Verification of color vegetation indices for automated crop imaging applications

Computers and Electronics in Agriculture

Volumn 63, Issue 2, 2008, Pages 282-293

  Meyer, George E ^a   Neto, João Camargo ^b  

^a UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^b EMBRAPA CERRADOS   (Brazil)

Author keywords

Color images; Machine vision; Plant; Residue; Soil; Vegetation index

Indexed keywords

AGRICULTURAL ENGINEERING; ARID REGIONS; BANDPASS FILTERS; BIOMASS; COMPUTER GRAPHICS; COMPUTER VISION; CROPS; ELECTRONIC DOCUMENT IDENTIFICATION SYSTEMS; FORESTRY; GREENHOUSES; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE SEGMENTATION; IMAGING SYSTEMS; OPTICAL DATA PROCESSING; PEST CONTROL; PLANT MANAGEMENT; Q FACTOR MEASUREMENT; REMOTE SENSING; RENEWABLE ENERGY RESOURCES; SEPARATION; SOILS; SPACE OPTICS; WEED CONTROL;

APPLIED (CO); BARE SOILS; CROP MANAGEMENTS; DIGITAL COLORS; ELSEVIER (CO); FIELD IMAGING; IMAGE PROCESSING OPERATIONS; IMAGING APPLICATIONS; MACHINE VISION; PLANT BIOMASS; PLANT SPECIES; QUALITY FACTOR (Q FACTOR); REGION -OF-INTEREST (ROI); REGIONS OF INTEREST (ROI); SINGLE PLANTS; SOYBEAN PLANTS; THRESHOLD VALUE(T); VEGETATION INDEX (VI); VEGETATIVE INDEX (VI); WHEAT STRAWS;

VEGETATION;

AUTOMATION; COLOR; COMPARATIVE STUDY; FARMING SYSTEM; IMAGING METHOD; PLANT RESIDUE; PRECISION AGRICULTURE; REMOTE SENSING; VEGETATION INDEX;

ALGORITHMS; BIOMASS; COLOR; COMPUTER GRAPHICS; ECOLOGY; FARM CROPS; FILTERS; GREENHOUSE EFFECT; IMAGE ANALYSIS; PEST CONTROL; PLANTS; REMOTE SENSING; RENEWABLE RESOURCES; SOIL; WEED CONTROL;

GLYCINE MAX; TRITICUM AESTIVUM;

EID: 47049087271     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2008.03.009     Document Type: Article

References (31)

