Robust methods for measurement of leaf-cover area and biomass from image data

Weed Science

Volumn 59, Issue 2, 2011, Pages 276-284

  Lati, Ran Nisim ^a   Filin, Sagi ^a   Eizenberg, Hanan ^b  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b VOLCANI CENTER   (Israel)

Author keywords

Biomass; homographic transformation; image processing; invariant image; leaf cover area; purple nutsedge

Indexed keywords

AUTOMATION; COLOR; DIGITAL IMAGE; DIGITAL PHOTOGRAMMETRY; ECOLOGICAL MODELING; GROWTH RATE; IMAGE PROCESSING; INTERSPECIFIC COMPETITION; LEAF AREA; PHYTOMASS; WEED;

CYPERUS ROTUNDUS;

EID: 79954518305     PISSN: 00431745     EISSN: None     Source Type: Journal    
DOI: 10.1614/WS-D-10-00054.1     Document Type: Article

References (44)

1. Alvarez, J. M., A. Lopez, and R. Baldrich. 2008. Illuminant-invariant model-based road segmentation. Pages 1175-1180 in Proceeding of IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.
2. Bosnic, A. C and C J. Swanton. 1997. Influence of barnyardgrass (Echino chloacrus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276-282. (Pubitemid 27218578)
3. Burgos-Artizzua, X. P., A. Ribeiroa, A. Tellaecheb, G. Pajaresc, and C Fernandez-Quintanillad. 2009. Improving weed pressure assessment using digital images from an experience-based reasoning approach. Comput. Electron. Agric. 65:176-185.
4. Campbell, J. B., ed. 1996. Introduction to Remote Sensing. New York: Guilford Press. 622 p.
5. Donald, W. W. 2006. Estimated corn yields using either weed cover or rated control after pre-emergence herbicides. Weed Sci. 54:373-379. (Pubitemid 44616956)
6. Donald, W. W., D. Archer, W. G. Johnson, and K. Nelson. 2004a. Zone herbicide application controls annual weeds and reduces residual herbicide use in corn. Weed Sci. 52:821-833. (Pubitemid 44272307)
7. Donald, W. W., W. G. Johnson, and K. A. Nelson. 2004b. In-row and between-row interference by corn (Zea mays) modifies annual weed control by preemergence residual herbicide. Weed Technol. 18:487-504.
8. Finlayson, G. D., M. S. Drew, and C. Lu. 2005. Invariant image improvement by sRGB colour space sharpening, in International Colour Association Congress. Granada: Spain.
9. Finlayson, G. D., S. D. Hordley, and M. S. Drew. 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.
10. Gee, C. H., J. Bossu, G. Jones, and F. Truchetet. 2008. Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric 60:49-59. (Pubitemid 350216921)
11. Gitelson, A. A., Y. J. Kaufman, R Stark, and D. Rundquist. 2002. Novel algorithm for remote estimation of vegetation fraction. Remote Sens. Environ. 80:76-87. (Pubitemid 34271203)
12. Goldshlager, N., S. Filin, and S. Abergel. 2009. A Linear Photogrammetric Method for Measuring Erosion in Agricultural Fields. Transactions of the American Society of Agriculture and Biological Engineering 52:437-445.
13. Hague, T., N. D. Tillet, and H. Wheeler. 2006. Automated crop and weed monitoring in widely spaced cereals. Precis. Agric 7:21-32. (Pubitemid 43376209)
14. Hernández-Andrés, J., R. L. Lee, Jr., and J. Romero. 1999. Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities. Appl. Optics 38:27.
15. Kasdorf, W. E., ed. 2003. The Columbia Guide to Digital Publishing Columbia University Press: NewYork. 750 p.
16. Knezevic, S. Z., S. F. Weise, and C. J. Swanton. 1995. Comparison ofempirical 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 (Pubitemid 26453756)
17. Kraus, K., ed. 1993. Photogrammetry. 4th ed, Volume 1. Bonn, Germany: Dümmler.
18. Kropff, M. J., S. E. Weaver, and M. A. Smits. 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.
19. Laliberte, A. S., A. Rango, J. E. Herrick, E. L. Fredrickson, and L. M. Burkett. 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. (Pubitemid 44838890)
