Abstract
For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.
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Andersen, H. and Granum, E. 1998. Classifying daylight conditions from colour histogram assessment. Presented at AgEng Oslo98, 24–27 August 1998, Oslo, Sweden, organized by EurAgEng, CIGR, NLH, AgEng Paper No. 98-a-037.
Borregaard, T., Nielsen, H., Nørgaard, L. and Have, H. 2000. Crop-weed discrimination by line imaging spectroscopy. Journal of Agricultural Engineering Research 75, 389–400.
Burks, T. F., Shearer, S. A., Gates, R. S. and Donohue, K. D. 2000a. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Transactions of the American Society of Agricultural Engineers 43(4), 1029–1037.
Burks, T. F., Shearer, S. A. and Payne, F. A. 2000b. Classification of weed species using color texture features and discriminant analysis. Transactions of the American Society of Agricultural Engineers 43(2), 441–448.
Cousens, R., Brain, P., O'Donovan, J. T. and O'sullivan, A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Science 35, 720–725.
Donald, W. 1994. Geostatistics for mapping weeds, with a Canada Thistle (Cirsium arvense) patch as a case study. Weed Science 42, 648–657.
El-Faki, M. S., Zhang, N. and Peterson, D. E. 2000a. Weed detection using color machine vision. Transactions of the American Society of Agricultural Engineers 43(6), 1969–1978.
El-Faki, M. S., Zhang, N. and Peterson, D. E. 2000b. Factors affecting color-based weed detection. Transactions of the American Society of Agricultural Engineers 43(4), 1001–1009.
Favier, J. F., Ross, D. W., Tsheko, R., Kennedy, D. D., Muir, A. Y. and Fleming, J. 1999. Discrimination of weeds in brassica crops using optical spectral reflectance and leaf texture analysis. In: Proceedings of SPIE 3543—Precision Agriculture and Biological Quality, edited by G. E. Meyer and J. A. DeShazer (SPIE, Bellingham, Washington, USA), p. 311–318.
Feyaerts, F., Pollet, P., Wambacq, P. and Van Gool, L. 1998. Sensor for weed detection based on spectral measurements. In: Proceedings of the 4th International Conference on Precision Agriculture, edited by P. C., Robert, R. H., Rust, and W. E. Larsen (ASA/CSSA/SSSA, Madison, WI, USA), Part B, p. 1537–1548.
Franz, E., Gebhardt, M. R. and Unklesbay, K. B. 1995. Algorithms for extracting leaf boundary information from digital images of plant foliage. Transactions of the ASAE 38(2), 625–633.
Gausman, H. W. 1985. Plant Leaf Optical Properties in Visible and Near-Infrared Light. Graduate Studies No. 29 (Texas Tech University, Lubbock, Texas, USA), p. 78
Gausman, H. W., Allen W. A., Wiegand, C. L., Escobar, D. E., Rodrigues R. R. and Richardson, A. J. 1973. The Leaf Mesophylls of Twenty Crops, Their Light Spectra and Optical and Geometrical Parameters. Technical Bulletin No. 1465 (Agricultural Research Service of the United States Department of Agriculture in cooperation with Texas Agricultural Experiment Station).
Guyer, D. E., Miles, G. E., Schreiber, M. M., Mitchell, O. R. and Vanderbilt, V. C. 1986. Machine vision and image processing for plant identification. Transactions of the American Society of Agricultural Engineers 29(6), 1500–1507.
Hahn, F. and Muir, A. Y. 1993. Weed-crop discrimination by optical reflectance. In: Proceedings of the IV International Symposium on Fruit, Nut, and Vegetable Production Engineering, edited by F. Juste (Ministerio de Agricultura, Pesca y Alimentación, INIA, March 1993, Valencia-Zaragoza, Spain), p. 221–228.
Kropff, M. J. 1988. Modelling the effects of weeds on crop production. Weed Research 28, 465–471.
Moshou, D., De Ketelaere, B., Vrindts, E., Kennes, P., De Baerdemaeker, J. and Ramon, H. 1998. Local linear mapping neural networks for pattern recognition of plants. In: Proceedings of the First IFAC Workshop on Control Application and Ergonomics in Agriculture, edited by N. Sigrimis and P. Groumpos (Pergamon), p. 61–66.
Murray, G. D. 1977. A cautionary note on selection of variables in discriminant analysis. Applied Statistics 26(3), 246–250.
Pollet, P., Feyaerts, F., Wambacq, P. and Van Gool, L. 1998. Weed detection based on structural information using an imaging spectrograph. In: Proceedings of the 4th International Conference on Precision Agriculture, edited by P. C. Robert, R. H., Rust, and W. E. Larsen (ASA/CSSA/SSSA, Madison, WI, USA), p. 1579–1591.
Rew, L. J., Miller, P. C. H. and Paice, M. E. R. 1997. The importance of patch mapping resolution for sprayer control. Aspects of Applied Biology—Optimizing Pesticide Applications 48, 49–55.
SAS Institute Inc. 1989. SAS/Stat* User's Guide, Version 6, Fourth edition, vol. 1, p. 686–693 and vol. 2, p. 493–1508.
Tang L., Tian, L. and Steward, B. L. 2000. Color image segmentation with genetic algorithm for in-field weed sensing. Transactions of the American Society of Agricultural Engineers 43(4), 1019–1027.
Tian, L., Slaughter, D. C. and Norris, R. F. 1997. Outdoor field machine vision identification of tomato seedlings for automated weed control. Transactions of the American Society of Agricultural Engineers 40(6), 1761–1768.
Vrindts, E. and De Baerdemaeker, J. 1996. Feasibility of weed detection with optical reflection measurements. In: Proceedings of Brighton Crop Protection Conference 1996—Pests and Diseases; 18–21 November 1996, (British Crop Protection Council, Brighton, UK) p. 443–444.
Vrindts, E., and De Baerdemaeker, J. 1997. Optical discrimination of crop, weed and soil for on-line weed detection. In: '97, Proceedings of the First European Conference on Precision Agriculture, edited by J. Stafford (BIOS Scientific Publishers, Oxford), vol. 2: Technology, IT and Management, p. 537–544.
Walter, H., and Koch, W. 1980. Light reflectance characteristics of weed and crop leaves as affected by plant species and herbicides. In: Proceedings of Brighton Crop Protection Conference—Weeds 1980, November 1980 (British Crop Protection Council, Brighton, UK), p. 243–250.
Wartenberg, G. 1996. Untersuchungen der Voraussetzungen für den teilflächenspezifischen chemischen Pflanzenschutz. (Investigation of the Premises of Site-Specific Chemical Crop Protection) Forschungsberichte des ATB 1996/1. Institut für Agrartechnik Bornim e.V. ATB, Abteilung Technik im Plfanzenbau, Potsdam-Bornim, p. 72.
Woebbecke D. M., Meyer, G. E., Von Bargen, K. and Mortensen, D. A. 1995. Shape features for identifying young weeds using image analysis. Transactions of the American Society of Agricultural Engineers 38(1), 271–281.
Zhang N. and Chaisattapagon C. 1995. Effective criteria for weed identification in wheat fields using machine vision. Transactions of the American Society of Agricultural Engineers 38(3), 965–974.
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Vrindts, E., De Baerdemaeker, J. & Ramon, H. Weed Detection Using Canopy Reflection. Precision Agriculture 3, 63–80 (2002). https://doi.org/10.1023/A:1013326304427
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DOI: https://doi.org/10.1023/A:1013326304427