1. Bhanu B., and Jones T.L. Image understanding research for automatic target recognition. Aerospace and Electronic Systems (IEEE) 8 10 (1993) 15-23
2. Camargo Neto, J., 2004. A Combined Statistical-Soft Computing Approach for Classification and Mapping Weed Species in Minimum Tillage Systems. Unpublished PhD Dissertation, University of Nebraska, Lincoln, NE.
3. Camargo Neto J., and Meyer G.E. Crop species identification using machine vision of computer extracted individual leaves. In: Chen Y.R., Meyer G.E., and Tu S. (Eds). Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality vol. 5996 (2005), Optical Engineering Press, Bellingham WA 64-74 (ISBN: 9780819460202)
4. Camargo Neto, Meyer G.E., and Jones D.D. Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm. Computers and Electronics in Agriculture 51 (2006) 65-85 (Elsevier)
5. Camargo Neto, Meyer G.E., Jones D.D., and Samal A.K. Plant species identification using elliptic Fourier analysis. Computers and Electronics in Agriculture 50 (2006) 121-134 (Elsevier)
6. Campbell J.B. Introduction to Remote Sensing (1996), Guilford Press, New York
7. El-Faki M.S., Zhang N., and Peterson D.E. Factors affecting color-based weed detection. Transactions of the ASAE 43 (2000) 1001-1009
8. El-Faki M.S., Zhang N., and Peterson D.E. Weed detection using color machine vision. Transactions of the ASAE 43 (2000) 1969-1978
9. Gebhardt S., Schellberg J., Lock R., and Kuhbauch W. Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Precision Agriculture 7 (2006) 165-178 (Springer)
10. Giltelson A.A., Kaufman Y.J., Stark R., and Rundquist D. Novel algorithm for remote estimation of vegetation fraction. Remote Sensing of Environment 80 (2002) 76-87 (Elsevier)
11. Hague T., Tillet N.D., and Wheeler H. Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture 7 (2006) 21-32 (Springer)
12. Hindman, T.W., 2001. A fuzzy logic approach for plant image segmentation and species identification in color images. Unpublished PhD Dissertation, University of Nebraska, Lincoln.
13. Hunt E.R., Cavigelli M., Daughtry C.T., McMurtrey J., and Walthall S.L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass. Precision Agriculture 6 4 (2005) 359-378 (Springer)
14. Lamm R.D., Slaughter D.C., and Giles D.K. Precision weed control for cotton. Transactions of the ASAE 45 (2002) 231-238
15. Lindquist J.L., Dieleman J.A., Mortensen D.A., Johnson G.A., and Pester-Wyse D.Y. Economic importance of managing spatially heterogeneous weed population. Weed Technology 12 (1998) 7-13
16. Mao, W., Wang, Y., Wang, Y., 2003. Real-time detection of between-row weeds using machine vision. ASAE paper number 031004. The Society for Agricultural, Food, and Biological Systems, St. Joseph, MI.
17. Marchant J.A., Andersen H.J., and Onyango C.M. Evaluation using different waveband combinations. Computers and Electronics in Agriculture 32 (2001) 101-117 (Elsevier)
18. Meyer G.E., Hindman T.W., and Lakshmi K. Machine vision detection parameters for plant species identification. In: Meyer G.E., and DeShazer J.A. (Eds). Precision Agriculture and Biological Quality, Proceedings of SPIE. vol. 3543, Bellingham, WA (1998) 327-335
19. Meyer G.E., Mehta T., Kocher M.F., Mortensen D.A., and Samal A. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASAE 41 (1998) 1189-1197
20. Meyer G.E., Camargo-Neto J., Jones D.D., and Hindman T.W. Intensified fuzzy cluster for determining plant, soil, and residue regions of interest from color images. Electronics in Agriculture 43 (2004) 161-180 (Elsevier)
21. Meyer G.E., Hindman T.W., Jones D.D., and Mortensen D.A. Digital camera operation and fuzzy logic classification of plant, soil, and residue color images. Applied Engineering in Agriculture 20 4 (2004) 519-529
22. Mortensen D.A., Johnson G.A., and Yang C.C. Weed distribution in agricultural fields. In Soil Specific Crop Management. Agronomy Society of America (1992) 113-124
23. Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics 9 (1979) 62-66
24. Perez A.J., Lopez F., Benlloch J.V., and Christensen S. Color and shape analysis techniques for weed detection in cereal fields. Computer and Electronics in Agriculture 25 (2000) 197-212 (Elsevier)
25. Tillet N.D., Hangue T., and Miles S.J. A field assessment of a potential method for weed and crop mapping on the basis of crop planting geometry. Computer and Electronics in Agriculture 32 (2001) 229-246 (Elsevier)
26. Wallinga J., Groeneveld R.M.W., and Lotz L.A.P. Measures that describe weed spatial patterns at different levels of resolution and their application to patch spraying of weeds. Weed Research 38 (1998) 351-359
27. Wang N., Zhang N., Dowell F.E., Sun Y., and Peterson D.E. Design of an optical weed sensor using plant spectral characteristics. Transactions of the ASAE 44 (2001) 409-419
28. Woebbecke D.M., Meyer G.E., B.K. V., and Mortensen D.A. Color indices for weed identification under various soil, residue and lighting conditions. Transactions of the ASAE 38 (1995) 259-269
29. Woebbecke D.M., Meyer G.E., B.K. V., and Mortensen D.A. Shape features for identifying young weeds using image analysis. Transactions of the ASAE 38 (1995) 271-281
30. Yang C.C., Prasher S.O., Landry J., and Ramaswamy H.S. Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agriculture 4 (2003) 5-18
31. Zanin G., Berti A., and Riello L. Incorporation of weed spatial variability into the weed control decision process. Weed Research 38 (1998) 107-118