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Detection and Identification of Weeds

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Precision Crop Protection - the Challenge and Use of Heterogeneity

Abstract

This chapter reviews the approaches for the automation of weed detection. Site-specific plant protection needs to address the varying weed infestation, but the automation is only partially solved and research is still ongoing. The properties for plant species distinction as well as approaches that use them are presented. The focus is on image based methods, of which an example is given.

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References

  • Andersen HJ, Reng L, Kirk K (2005) Geometric plant properties by relaxed stereo vision using simulated annealing. Comput Electron Agric 49:219–232

    Article  Google Scholar 

  • Ã…strand B, Baerveldt AJ (2004) Plant recognition and localization using context information. In: Proceedings of the Mechatronics and Robotics 2004 (MechRob2004), Sascha Eysoldt Verlag, Aachen, pp 1191–1196

    Google Scholar 

  • Backes M, Jacobi J (2006) Classification of weed patches in Quickbird images: verification by ground truth data. EARSeL eProceedings 5:173–179

    Google Scholar 

  • Backes M, Schumacher D, Plümer L (2005) The sampling problem in weed control. Are currently applied sampling strategies adequate for site-specific weed control. In: Stafford J (ed) Precision agriculture 2005. Wageningen Academic Publishers, Wageningen, pp 155–161

    Google Scholar 

  • Bakker T, Wouters H, van Asselt K et al (2008) A vision based row detection system for sugar beet. Comput Electron Agric 60:87–95

    Article  Google Scholar 

  • Berge T, Aastveit A, Fykse H (2008) Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals. Prec Agric 9:391–405

    Article  Google Scholar 

  • Biller RH (1998) Reduced input of herbicides by use of optoelectronic sensors. J Agric Eng Res 71:357–362

    Article  Google Scholar 

  • Borregaard T, Nielsen H, Nørgaard L, Have H (2000) Crop-weed discrimination by line imaging spectroscopy. J Agric Eng Res 75:389–400

    Article  Google Scholar 

  • Bossu J, Gée C, Jones G, Truchetet F (2009) Wavelet transform to discriminate between crop and weed in perspective agronomic images. Comput Electron Agric 65:133–143

    Article  Google Scholar 

  • Brown R, Noble S (2005) Site-specific weed management: sensing requirements – what do we need to see? Weed Sci 53:252–258

    Article  CAS  Google Scholar 

  • Burgos-Artizzu XP, Ribeiro A, Tellaeche A et al (2009) Improving weed pressure assessment using digital images from an experience-based reasoning approach. Comput Electron Agric 65:176–185, doi:10.1016/j.compag.2008.09.001

    Google Scholar 

  • Burks T, Shearer S, Heath J, Donohue K (2005) Evaluation of neural-network classifiers for weed species discrimination. Biosyst Eng 91:293–304

    Article  Google Scholar 

  • Chapron M, Requena-Esteso M, Boissard P, Assemat L (1999) A method for recognizing vegetal species from multispectral images. In: Stafford J (ed) Precision agriculture 1999. Sheffield Academic Press, Sheffield, pp 239–248

    Google Scholar 

  • Cho SI, Lee DS, Jeong JY (2002) Weed-plant discrimination by machine vision and artificial neural network. Biosyst Eng 83:275–280

    Article  Google Scholar 

  • Dille JA, Mortensen DA, Young LJ (2002) Predicting weed species occurrence based on site properties and previous year’s weed presence. Prec Agric 3:193–207

    Article  Google Scholar 

  • van Evert F, Polder G, van der Heijden G et al (2009) Real-time vision-based detection of Rumex obtusifolius in grassland. Weed Res 49:164–174

    Article  Google Scholar 

  • Gebhardt S, Kühbauch W (2007a) A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution. Prec Agric 8:1–13

    Article  Google Scholar 

  • Gebhardt S, Kühbauch W (2007b) Continous mapping of Rumex obtusifolius during different grassland growths based on automatic image classification and GIS-based post processing. In: Stafford J (ed) Precision agriculture ’07, 6th European Conference on Precision Agriculture (ECPA), Wageningen Academic Publishers, Wageningen, pp 499–506

    Google Scholar 

  • Gerhards R, Christensen S (2003) Real-time weed detection, decision making and patch spraying in maize, sugar beet, winter wheat and winter barley. Weed Res 43:385–392

    Article  Google Scholar 

  • Girma K, Mosali J, Raun WR et al (2005) Identification of optical spectral signatures for detecting cheat and ryegrass in winter wheat. Crop Sci 45:477–485

    Article  Google Scholar 

  • Godwin R, Miller P (2003) A review of the technologies for mapping within-field variability. Biosyst Eng 84:393–407

