Skip to main content
SpringerLink
Log in

The Biological Engineer: Sensing the Difference Between Crops and Weeds

  • Chapter
  • First Online:
Automation: The Future of Weed Control in Cropping Systems

Abstract

This chapter describes the current state of the art in technology and methodology being used to develop sensors for automated weed control in cropping systems. The development of a reliable universal weed vs. crop plant sensor that works well in a wide range of crops and cropping systems is a formidable task. The discussion in this chapter highlights the significant progress that has been made in developing new, more robust, automatic sensing systems that can differentiate crop plants from weeds. Case studies documenting high levels of success in trials conducted outdoors in the natural, largely uncontrolled environment of an agricultural cropping system are presented. A discussion of the strengths and current challenges of the more successful weed and crop sensing techniques is provided. In many cases the methodology has utilized site- or condition-specific a priori knowledge to make the sensors smarter in a local context. This chapter highlights the advantages and compromises made in using these techniques. The chapter concludes with a discussion of the remaining engineering challenges to the development of a comprehensive, multifaceted fusion of several methods for sensing the differences between crops and weeds across the entire crop production cycle, and how the rapid development of advanced sensing and machine learning technologies will facilitate new plant recognition architectures and systems to achieve the level of machine recognition of weeds needed for automated weed control in cropping systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ahmad U, Kondo N, Arima S, Monta M, Mohri K (1991) Weed detection in lawn field using machine vision. J JSAM 61(2):61–69

    Google Scholar 

  • Andújar D, Excolà A, Dorado J, Fernández-Quintanilla C (2011) Weed discrimination using ultrasonic sensors. Weed Res 51:543–547

    Article  Google Scholar 

  • Ã…strand B, Baerveldt A-J (2005) Plant recognition and localization using context information and individual plant features. Paper IV In: Vision based perception for mechatronic weed control. Ã…strand, B. PhD thesis. Chalmers University of Technology. Göteborg, Sweden. fou.sjv.se/fou/download.lasso?id=Fil-000788. Last accessed 24 Sept 2012

    Google Scholar 

  • Booth DT, Cox SE (2008) Image-based monitoring to measure ecological change. Front Ecol Environ 6:185–190

    Article  Google Scholar 

  • Booth DT, Cox SE (2009) Dual-camera, high-resolution aerial assessment of pipeline revegetation. Environ Monit Assess 158:23–33

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Burger W, Burge MJ (2008) Digital image processing. An algorithmic introduction using Java. Springer, New York. ISBN 978-1-84628-379-6

    Book  Google Scholar 

  • Burks TF, Shearer SA, Green JD, Heath JR (2002) Influence of weed maturity levels on species classification using machine vision. Weed Sci 50(6):802–811

    Article  CAS  Google Scholar 

  • Chi YT, Chien CF, Lin TT (2002) Leaf shape modeling and analysis using geometric descriptors derived from Bezier curves. Trans Am Soc Agric Eng 46(1):175–185

    Google Scholar 

  • Christensen S, Søgaard HT, Kudsk P, Nørremark M, Lund I, Nadimi ES, Jørgensen R (2009) Site-specific weed control technologies. Weed Res 49:233–241

    Article  Google Scholar 

  • Cope JS, Corney D, Clark JY, Remagnino P, Wilkin P (2012) Plant species identification using digital morphometrics: a review. Expert Syst Appl 39:7562–7573

    Article  Google Scholar 

  • Cox SWR, McLean KA (1969) Electro-chemical thinning of sugar beet. J Agric Eng Res 14(4):332–343

    Article  Google Scholar 

  • Davies ER (2012) Computer and machine vision: theory, algorithms, practicalities, 4th edn. Elsevier, Waltham, 871 p. ISBN 9780123869081

    Google Scholar 

  • Diprose MF, Benson FA (1984) Electrical methods of killing plants. J Agric Eng Res 30:197–209

    Article  Google Scholar 

  • Du JX, Wang XF, Zhang GJ (2007) Leaf shape based plant species recognition. Appl Math Comput 185:883–893

