Optimal color space selection method for plant/soil segmentation in agriculture

Computers and Electronics in Agriculture

Volumn 122, Issue , 2016, Pages 124-132

  Hernandez Hernandez J L ^a   Garcia Mateos G ^b   Gonzalez Esquiva J M ^c   Escarabajal Henarejos, D ^c   Ruiz Canales, A ^d   Molina Martinez J M ^c  

^a UNIVERSIDAD AUTÓNOMA DE GUERRERO   (Mexico)

^b UNIVERSITY OF MURCIA   (Spain)

^c TECHNICAL UNIVERSITY OF CARTAGENA   (Spain)

^d MIGUEL HERNÁNDEZ UNIVERSITY   (Spain)

Author keywords

Color classification; Color spaces; Image processing in agriculture; Percentage of green cover

Indexed keywords

AGRICULTURE; CAMERAS; CLASSIFICATION (OF INFORMATION); COLOR CODES; COLOR IMAGE PROCESSING; CROPS; IMAGE PROCESSING; MOBILE DEVICES; PROBABILITY DENSITY FUNCTION;

AUTOMATIC CLASSIFICATION; COLOR CLASSIFICATION; COLOR SPACE; COLOR SPACE SELECTION; INTRINSIC PARAMETERS; NON-PARAMETRIC MODEL; PERCENTAGE OF GREEN COVER; PROBABILISTIC APPROACHES;

COLOR;

AGRICULTURAL ECOSYSTEM; COLOR; GROUND COVER; IMAGE PROCESSING; PROBABILITY; SEGMENTATION; SOFTWARE; SOIL COLOR;

EID: 84960343639     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2016.01.020     Document Type: Article

References (32)

