Automatic identification of crop and weed species with chlorophyll fluorescence induction curves |
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Authors: | Esa Tyystjärvi Michael Nørremark Heta Mattila Mika Keränen Marja Hakala-Yatkin Carl-Otto Ottosen Eva Rosenqvist |
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Institution: | 1.Molecular Plant Biology, Department of Biochemistry and Food Chemistry,University of Turku,Turku,Finland;2.Department of Biosystem Engineering, Faculty of Agricultural Sciences,Aarhus University,Tjele,Denmark;3.Institute of Horticulture, Faculty of Agricultural Sciences,Aarhus University,?rslev,Denmark;4.Institute of Agriculture and Ecology, Section of Crop Science,University of Copenhagen,Taastrup,Denmark |
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Abstract: | Automatic identification of crop and weed species is required for many precision farming practices. The use of chlorophyll
fluorescence fingerprinting for identification of maize and barley among six weed species was tested. The plants were grown
in outdoor pots and the fluorescence measurements were done in variable natural conditions. The measurement protocol consisted
of 1 s of shading followed by two short pulses of strong light (photosynthetic photon flux density 1700 μmol m−2 s−1) with 0.2 s of darkness in between. Both illumination pulses caused the fluorescence yield to increase by 30–60% and to display
a rapid fluorescence transient resembling transients obtained after long dark incubation. A neural network classifier, working
on 17 features extracted from each fluorescence induction curve, correctly classified 86.7–96.1% of the curves as crop (maize
or barley) or weed. Classification of individual species yielded a 50.2–80.8% rate of correct classifications. The best results
were obtained if the training and test sets were measured on the same day, but good results were also obtained when the training
and test sets were measured on different dates, and even if fluorescence induction curves measured from both leaf sides were
mixed. The results indicate that fluorescence fingerprinting has potential for rapid field separation of crop and weed species. |
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