    Article  Google Scholar 

  • Duniway MC, Karl JW, Schrader S, Baquera N, Herrick JE (2012) Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability. Environ Monit Assess 184:3789–3804

    Article  PubMed  Google Scholar 

  • Ehsani MR, Upadhyaya SK, Mattson ML (2004) Seed location mapping using RTK-GPS. Trans Am Soc Agric Eng 47:909–914

    Article  Google Scholar 

  • Feyaerts F, van Gool L (2001) Multi-spectral vision system for weed detection. Pattern Recognit Lett 22:667–674

    Article  Google Scholar 

  • Franz E, Gebhardt MR, Unklesbay KB (1991) Shape description of completely visible and partially occluded leaves for identifying plants in digital images. Trans Am Soc Agric Eng 34(2):673–681

    Article  Google Scholar 

  • Galvão LS, Formaggio AR, Tisot DA (2005) Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sens Environ 94:523–534

    Article  Google Scholar 

  • Garrett RE (1966) Development of a synchronous thinner. J Am Soc Sugar Beet Technol 14(3):206–213

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gebhardt S, Schellberg J, Lock R, Kühbauch W (2006) Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Precis Agric 7:165–178

    Article  Google Scholar 

  • Guyot G (1990) Optical properties of vegetation canopies. In: Steven MD, Clark JA (eds) Applications of remote sensing in agriculture. Butterworths, London, pp 19–44

    Chapter  Google Scholar 

  • Haff RP, Slaughter DC, Jackson ES (2011) X-ray based stem detection in an automatic tomato weeding system. Appl Eng Agric 27(5):803–810

    Article  Google Scholar 

  • Tillett and Hague Technology Ltd. (2005) Autonomous crop treatment vehicle. http://www.thtechnology.co.uk/Past%20Projects.html. Last accessed 24 Sept 2012

  • Haralick RM, Shanmuga K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC3(6):610–621

    Article  Google Scholar 

  • Hearn DJ (2009) Shape analysis for the automated identification of plants from images of leaves. Taxon 58:934–954

    Google Scholar 

  • Henry WB, Shaw DR, Reddy KR, Bruce LM, Tamhankar HD (2004) Spectral reflectance curves to distinguish soybean from common cocklebur (Xanthium strumarium) and sicklepod (Cassia obtusifolia) grown with varying soil moisture. Weed Sci 52(5):788–796

    Article  CAS  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 

  • Ishihama F, Watabe Y, Oguma H (2012) Validation of a high-resolution, remotely operated aerial remote-sensing system for the identification of herbaceous plant species. Appl Veg Sci 15:383–389

    Article  Google Scholar 

  • Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1:321–331

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lamb DW, Weedon MM, Rew LJ (1999) Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging. Avena spp. in seedling triticale. Weed Res 39(6):481–492

    Article  Google Scholar 

  • Lee WS (1998) Robotic weed control system for tomatoes. PhD dissertation. University of California, Davis, 287 p

    Google Scholar 

  • Lee CL, Chen SY (2006) Classification of leaf images. Int J Imaging Syst Technol 16:15–23

    Article  Google Scholar 

  • Leica (2012) RCD30 Series, 60MP single camera head delivers co-registered, multispectral RGBN imagery. http://www.leica-geosystems.com/en/Leica-RCD30_86987.htm. Last accessed 24 Sept 2012

  • Manh AG, Rabatel G, Assemat L, Aldon MJ (2001) Weed leaf image segmentation by deformable templates. J Agric Eng Res 80(2):139–146

    Article  Google Scholar 

  • Meyer GE, Mehta T, Kocher F, Mortensen DA, Samal A (1998) Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans Am Soc Agric Eng 41(4):1189–1197

    Article  Google Scholar 

  • Mikrokopter (2012) XL Hexakopter (MK Hexa XL). http://gallery.mikrokopter.de/main.php?g2_view=dynamicalbum.PopularAlbum&g2_itemId=63419&g2_imageViewsIndex=0. Last accessed 24 Sept 2012

  • Nam Y, Hwang E, Kim D (2008) A similarity-based leaf image retrieval scheme: joining shape and venation features. Comput Vis Image Underst 110:245–259

