Precision Agriculture

, Volume 5, Issue 5, pp 477–508 | Cite as

A Review on Remote Sensing of Weeds in Agriculture

  • K.R. Thorp
  • L.F. Tian


In the effort of developing precision agriculture tools, remote sensing has been commonly considered as an effective technique for weed patch delineation, where weed infestations are detected based on variations in the plant canopy spectral response. Because the canopy spectral response is important for weed detection, discussions on the irradiative interaction of light in plant canopies and the effect of variable soil background on the canopy spectral response are presented in this review. Also, a presentation of the current techniques for removal of soil effects, including vegetation indices and spectral mixture analysis, shows that these techniques have not been adequately developed for use in remote sensing-based weed detection applications. Given the nature of light interaction in a plant canopy, this review proposes that the spectral response of a plant canopy depends on both the species and the biomass density. Remote detection of weeds from ground-, aircraft-, and satellite-based platforms has been accomplished on a wide scale, yet the use of these weed detection methods to make variable-rate herbicide applications has not occurred as often. By judging success based on variable-rate herbicide applications rather than precise weed localization, some of the current problems in weed sensing may be skirted.

remote sensing weed distribution weed detection vegetation indices vegetative reflectance 


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  1. Allen, W. A. and Richardson, A. J. 1968. Interaction of light with a plant canopy. Journal of the Optical Society of America 58(8), 1023-1028.Google Scholar
  2. Anderson, G. L., Everitt, J. H., Richardson, A. J. and Escobar, D. E. 1993. Using satellite data to map false broomweed (Ericameria austrotexana) infestations on south Texas rangelands. Weed Technology 7(4), 865-871.Google Scholar
  3. Andreasen, C., Rudemo, M. and Sevestre, S. 1997. Assessment of weed density at an early stage by use of image processing. Weed Research 37, 5-18.Google Scholar
  4. Ashley, M. D. and Rea, J. 1975. Seasonal vegetation differences from ERTS imagery. Photogrammetric Engineering and Remote Sensing 41, 713-719.Google Scholar
  5. Audsley, E. 1993. Operational research analysis for patch spraying. Crop Protection 12, 111-119.Google Scholar
  6. Bach, H. and Mauser, W. 1994. Modelling and model verification of the spectral reflectance of soils under varying moisture conditions. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS '94 (IEEE, Piscataway, NJ, USA) Vol. 4, pp. 2354-2356.Google Scholar
  7. Bannari, A., Morin, D., Bonn, F. and Huete, A. R. 1995. A review of vegetation indices. Remote Sensing Reviews 13, 95-120.Google Scholar
  8. Baret, F., Guyot, G. and Major, D. J. 1989. TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In: Proceedings of the 12th Canadian Symposium on Remote Sensing, IGARSS'89, July 10-14 1989 (IEEE,Vancouver, Canada) Vol. 3, pp. 1355-1358.Google Scholar
  9. Bechdol, M. A., Gualtieri, J. A., Hunt, J. T., Chettri, S. and Garegnani, J. 2000. Hyperspectral imaging: a potential tool for improving weed and herbicide management. In: Proceedings of the 5th International Conference on Precision Agriculture (ASA-CSSA-SSSA, Madison, WI, USA).Google Scholar
  10. Blackburn, G. A. 1998. Quantifying the chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment 66, 273-285.Google Scholar
  11. Blackshaw, R. E., Molnar, L. J., Chevalier, D. F. and Lindwall, C. W. 1998a. Factors affecting the operation of the weed-sensing Detectspray system. Weed Science 46, 127-131.Google Scholar
  12. Blackshaw, R. E., Molnar, L. J. and Lindwall, C. W. 1998b. Merits of a weed-sensing sprayer to control weeds in conservation fallow and cropping systems. Weed Science 46, 120-126.Google Scholar
