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

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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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References

  • R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org. Accessed October 9, 2010.

  • Aitkenhead, M. J., Dalgetty, I. A., Mullins, C. E., McDonald, A. J. S., & Strachan, N. J. C. (2003). Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture, 39, 157–171.

    Article  Google Scholar 

  • Baker, N. R., & Rosenqvist, E. (2004). Applications of chlorophyll fluorescence can improve crop production strategies: An examination of future possibilities. Journal of Experimental Botany, 55, 1607–1621.

    Article  PubMed  CAS  Google Scholar 

  • Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31.

    Article  PubMed  CAS  Google Scholar 

  • Berge, T. W., Aastweit, A. H., & Fykse, H. (2008). Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals. Precision Agriculture, 8, 391–405.

    Article  Google Scholar 

  • Berger, S., Papadopoulos, M., Schreiber, U., Kaiser, W., & Roitsch, T. (2004). Complex regulation of gene expression, photosynthesis and sugar levels by pathogen infection in tomato. Physiologia Plantarum, 122, 419–428.

    Article  CAS  Google Scholar 

  • Bossu, J., Gée, Ch., Jones, J., & Truchetet, F. (2009). Wavelet transform to discriminate between crop and weed in perspective agronomic images. Computers and Electronics in Agriculture, 65, 133–143.

    Article  Google Scholar 

  • Christensen, S., Søgaard, H. T., Kudsk, P., Nørremark, M., Lund, I., Nadimi, E. S., et al. (2009). Site-specific weed control technologies. Weed Research, 49, 233–241.

    Article  Google Scholar 

  • Codrea, M. C., Aittokallio, T., Keränen, M., Tyystjärvi, E., & Nevalainen, O. S. (2003). Feature learning with a genetic algorithm for fluorescence fingerprinting of plant species. Pattern Recognition Letters, 24, 2663–2673.

    Article  Google Scholar 

  • Codrea, M. C., Aittokallio, T., Keränen, M., Tyystjärvi, E., & Nevalainen, O. S. (2004). Genetic algorithms-based feature learning method; an application to fluorescence fingerprinting. Lecture Notes in Computer Science, 2936, 371–383.

    Article  Google Scholar 

  • Gebhardt, S., & Kühlbauch, W. (2007). A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution. Precision Agriculture, 8, 1–13.

    Article  Google Scholar 

  • Gerhards, R., & Christensen, S. (2006). Site-specific weed management. In A. Srinivasan (Ed.), Handbook of precision agriculture. Principles and applications (pp. 185–205). New York: The Haworth Press Inc.

    Google Scholar 

  • Hague, T., Tillett, N. D., & Wheeler, H. (2006). Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture, 7, 21–32.

    Article  Google Scholar 

  • Hemming, J., & Rath, T. (2006). Computer vision based weed identification under field conditions using controlled lightning. Journal of agricultural Engineering Research, 78, 233–243.

    Article  Google Scholar 

  • Ishak, A. J., Hussain, A., & Mustafa, M. M. (2009). Weed image classification using Gabor wavelet and gradient field distribution. Computers and Electronics in Agriculture, 66, 53–61.

    Article  Google Scholar 

  • Keränen, M., Aro, E. M., Nevalainen, O., & Tyystjärvi, E. (2009). Toxic and non-toxic Nodularia strains can be distinguished from each other and from eukaryotic algae with chlorophyll fluorescence fingerprinting. Harmful Algae, 8, 817–822.

    Article  Google Scholar 

  • Keränen, M., Aro, E.-M., Tyystjärvi, E., & Nevalainen, O. (2003). Automatic plant identification with chlorophyll fluorescence fingerprinting. Precision Agriculture, 4, 53–67.

    Article  Google Scholar 

  • Loghavi, M., & Mackvandi, B. B. (2008). Development of a target oriented weed control system. Computers and Electronics in Agriculture, 63, 112–118.

    Article  Google Scholar 

  • Longchamps, L., Panneton, B., Samson, G., Leroux, G. D., & Thériault, R. (2010). Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature. Precision Agriculture, 11, 181–197.