    Article  Google Scholar 

  • Neto J, Meyer G, Jones D, Samal A (2006) Plant species identification using elliptic Fourier leaf shape analysis. Comput Electron Agric 50:121–134

    Article  Google Scholar 

  • Nightingale GT (1933) Effects of temperature on metabolism in tomato. Bot Gaz 95:35–58

    Article  CAS  Google Scholar 

  • Nikon (2012) Nikon D800 36.3MP FX-format CMOS sensor. http://www.nikonusa.com/en/Nikon-Products/Product/Digital-SLR-Cameras/25480/D800.html. Last accessed 24 Sept 2012

  • Nokia (2012) Nokia 808 PureView 41.0 megapixel camera sensor. http://www.nokia.com/us-en/products/phone/808/specifications/. Last accessed 24 Sept 2012

  • Nørremark M, Søgaard HT, Griepentrog HW, Nielsen H (2007) Instrumentation and method for high accuracy georeferencing of sugar beet plants. Comput Electron Agric 56:130–146

    Article  Google Scholar 

  • NTech (2012) WeedSeeker automatic spot spraying system. http://www.ntechindustries.com/weedseeker-home.html. Last accessed 24 Sept 2012

  • 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 

  • Onyango CM, Marchant JA (2003) Segmentation of row crop plants from weeds using colour and morphology. Comput Electron Agric 39:141–155

    Article  Google Scholar 

  • Peñuelas J, Filella I, Biel C, Serrano L, Save R (1993) The reflectance at the 950–970 nm region as an indicator of plant water status. Int J Remote Sens 14:1887–1905

    Article  Google Scholar 

  • Pérez-Ruiz M, Slaughter DC, Gliever CJ, Upadhyaya SK (2012) Automatic GPS-based intra-row weed knife control system for transplanted row crops. Comput Electron Agric 80:41–49. doi:10.1016/j.compag.2011.10.006

    Article  Google Scholar 

  • Persson M, Ã…strand B (2008) Classification of crops and weeds extracted by active shape models. Biosyst Eng 100:484–497

    Article  Google Scholar 

  • Piron A, Leemans V, Lebeau F, Destain MF (2009) Improving in-row weed detection in multispectral stereoscopic images. Comput Electron Agric 69:73–79

    Article  Google Scholar 

  • Piron A, van der Heijden F, Destain MF (2011) Weed detection in 3D images. Precis Agric 12:607–622

    Article  Google Scholar 

  • Price JC (1994) How unique are spectral signatures? Remote Sens Environ 49:181–186

    Article  Google Scholar 

  • Scotford IM, Miller PCH (2005) Applications of spectral reflectance techniques in Northern European cereal production: a review. Biosyst Eng 90(3):235–250

    Article  Google Scholar 

  • Shearer SA, Holmes RG (1990) Plant identification using color co-occurrence matrices. Trans Am Soc Agric Eng 33(6):1037–1044

    Google Scholar 

  • Slaughter DC, Lanini WT, Giles DK (2004) Discriminating weeds from processing tomato plants using visible and near-infrared spectroscopy. Trans Am Soc Agric Eng 47(6):1907–1911

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Slaughter DC, Giles DK, Fennimore SA, Smith RF (2008b) Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technol 22(2):378–384

    Article  Google Scholar 

  • Søgaard HT (2005) Weed classification by active shape models. Biosyst Eng 91(3):271–281

    Article  Google Scholar 

  • Soille P (1999) Morphological image analysis: principles and applications. Springer, Berlin, 316 p

    Book  Google Scholar 

  • Soille P (2000a) Morphological image analysis applied to crop field mapping. Image Vis Comput 18:1025–1032

    Article  Google Scholar 

  • Soille P (2000b) Morphological image analysis applied to crop field mapping: electronic appendix. http://mdigest.jrc.ec.europa.eu/soille/ivc2000/elecapp.html. Last accessed 24 Sept 2012

  • Sonka M, Hlavac V, Boyle R (2007) Image processing, analysis, and machine vision, 3rd edn. Thomson Engineering, Toronto, 972 pp. ISBN 13: 978–0495082521