  13. Borel, C. C. and Gerstl, S. A. W. 1994. Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sensing of Environment 47, 403-416.Google Scholar
  14. Bouman, B. A. M. 1992. Accuracy of estimating the leaf area index from vegetation indices derived from crop reflectance characteristics, a simulation study. International Journal of Remote Sensing 13(16), 3069-3084.Google Scholar
  15. Bowers, S. A. and Hanks, R. J. 1965. Reflection of radiant energy from soils. Soil Science 100(2), 130-138.Google Scholar
  16. Brian, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. Journal of Applied Ecology 27(2), 735-742.Google Scholar
  17. Brown, R. B. and Steckler, J.-P. G. A. 1995. Prescription maps for spatially variable herbicide application in no-till corn. Transactions of the ASAE 38(6), 1659-1666.Google Scholar
  18. Brown, R. B., Steckler, J.-P. G. A. and Anderson, G. W. 1994. Remote sensing for identification of weeds in no-till corn. Transactions of the ASAE 37(1), 297-302.Google Scholar
  19. Burks, T. F., Shearer, S. A. and Payne, F. A. 2000. Classification of weed species using color texture features and discriminant analysis. Transactions of the ASAE 43(2), 441-448.Google Scholar
  20. Cardina, J., Johnson, G. A. and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Science 45, 364-373.Google Scholar
  21. Cardina, J., Sparrow, D. H. and McCoy, E. L. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in no-till soybeans (Glycine max). Weed Science 43(2), 258-268.Google Scholar
  22. Cardina, J., Sparrow, D. H. and McCoy, E. L. 1996. Spatial relationships between seedbank and seedling populations of common lambsquarters (Chenopodium album) and annual grasses. Weed Science 44(2), 298-308.Google Scholar
  23. Chancellor, W. J. and Goronea, M. A. 1994. Effects of spatial variability of nitrogen, moisture, and weeds on the advantages of site-specific applications for wheat. Transactions of the ASAE 37(3), 717-724.Google Scholar
  24. Chen, Z., Elvidge, C. D. and Jansen, W. T. 1993. Description of derivative-based high spectral-resolution (AVIRIS) green vegetation index. In: Proceedings of SPIE: Imaging Spectroscopy of the Terrestrial Environment, April 14-15 1993, edited by G. Vane (Orlando, Florida, USA) Vol. 1937, pp. 43-54.Google Scholar
  25. Christensen, S., Nordbo, E. and Kristensen, K. 1994. Weed cover mapping with spectral reflectance measurements. Aspects of Applied Biology-Sampling to Make Decisions 37, 171-179.Google Scholar
  26. Cipra, J. E., Baumgardner, M. F., Stoner, E. R. and MacDonald, R. B. 1971. Measuring radiance characteristics of soil with a field spectroradiometer. Soil Science Society of America Proceedings 35, 1014-1017.Google Scholar
  27. Cochran, W. 1977. Sampling Techniques (John Wiley & Sons Inc., New York, NY, USA).Google Scholar
  28. Colwell, J. E. 1974. Vegetation canopy reflectance. Remote Sensing of Environment 3, 175-183.Google Scholar
  29. Condit, H. R. 1971. The spectral reflectance of American soils. Photogrammetric Engineering 36, 955-966.Google Scholar
  30. Copenhaver, K., Gress, T., White, S. and Varner, B. 2001. Weed detection and delineation in soybeans. Final report of ITD/Spectral Visions to NASA Earth Science Application Directorate (ESAD) 2001. Scholar
  31. Curran, P. J., Dungan, J. L., Macler, B. A. and Plummer, S. E. 1991. The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment 35, 69-76.Google Scholar
  32. Datt, B. and Paterson, M. 2000. Vegetation-soil spectral mixture analysis. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS '00 (IEEE, Piscataway, NJ, USA) Vol. 5, pp. 1936-1938.Google Scholar
  33. Demetriades-Shah, T. H., Steven, M. D. and Clark, J. A. 1990. High resolution derivative spectra in remote sensing. Remote Sensing of Environment 33, 55-64.Google Scholar
  34. Donald, W. W. 1994. Geostatistics for mapping weeds, with a Canada thistle (Cirsium arvense) patch as a case study. Weed Science 42(4), 648-657.Google Scholar
