Automatic identification of crop and weed species with chlorophyll fluorescence induction curves

Automatic identification of crop and weed species with chlorophyll fluorescence induction curves 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Automatic identification of crop and weed species with chlorophyll fluorescence induction curves

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Publisher
Springer US
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
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/s11119-010-9201-6
Publisher site
See Article on Publisher Site

References

  • Weed and crop discrimination using image analysis and artificial intelligence methods
    Aitkenhead, MJ; Dalgetty, IA; Mullins, CE; McDonald, AJS; Strachan, NJC
  • Artificial neural networks: fundamentals, computing, design, and application
    Basheer, IA; Hajmeer, M
  • Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals
    Berge, TW; Aastweit, AH; Fykse, H
  • Wavelet transform to discriminate between crop and weed in perspective agronomic images
    Bossu, J; Gée, Ch; Jones, J; Truchetet, F
  • Site-specific weed control technologies
    Christensen, S; Søgaard, HT; Kudsk, P; Nørremark, M; Lund, I; Nadimi, ES; Jørgensen, R
  • A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution
    Gebhardt, S; Kühlbauch, W

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