Next Article in Journal
Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring
Next Article in Special Issue
Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis
Previous Article in Journal
Estimation of Prestress Force Distribution in Multi-Strand System of Prestressed Concrete Structures Using Field Data Measured by Electromagnetic Sensor
Previous Article in Special Issue
Low-Cost Soil Moisture Profile Probe Using Thin-Film Capacitors and a Capacitive Touch Sensor
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1319; doi:10.3390/s16081319

Colour-Based Binary Discrimination of Scarified Quercus robur Acorns under Varying Illumination

1
Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30 Mickiewicza Ave., Kraków 30-059, Poland
2
Department of Forest Work Mechanisation, Faculty of Forestry, University of Agriculture in Krakow, 46 29-listopada Ave., Kraków 31-425, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 1 June 2016 / Revised: 30 July 2016 / Accepted: 3 August 2016 / Published: 18 August 2016
(This article belongs to the Special Issue Sensors for Agriculture)
View Full-Text   |   Download PDF [2843 KB, uploaded 18 August 2016]   |  

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. View Full-Text
Keywords: pedunculate oak; scarification; viability prediction; colour image processing; machine vision; image classification pedunculate oak; scarification; viability prediction; colour image processing; machine vision; image classification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jabłoński, M.; Tylek, P.; Walczyk, J.; Tadeusiewicz, R.; Piłat, A. Colour-Based Binary Discrimination of Scarified Quercus robur Acorns under Varying Illumination. Sensors 2016, 16, 1319.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top