1 |
Burgos-Artizzu, X.P., Ribeiro, A., Guijarro, M. and Pajares, G. 2011. Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture 75(2): 337-346.
DOI
|
2 |
CIRIA and USACE. 2013. The International Levee Handbook. London.
|
3 |
Hague, T., Tillet, N. and Wheeler, H. 2006. Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture 1(1): 95-113.
|
4 |
Hamuda, E., Glavin, M., and Jones, E. 2016. A survey of image processing techniques for plant extraction and segmentation in the field: Computers and Electronics in Agriculture 125: 184-199.
DOI
|
5 |
Kataoka, T., Kaneko, T., Okamoto, H. and Hata, S. 2003. Crop growth estimation system using machine vision. Proceeding of 2003 IEEE/ASME International Conference. Advanced Intelligent Mechatronics (AIM 2003) 2: 1079-1083.
|
6 |
Kraus, K. and Pfeifer, N. 1998. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 53: 193-203.
DOI
|
7 |
Meng, X., Currit, N. and Zhao, K. 2010. Ground filtering algorithms for airborne LiDAR Data: a review of critical issues. Remote Sensing 2: 833-860.
DOI
|
8 |
Tian, L.F. and Slaughter, D.C. 1998. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture 21: 153-168.
DOI
|
9 |
Meyer, G.E., Camargo Neto, J., Jones, D.D., and Hindman, T.W. 2004. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images: Computers and Electronics in Agriculture 42(3): 161-180.
DOI
|
10 |
Planetary Habitability Laboratory (PHL). Visible vegetation index (VVI). Available online: https://phl.upr.edu/projects/visible-vegetation-index-vvi (accessed on 2 Dec. 2021).
|
11 |
Yilmaz, V., Konakoglu, B., Serifoglu, C., Gungor, O. and Gokalp, E. 2016. Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos. Geocarto International 33(3): 310-320.
DOI
|
12 |
Montealegre, A.L., Lamelas, M.T. and de la Riva, J. 2015. A comparison of open-source LiDAR filtering algorithms in a mediterranean forest environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8: 4072-4085, doi:10.1109/JSTARS.2015.2436974.
DOI
|
13 |
Ribeiro, A., Fernandez-Quintanilla, C., Barroso, J. and Garcia-Alegre, M.C. 2005. Development of an image analysis system for estimation of weed. Proceedings of the 5th European Conference on Precision Agriculture (5ECPA): 169-174.
|
14 |
Axelsson, P. 1999. Processing of laser scanner data-algorithms and applications. Journal of Photogrammetry and Remote Sensing 54: 138-147, doi:10.1016/S0924-2716(99)00008-8.
DOI
|
15 |
Axelsson, P. 2000. DEM generation from laser scanner data using adaptive TIN models. International Archives of the Photogrammetry, Remote Sensing and Spatial Information 33: 110-117.
|
16 |
Guerrero, J.M., Pajares, G., Montalvo, M., Romeo, J. and Guijarro, M. 2012. Support vector machines for crop/weeds identification in maize fields. Expert Systems with Applications 39: 11149-11155.
DOI
|
17 |
Hunt, E.R., Cavigelli, M., Daughtry, C.S.T., McMurtrey, J.E. and Walthall, C.L. 2005. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture 6: 359-378.
DOI
|
18 |
Lamm, R.D., Slaughter, D.C. and Giles, D.K. 2002. Precision weed control for cotton. Transactions of the American Society of Agricultural Engineers 45: 231-238.
|
19 |
Meyer, G.E., Hindman, T.W. and Lakshmi, K. 1999. Machine vision detection parameters for plant species identification. In: Meyer, G.E., DeShazer, J.A. (Eds.), Precision Agriculture and Biological Quality, Proceedings of SPIE 3543: 327-335.
|
20 |
Sithole, G. and Vosselman, G. 2004. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. Journal of Photogrammetry and Remote Sensing 59: 85-101.
DOI
|
21 |
Woebbecke, D., Meyer, G., VonBargen, K. and Mortensen, D. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the American Society of Agricultural Engineers 38(1): 271-281.
DOI
|