1. Ahamed T., Tian L., Jiang Y.S., Zhao B., Liu H., Ting K.C. Tower remote-sensing system for monitoring energy crops; image acquisition and geometric corrections. Biosyst. Eng. 2012, 112(2):93-107.
2. Allen R.G., Pereira L.S. Estimating crop coefficients from fraction of ground cover and height. Irrig. Sci. 2009, 28:17-34.
3. Blasco J., Aleixos N., Roger J.M., Rabatel G., Molto E. Robotic weed control using machine vision. Biosyst. Eng. 2002, 83(2):149-157.
4. Campos I., Neale C.M., Calera A., Balbontín C., González-Piqueras J. Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.). Agric. Water Manag. 2010, 98:45-54.
5. Dobrzański B., Rybczyński R. Color change of apple as a result of storage, shelf-life, and bruising. Int. Agrophys. 2002, 16(4):261-268.
6. Escarabajal-Henarejos D., Molina-Martínez J.M., Fernández-Pacheco D.G., García-Mateos G. Methodology for obtaining prediction models of root depth of lettuce for its application in irrigation automation. Agric. Water Manage. 2015, 151:167-173.
7. Fernández-Pacheco D.G., Escarabajal D., Ruiz-Canales A., Conesa J., Molina-Martínez J.M. A digital image-processing-based method for determining the crop coefficient of lettuce crops in the southeast of Spain. Biosyst. Eng. 2014, 117:23-34.
8. García-Mateos G., Hernández-Hernández J.L., Escarabajal-Henarejos D., Jaen-Terrones S., Molina-Martínez J.M. Study and comparison of color models for automatic image analysis in irrigation management applications. Agric. Water Manage. 2015, 151:158-166.
9. Gonçalves J.M., Muga A.P., Horst M.G., Pereira L.S. Furrow irrigation design with multicriteria analysis. Biosyst. Eng. 2011, 109(4):266-275.
10. Hanson B.R., May D.M. Crop coefficients for drip-irrigated processing tomato. Agric. Water Manage. 2005, 81(3):381-399.
11. Hernández-Hernández, J.L., García-Mateos, G., González Esquiva, J.M., Escarabajal-Henarejos, D., Molina-Martínez, J.M., 2015. A software for automatic classification of plants and soil (CAPS), VIII Congreso Ibérico de Agroingeniería, Orihuela, Alicante, 1-3 June.
12. Jain A.K., Duin R.P.W., Mao J. Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22:4-37.
13. Kakumanu P., Makrogiannis S., Bourbakis N. A survey of skin-color modeling and detection methods. Pattern Recogn. 2007, 40(3):1106-1122.
14. Kodagali J.A., Balaji S. Computer vision and image analysis based techniques for automatic characterization of fruits - a review. Int. J. Comput. Appl. 2012, 50(6):6-12.
15. Kumar, P., Sengupta, K., Lee, A., 2002. A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system. The IEEE 5th International Conference on Intelligent Transportation Systems, 100-105.
16. Lin K., Chen J., Si H., Junhui W. A review on computer vision technologies applied in greenhouse plant stress detection. Adv. Image Graphics Technol. 2013, 363:192-200.
17. López-Urrea R., de Santa Martín., Olalla F., Montoro A., López-Fuster P. Single and dual crop coefficients and water requirements for onion (Allium cepa L.) under semiarid conditions. Agric. Water Manage. 2009, 96:1031-1036.
18. Lorente D., Aleixos N., Gómez-Sanchís J., Cubero S., García-Navarrete O.L., Blasco J. Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol. 2012, 5(4):1121-1142.
19. Luszczkiewicz-Piatek M. Which color space should be chosen for robust color image retrieval based on mixture modeling. Adv. Intell. Syst. Comput. 2014, 233:55-64.
20. McCarthy C.L., Cheryl N.H., Hancock S.R. Applied machine vision of plants - a review with implications for field deployment in automated farming operations. Intel. Serv. Robot. 2010, 3(4):209-217.
21. Meyer G.E., Mehta T., Kocher M.F., Mortensen D., Samal A. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. ASAE 1998, 41(4):1189-1197. A.
22. Ohta Y., Kanade T., Sakai T. Color information for region segmentation. Comput. Graphics Image Process. 1980, 13(3):222-241.
23. Ortuño M.F., Conejero W., Moreno F., Moriana A., Intrigliolo D.S., Biel C., Torrecillas A. Could trunk diameter sensors be used in woody crops for irrigation scheduling? A review of current knowledge and future perspectives. Agric. Water Manage. 2010, 97(1):1-11.
24. Richardson M.D., Karcher D.E., Purcell L.C. Quantifying turfgrass cover using digital image analysis. Crop Sci. 2001, 41(6):1884-1888.
25. Shih P., Liu C. Comparative assessment of content-based face image retrieval in different color spaces. Int. J. Pattern Recognit. Artif. Intell. 2005, 19(7):873-893.
26. Shiraishi M., Sumiya H. Plant identification from leaves using quasi-sensor fusion. J. Manuf. Sci. Eng. Trans. ASME 1996, 118(3):382-387.
27. Slaughter D.C., Giles D.K., Downey D. Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 2008, 61:63-78.
28. Steward B.L., Tian L.F., Nettleton D., Tang L. Reduced-dimension clustering for vegetation segmentation. Trans. ASAE 2004, 47(2):609-616.
29. Story D., Kacira M., Kubota C., Akoglu A., An L.L. Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Comput. Electron. Agric. 2010, 74(2):238-243.
30. Terrillon, J.C., Akamatsu, S., 2000. Comparative performance of different chrominance spaces for color segmentation and detection of human faces in complex scene images. International Conf. on Face and Gesture Recognition, 54-61.
31. Xu, X.G., Wang, J.H., Li, C.J., Song, X.U., Huang, W.J., 2010. Estimating growth height of winter wheat with remote sensing. In: Proceedings of the SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII.
32. Woebbecke D.M., Meyer G.E., Von Bargen K., Mortensen D.A. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38(1):259-269.