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

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Publisher
Springer US
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

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