Assessment of colour indices derived from conventional digital camera for determining nitrogen status in rice plants

Journal of Food, Agriculture and Environment

Volumn 11, Issue 2, 2013, Pages 655-662

  Saberioon, Mohammad Mehdi ^a   Amin, Mohd Soom Mohd ^a   Aimrun, Wayayok ^a   Gholizadeh, Asa ^a   Rahim Anuar, Anuar Abd ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Colour indices; Conventional digital camera; Nitrogen; Remote sensing image; Rice

Indexed keywords

NITROGEN;

ARTICLE; CAMERA; FERTILIZATION; FOOD COLOR; GREENHOUSE; HARVESTING; IMAGE PROCESSING; INFORMATION PROCESSING; NITROGEN MINERALIZATION; REMOTE SENSING; RICE;

EID: 84880253750     PISSN: 14590255     EISSN: 14590263     Source Type: Journal    
DOI: None     Document Type: Article

References (32)

1. Huber, D. M. and Thompson, I. A. 2007. Nitrogen and plant disease. In Datnoff, L.E., Elmer, W.H. and Huber, D.M. (eds.). Mineral Nutrition and plant disease. APS Press-The American Phytopathological Society, St. Paul, Minnesota, U.S.A., pp. 31-44.
2. Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J. and Koti, S. 2005 Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agron. J. 97:89-98.
3. Jaynes, D. B., Colvin, T. S., Karlen, D. L., Cambardella, C. A. and Meek, D. W. 2001. Nitrate loss in subsurface drainage as affected by nitrogen fertilizer rate. J. Environ. Qual. 30:1305-1314.
4. Angus, J. F. 2001. Nitrogen supply and demand in Australian agriculture, Aust. J. Exp. Agr. 41:277-288.
5. nd edn. American Society of Agronomy, Madison, WI, pp. 595-624.
6. Gholizadeh, A., Amin, M. S. M., Anuar, A. R., Aimrun, W. and Saberioon, M. M. 2011. Temporal variability of SPAD chlorophyll meter readings and its relationship to total nitrogen in leaves within a Malaysian paddy field. Aust. J. Basic Applied Sci. 5(5):236-245.
7. Confalonieri, R., Stroppiana, D., Boschetti, M., Gusberti, D., Bocchi, S. and Acutis, M. 2006. Analysis of rice sample size variability due to development stage, nitrogen fertilization, sowing technique and variety using the visual jackknife. Field Crops Res. 97:135-141.
8. Hairuddin, A. M., Md Tahir, N. and, Shah Baki S. R. 2011. Overview of image processing approach for nutrient deficiencies detection in Elaeis guineensis. Presented at the 2011 IEEE International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia, pp.116-120.
9. Yao, X., Du, W., Feng, S. and Zou, J. 2010. Image-based plant nutrient status analysis: An overview. 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS) 1:460-464.
10. Blackmer, T. M., Schepers, J. S. and Varvel, G. E. 1994. Light reflectance compared with other nitrogen stress measurements in corn leaves. Agron. J. 86:934-938.
11. Xue, L., Cao, W., Zhu, Y. and Dai, T. 2004. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agron. J. 96(1):135-142.
12. Meyer, G. E. and Neto, J. C. 2008. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63(2):282-293.
13. Richardson, M. D. and Karcher, D. E. 2003. Quantifying turfgrass color using digital image analysis. Crop Sci. 43(3):943-951.
14. Gitelson, A. A., Kaufman, Y. J., Stark, R. and Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80(1):76-87.
15. Jia, L., Chen, X., Zhang, F., Buerkert, A. and Roemheld, V. 2007. Optimum nitrogen fertilization of winter wheat based on color digital camera images. Commun. Soil Sci. Plant Anal. 38(11-12):1385-1394.
16. Li, Y., Chen, D., Walker, C. N. and Angus, J. F. 2010. Estimating the nitrogen status of crops using a digital camera. Field Crop. Res. 118:221-227.
17. Mercado-Luna, A., Rico-Garcia, E., Lara-Herrera, A., Soto-Zarazua, G., Ocampo-Velazquez, R., Guevara-Gonzalez, R. and Herrera-Ruiz, G. 2010. Nitrogen determination on tomato (Lycopersicon esculentum Mill.) seedlings by color image analysis (RGB). Afr. J. Biotechnol. 9(33):5326-5332.
18. Graeff, S. J., Fenning, P., Claupein, W. and Liebig, H. 2008. Evaluation of Image Analysis to Determine the N-Fertilizer Demand of Broccoli Plants (Brassica oleracea convar. botrytis var. italica). Adv. Optic. Technol. pp. 1-8.
19. Varinderpal-Singh, Bijay-Singh, Yadvinder-Singh, Thind, H. S. and Gupta, R. K. 2010. Need based nitrogen management using the chlorophyll meter and leaf colour chart in rice and wheat in South Asia: A review. Nutr. Cycl. Agroecosyst. 88(3):361-380.
20. Verhoeven, G. J. J. 2010. It's all about the format-unleashing the power of RAW aerial photography. Int. J. Remote Sens. 31(8):2009-2042.
21. Tang, L., Tian, L. F. and Steward, B. L. 2003. Classification of broadleaf and grass weeds using Gabor wavelets and an artificial neural network. Trans. ASAE 46:1247-1254.
22. Trussell, H. J., Vrhel, M. J. and Saber, E. 2005. Color image processing. IEEE Signal Process. Mag. 22:14-22.
23. Cheng, D. H., Jiang, X. H., Sun, Y. and Wang, J. 2001. Color image segmentation: Advances and prospects. Pattern Recognit. 34:2259-2281.
24. Xu, Y. F., Wang, X. M., Sun, H., Wang, H. H. and Zhan, Y. 2010. Study of monitoring maize leaf nutrition based on image processing and spectral analysis. World Automation Congress, pp. 465-468.
25. Yoshida, S. 1981. Fundamentals of Rice Crop Science. International Rice Research Institute, Los Banos, Philippines, 269 p.
26. Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127-150.
27. Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker C. J. and Stenseth, N. C. 2005 Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20:503-510.
28. Mao, W., Wang, Y. and Wang, Y. 2003. Real time detection of between-row weeds using machine vision. Paper No. 031004, ASAE Annual meeting, St Joseph, MI, USA
29. Kawashima, S. and Nakatani, M. 1998. An algorithm for estimating chlorophyll content in leaves using a video camera. Ann. Bot. 81:49-54.
30. Louhaichi, M., Borman, M. M. and Johnson, D. E. 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International 16:65-70.
31. Wang, J., Wei, C., Guo, J. Q and Lei, Y. W. 2011. A method based on digital image analysis for estimating crop canopy parameters. Presented at the International Conference on Intelligent Computation Technology and Automation (ICICTA), Shenzhen, China, Vol. 2, 7 p.
32. Cui, R. X. and Lee, B. W. 2002. Spikelet number estimation model using nitrogen nutrition status and biomass at panicle initiation and heading stage of rice. Korean J. Crop Sci. 47:390-394.