  35. El-Faki, M. S., Zhang, N. and Peterson, D. E. 2000. Weed detection using color machine vision. Transactions of the ASAE 43(6), 1969-1978.Google Scholar
  36. Elvidge, C. D. and Chen, Z. 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment 54, 38-48.Google Scholar
  37. Elvidge, C. D., Chen, Z. and Groeneveld, D. P. 1993. Detection of trace quantities of green vegetation in 1990 AVIRIS data. Remote Sensing of Environment 44, 271-279.Google Scholar
  38. Elvidge, C. D. and Lyon, R. J. P. 1985. Influence of rock-soil spectral variation on the assessment of green biomass. Remote Sensing of Environment 17, 265-279.Google Scholar
  39. Everitt, J. H., Alaniz, M. A., Escobar, D. E. and Davis, M. R. 1992a. Using remote sensing to distinguish common (Isocoma coronopifolia) and Drummond goldenweed (Isocoma drummondii). Weed Science 40(4), 621-628.Google Scholar
  40. Everitt, J. H., Anderson, G. L., Escobar, D. E., Davis, M. R., Spencer, N. R. and Andrascik, R. J. 1995. Use of remote sensing for detecting and mapping leafy spurge (Euphorbia esula). Weed Technology 9(3), 599-609.Google Scholar
  41. Everitt, J. H., Escobar, D. E., Alaniz, M. A., Villarreal, R. and Davis, M. R. 1992b. Distinguishing brush and weeds on rangelands using video remote sensing. Weed Technology 6(4), 913-921.Google Scholar
  42. Everitt, J. H., Escobar, D. E., Villarreal, R., Alaniz, M. A. and Davis, M. R. 1993a. Canopy light reflectance and remote sensing of shin oak (Quercus havardii) and associated vegetation. Weed Science 41(2), 291-297.Google Scholar
  43. Everitt, J. H., Escobar, D. E., Villarreal, R., Alaniz, M. A. and Davis, M. R. 1993b. Integration of airborne video, global positioning system, and geographic information system technologies for detecting and mapping two woody legumes on rangelands. Weed Technology 7(4), 981-987.Google Scholar
  44. Everitt, J. H., Pettit, R. D. and Alaniz, M. A. 1987. Remote sensing of broom snakeweed (Gutierrezia sarothrae) and spiny aster (Aster spinosus). Weed Science 35(2), 295-302.Google Scholar
  45. Everitt, J. H., Richerson, J. V., Alaniz, M. A., Escobar, D. E., Villarreal, R. and Davis, M. R. 1994. Light reflectance characteristics and remote sensing of Big Bend loco (Astragalus mollissimus var. earlei) and Wooton loco (Astragalus wootonii). Weed Science 42(1), 115-122.Google Scholar
  46. Franz, E., Gebhardt,M.R. and Unklesbay, K. B. 1991a. Shape description of completely visible and partially occluded leaves for identifying plants in digital images. Transactions of the ASAE 34(2), 673-681.Google Scholar
  47. Franz, E., Gebhardt, M. R. and Unklesbay, K. B. 1991b. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Transactions of the ASAE 34(2), 682-687.Google Scholar
  48. Gates, D. M., Keegan, H. J., Schleter, J. C. and Weidner, V. R. 1965. Spectral properties of plants. Applied Optics 4(1), 11-20.Google Scholar
  49. Gausman, H. W., Gerbermann, A. H., Wiegand, C. L., Leamer, R. W., Rodriguez, R. R. and Noriega, J. R. 1975. Reflectance differences between crop residues and soils. Soil Science Society of America Proceedings 39, 752-755.Google Scholar
  50. 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. Transactions of the ASAE 36(1), 163-171.Google Scholar
  51. 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 ASAE 29(6), 1500-1507.Google Scholar
  52. Haggar, R. J., Stent, C. J. and Isaac, S. 1983. A prototype hand-held patch sprayer for killing weeds, activated by spectral differences in crop/weed canopies. Journal of Agricultural Engineering Research 28, 349-358.Google Scholar
  53. Hall, F. G., Huemmrich, K. F., Goward, S. N. 1990. Use of narrow-band spectra to estimate the fraction of absorbed photosynthetically active radiation. Remote Sensing of Environment 32, 47-54.Google Scholar
  54. Hanks, J. E. and Beck, J. L. 1998. Sensor-controlled hooded sprayer for row crops. Weed Technology 12, 308-314.Google Scholar
