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Robust Methods for Measurement of Leaf-Cover Area and Biomass from Image Data

Published online by Cambridge University Press:  20 January 2017

Ran Nisim Lati ,
Sagi Filin  and
Hanan Eizenberg
Ran Nisim Lati*
Affiliation:
Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Sagi Filin
Affiliation:
Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Hanan Eizenberg
Affiliation:
Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel
*
Corresponding author's E-mail: ranlati@tx.technion.ac.il
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Abstract

Leaf-cover area is a widely required plant development parameter for predictive models of weed growth and competition. Its assessment is performed either manually, which is labor intensive, or via visual inspection, which provides biased results. In contrast, digital image processing enables a high level of automation, thereby offering an attractive means for estimating vegetative leaf-cover area. Nonetheless, image-driven analysis is greatly affected by illumination conditions and camera position at the time of imaging and therefore may introduce bias into the analysis. Addressing both of these factors, this paper proposes an image-based model for leaf-cover area and biomass measurements. The proposed model transforms color images into an illumination-invariant representation, thus facilitating accurate leaf-cover detection under varying light conditions. To eliminate the need for fixed camera position, images are transformed into an object–space reference frame, enabling measurement in absolute metric units. Application of the proposed model shows stability in leaf-cover detection and measurement irrespective of camera position and external illumination conditions. When tested on purple nutsedge, one of the world's most troublesome weeds, a linear relation between measured leaf-cover area and plant biomass was obtained regardless of plant developmental stage. Data on the expansion of purple nutsedge leaf-cover area is essential for modeling its spatial growth. The proposed model offers the possibility of acquiring reliable and accurate biological data from digital images without extensive photogrammetric or image-processing expertise.

