Comparison of chlorophyll fluorescence curves and texture analysis for automatic plant identification

Comparison of chlorophyll fluorescence curves and texture analysis for automatic plant... With automatic plant identification methods, the amount of herbicides used in agriculture can be reduced when herbicides are sprayed only on weeds. In the present study, leaves of oat (Avena sativa) and dandelion (Taraxacum officinale, TAROF) were arranged so that there was overlap between the species, imaged with a pulse amplitude modulation fluorescence camera and photographed with a digital color camera. The fluorescence induction curves from each pixel were parameterized to obtain a set of features and from color photographs, texture features were calculated. A support vector algorithm that also performed feature selection was used for pattern recognition of both data sets. Fluorescence-based identification worked well with oat leaves, producing 92.2 % of correctly identified pixels, whereas the texture-based method often mis-identified the central vein of a TAROF leaf as oat, identifying correctly only 66.5 % of oat pixels. With TAROF that shows a clear dicot-type texture, the texture method was slightly better (96.4 % correctly identified pixels) than the fluorescence method (94.6 %). In fluorescence-based identification, the accuracy varied between entire TAROF leaves, probably reflecting the genetic variability of TAROF. The results suggest that the accuracy of identification could be improved by combining two identification methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Comparison of chlorophyll fluorescence curves and texture analysis for automatic plant identification

Loading next page...
 
/lp/springer_journal/comparison-of-chlorophyll-fluorescence-curves-and-texture-analysis-for-2OcFGxgvun
Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-013-9320-y
Publisher site
See Article on Publisher Site

Abstract

With automatic plant identification methods, the amount of herbicides used in agriculture can be reduced when herbicides are sprayed only on weeds. In the present study, leaves of oat (Avena sativa) and dandelion (Taraxacum officinale, TAROF) were arranged so that there was overlap between the species, imaged with a pulse amplitude modulation fluorescence camera and photographed with a digital color camera. The fluorescence induction curves from each pixel were parameterized to obtain a set of features and from color photographs, texture features were calculated. A support vector algorithm that also performed feature selection was used for pattern recognition of both data sets. Fluorescence-based identification worked well with oat leaves, producing 92.2 % of correctly identified pixels, whereas the texture-based method often mis-identified the central vein of a TAROF leaf as oat, identifying correctly only 66.5 % of oat pixels. With TAROF that shows a clear dicot-type texture, the texture method was slightly better (96.4 % correctly identified pixels) than the fluorescence method (94.6 %). In fluorescence-based identification, the accuracy varied between entire TAROF leaves, probably reflecting the genetic variability of TAROF. The results suggest that the accuracy of identification could be improved by combining two identification methods.

Journal

Precision AgricultureSpringer Journals

Published: Jun 1, 2013

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off