  55. Hatfield, J. L. and Pinter Jr., P. J. 1993. Remote sensing for crop protection. Crop Protection 12(6), 403-413.Google Scholar
  56. Heilman, J. L. and Kress, M. R. 1987. Effects of vegetation on spectral irradiance at the soil surface. Agronomy Journal 79, 765-768.Google Scholar
  57. Hooper, A. W., Harries, G. O. and Ambler, B. 1976. A photoelectric sensor for distinguishing between plant material and soil. Journal of Agricultural Engineering Research 21, 145-155.Google Scholar
  58. Horler, D. N. H., Dockray, M. and Barber, J. 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing 4(2), 273-288.Google Scholar
  59. Huete, A. R. 1986. Separation of soil-plant mixtures by factor analysis. Remote Sensing of Environment 19, 237-251.Google Scholar
  60. Huete, A. R. 1987. Soil-dependent spectral response in a developing plant canopy. Agronomy Journal 79, 61-68.Google Scholar
  61. Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295-309.Google Scholar
  62. Huete, A. R. and Jackson, R. D. 1988. Soil and atmosphere influences on the spectra of partial canopies. Remote Sensing of Environment 25, 89-105.Google Scholar
  63. Huete, A. R., Jackson, R. D. and Post, D. F. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment 17, 37-53.Google Scholar
  64. Hurcom, S. J. and Harrison, A. R. 1998. The NDVI and spectral decomposition for semi-arid vegetation abundance estimation. International Journal of Remote Sensing. 19(16), 3109-3125.Google Scholar
  65. Jackson, R. D. 1983. Spectral indices in n-space. Remote Sensing of Environment 13, 409-421.Google Scholar
  66. Jackson, R. D., Reginato, R. J., Pinter, Jr. P. J. and Idso, S. B. 1979. Plant canopy information extraction from composite scene reflectance of row crops. Applied Optics 18(22), 3775-3782.Google Scholar
  67. Jia, J. and Krutz, G. W. 1992. Location of the maize plant with machine vision. Journal of Agricultural Engineering Research 52, 169-181.Google Scholar
  68. Johnson, G. A., Mortensen, D. A. and Martin, A. R. 1995. A simulation of herbicide use based on weed spatial distribution. Weed Research 35, 197-205.Google Scholar
  69. Jordan, C. F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50(4), 663-666.Google Scholar
  70. Kauth, R. J. and Thomas, G. S. 1976. The tasseled cap-a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In: Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, June 29-July 1 1976 (Purdue University, West Lafayette, IN, USA) pp. 41-51.Google Scholar
  71. Kawata, S. and Minami, S. 1984. Adaptive smoothing of spectroscopic data by a linear mean-square estimation. Applied Spectroscopy 38(1), 49-58.Google Scholar
  72. King, R. P., Lybecker, D. W., Schweizer, E. E. and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Science 34(6), 972-979.Google Scholar
  73. Knipling, E. B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1, 155-159.Google Scholar
  74. LaMastus, F. E., Smith, C. M., Shaw, D. R. and King, R. L. 2000. Potential for weed species differentiation using remote sensing. In: Proceedings of the 5th International Conference on Precision Agriculture (ASA-CSSA-SSSA, Madison, WI, USA) (CD-ROM).Google Scholar
  75. Lamb, D. W. and Brown, R. B. 2001. Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research. 78(2), 117-125.Google Scholar
  76. Lamb, D. W. and Weedon, M. 1998. Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imagery: Panicum effusum in oilseed rape stubble. Weed Research 38, 443-451.Google Scholar
  77. 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. Weed Research 39, 481-492.Google Scholar
  78. Lass, L. W. and Callihan, R. H. 1997. The effect of phenological stage on the detectability of yellow hawkweed (Heiracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technology 11(2), 248-256.Google Scholar
  79. Lass, L. W., Carson, H. W. and Callihan, R. H. 1996. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technology 10(3), 466-474.Google Scholar