20. Lemieux, C., L. Vallee, and A. Vanasse. 2003. Predicting yield loss in maize fields and developing decision support for post-emergence herbicide applications. Weed Res. 43:323-332. (Pubitemid 37231782)
21. Lindquist, J. L. and S. Z. Knezevic 2001. Quantifying crop yield response to weed populations: applications and limitations. Pages 205-232 in R.K.D. Peterson and L. G. Higley, eds. Biotic Stress. Boca Raton, FL: CRC Press.
22. Lucchese, L. and S. Mitra 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.
23. Malacara, D., ed 2002. Color Vision and Colorimetry, Theory and Applications. Bellingham WA: SPIE Press. 164 p.
24. Mao, W., Y. Wang, and Y. Wang. 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.
25. Marchant, J. A., R D. Tillett, and R Brivot. 1998. Real-time segmentation of plants and weeds. Real-Time Imaging 4:243-253.
26. Meyer, G. E. and J. C Neto. 2008. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric 63:282-293.
27. Murphy, C., D. Lemerle, R Jones, and S. Harden. 2002. Use of density to predict crop yield loss between variable seasons. Weed Res. 42:377-384. (Pubitemid 35241125)
28. Ngouajio, M., C Lemieux, J. J. Fortier, D. Careau, and G. D. Leroux. 1998. Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technol. 12:446-453. (Pubitemid 28436972)
29. Ngouajio, M., C Lemieux, and G. D. Leroux. 1999a. Prediction of corn (Zea mays) yield loss from early observations of the relative leafarea and the relative leaf cover of weeds. Weed Sci. 47:297-304. (Pubitemid 29432454)
30. Ngouajio, M., G. D. Leroux, and C Lemieux. 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. (Pubitemid 29492372)
31. Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man. Cyber. 9:62-66. (Pubitemid 9413341)
32. Perez, A. J., F. Lopez, J. V. Benlloch, and S. Christensen. 2000. Color and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric 25:197-212. (Pubitemid 30165765)
33. Pratt, W. K., ed 2007. Digital Image Processing. 4th ed. Los Altos, CA: John Wiley & Sons. 782 p.
34. Rasmussen, J., M. Norremark, and B. M. Bibby. 2007. Assessment of leaf cover and crop soil cover in weed harrowing research using digital images. Weed Res. 47:299-310. (Pubitemid 47052245)
35. Reid, J. and S. Searcy. 1987. Vision-based guidance of an agricultural tractor. IEEE Control Systems Magazine 7:39-43.
36. Ribeiro, A., C Fernández-Quintanilla, J. Barroso, and M. C García-Alegre. 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).
37. Ruiz-Ruiz, G., J. Gómez-Gil, and L. M. Navas-Gracia. 2009. Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Comput. Electron. Agric 68(1):88-96.
38. Shrestha, D. S., B. L. Steward, and S. J. Birrell. 2004. Video processing for early stage maize plant detection. Biosys. Eng. 89:119-129. (Pubitemid 39444293)
39. Tellaeche, A., X. P. BurgosArtizzu, G. Pajares, A. Ribeiro, and F. Q. César. 2008. A new vision-based approach to differential spraying in precision agriculture. Precis. Agric 60:144-155.
40. Thorp, K. R and L. F. Tian. 2004. A review on remote sensing of weeds in agriculture. Precis. Agric. 5:477-508. (Pubitemid 39459656)
41. Tian, L. F. and D. C Slaughter. 1998. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Comput. Electron. Agric 21(3):153-168. (Pubitemid 29060113)
42. Wang, G., M. E. McGiffen, Jr., and E. J. Ogbuchiekwe. 2008. Crop rotation effects on Cyperus rotundus and C esculentus population dynamics in southern California vegetable production. Weed Res. 48:420-428.
43. 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. (Pubitemid 44298093)
44. Woebbecke, D. M., G. E. Meyer, K. Von Bargen, and D. A. Mortensen. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38(1):259-269.