    Article  Google Scholar 

  • Maxwell, K., & Johnson, G. N. (2000). Chlorophyll fluorescence—a practical guide. Journal of Experimental Botany, 51, 659–668.

    Article  PubMed  CAS  Google Scholar 

  • Meier, U. (Ed.). (2001). Growth stages of mono- and dicotyledonous plants. BBCH monograph. Blackwell, Berlin-Wien: Federal Biologische Research Centre for Agriculture and Forestry.

    Google Scholar 

  • Meyer, G. E., Mehta, T., Kocher, M. F., Mortensen, D. A., & Samal, A. (1998). Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASAE, 41, 1189–1197.

    Google Scholar 

  • Meyer, G. E., & Neto, J. C. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 63, 282–293.

    Article  Google Scholar 

  • Mishra, A., Matouš, K., Mishra, K. B., & Nedbal, L. (2009). Towards discrimination of plant species by machine vision: Advanced statistical analysis of chlorophyll fluorescence transients. Journal of Fluorescence, 19, 905–913.

    Article  PubMed  CAS  Google Scholar 

  • Moshou, D., Vrindts, E., De Ketelaere, B., De Baerdemaeker, J., & Ramon, H. (2001). A neural network based plant classifier. Computers and Electronics in Agriculture, 31, 5–16.

    Article  Google Scholar 

  • Nedbal, L., Soukupová, J., Kaftan, D., Whitmarsh, J., & Trílek, M. (2000). Kinetic imaging of chlorophyll fluorescence using modulated light. Photosynthesis Research, 66, 3–12.

    Article  PubMed  CAS  Google Scholar 

  • Neubauer, C., & Schreiber, U. (1987). The polyphasic rise of chlorophyll fluorescence upon onset of strong continuous illumination. 1. Saturation characteristics and partial control by the Photosystem-II acceptor side. Zeitschrift für Naturforschung C—A Journal of Biosciences, 42, 1246–1254.

    CAS  Google Scholar 

  • Nørremark, M., Griepentrog, H. W., Nielsen, J., & Søgaard, H. T. (2008). The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops. Biosystems Engineering, 101, 396–410.

    Article  Google Scholar 

  • Nørremark, M., Søgaard, H. T., Griepentrog, H. W., & Nielsen, H. (2007). Instrumentation and method for high accuracy geo-referencing of sugar beet plants. Computers and Electronics in Agriculture, 56, 130–146.

    Article  Google Scholar 

  • Ounis, A., Evain, J., Flexas, J., Tosti, S., & Moya, I. (2001). Adaptation of a PAM-fluorometer for remote sensing of chlorophyll fluorescence. Photosynthesis Research, 68, 113–120.

    Article  PubMed  CAS  Google Scholar 

  • Persson, M., & Åstrand, B. (2008). Classification of crops and weeds extracted by active shape models. Biosystems Engineering, 100, 484–497.

    Article  Google Scholar 

  • Rasmussen, J., Nørremark, M., & Bibby, B. M. (2007). Assessment of leaf cover and crop soil cover in weed harrowing research using digital images. Weed Research, 47, 299–310.

    Article  Google Scholar 

  • Ripley, R. B. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.

    Google Scholar 

  • Slaughter, D. C., Giles, D. K., & Downey, D. (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61, 63–78.

    Article  Google Scholar 

  • Søgaard, H. T., & Lund, I. (2007). Application accuracy of a machine vision-controlled robotic micro-dosing system. Biosystems Engineering, 96, 315–322.

    Article  Google Scholar 

  • Srinivasan, A. (Ed.). (2006). Handbook of precision agriculture. Principles and applications. New York: The Haworth Press Inc.

    Google Scholar 

  • Strasser, R. J., Srivastava, A., & Govindjee, (1995). Polyphasic chlorophyll a fluorescence transient in plants and cyanobacteria. Photochemistry and Photobiology, 61, 32–42.

