Automatic segmentation of relevant textures in agricultural images

Computers and Electronics in Agriculture

Volumn 75, Issue 1, 2011, Pages 75-83

  Guijarro, M ^a   Pajares, G ^a   Riomoros, I ^a   Herrera, P J ^a   Burgos Artizzu, X P ^b   Ribeiro, A ^b  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^b Consejo Superior de Investigaciones Científicas   (Spain)

Author keywords

Automatic tasks in agriculture; Image segmentation; Machine vision; Texture identification in crops

Indexed keywords

AUTOMATIC SEGMENTATIONS; AUTOMATIC TASKS IN AGRICULTURE; AUTONOMOUS ROBOT NAVIGATION; CHEMICAL PRODUCTS; GREEN PLANTS; MACHINE VISION; MECHANICAL MANIPULATION; NEW APPROACHES; PLANTS GROWING; PROCESSING PROCEDURES; SITE-SPECIFIC; SPECIFIC AREAS; SUPERVISED FUZZY CLUSTERING; THRESHOLDING;

AGRICULTURAL MACHINERY; COMPUTER VISION; CROPS; DIGITAL IMAGE STORAGE; IMAGING SYSTEMS; OPTICAL DATA PROCESSING; ROBOTS; SOILS; TEXTURES;

IMAGE SEGMENTATION;

AGRICULTURAL SOIL; BARLEY; HUMIDITY; IMAGE PROCESSING; MAIZE; TEXTURE;

HORDEUM; VIRIDIPLANTAE; ZEA MAYS;

EID: 78650512819     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2010.09.013     Document Type: Article

References (34)

1. Balasko B., Abonyi J., Feil B. Fuzzy Clustering and Data Analysis Toolbox for Use with Matlab 2008, Veszprem University, Hungary, available on-line from the web site: http://www.fmt.vein.hu/softcomp/fclusttoolbox/FuzzyClústeringToolbox.pdf.
2. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms 1981, Kluwer, Plenum Press, New York.
3. Burgos-Artizzu X.P., Ribeiro A., Tellaeche A., Pajares G., Fernández-Quintanilla C. Improving weed pressure assessment using digital images from an experience-based reasoning approach. Computers and Electronics in Agriculture 2009, 65:176-185.
4. Davies G., Casady W., Massey R. Precision agriculture: an introduction. Water Quality Focus Guide 1998, WQ450, available on-line:. http://extension.missouri.edu/explorepdf/envqual/wq0450.pdf.
5. Duda R.O., Hart P.E., Stork D.S. Pattern Classification 2000, Wiley.
6. Gebhardt S., Schellberg J., Lock R., Kaühbauch W.A. Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Precision Agriculture 2006, 7(3):165-178.
7. Gebhardt S., Kaühbauch W.A. A new algorithm for automatic Rumex obtusifolius detection in digital image using colour and texture features and the influence of image resolution. Precision Agriculture 2007, 8(1):1-13.
8. Gée Ch., Bossu J., Jones G., Truchetet F. Crop/weed discrimination in perspective agronomic images. Computers and Electronics in Agriculture 2008, 60:49-59.
9. Gonzalez R.C., Woods R.E. Digital Image Processing 2008, Prentice-Hall, Upper Saddle River, NY.
10. Gonzalez R.C., Woods R.E., Eddins S.L. Digital Image Processing using Matlab 2004, Prentice-Hall, Upper Saddle River, NY.
11. Hague T., Tillet N., Wheeler H. Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture 2006, 1(1):95-113.
12. Kataoka T., et al. Crop growth estimation system using machine vision. The 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics 2003.
13. Kirk K., Andersen H.J., Thomsen A.G., Jørgensen J.R. Estimation of leaf area index in ceral crops using red-green images. Biosystems Engineering 2009, 104:308-317.
14. Ling P.P., Ruzhitsky V.N. Machine vision techniques for measuring the canopy of tomato seedling. Journal Agricultural Engineering Research 1996, 65(2):85-95.
15. Luscier J.D., Thompson W.L., Wilson J.M., Gorham B.E., Dragut L.D. Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots. Frontiers in Ecology and the Environment 2006, 4(8):408-413.
16. Meyer G.E., Hindman T.W., Lakshmi K. Machine Vision Detection Parameters for Plant Species Identification 1998, SPIE, Bellingham, WA.
17. Meyer G.E., Camargo-Neto J., Jones D.D., Hindman T.W. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and Electronics in Agriculture 2004, 42:161-180.
18. Meyer G.E., Camargo-Neto J. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture 2008, 63:282-293.
19. Neto J.C. A Combined Statistical-Soft Computing Approach for Classification and Mapping Weed Species in Minimum Tillage Systems 2004, University of Nebraska, Lincoln, NE.
20. Onyango C.M., Marchant J.A. Segmentation of row crop plants from weeds using colour and morphology. Computers and Electronics in Agriculture 2003, 39:141-155.
21. Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on System Man and Cybernetics 1979, 9:62-66.
22. Pajares G., Ruz J.J., Cruz J.M. Performance analysis Performance Analysis of Homomorphic Systems for Image Change Detection. Pattern Recognition and Image Analysis 2005, vol. 3522:563-570. Springer-Verlag, Berlín.
23. Reid J.F., Searcy S.W. Vision-based guidance of an agricultural tractor. IEEE Control Systems 1987, 7(12):39-43.
24. Ribeiro A., Fernández-Quintanilla C., Barroso J., García-Alegre M.C. Development of an image analysis system for estimation of weed. Proceedings of the 5th European Conference On Precision Agriculture (5ECPA) 2005, 169-174.
25. Ruiz-Ruiz G., Gómez-Gil J., Navas-Gracia L.M. Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Computers and Electronics in Agriculture 2009, 68:88-96.
26. Shrestha D.S., Steward B.L., Birrell S.J. Video processing for early stage maize plant detection. Biosystems in Engineering 2004, 89(2):119-129.
27. Tellaeche A., Burgos-Artizzu X., Pajares G., Ribeiro A., Fernández-Quintanilla C. A new vision-based approach to differential spraying in precision agriculture. Computers and Electronics in Agriculture 2008, 60(2):144-155.
28. Tellaeche A., Burgos-Artizzu X.P., Pajares G., Ribeiro A. A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition 2008, 41:521-530.
29. The Mathworks 2010. http://www.mathworks.com/.
30. Tian, Slaughter Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture 1998, 21:153-168.
31. Woebbecke D.M., Meyer G.E., von Bargen K., Mortensen D.A. Shape features for identifying young weeds using image analysis. Transactions on American Society of Agricultural Engineering 1995, 38(1):271-281.
32. Zheng L., Zhang J., y Wang Q. Mean-shift-based color segmentation of images containing green vegetation. Computers and Electronics in Agriculture 2009, 65:93-98.
33. Zheng L., Shi D., Zhang J. Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminate. Pattern Recognition Letters 2010, 10.1016/j.patrec.2010.01.016.
34. Zimmermann H.J. Fuzzy Set Theory and its Applications 1991, Kluwer Academic Publishers, Norwell.