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Site-specific weed management: sensing requirements— what do we need to see?

Published online by Cambridge University Press:  20 January 2017

Ralph B. Brown  and
Scott D. Noble
Scott D. Noble
Affiliation:
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
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Abstract

Automated detection and identification of weeds in crop fields is the greatest obstacle to development of practical site-specific weed management systems. Research progress is summarized for two different approaches to the problem, remote sensing weed mapping and ground-based detection using digital cameras or nonimaging sensors. The general spectral and spatial limitations reported for each type of weed identification system are reviewed. Airborne remote sensing has been successful for detection of distinct weed patches when the patches are dense and uniform and have unique spectral characteristics. Identification of weeds is hampered by spectral mixing in the relatively large pixels (typically larger than 1 by 1 m) and will not be possible from imagery where weed seedlings are sparsely distributed among crop plants. The use of multispectral imaging sensors such as color digital cameras on a ground-based mobile platform shows more promise for weed identification in field crops. Spectral features plus spatial features such as leaf shape and texture and plant organization may be extracted from these images. However, there is a need for research in areas such as artificial lighting, spectral band requirements, image processing, multiple spatial resolution systems, and multiperspective images.

Keywords

Machine visionremote sensingsite-specificspectral reflectanceweed identification
Type
Symposium
Information
Weed Science , Volume 53 , Issue 2 , April 2005 , pp. 252 - 258
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Aitkenhead, M. J., Dalgetty, I. A., Mullins, C. E., McDonald, A. J. S., and Strachan, N. J. C. 2003. Weed and crop discrimination using image analysis and artificial intelligence methods. Comput. Electron. Agric 39:157–171.Google Scholar
Apostol, S., Viau, A. A., Tremblay, N., Briantais, J-M., Prasher, S., Parent, L-E., and Moya, I. 2003. Laser-induced fluorescence signatures as a tool for remote monitoring of water and nitrogen stresses in plants. Can. J. Remote Sens 29:57–65.Google Scholar
Bajwa, S. G. and Tian, L. F. 2001. Aerial CIR remote sensing for weed density mapping in a soybean field. Trans. Am. Soc. Agric. Eng 44:1965–1974.Google Scholar
Baron, R. J., Crowe, T. G., and Wolf, T. M. 2002. Dual camera measurement of crop canopy using reflectance. Paper No. 02-209 in The CSAE/SCGR Annual Meeting, Saskatoon, SK, July 14–17. Winnipeg, MB: Canadian Society of Agricultural Engineers.Google Scholar
Blackshaw, R. E., Molnar, L. J., Chevalier, D. F., and Lindwall, C. W. 1998. Factors affecting the operation of the weed-sensing Detectspray system. Weed Sci 46:127–131.Google Scholar
Borkowski, W. 1999. Fractal dimension based features are useful descriptors of leaf complexity and shape. Can. J. For. Res 29:1301–1310.Google Scholar
Borregaard, T., Nielsen, H., Nørgaard, L., and Have, H. 2000. Crop-weed discrimination by line imaging spectroscopy. J. Agric. Eng. Res 75:389–400.CrossRefGoogle Scholar
Brown, R. B. and Steckler, J-P. G. A. 1993. Weed patch identification in no-till corn using digital imagery. Can. J. Remote Sens 19:88–91.Google Scholar
Brown, R. B., Steckler, J-P. G. A., and Anderson, G. W. 1994. Remote sensing for identification of weeds in no-till corn. Trans. Am. Soc. Agric. Eng 37:297–302.CrossRefGoogle Scholar
Burks, T. F., Shearer, S. A., Green, J. D., and Heath, J. R. 2002. Influence of weed maturity levels on species classification using machine vision. Weed. Sci 50:802–811.Google Scholar
Chaisattapagon, C. and Zhang, N. 1991. Weed detection using machine vision. Paper No. 91-3508 in International Winter Meeting. St. Joseph, MI: American Society of Agricultural Engineers. 14 p.Google Scholar