Keywords

Purple nutsedge, Cyperus rotundus L. CYPROBiomasshomographic transformationimage processinginvariant imageleaf-cover areapurple nutsedge
Type
Special Topics
Information
Weed Science , Volume 59 , Issue 2 , June 2011 , pp. 276 - 284
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Alvarez, J. M., Lopez, A., and Baldrich, R. 2008. Illuminant-invariant model-based road segmentation. Pages 1175–1180 in Proceeding of IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.CrossRefGoogle Scholar
Bosnic, A. C. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276–282.CrossRefGoogle Scholar
Burgos-Artizzua, X. P., Ribeiroa, A., Tellaecheb, A., Pajaresc, G., and Fernandez-Quintanillad, C. 2009. Improving weed pressure assessment using digital images from an experience-based reasoning approach. Comput. Electron. Agric. 65:176–185.CrossRefGoogle Scholar
Campbell, J. B., ed. 1996. Introduction to Remote Sensing. New York Guilford Press. 622 p.Google Scholar
Donald, W. W. 2006. Estimated corn yields using either weed cover or rated control after pre-emergence herbicides. Weed Sci. 54:373–379.CrossRefGoogle Scholar
Donald, W. W., Archer, D., Johnson, W. G., and Nelson, K. 2004a. Zone herbicide application controls annual weeds and reduces residual herbicide use in corn. Weed Sci. 52:821–833.CrossRefGoogle Scholar
Donald, W. W., Johnson, W. G., and Nelson, K. A. 2004b. In-row and between-row interference by corn (Zea mays) modifies annual weed control by preemergence residual herbicide. Weed Technol. 18:487–504.CrossRefGoogle Scholar
Finlayson, G. D., Drew, M. S., and Lu, C. 2005. Invariant image improvement by sRGB colour space sharpening, in International Colour Association Congress. Granada Spain.Google Scholar
Finlayson, G. D., Hordley, S. D., and Drew, M. S. 2002. Removing shadows from images. Pages 823–836 in ECCV '02: Proceedings of the 7th European Conference on Computer Vision-Part IV. London, UK Springer-Verlag.CrossRefGoogle Scholar
Gee, C. H., Bossu, J., Jones, G., and Truchetet, F. 2008. Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric. 60:49–59.CrossRefGoogle Scholar
Gitelson, A. A., Kaufman, Y. J., Stark, R., and Rundquist, D. 2002. Novel algorithm for remote estimation of vegetation fraction. Remote Sens. Environ. 80:76–87.CrossRefGoogle Scholar
Goldshlager, N., Filin, S., and Abergel, S. 2009. A Linear Photogrammetric Method for Measuring Erosion in Agricultural Fields. Transactions of the American Society of Agriculture and Biological Engineering. 52:437–445.Google Scholar
Hague, T., Tillet, N. D., and Wheeler, H. 2006. Automated crop and weed monitoring in widely spaced cereals. Precis. Agric. 7:21–32.CrossRefGoogle Scholar
Hernández-Andrés, J., Lee, R. L. Jr., and Romero, J. 1999. Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities. Appl. Optics. 38:27.CrossRefGoogle ScholarPubMed
Kasdorf, W. E., ed. 2003. The Columbia Guide to Digital Publishing Columbia University Press, NewYork: 750 p.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize (Zea mays L.). Weed Res. 35:207–214.CrossRefGoogle Scholar
Kraus, K., ed. 1993. Photogrammetry. 4th ed, Volume 1. Bonn, Germany Dümmler.Google Scholar
Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop-weed interference: relations amongst weed density, relative time of weed emergence, relative leaf area, and yield loss. Weed Sci. 40:296–301.CrossRefGoogle Scholar
Laliberte, A. S., Rango, A., Herrick, J. E., Fredrickson, E. L., and Burkett, L. M. 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. J. Arid Environ. 69:1–14.CrossRefGoogle Scholar
Lemieux, C., Vallee, L., and Vanasse, A. 2003. Predicting yield loss in maize fields and developing decision support for post-emergence herbicide applications. Weed Res. 43:323–332.CrossRefGoogle Scholar
Lindquist, J. L. and Knezevic, S. Z. 2001. Quantifying crop yield response to weed populations: applications and limitations. Pages 205–232 in Peterson, R. K. D., and Higley, L. G., eds. Biotic Stress. Boca Raton, FL CRC Press.Google Scholar
Lucchese, L. and Mitra, S., K. 2000. Filtering color images in the xyY color space, In Proceedings of IEEE International Conference on Image Processing (ICIP '00), vol. 3, Pp. 500–503 Vancouver, BC, Canada.Google Scholar
Malacara, D., ed. 2002. Color Vision and Colorimetry, Theory and Applications. Bellingham WA SPIE Press. 164 p.Google Scholar
Mao, W., Wang, Y., and Wang, Y. 2003. Real-Time Detection of Between-Row Weeds Using Machine Vision. ASAE paper number 031004. St. Joseph, MI The Society for Agricultural, Food, and Biological Systems.Google Scholar
Marchant, J. A., Tillett, R. D., and Brivot, R. 1998. Real-time segmentation of plants and weeds. Real-Time Imaging. 4:243–253.CrossRefGoogle Scholar
Meyer, G. E. and Neto, J. C. 2008. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63:282–293.CrossRefGoogle Scholar
Murphy, C., Lemerle, D., Jones, R., and Harden, S. 2002. Use of density to predict crop yield loss between variable seasons. Weed Res. 42:377–384.CrossRefGoogle Scholar
Ngouajio, M., Lemieux, C., Fortier, J. J., Careau, D., and Leroux, G. D. 1998. Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technol. 12:446–453.CrossRefGoogle Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999a. Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Sci. 47:297–304.CrossRefGoogle Scholar
Ngouajio, M., Leroux, G. D., and Lemieux, C. 1999b. Influence of images recording height and crop growth stage on leaf cover estimates and their performance in yield prediction models. Crop Prot. 18:501–508.CrossRefGoogle Scholar
Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man. Cyber. 9:62–66.CrossRefGoogle Scholar
Perez, A. J., Lopez, F., Benlloch, J. V., and Christensen, S. 2000. Color and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric. 25:197–212.CrossRefGoogle Scholar
Pratt, W. K., ed. 2007. Digital Image Processing. 4th ed. Los Altos, CA John Wiley & Sons. 782 p.CrossRefGoogle Scholar
Rasmussen, J., Norremark, M., and Bibby, B. M. 2007. Assessment of leaf cover and crop soil cover in weed harrowing research using digital images. Weed Res. 47:299–310.CrossRefGoogle Scholar
Reid, J. and Searcy, S. 1987. Vision-based guidance of an agricultural tractor. IEEE Control Systems Magazine. 7:39–43.CrossRefGoogle Scholar
Ribeiro, A., Fernández-Quintanilla, C., Barroso, J., and García-Alegre, M. C. 2005. Development of an image analysis system for estimation of weed. Pages 169–174 in Proceedings of the 5th European Conference on Precision Agriculture (5ECPA).Google Scholar
Ruiz-Ruiz, G., Gómez-Gil, J., and Navas-Gracia, L. M. 2009. Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Comput. Electron. Agric. 68(1):88–96.CrossRefGoogle Scholar
Shrestha, D. S., Steward, B. L., and Birrell, S. J. 2004. Video processing for early stage maize plant detection. Biosys. Eng. 89:119–129.CrossRefGoogle Scholar
Tellaeche, A., BurgosArtizzu, X. P., Pajares, G., Ribeiro, A., and César, F. Q. 2008. A new vision-based approach to differential spraying in precision agriculture. Precis. Agric. 60:144–155.Google Scholar
Thorp, K. R. and Tian, L. F. 2004. A review on remote sensing of weeds in agriculture. Precis. Agric. 5:477–508.CrossRefGoogle Scholar
Tian, L. F. and Slaughter, D. C. 1998. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Comput. Electron. Agric. 21(3):153–168.CrossRefGoogle Scholar
Wang, G., McGiffen, M. E. Jr., and Ogbuchiekwe, E. J. 2008. Crop rotation effects on Cyperus rotundus and C. esculentus population dynamics in southern California vegetable production. Weed Res. 48:420–428.CrossRefGoogle Scholar
Webster, T. M. 2005. Patch expansion of purple nutsedge (Cyperus rotundus) and yellow nutsedge (Cyperus esculentus) with and without polyethylene mulch. Weed Sci. 53:839–845.CrossRefGoogle Scholar
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., and Mortensen, D. A. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE. 38(1):259–269.CrossRefGoogle Scholar