Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T05:30:48.713Z Has data issue: false hasContentIssue false
  • English
  • Français

Multispectral Machine Vision Identification of Lettuce and Weed Seedlings for Automated Weed Control

Published online by Cambridge University Press:  20 January 2017

David C. Slaughter ,
D. Ken Giles ,
Steven A. Fennimore  and
Richard F. Smith
David C. Slaughter*
Affiliation:
Biological and Agricultural Engineering Department, University of California–Davis, Davis, CA 95616
D. Ken Giles
Affiliation:
Biological and Agricultural Engineering Department, University of California–Davis, Davis, CA 95616
Steven A. Fennimore
Affiliation:
Department of Plant Sciences, University of California–Davis, Salinas, CA 93905
Richard F. Smith
Affiliation:
University of California Cooperative Extension, Salinas, CA 93901
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Multispectral images of leaf reflectance in the visible and near infrared region from 384 to 810 nm were used to establish the feasibility of developing a site-specific classifier to distinguish lettuce plants from weeds in California direct-seeded lettuce fields. An average crop vs. weed classification accuracy of 90.3% was obtained in a study of over 7,000 individual spectra representing 150 plants. The classifier utilized reflectance values from a small spatial area (3 mm diameter) of the leaf in order to allow the method to be robust to occlusion and to eliminate the need to identify leaf boundaries for shape-based machine vision recognition. Reflectance spectra were collected in the field using equipment suitable for real-time operation as a weed sensor in an autonomous system for automated weed control.

Keywords

Lettuce, Lactuca sativa L. ‘Capitata’ and ‘Crispa’Machine visionmultispectral imagesreflectanceroboticsweed detectionweed recognition
Type
Symposium
Information
Weed Technology , Volume 22 , Issue 2 , June 2008 , pp. 378 - 384
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Åstrand, B. and Baerveldt, A. J. 2002. An agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots. 13:21–35.Google Scholar
Biller, R. H. 1998. Reduced input of herbicides by use of optoelectronic sensors. J. Agric. Eng. Res. 71:357–362.Google Scholar
Borregaard, T., Nielsen, H., Norgaard, L., and Have, H. 2000. Crop–weed discrimination by line imaging spectroscopy. J. Agric. Eng. Res. 75:389–400.Google Scholar
Burks, T. A., 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.CrossRefGoogle Scholar
Chandler, J. M. and Cooke, F. T. 1992. Economics of cotton losses caused by weeds. Pages 85–116. in McWhorter, C. G. and Abernathy, J. R., editors. Weeds of Cotton: Characterization and Control. Memphis, TN The Cotton Foundation.Google Scholar
El-Faki, M. S., Zhang, N., and Peterson, D. E. 2000a. Factors affecting color-based weed detection. Trans. ASAE. 43:1001–1009.CrossRefGoogle Scholar
El-Faki, M. S., Zhang, N., and Peterson, D. E. 2000b. Weed detection using color machine vision. Trans. ASAE. 43:1969–1978.Google Scholar
Franz, E., Gebhardt, M. R., and Unklesbay, K. B. 1991. Shape description of completely visible and partially occluded leaves for identifying plants in digital images. Trans. ASAE. 34:673–681.CrossRefGoogle Scholar
Gray, C. J., Shaw, D. R., Bond, J. A., Stephenson, D. O. IV, and Oliver, L. R. 2007. Assessing the reflective characteristics of Palmer amaranth (Amaranthus palmeri) and pitted morningglory (Ipomoea lacunosa) accessions. Weed Sci. 55:293–298.Google Scholar
Guyot, G. 1990. Optical properties of vegetation canopies. Pages 19–43. in Steven, M. D. and Clark, J. A., editors. Applications of Remote Sensing in Agriculture. Kent, U.K Butterworth-Heinemann.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
Hollaender, A. 1956. Radiation Biology. Vol. III. New York McGraw-Hill.Google Scholar
Hutto, K. C., Shaw, D. R., Byrd, J. D. Jr, and King, R. L. 2006. Differentiation of turfgrass and common weed species using hyperspectral radiometry. Weed Sci. 54:335–339.CrossRefGoogle Scholar
Jordan, C. F. 1969. Derivation of leaf area index from quality of light on the forest floor. Ecology. 50:663–666.CrossRefGoogle Scholar
Lanini, W. T. and Le Strange, M. 1991. Low-input management of weeds in vegetable fields. Calif. Agric. 45:11–13.CrossRefGoogle Scholar
Oberti, R. and De Baerdemaeker, J. 2000. Assessing the nitrogen status of plants by optical measurements. EuroAgEng Paper No. 00 PA 008 in AgEng 2000 conference, Warwick, U.K., July 2–7, 2000.Google Scholar
Roberts, H. A., Hewson, R. T., and Ricketts, M. A. 1977. Weed competition in drilled summer lettuce. Hortic. Res. 17:39–45.Google Scholar
SAS Institute, , 2007. SAS OnlineDoc, Version 9.1.3, Cary, NC. http://support.sas.com/onlinedoc/913/docMainpage.jsp. Accessed: February 6, 2008.Google Scholar
Savitzky, A. and Golay, M. J. E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36:1627–1638.Google Scholar
Scotford, I. M. and Miller, P. C. H. 2005. Applications of spectral reflectance techniques in northern European cereal production: a review. Biosyst. Eng. 90:235–250.Google Scholar
Shrefler, J. W., Stall, W. M., and Dusky, J. A. 1996. Spiny amaranth (Amaranthus spinosus L.) a serious competitor to crisphead lettuce (Lactuca sativa L.). HortSci. 31:347–348.Google Scholar
Slaughter, D. C., Giles, D. K., and Downey, D. 2008. Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61:63–78.Google Scholar
Slaughter, D. C., Lanini, W. T., and Giles, D. K. 2004. Discriminating weeds from processing tomato plants using visible and near-infrared spectroscopy. Trans. ASAE. 47:1907–1911.Google Scholar
Steinier, J., Termonia, Y., and Deltour, J. 1972. Comments on smoothing and differentiation of data by simplified least square procedure. Anal. Chem. 44:1906–1909.Google Scholar
Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127–150.Google Scholar
van Emden, H. F. 1965. The role of uncultivated land in the biology of crop pests and beneficial insects. Sci. Hortic. 17:121–136.Google Scholar
Vargas, R., Fischer, W. B., Kempen, H. M., and Wright, S. D. 1996. Cotton weed management. Pages 187–202. in Hake, S. J., Kerby, T. A., and Hake, K. D., editors. Cotton Production Manual. Publication 3352. Oakland, CA University of California, Division of Agriculture and Natural Resources.Google Scholar
Vrindts, E., De Baerdemeaeker, J., and Ramon, H. 2002. Weed detection using canopy reflection. Prec. Agric. 3:63–80.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. ASAE. 38:271–281.Google Scholar
Yang, C-C., Prasher, S. O., and Goel, P. K. 2004. Differentiation of crop and weeds by decision-tree analysis of multi-spectral data. Trans. ASAE. 47:873–879.Google Scholar
Zwiggelaar, R. 1998. A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Protect. 17:189–206.Google Scholar