Chi, Y-T., Chien, C-F., and Lin, T-T. 2002. Leaf shape modeling and analysis using geometric descriptors derived from Bezier curves. Trans. Am. Soc. Agric. Eng 46:175–185.Google Scholar
Cho, S. I., Lee, D. S., and Jeong, J. Y. 2002. Weed-plant discrimination by machine vision and artificial neural network. Biosystems Eng 82:275–280.Google Scholar
Critten, D. L. 1997. Fractal dimension relationships and values associated with certain plant canopies. J. Agric. Eng. Res 67:61–72.Google Scholar
El-Faki, M. S., Zhang, N., and Peterson, D. E. 2000. Weed detection using color machine vision. Trans. Am. Soc. Agric. Eng 43:1969–1978.Google Scholar
Elmore, A. J., Mustard, J. F., Manning, S. J., and Lobell, D. B. 2000. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sens. Environ 73:87–102.Google Scholar
Everitt, J. H., Alaniz, M. A., Escobar, D. E., and Davis, M. R. 1992. Using remote sensing to distinguish common (Isocoma coronopifolia) and Drummond goldenweed (Isocoma drumondii). Weed Sci 40:621–628.Google Scholar
Everitt, J. H. and Deloach, C. J. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci 38:273–278.Google Scholar
Felton, W. L. and McCloy, K. R. 1992. Spot spraying. Agric. Eng 73:9–12.Google Scholar
Feyaerts, F. and van Gool, L. 2001. Multi-spectral vision system for weed detection. Pattern Recogn. Lett 22:667–674.Google Scholar
Foroutan-pour, K., Dutilleul, P., and Smith, D. L. 2001. Inclusion of the fractal dimension of leafless plant structure in the Beer-Lambert Law. Agron. J 93:333–338.Google Scholar
Franz, E., Gebhardt, M. R., and Unklesbay, K. N. 1991a. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Trans. Am. Soc. Agric. Eng 34:682–687.CrossRefGoogle Scholar
Franz, E., Gebhardt, M. R., and Unklesbay, K. B. 1991b. Shape description of completely visible and partially occluded leaves for identifying plants in digital images. Trans. Am. Soc. Agric. Eng 34:673–681.Google Scholar
Guyer, D. E., Miles, G. E., Gaultney, L. D., and Schreiber, M. M. 1993. Application of machine vision to shape analysis in leaf and plant identification. Trans. Am. Soc. Agric. Eng 36:163–171.Google Scholar
Guyer, D. E., Miles, G. E., Schreiber, M. M., Mitchel, O. R., and Vanderbilt, V. C. 1986. Machine vision and image processing for plant identification. Trans. Am. Soc. Agric. Eng 29:1500–1507.Google Scholar
Hemming, J. and Rath, T. 2001. Computer-vision-based weed identification under field conditions using controlled lighting. J. Agric. Eng. Res 78:233–243.Google Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D. 2004. Spectral reflectance curves to distinguish soybean from common cocklebur (Xanthium strumarium) and sicklepod (Cassia obtusifolia) grown with varying soil moisture. Weed Sci 52:788–796.Google Scholar
Jurado-Expósito, M., López-Granados, F., Atenciano, S., Garcia-Torres, L., and González-Andújar, J. L. 2003. Discrimination of weed seedlings, wheat (Triticum aestivum) and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS). Crop Prot 22:1177–1180.Google Scholar
Keränen, M., Aro, E-M., and Tyystjärvi, E. 2003. Automatic plant identification with chlorophyll fluorescence fingerprinting. Precision Agric 4:53–67.Google Scholar
Kincaid, D. T. and Schneider, R. B. 1983. Quantification of leaf shape with a microcomputer and Fourier transform. Can. J. Bot 61:2333–2342.Google Scholar
Lamb, D. W. and Brown, R. B. 2001. Remote-sensing and mapping of weeds in crops. J. Agric. Eng. Res 78:117–125.Google Scholar
Lamb, D. W. and Weedon, M. 1998. Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imaging. Panicum effusum in oilseed rape stubble. Weed Res 38:443–451.Google Scholar
Lamb, D. W., Weedon, M. M., and Rew, L. J. 1999. Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging. Avena spp. in seedling triticale (X Tritosecale). Weed Res 39:481–492.Google Scholar
Landgrebe, D. 1986. A Brief History of the Laboratory for Applications of Remote Sensing (LARS). www.lars.purdue.edu/home/LARSHistory.html.Google Scholar
Manh, A-G., Rabatel, G., Assemat, L., and Aldon, M-J. 2001. Weed leaf image segmentation by deformable templates. J. Agric. Eng. Res 80:139–146.Google Scholar