    Article  Google Scholar 

  • Gorretta N, Fiorio C, Rabatel G, Marchant J (2005) Cabbage/weed discrimination with a region/contour based segmentation approach for multispectral images. In: Bellon Maurel V, Carbonneau A, Regnard JL et al (eds) Information and technology for sustainable fruit and vegetable production. Production, Proceedings of FRUTIC’05, AgroM ENSA Montpellier; Cemagref Montpellier; CIRAD; INRA, Cemagref, Montpellier France, pp 371–380, 7th Fruit nut and vegetable production engineering symposium, 12–16 Sep 2005

    Google Scholar 

  • Guyot G, Baret F, Jacquemoud S (1992) Imaging spectroscopy for vegetation studies. In: Toselli F, Bodechtel J (eds) Spectroscopy: fundamentals and prospective applications. Kluwer Academic Publishers, Dordrecht, pp 145–165

    Google Scholar 

  • Haboudane D, Miller JR, Pattey E et al (2004) Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies: modeling and validation in the context of precision agriculture. Rem Sens Environ 90:337–352

    Article  Google Scholar 

  • Hamouz P, Novakova K, Soukup J, Tyser L (2006) Evaluation of sampling and interpolation methods used for weed mapping. J Plant Dis Prot XX (special issue):205–215

    Google Scholar 

  • Heijting S, van der Werf W, Stein A, Kropff MJ (2007) Are weed patches stable in location? Application of an explicitly two-dimensional methodology. Weed Res 47:381–395

    Article  Google Scholar 

  • Hemming J, Rath T (2001) Computer-vision-based weed identification under field conditions using controlled lighting. J Agric Eng Res 78:233–243

    Article  Google Scholar 

  • Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8:179–187

    Google Scholar 

  • Ishak AJ, Hussain A, Mustafa MM (2009) Weed image classification using gabor wavelet and gradient field distribution. Comput Electron Agric 66:53–61

    Article  Google Scholar 

  • Jähne B (2001) Digital image processing, 5th edn. Springer, Berlin

    Google Scholar 

  • Jones G, Gée C, Truchetet F (2007) Simulation of perspective agronomic images for weed detection. In: Stafford J (ed) Precision agriculture ’07, 6th European Conference on Precision Agriculture (ECPA), Wageningen Academic Publishers, Wageningen, pp 507–515

    Google Scholar 

  • Keränen M, Aro EM, Tyystjärvi E, Nevalainen O (2003) Automatic plant identification with chlorophyll fluorescence fingerprinting. Prec Agric 4:53–67

    Article  Google Scholar 

  • Klose R, Thiel M, Ruckelshausen A, Marquering J (2008) Weedy – a sensor fusion based autonomous field robot for selective weed control. In: VDI (ed) Land technik 2008. VDI Verlag, Stuttgart, pp 167–172

    Google Scholar 

  • Lamb D, Brown R (2001) Remote-sensing and mapping of weeds in crops. J Agric Eng Res 78:117–125

    Article  Google Scholar 

  • Langner HR, Böttger H, Schmidt H (2006) A special vegetation index for the weed detection in sensor based precision agriculture. Environ Monit Assessm 117:505–518

    Article  Google Scholar 

  • López-Granados F, Jurado-Expósito M, Peña-Barragán JM, García-Torres L (2006) Using remote sensing for identification of late-season grass weed patches in wheat. Weed Sci 54:346–353

    Google Scholar 

  • Manh A, Rabatel G, Assemat L, Aldon M (2001) Weed leaf image segmentation by deformable templates. J Agric Eng Res 80:139–146

    Article  Google Scholar 

  • Meyer GE, Neto JC (2008) Verification of color vegetation indices for automated crop imaging applications. Comput Electron Agric 63:282–293

    Article  Google Scholar 

  • Mokhtarian F, Abbasi S, Kittler J (1996) Robust and efficient shape indexing through curvature scale space. In: Pycock D (ed) Proceedings of the British Machine Vision Conference 1996, BMVC, British Machine Vision Association, Edinburgh, pp 53–62

    Google Scholar 

  • Mortensen DA (2002) Crop/weed outcomes from site-specific and uniform soil-applied herbicide applications. Prec Agric 3:95

    Article  Google Scholar 

  • Neto JC, Meyer GE, Jones DD (2006a) Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm. Comput Electron Agric 51: 66–85

    Article  Google Scholar 

  • Neto JC, Meyer GE, Jones DD, Samal AK (2006b) Plant species identification using elliptic fourier leaf shape analysis. Comput Electron Agric 50:121–134

    Article  Google Scholar 

  • Oebel H (2006) Teilschlagspezifische Unkrautbekämpfung durch raumbezogene Bildverarbeitung im Offline- und (Online-) Verfahren (TURBO). PhD thesis, Universität Hohenheim, Fakultät Agrarwissenschaften

    Google Scholar 

  • Oebel H, Gerhards R (2005) Site-specific weed control using digital image analysis and georeferenced application maps – first on-farm experiences. In: Stafford JV (ed) 5th ECPA, Uppsala, Wageningen Academic Publishers, Wageningen, pp 131–138

