Next Article in Journal
A Cost-Effective Geodetic Strainmeter Based on Dual Coaxial Cable Bragg Gratings
Previous Article in Journal
Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(4), 845; doi:10.3390/s17040845

A Classification Method for Seed Viability Assessment with Infrared Thermography

School of Technology, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 9 January 2017 / Revised: 3 April 2017 / Accepted: 10 April 2017 / Published: 12 April 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2423 KB, uploaded 13 April 2017]   |  

Abstract

This paper presents a viability assessment method for Pisum sativum L. seeds based on the infrared thermography technique. In this work, different artificial treatments were conducted to prepare seeds samples with different viability. Thermal images and visible images were recorded every five minutes during the standard five day germination test. After the test, the root length of each sample was measured, which can be used as the viability index of that seed. Each individual seed area in the visible images was segmented with an edge detection method, and the average temperature of the corresponding area in the infrared images was calculated as the representative temperature for this seed at that time. The temperature curve of each seed during germination was plotted. Thirteen characteristic parameters extracted from the temperature curve were analyzed to show the difference of the temperature fluctuations between the seeds samples with different viability. With above parameters, support vector machine (SVM) was used to classify the seed samples into three categories: viable, aged and dead according to the root length, the classification accuracy rate was 95%. On this basis, with the temperature data of only the first three hours during the germination, another SVM model was proposed to classify the seed samples, and the accuracy rate was about 91.67%. From these experimental results, it can be seen that infrared thermography can be applied for the prediction of seed viability, based on the SVM algorithm. View Full-Text
Keywords: thermal imaging; support vector machine (SVM); seed germination; multi classifier; image processing; classification thermal imaging; support vector machine (SVM); seed germination; multi classifier; image processing; 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

Men, S.; Yan, L.; Liu, J.; Qian, H.; Luo, Q. A Classification Method for Seed Viability Assessment with Infrared Thermography. Sensors 2017, 17, 845.

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