Phenology monitoring of agricultural plants using texture analysis

2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015

Volumn , Issue , 2015, Pages 338-342

  Yalcin, Hulya ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

agriculture; glcm features; HOG features; image processing; plant phenology; textural analysis

Indexed keywords

AGRICULTURAL MACHINERY; ARTIFICIAL INTELLIGENCE; BIOLOGY; CAMERAS; IMAGE PROCESSING; LEARNING SYSTEMS; POPULATION STATISTICS; VEGETATION;

ENVIRONMENTAL CONDITIONS; GLCM FEATURE; GRAY LEVEL CO OCCURRENCE MATRIX(GLCM); HISTOGRAM OF ORIENTED GRADIENTS (HOG); HOG FEATURES; MACHINE LEARNING TECHNIQUES; PLANT PHENOLOGY; TEXTURAL ANALYSIS;

AGRICULTURE;

EID: 84960398901     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/Agro-Geoinformatics.2015.7248114     Document Type: Conference Paper

References (23)

1. Broich, M. , Huete, A, "A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications," Environmental Modelling & Software, 64: 191-204, 2015.
2. Richardson, A, Keenan, T. , Migliavacca, M. , Ryu, Y. , Sonnentag, 0., Toomey, M. , "Climate change, phenology, and phenological control of vegetation feedbacks to the climate system," Agricultural and Forest Meteorology, 169:156-173, 2013.
3. Cleland, E. E. ; Chuine, 1.; Menzel, A. ; Mooney, H. A. ; Schwartz, M.D. Shifting plant phenology in response to global change. Trends Eco!. Evo!., 22, 357-365, 2007.
4. Iv its, E. , Cherlet, M. , Mehl, W. , Sommer, S. , "Ecosystem functional units characterized by satellite observed phenology and productivity graDients: A case study for Europe," Ecological Indicators, 27:17-28, 2013.
5. Eerens, H. , Haesen, D. , Rembold, F. , Urbano, F. , Tote, C., Bydekerke, L. , "Image time series processing for agriculture monitoring," Environmental Modelling & Software, 53:154-162, 2014.
6. Srbinovska, M., Gavrovski, C., Dimcev, V. , Krkoleva, A, Borozan, V. , "Environmental parameters monitoring in precision agriculture using wireless sensor networks," Journal of Cleaner Production, 88:297-307, 2015.
7. Bagis, S. , Ustundag, S., Ozelkan, E. , "An adaptive spatiotemporal agricultural cropland temperature prediction system based on ground and satellite measurements," International Conference on Agro Geoinformatics, pages: 1-6, 2012.
8. Xiao, X. , Li, J. , He, H. , Fei, T. , Zhou, Y. , "Framework for phenology analyses from observation data of digital images and meteorological data," IEEE International Conference on Computer Science and Automation Engineering, pages: 373-377, 2012.
9. Zhou, X. , Luo, x., "Advances in Non-Destructive Measurement and 3D Visualization Methods for Plant Root Based on Machine Vision,"lnternational Conference on Biomedical Engineering and Informatics, pp: 1-5, 2009.
10. Bagis, S. ; Ustundag, B. B. , "Image based automated phenological stage detection of cereal plants," International Conference on Agro Geoinformatics (Agro-Geoinfonnatics), pages: 1-6, 2012.
11. Burgos-Artizzua, X. , Ribeiroa, A, Guijarrob, M., Pajaresb, G. , "Realtime image processing for crop/weed discrimination in maize fields," Computers and Electronics in Agriculture, 75(2): 337-346, 2011.
12. Bianconi F. , Fernandez A, Gonzalez A. , Caride D. , Calvino A. , "Rotation-invariant colour texture classification through multilayer CCR," Pattern Recognition Letters 30: 765-773, 2009.
13. Huazhong Shu, Limin Luo, Jean Louis Coatrieux, "Moment-based Approaches in Image. Part I: basic features", IEEE Trans. Engineering in Medicine and Biology Magazine, 26(5):70-74, 2007.
14. Javidan.R, Masnadi-Shirazi.M. A, Azimifar. Z, and Sadreddini. M.H, "A Comparative Study between Wavelet and Contourlet Transform Features for Textural Image Classification," ICTT A, pp. 1-5, 2008.
15. Liao.S, Law. M.W. K, Chung. A.C. S, "Dominant Local Binary Patterns for Texture Classification," IEEE TIP, 18(5): 1107-1118, 2009.
16. Long.Z, and Younan. N.H, "Contourlet Spectral Histogram for Texture Classification," IEEE SSIAI, pp. 31-35, 2006.
17. Othmen. M. B, Sayadi.M, and Fnaiech.F, "Interest of the MultiResolution Analysis Based on the Co-occurrence Matrix for Texture Classification," IEEE MELECON, pp. 852-856, 2008.
18. Qiao. Y, and Sun.S, 'Texture Classification Using Wavelet Frame Representation Based Feature," IEEE ICEIS, pp. 1-4, 2006.
19. Salem. Y.B, and Nasri.S, "Texture Classification of Woven Fabric Based on a GLCM Method and using Multiclass Support Vector Machine," SSD, pp. 1-8, 2009.
20. Gunes, E. O. , Aygun, S., Kirci, M. , Kalateh, A, Cakir, Y. , "Determination of the varieties and characteristics of wheat seeds grown in Turkey using image processing techniques," International Conference on Agro-geoinformatics, pages: I-4, 2014.
21. Baraldi, A, Parmiggiani, F. , "An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters," IEEE Transactions on Geoscience and Remote Sensing, 33(2):293-304, 1995.
22. Pesaresi, M. , Gerhardinger, A, Kayitakire, F. , "A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(3): 180-192, 2008.
23. Dalal, N. , Triggs, B. , "Histograms of oriented graDients for human detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1:886-893, 2005.