A1 Refereed original research article in a scientific journal

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




AuthorsMattila Heta, Valli Pertti, Pahikkala Tapio, Teuhola Jukka, Nevalainen Olli S, Tyystjärvi Esa

PublisherSpringer

Publication year2013

JournalPrecision Agriculture

Journal acronymPrecision Agric

Number in series6

Volume14

Issue6

First page 621

Last page636

Number of pages16

ISSN1385-2256

DOIhttps://doi.org/10.1007/s11119-013-9320-y(external)


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.



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