    Google Scholar 

  • Okamoto H, Murata T, Kataoka T, Hata SI (2007) Plant classification for weed detection using hyperspectral imaging with wavelet analysis. Weed Biol Manag 7:31–37

    Article  Google Scholar 

  • Paap A, Askraba S, Alameh K, Rowe J (2008) Photonic-based spectral reflectance sensor for ground-based plant detection and weed discrimination. Opt Express 16:1051–1055

    Article  PubMed  Google Scholar 

  • Pérez A, López F, Benlloch J, Christensen S (2000) Colour and shape analysis techniques for weed detection in cereal fields. Comput Electron Agric 25:197–212

    Article  Google Scholar 

  • Piron A, Leemans V, Kleynen O et al (2008) Selection of the most efficient wavelength bands for discriminating weeds from crop. Comput Electron Agric 62:141–148

    Article  Google Scholar 

  • Piron A, Leemans V, Lebeau F, Destain M (2009) Improving in-row weed detection in multispectral stereoscopic images. Comput Electron Agric 69:73–79, doi: 10.1016/j.compag.2009.07.001

    Google Scholar 

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

    Article  Google Scholar 

  • Reyniers M, Vrindts E, De Baerdemaeker J (2006) Comparison of an aerial-based system and an on the ground continuous measuring device to predict yield of winter wheat. Eur J Agron 24:87–94

    Article  Google Scholar 

  • Shafri HZM, Salleh MAM, Ghiyamat A (2006) Hyperspectral remote sensing of vegetation using red edge position techniques. Am J Appl Sci 3:1864–1871

    Article  Google Scholar 

  • Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61:63–78

    Article  Google Scholar 

  • Søgaard H, Heisel T (2002) Machine vision identification of weed species based on active shape models. In: van Laar HH, Bastiaans L, Baumann DT et al (eds) EWRS 12th EWRS Symposium, European Weed Research Society. Grafisch Service Centrum Van Gils BV, Wageningen, pp 402–403

    Google Scholar 

  • Soille P (2003) Morphological image analysis, 2nd edn. Springer, Heidelberg

    Google Scholar 

  • Sui R, Thomasson JA, Hanks J, Wooten J (2008) Ground-based sensing system for weed mapping in cotton. Comput Electron Agric 60:31–38

    Article  Google Scholar 

  • Thorp K, Tian L (2004) A review on remote sensing of weeds in agriculture. Prec Agric 5:477–508

    Article  Google Scholar 

  • Å eatović D (2008) A segmentation approach in novel real time 3D plant recognition system. In: Proceedings of the Computer Vision Systems, Lecture Notes in Computer Science, vol 5008. Springer, Berlin/Heidelberg, pp 363–372

    Google Scholar 

  • Wang N, Zhang N, Dowell FE, Sun Y, Peterson DE (2001) Design of an optical weed sensor using plant spectral characteristics. In: ASAE (ed) Transactions of the ASAE, vol 44. American Society of Agricultural Engineers, St. Joseph, pp 409–419

    Google Scholar 

  • Weis M, Gerhards R (2007) Feature extraction for the identification of weed species in digital images for the purpose of site-specific weed control. In: Stafford J (ed) Precision agriculture ’07, 6th European Conference on Precision Agriculture (ECPA). Wageningen Academic Publishers, Wageningen, pp 537–545

    Google Scholar 

  • Woebbecke D, Meyer G, von Bargen K, Mortensen D (1995) Color indices for weed identification under various soil, residue and lighting conditions. American Society of Agricultural Engineers, St. Joseph, pp 259–269

    Google Scholar 

  • Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recognit 37:1–19

    Article  Google Scholar 

  • Zwiggelaar R (1998) A review of spectral properties of plants and their potential use for crop/weed discrimination. Crop Prot 17:189–206

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Weed Science, Institute for Phytomedicine, University of Hohenheim, Stuttgart, D-70599, Germany

    Martin Weis & Markus Sökefeld

Authors
  1. Martin Weis
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  2. Markus Sökefeld
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Corresponding author

Correspondence to Martin Weis .

Editor information

Editors and Affiliations

  1. , Institute of Crop Science and Resource C, University of Bonn, Nussallee 9, Bonn, 53115, Germany

    Erich-Christian Oerke

  2. , Department of Weed Sciences, University of Hohenheim, Otto-Sanderstrasse 5, Stuttgart, 70599, Germany

    Roland Gerhards

  3. , Department of Geography, Remote Sensing, University of Bonn, Meckenheimer Allee 166, Bonn, 53115, Germany

    Gunter Menz

  4. , Institute of Crop Science and Resource C, University of Bonn, Nussallee 9, Bonn, 53115, Germany

    Richard A. Sikora

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Weis, M., Sökefeld, M. (2010). Detection and Identification of Weeds. In: Oerke, EC., Gerhards, R., Menz, G., Sikora, R. (eds) Precision Crop Protection - the Challenge and Use of Heterogeneity. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9277-9_8

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