1. Ahmed, F., H. A. Al-Mamun, A. S. M. Hossain Bari, E. Hossain, and P. Kwan. 2012. Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection 40: 90-104.
2. Arribas, J. I., G. V. Sanchez-Ferrero, G. Ruiz-Ruiz, and J. Gomez-Gil. 2011. Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture 78 (1): 9-18.
3. Ballard, D. H. 1981. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition 13 (2): 111-122.
4. Blasco, J., N. Aleixos, J. Roger, E. Rabatel, and E. Molto. 2002. Robotic weed control using machine vision. Biosystems Engineering 83 (2): 149-157.
5. Cooke, D. A., and R. K. Scott. 1993. The Sugar Beet Crop. Chapman and Hall, Publishers. pp: 675.
6. Cussans, G. W. 1986. The potential for integrated weed management (IWM) control. 49th winter congress. International Institute for Sugar Beet Research 253-262.
7. Gee, C., J. Bossu, G. Jones, and F. Truchetet. 2008. Crop/weed Discrimination in Perspective Agronomic Images. Computers and Electronics in Agriculture 60 (1):49-59.
8. Ghadiri, H. 1996. Concept and application of critical period of weed control. Collections of full papers of 4th Iranian crop production and breeding congress Isfahan 257-265.
9. Gillott, I. 2001. Critical herbicide uses in minor crops- an agronomist’s view. Proceedings of the BCPC Conference-Weeds, Brighton, UK, 799-802.
10. Hakimi, M., and J. Gohari. 1993. Determination of the most suitable row distance in sugar beet cultivation. Publication of Iran sugarbeet seed institute.
11. Hemming, J., and T. Rath. 2001. Computer vision-based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research 78 (3): 233-243.
12. Jafari, A. 2005. Developing a Suitable Algorithm for Weeds Segmentation from Sugar Beet Crop Using Machine Vision and Artificial Neural Networks. PhD Thesis, Department of Agricultural Machinery, Faculty of Agriculture, Tehran University.
13. Jafari, A., S. S. Mohtasebi, H. E. Jahromi, and M. Omid. 2006. Weed detection in sugar beet fields using machine vision. International Journal of Agriculture and Biology 8 (5): 602-605.
14. Jahadakbar, M. R., R. Tabatabai, and H. R. Ebrahimian. 2004. Critical period of weed competition with sugar beet in Kabotarabad-Esfahan. Journal of Sugar Beet 20 (1): 73-92.
15. Kavdır, I. 2004. Discrimination of sunflower, weed and soil by artificial neural networks. Computers and Electronics in Agriculture 44 (2): 153-160.
16. Kaya, R., and S. Buzluk. 2006. Integrated weed control in sugar beet through combinations of tractor hoeing and reduced dosage of herbicide mixture. Turkish Journal of Agriculture and Forestry 30: 137-144.
17. Kolivand, M. 1995. Study of sugar beet growth pattern in Kermanshah. Journal of Sugarbeet 11 (1): 1-19.
18. Leemans, V., and M. F. Destain. 2006. Application of the Hough Transform for Seed Row Localization using Machine Vision. Biosystems Engineering 94 (3): 325-336.
19. Morishita, D. W., and M. J. Wille. 2001. Broadleaf weed control in sugar beet with soilapplied and sequential post emergence herbicides compared to micro herbicide rates. Available from: www.uidaho.edu/sugar beet/weed/00-12.htm.
20. Moshashai, K., M. Almasi, S. Minaei, and A. M. Borghei. 2008. Identification of sugarcane nodes using image processing and machine vision technology. International Journal of Agricultural Researches 3: 357-364.
21. Perez, A. J., F. Lopez, J. V. Benlloch, and S. Christensen. 2000. Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture 25: 197-212.
22. Shahbazi, H. A., and M. Abdollahian-Noghabi. 2000. Critical period of weed competition in sugar beet in Mashhad, 16 (1): 58-74.
23. Shapiro, L. G., and G. C. Stockman. 2001. Computer Vision, Prentice-Hall Inc., Upper. Saddle River, New Jersey, pp. 41.
24. Sogaard, H. T. 2005. Weed Classification by Active Shape Models. Biosystems Engineering 91 (3): 271-281.
25. Sonka, M., V. Hlavac, and R. Boyle. 1993. Image Processing, Analysis, and Machine Vision. Brooks/Cole Publishing Company.
26. Tellaeche, A., X. P. BurgosArtizzu, G. Pajares, A. Ribeiro, and C. Fernandez-Quintanilla. 2008. A new vision-based approach to differential spraying in precision agriculture. Computers and Electronics in Agriculture 60 (2): 144-155.
27. Terawaki, M., T. Kataoka, H. Okamoto, and S. Hata. 2002. Distinction between sugar beet and weeds for development of automatic thinner and weeding machine of sugar beet. Proceeding of the Automation Technology for Off-Road Equipment Conference (Chicago, Illinois, USA).
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