professionals. It has been shown that both automatic and manual methods reach an accuracy level of 84%, assuming that the images of the sections are properly normalised. The achieved recognition ratio was higher when referenced to predictions provided by professionals. Results of discrimination by means of Bayes classifier have been also presented as a reference.
authors.affiliation: Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., Kraków 30-059, Poland. mjk@agh.edu.pl.
authors.forename: Mirosław
authors.initials: M
authors.lastname: Jabłoński
authors.affiliation: Department of Forest Work Mechanisation, Faculty of Forestry, University of Agriculture in Krakow, 46 29-listopada Ave., Kraków 31-425, Poland. rltylek@cyf-kr.edu.pl.
authors.forename: Paweł
authors.initials: P
authors.lastname: Tylek
authors.affiliation: Department of Forest Work Mechanisation, Faculty of Forestry, University of Agriculture in Krakow, 46 29-listopada Ave., Kraków 31-425, Poland. rlwalczy@cyf-kr.edu.pl.
authors.forename: Józef
authors.initials: J
authors.lastname: Walczyk
authors.affiliation: Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., Kraków 30-059, Poland. rtad@ahg.edu.pl.
authors.forename: Ryszard
authors.initials: R
authors.lastname: Tadeusiewicz
authors.affiliation: Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., Kraków 30-059, Poland. ap@agh.edu.pl.
authors.forename: Adam
authors.initials: A
authors.lastname: Piłat
mesh_terms: D001499:Bayes Theorem; D003116:Color; D018525:Germination / Q000502:physiology*; D007091:Image Processing, Computer-Assisted; D008027:Light; D012639:Seeds / Q000254:growth & development*
keywords: colour image processing; image classification; machine vision; pedunculate oak; scarification; viability prediction