    Article  CAS  Google Scholar 

  • Strasser, R. J., Tsimilli-Michael, M., & Srivastava, A. (2004). Analysis of the chlorophyll a fluorescence transient. In G. C. Papageorgiou & Govindjee (Eds.), Chlorophyll fluorescence: A signature of photosynthesis (pp. 363–388). The Netherlands: Springer.

    Google Scholar 

  • Sui, R., Thomasson, J. A., Hanks, J., & Wooten, J. (2008). Ground-based sensing system for weed mapping in cotton. Computers and Electronics in Agriculture, 60, 31–38.

    Article  Google Scholar 

  • Tyystjärvi, E., & Karunen, J. (1990). A microcomputer program and fast analog to digital converter card for the analysis of fluorescence induction transients. Photosynthesis Research, 26, 127–132.

    Article  Google Scholar 

  • Tyystjärvi, E., Koski, A., Keränen, M., & Nevalainen, O. (1999). The Kautsky curve is a built-in barcode. Biophysical Journal, 77, 1159–1167.

    Article  PubMed  Google Scholar 

  • Van Kooten, O., & Snel, J. F. H. (1990). The use of chlorophyll fluorescence terminology in plant stress physiology. Photosynthesis Research, 25, 147–150.

    Article  Google Scholar 

  • Vass, I., Kirilovsky, D., & Etienne, A. L. (1999). UV-B radiation-induced donor- and acceptor-side modifications of photosystem II in the cyanobacterium Synechocystis sp PCC 6803. Biochemistry, 38, 12786–12794.

    Article  PubMed  CAS  Google Scholar 

  • Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (4th ed.). New York: Springer.

    Google Scholar 

  • Vioix, J.-B., Douzals, J.-P., Truchetet, F., Assémat, L., & Guillemin, J.-P. (2002). Spatial and spectral methods for weed detection and localization. Journal on Applied Signal Processing, 7, 679–685.

    Article  Google Scholar 

  • Wang, N., Zhang, N., Wei, J., Stoll, Q., & Peterson, D. E. (2007). A real-time, embedded, weed-detection system for use in wheat fields. Biosystems Engineering, 98, 276–285.

    Article  Google Scholar 

  • Wiles, L. J. (2009). Beyond patch spraying: site-specific weed management with several herbicides. Precision Agriculture, 10, 277–290.

    Article  Google Scholar 

  • Zhang, Y., Staab, E. S., Slaughter, D. C., Giles, D. K., & Downey, D. (2009). Precision automated weed control using hyperspectral vision identification and heated oil. ASABE Paper No. 096365. St. Joseph, MI: ASABE

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Acknowledgments

The authors thank Senior Scientist Solvejg K. Mathiassen, University of Aarhus, Faculty of Agricultural Sciences, Department of Integrated Pest Management, and green house manager Willy Rasmussen, for providing plants. This work was financially supported by the Danish Food Industry Agency, Ministry of Food, Agriculture and Fisheries, and Academy of Finland.

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Authors and Affiliations

  1. Molecular Plant Biology, Department of Biochemistry and Food Chemistry, University of Turku, 20014, Turku, Finland

    Esa Tyystjärvi, Heta Mattila, Mika Keränen & Marja Hakala-Yatkin

  2. Department of Biosystem Engineering, Faculty of Agricultural Sciences, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark

    Michael Nørremark

  3. Institute of Horticulture, Faculty of Agricultural Sciences, Aarhus University, Kirstinebjergvej 10, 5792, Årslev, Denmark

    Carl-Otto Ottosen

  4. Institute of Agriculture and Ecology, Section of Crop Science, University of Copenhagen, Hojbakkegaard Allé 21, 2630, Taastrup, Denmark

    Eva Rosenqvist

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  1. Esa Tyystjärvi
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Correspondence to Esa Tyystjärvi.

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Tyystjärvi, E., Nørremark, M., Mattila, H. et al. Automatic identification of crop and weed species with chlorophyll fluorescence induction curves. Precision Agric 12, 546–563 (2011). https://doi.org/10.1007/s11119-010-9201-6

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