  80. Lawrence, R. L. and Ripple, W. J. 1998. Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape: Mount St. Helens, Washington. Remote Sensing of Environment 64, 91-102.Google Scholar
  81. Lee, W. S., Slaughter, D. C. and Giles, D. K. 1999. Robotic weed control system for tomatoes. Precision Agric. 1(1): 95-113.Google Scholar
  82. Lillesaeter, O. 1982. Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling. Remote Sensing of Environment 12, 247-254.Google Scholar
  83. Lillesand, T. M. and Kiefer, R. W. 2000. Remote Sensing and Image Interpretation. 4th edn (John Wiley & Sons Inc., New York, NY, USA).Google Scholar
  84. Lopez-Backovic, I. 1988. Using geostatistics to consider variability of soil and crop properties. ASAE Paper No. 88-1605. (ASAE, St. Joseph, MI, USA).Google Scholar
  85. Lord, D., Desjardins, R. L., Dube, P. A. and Brach, E. J. 1985. Variations of crop canopy spectral reflectance measurements under changing sky conditions. Photogrammetric Engineering and Remote Sensing 51(6), 689-695.Google Scholar
  86. Lybecker, D. W., Schweizer, E. E. and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Science 39(1), 124-129.Google Scholar
  87. Major, D. J., Baret, F. and Guyot, G. 1990. A ratio vegetation index adjusted for soil brightness.International Journal of Remote Sensing 11(5), 727-740.Google Scholar
  88. Marshall, E. J. P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Research 28, 191-198.Google Scholar
  89. Maxwell, B. D. and Ghersa, C. 1992. The influence of weed seed dispersion versus the effect of competition on crop yield. Weed Technology 6(1), 196-204.Google Scholar
  90. Medlin, C. R. and Shaw, D. R. 2000. Economic comparison of broadcast and site-specific herbicide applications in nontransgenic and glyphosate-tolerant Glycine max. Weed Science 48, 653-661.Google Scholar
  91. 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 Science 48, 393-398.Google Scholar
  92. Menges, R. M., Nixon, P. R. and Richardson, A. J. 1985. Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Science 33(4), 569-581.Google Scholar
  93. Mestre, H. 1935. The absorption of radiation by leaves and algae. Cold Spring Harbor Symposium on Quantitative Biology 3, 191-209.Google Scholar
  94. Meyer, G. E., Mehta, T., Kocher, M. F., Mortensen, D. A. and Samal, A. 1998. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASAE 41(4), 1189-1197.Google Scholar
  95. Moran, M. S., Inoue, Y. and Barnes, E. M. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment 61, 319-346.Google Scholar
  96. Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technology 5(2), 445-452.Google Scholar
  97. Mortensen, D. A., Johnson, G. A. and Young, L. J. 1993. Weed distribution in agricultural fields. In: Soil Specific Crop Management, edited by P. C. Roberts, R. H. Rust, and W. E. Larson (ASA-CSSA-SSSA, Madison, WI, USA), ch. 9, pp. 113-124.Google Scholar
  98. Navas, M. L. 1991. Using plant population biology in weed research: A strategy to improve weed management. Weed Research 31, 171-179.Google Scholar
  99. Nitsch, B. B., VonBargen, K., Meyer, G. E. and Mortensen, D. A. 1991. Visible and near infrared plant, soil, and crop residue reflectivity for weed sensor design. ASAE Paper No. 91-3006. (ASAE, St. Joseph, MI, USA).Google Scholar
  100. Philpot, W. D. 1991. The derivative ratio algorithm: Avoiding atmospheric effects in remote sensing. IEEE Transactions on Geoscience and Remote Sensing 29(3), 350-357.Google Scholar
  101. Planet, W. G. 1970. Some comments on reflectance measurements of wet soils. Remote Sensing of Environment 1, 127-129.Google Scholar
  102. Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. and Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment 24, 119-126.Google Scholar
  103. Ray, T. W. and Murray, B. C. 1996. Nonlinear spectral mixing in desert vegetation. Remote Sensing of Environment 55, 59-64.Google Scholar