McGwire, K., Minor, T., and Fenstermaker, L. 2000. Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. Remote Sens. Environ 72:360–374.Google Scholar
Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max. Weed Sci 48:393–398.Google Scholar
Menges, R. M., Nixon, P. R., and Richardson, A. J. 1985. Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Sci 33:569–581.Google Scholar
Merrit, S. J., Meyer, G. E., Von Bargen, K., and Mortensen, D. A. 1994. Reflectance sensor and control system for spot spraying. Paper No. 941057 in The 1994 International Summer Meeting. St. Joseph, MI: American Society of Agricultural Engineers.Google Scholar
Moshou, D., Ramon, H., and De Baerdemaeker, J. 2002. A weed species spectral detector based on neural networks. Precision Agric 3:209–223.Google Scholar
Noble, S. D. 2002. Crop and Weed Leaf Reflectance and Classification. . University of Saskatchewan, Saskatoon, SK, Canada. 137 p.Google Scholar
Noble, S. D., Brown, R. B., and Crowe, T. G. 2002. The use of spectral properties for weed detection and identification—A review. Paper No. 02-208 in CSAE/SCGR Annual Meeting, Saskatoon, SK. Winnipeg, MB: Canadian Society of Agricultural Engineers. 17 p.Google Scholar
Pérez, A. J., López, F., Benlloch, J. V., and Christensen, S. 1997. Colour and shape analysis techniques for weed detection in cereal fields. Pages 45–50 in Proceedings of the 1st European Conference on Information Technology in Agriculture, Copenhagen, Denmark: The Royal Veterinary and Agricultural University.Google Scholar
Price, J. C. 1994. How unique are spectral signatures? Remote Sens. Environ 49:181–186.Google Scholar
Ramon, H., Anthonis, J., Vrindts, E., Delen, R., Reumers, J., Moshou, D., Deprez, K., and De Baerdemaeker, J. 2002. Development of a weed activated spraying machine for targeted application of herbicides. Asp. Appl. Biol 66:147–164.Google Scholar
Rew, L. J., Miller, P. C. H., and Paice, M. E. R. 1997. The importance of patch mapping resolution for sprayer control. Asp. Appl. Biol 48:49–55.Google Scholar
Shearer, S. A. and Holmes, R. G. 1990. Plant identification using color co-occurrence matrices. Trans. Am. Soc. Agric. Eng 33:2037–2044.Google Scholar
Shiraishi, M. and Sumiya, H. 1996. Plant identification from leaves using quasi-sensor fusion. Trans. ASME 118:382–387.Google Scholar
Tang, L., Tian, L., and Steward, B. L. 2000. Color image segmentation with genetic algorithm for in-field weed sensing. Trans. Am. Soc. Agric. Eng 43:1019–1027.Google Scholar
Vrindts, E. and De Baerdemaeker, J. 1997. Optical discrimination of crop, weed and soil for on-line weed detection. Pages 537–544 in Proceedings of the First European Conference on Precision Agriculture, Warwick, UK, 8–10 September, 1997, Volume 1. Oxford, UK: BioScientific.Google Scholar
Vrindts, E. and De Baerdemaeker, J. 2000. Using spectral information for weed detection in field circumstances. EurAgEng Paper No. 00-PA-010 in AgEng 2000, Warwick, UK: European Society of Agricultural Engineers.Google Scholar
Wang, N., Zhang, N., Dowell, F. E., Sun, Y., and Peterson, D. E. 2001. Design of an optical weed sensor using plant spectral characteristics. Trans. Am. Soc. Agric. Eng 44:409–419.Google Scholar
Wang, N., Zhang, N., Peterson, D., and Dowell, F. 2000. Design of an optical weed sensor using plant spectral characteristics. Biological quality and precision agriculture II, Proc. SPIE 4203:63–72.Google Scholar
Williams, A. P. and Hunt, E. R. Jr. 2002. Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ 82:446–456.Google Scholar
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., and Mortensen, D. A. 1992. Plant species identification, size, and enumeration using machine vision techniques on near-binary images. SPIE Opt. Agric. For 1836:208–219.Google Scholar
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., and Mortensen, D. A. 1995. Shape features for identifying young weeds using image analysis. Trans. Am. Soc. Agric. Eng 38:271–281.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 17:189–206.Google Scholar