    Google Scholar 

  • Southall B, Hague T, Marchant JA, Buxton BF (2002) An autonomous crop treatment robot: Part I. A Kalman filter model for localization and crop/weed classification. Int J Robot Res 21(1):61–74

    Article  Google Scholar 

  • Sun H, Slaughter DC, Pérez-Ruiz M, Gliever C, Upadhyaya SK, Smith RF (2010) RTK GPS mapping of transplanted row crops. Comput Electron Agric 71:32–37

    Article  Google Scholar 

  • Tang L, Tian L, Steward BL (2003) Classification of broadleaf and grass weeds using Gabor wavelets and an artificial neural network. Trans Am Soc Agric Eng 46(4):1247–1254

    Google Scholar 

  • Tillett ND, Hague T, Miles SJ (2001) A field assessment of a potential method for weed and crop mapping on the basis of crop planting geometry. Comput Electron Agric 32:229–246

    Article  Google Scholar 

  • Tillett ND, Hague T, Grundy AC, Dedousis AP (2008) Mechanical within-row weed control for transplanted crops using computer vision. Biosyst Eng 99:171–178

    Article  Google Scholar 

  • van Evert FK, Polder G, van der Heijden GWAM, Kempenaar C, Lotz LAP (2009) Real-time vision-based detection of Rumex obtusifolius in grassland. Weed Res 49:164–174

    Article  Google Scholar 

  • Vrindts E, De Baerdemeaeker J, Ramon H (2002) Weed detection using canopy reflection. Precis Agric 3(1):63–80

    Article  Google Scholar 

  • Watchareeruetai U, Takeuchi Y, Matsumoto T, Kudo H, Ohnishi N (2006) Computer vision based methods for detecting weeds in lawns. Mach Vis Appl 17:287–296

    Article  Google Scholar 

  • Weis M, Sökefeld M (2010) Chapter 8: Detection and identification of weeds. In: Oerke E-C et al (eds) Precision crop protection – the challenge and use of heterogeneity. Springer, Dordrecht, pp 119–134. doi:10.1007/978-90-481-9277-9_8

    Chapter  Google Scholar 

  • Zhang Y (2011) Hyperspectral vision-based machine learning for robust plant recognition in autonomous weed control. PhD dissertation. University of California, Davis

    Google Scholar 

  • Zhang Y, Slaughter DC (2011a) Influence of solar irradiance on hyperspectral imaging-based plant recognition for autonomous weed control. Biosyst Eng 110(3):330–339. doi:10.1016/j.biosystemseng.2011.09.006

    Article  Google Scholar 

  • Zhang Y, Slaughter DC (2011b) Hyperspectral species mapping for automatic weed control in tomato under thermal environmental stress. Comput Electron Agric 77(1):95–104. doi:10.1016/j.compag.2011.04.001

    Article  Google Scholar 

  • Zhang Y, Slaughter DC, Staab ES (2012) Robusthyperspectral vision-based classification for multi-season weed mapping. J Photogramm Remote Sens 69:65–73. doi:10.1016/j.isprsjprs.2012.02.006

    Article  Google Scholar 

  • Zwiggelaar R (1998) A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Prot 17(3):189–206

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biological and Agricultural Engineering, University of California, 2030 Bainer Hall, One Shields Avenue, Davis, CA, 95616, USA

    David C. Slaughter

Authors
  1. David C. Slaughter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David C. Slaughter .

Editor information

Editors and Affiliations

  1. Department of Agronomy and Haorticulture, West Central Research & Extension Center, University of Nebraska-Lincoln, North Platte, Nebraska, USA

    Stephen L. Young

  2. Depts. Crop & Soil Sc. & Bio.l Sys. Eng., Washington State University, Prosser, Washington, USA

    Francis J. Pierce

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Slaughter, D.C. (2014). The Biological Engineer: Sensing the Difference Between Crops and Weeds. In: Young, S., Pierce, F. (eds) Automation: The Future of Weed Control in Cropping Systems. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7512-1_5

Download citation

Publish with us

Policies and ethics

Search

Navigation