  104. Rew, L. J., Cussans, G. W., Mugglestone, M. A. and Miller, P. C. H. 1996. A technique for mapping the spatial distribution of Elymus repens, with estimates of the potential reduction in herbicide usage from patch spraying. Weed Research 36, 283-292.Google Scholar
  105. Richardson, A. J., Menges, R. M. and Nixon, P. R. 1985. Distinguishing weed from crop plants using video remote sensing. Photogrammetric Engineering and Remote Sensing 51(11), 1785-1790.Google Scholar
  106. Richardson, A. J. and Wiegand, C. L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43(12), 1541-1552.Google Scholar
  107. Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G. and Green, R. O. 1998. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment 65, 267-279.Google Scholar
  108. Roberts, D. A., Smith, M. O. and Adams, J. B. 1993. Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data. Remote Sensing of Environment 44, 255-269.Google Scholar
  109. Rondeaux, G., Steven, M. and Baret, F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55, 95-107.Google Scholar
  110. Savitzky, A. and Golay, M. J. E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36(8), 1627-1639.Google Scholar
  111. Schowengerdt, R. A. 1997. Remote Sensing: Models and Methods for Image Processing. 2nd edn. (Academic Press, San Deigo, CA, USA).Google Scholar
  112. Shearer, S. A. and Holmes, R. G. 1990. Plant identification using color co-occurrence matrices. Transactions of the ASAE 33(6), 2037-2044.Google Scholar
  113. Shearer, S. A. and Jones, P. T. 1991. Selective application of post-emergence herbicides using photoelectrics. Transactions of the ASAE 34(4), 1661-1666.Google Scholar
  114. Smith, G. M. and Milton, E. J. 1999. The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing 20(13), 2653-2662.Google Scholar
  115. Stafford, J. V. and Miller, P. C. H. 1993. Spatially selective application of herbicide to cereal crops. Computers and Electronics in Agriculture 9, 217-229.Google Scholar
  116. Stoner, E. R. and Baumgardner, M. F. 1981. Characteristic variations in reflectance of surface soils. Soil Science Society of America Journal 45, 1161-1165.Google Scholar
  117. Stoner, E. R., Baumgardner, M. F., Weismiller, R. A., Biehl, L. L. and Robinson, B. F. 1980. Extension of laboratory-measured soil spectra to field conditions. Soil Science Society of America Journal 44, 572-574.Google Scholar
  118. Tang, L., Tian, L. and Steward, B. L. 2000. Color image segmentation with genetic algorithm for in-field weed sensing. Transactions of the ASAE 43(4), 1019-1027.Google Scholar
  119. Thenkabail, P. S., Smith, R. B. and DePauw, E. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71, 158-182.Google Scholar
  120. Thomas, D. L., daSilva, F. J. K. and Cromer, W. A. 1988. Image processing technique for plant canopy cover evaluation. Transactions of the ASAE 31(2), 428-434.Google Scholar
  121. Thompson, J. F., Stafford, J. V. and Miller, P. C. H. 1991. Potential for automatic weed detection and selective herbicide application. Crop Protection 10, 254-259.Google Scholar
  122. Thornton, P. K., Fawcett, R. H., Dent, J. B. and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Protection 9, 337-342.Google Scholar
  123. Thorp, K. R. 2002. Variable-rate Applications of Herbicide Using Weed Maps Generated from Remote Sensing Imagery. M.S. thesis, University of Illinois at Urbana-Champaign, Urbana, IL, USA.Google Scholar
  124. Tian, L. F. and Slaughter, D. C. 1998. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture 21(3), 153-168.Google Scholar
  125. Tian, L., Slaughter, D. C. and Norris, R. F. 1997. Outdoor field machine vision of tomato seedlings for automated weed control. Transactions of the ASAE 40(6), 1761-1768.Google Scholar
  126. Tian, L. 2002. Sensor-based precision chemical application system. Journal of Computers and Electronics in Agriculture 36(23), 133-149.Google Scholar
  127. Timmermann, C., Gerhards, R., Krohmann, P., Sokefeld, M. and Kuhbauch, W. 2001. The economical and ecological impact of the site-specific weed control. In: Proceedings of the 3rd European Conference on Precision Agriculture, edited by G. Grenier and S. Blackmore (agro Montpellier, Montpellier, France) Vol. 2, pp. 563-568.Google Scholar
  128. Tipler, P.A. 1991. Physics for Scientists and Engineers: Extended Version. (Worth Publishers, Inc., New York, NY, USA).Google Scholar
  129. Tsai, F. and Philpot, W. 1998. Derivative analysis of hyperspectral data. Remote Sensing of Environment 66, 41-51.Google Scholar
  130. Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127-150.Google Scholar
  131. VanGroenendael, J. M. 1988. Patchy distribution of weeds and some implications for modeling population dynamics: a short literature review. Weed Research 28, 437-441.Google Scholar
  132. Varner, B. L., Gress, T. A., Copenhaver, K., Wax, L. M., Sprague, C. L. and Tranel, P. J. 2000. Detection of cockleburs (Xanthium strumarium L.) in soybeans using hyperspectral imagery. In: Proceedings of the 5th International Conference on Precision Agriculture (ASA-CSSA-SSSA, Madison, WI, USA).Google Scholar
  133. Vioix, J-B., Douzals, J-P., Assemat, L., LeCorre, V., Dessaint, F. and Guillemin, J-P. 2001. Development of a combined spatial and spectral method for weed detection and localisation. In: Proceedings of the 3rd European Conference on Precision Agriculture, edited by G. Grenier and S. Blackmore (agro Montpellier, Montpellier, France) Vol. 2, pp. 605-610.Google Scholar
  134. Walter-Shea, E. A., Norman, J. M. and Blad, B. L. 1989. Leaf bidirectional reflectance and transmittancein corn and soybeans. Remote Sensing of Environment 29, 161-174.Google Scholar
  135. Wanjura, D. F. and Hatfield, J. L. 1986. PAR and IR reflectance, transmittance, and absorptance of four crop canopies. Transactions of the ASAE 29(1), 143-150.Google Scholar
  136. Wanjura, D. F. and Hatfield, J. L. 1987. Sensitivity of spectral vegetative indices to crop biomass. Transactions of the ASAE 30(3), 810-816.Google Scholar
  137. Wiegand, C. L., Richardson, A. J., Escobar, D. E. and Gerbermann, A. H. 1991. Vegetation indices in crop assessments. Remote Sensing of Environment 35, 105-119.Google Scholar
  138. Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J. and Wilkerson, G. G. 1992. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Science 40(4), 554-557.Google Scholar
  139. Woebbecke, D. M., Meyer, G. E., VonBargen, K. and Mortensen, D. A. 1995a. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38(1), 259-269.Google Scholar
  140. Woebbecke, D. M., Meyer, G. E., VonBargen, K. and Mortensen, D. A. 1995b. Shape features for identifying young weeds using image analysis. Transactions of the ASAE 38(1), 271-281.Google Scholar
  141. Woolley, J. T. 1971. Reflectance and transmittance of light by leaves. Plant Physiology 47, 656-662.Google Scholar
  142. Yao, H., Tian, L., Tang, L. and Thorp, K. 2002. Corn canopy reflectance study with a real-time highdensityspectral-image mapping system. ASAE Paper No. 02-3144. (ASAE, St. Joseph, MI, USA).Google Scholar
  143. Zhang, N. and Chaisattapagon, C. 1995. Effective criteria for weed identification in wheat fields using machine vision. Transactions of the ASAE 38(3), 965-974.Google Scholar
  144. Zhang, L., Li, D., Tong, Q. and Zheng, L. 1998. Study of the spectral mixture model of soil and vegetation in PoYang Lake area, China. International Journal of Remote Sensing 19(11), 2077-2084.Google Scholar
  145. Zwiggelaar, R. 1998. A review of spectral properties of plants and their potential use for crop/weed discrimination in row crops. Crop Protection 17(3), 189-206.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • K.R. Thorp
    • 1
  • L.F. Tian
    • 1
  1. 1.Illinois Laboratory for Agricultural Remote SensingUniversity of IllinoisUSA

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