A review of optical methods for assessing nitrogen contents during rice growth

Applied Engineering in Agriculture

Volumn 30, Issue 4, 2014, Pages 657-669

  Saberioon, Mohammadmehdi ^a   Amin, Mohd S M ^a   Gholizadeh, Asa ^b   Ezri, Mohd H ^a  

^a Universiti Putra Malaysai   (Malaysia)

^b CZECH UNIVERSITY OF LIFE SCIENCES PRAGUE   (Czech Republic)

Author keywords

Chlorophyll content; Digital camera; Low altitude remote sensing system; Nitrogen; Remote sensing

Indexed keywords

NITROGEN; REMOTE SENSING;

CHLOROPHYLL CONTENTS; NITROGEN CONTENT; OPTICAL METHODS; REMOTE SENSING SYSTEM; RICE GROWTHS;

DIGITAL CAMERAS;

EID: 84908552883     PISSN: 08838542     EISSN: None     Source Type: Journal    
DOI: 10.13031/aea.30.10478     Document Type: Article

References (136)

1. Aber, J. S., Aaviksoo, K., Karofeld, E., & Aber, S. W. (2002). Patterns in Estonian bogs as depicted in colour kite aerial photographs Suo., 53(1), 1-15.
2. Adamsen, F. J., Pinter, P. J., Barnes, E. M., LaMorte, R. L., Wall, G. W., Leavitt, S. W., & Kimbau, B. A. (1999). Measuring wheat senescence with a digital camera. Crop Sci., 39(3), 719-724. doi:http://dx.doi.org/10.2135/cropsci1999.0011183X003900030019x
3. Bajwa, S. G., & Mozzaffari, M. (2007). Effect of N availability on vegetative index of cotton canopy: A spatial regression approach. Trans. ASABE, 50(5), 1883-1892. doi:http://dx.doi.org/10.13031/2013.23940
4. Bajwa, S. G., Mishra, A. R., & Norman, R. J. (2010). Canopy reflectance response to plant nitrogen accumulation in rice. Precis. Agric., 11(5), 488-506. doi:http://dx.doi.org/10.1007/s11119-009-9142-0
5. Balasubramanian, V., Morales, A. C., Cruz, R. T., & Abdulrachman, S. (1999). On-farm adaptation of knowledge-intensive nitrogen management technologies for rice system. Nutr. Cycling Agroecosyst., 53(1), 59-69. doi:http://dx.doi.org/10.1023/A:1009744605920
6. Balasubramanian, V., Morales, A. C., Cruz, R. T., Thiyagarajan, T. M., Nagara-jan, R., Babu, M., Abdulrachman, S., & Hai, L. H. (2000). Adaptation of the chlorophyll meter (SPAD) technology for real-time N management in rice: A review. Manila, the Philippines: IRRI. Retrieved from http://www.irri.org/publications/irrn/pdfs/vol25no1/IRRN25-1Minireview.pdf
7. Berntsen, J., Thomsen, A., Schelde, K., Hansen, O., Knudsen, L., Broge, N., Hougaard, H., & Horfarter, R. (2006). Algorithms for sensor-based redistri-bution of nitrogen fertilizer in winter wheat. Precis. Agric., 7(2), 65-83. doi:http://dx.doi.org/10.1007/s11119-006-9000-2
8. Blackmer, T. M., & Schepers, J. S. (1995). Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. J. Prod. Agr., 8(1), 56-60. doi:http://dx.doi.org/10.2134/jpa1995.0056
9. Blackmer, T. M., Schepers, J. S., Varvel, G. E., & Walter-Shea, E. A. (1996). Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron. J., 88(1), 1-5. doi:http://dx.doi.org/10.2134/agronj1996.000219620088000100 01x
10. Boegh, E., Soegaard, H., Broge, N., Hasager, C. B., Jensen, N. O., Schelde, K., & Thomsen, A. (2002). Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sens. Environ., 81(2-3), 179-193. doi:http://dx.doi.org/10.1016/S0034-4257(01)00342-X
11. Bouman, B. A. M., Lampayan, R. M., & Tuong, T. P. (2007). Management in Irrigated Rice: Coping with Water Scarcity. Los Banos, Phillipines : International Rice Research Institute.
12. Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ., 76(2), 156-172. doi:http://dx.doi.org/10.1016/S0034-4257(00)00197-8
13. Bufogle, A., Bollich, P. K., Kovar, J. L., Lindau, C. W., & Macchiavellid, R. R. (1998). Comparison of ammonium sulfate and urea as nitrogen sources in rice production. J. Plant Nutr., 21(8), 1601-1614. doi:http://dx.doi.org/10.1080/01904169809365507
14. Buresh, R. J. (2007). Fertile progress Rice today, 6(3), 32-33.
15. Buresh, R. J., & Datta, S. K. ( 1990). Denitrification losses from puddled rice soils in the tropics. Biol. Fertil. Soils., 9(1), 1-13. doi:http://dx.doi.org/10.1007/BF00335854
16. Canisius, F., Turral, H., & Molden, D. (2007). Fourier analysis of historical NOAA time series data to estimate bimodal agriculture. Int. J. Remote Sens., 28(24), 5503-5522. doi:http://dx.doi.org/10.1080/01431160601086043
17. Cao, Q., Miao, Y., Wang, H., Huang, S., & Cheng, S. (2013). Non-destructive estimation of rice plant nitrogen status with crop circle multispectral active canopy sensor. Field Crops Res., 154, 133-144. doi:http://dx.doi.org/10.1016/j.fcr.2013.08.005
18. Cartelat, A., Cerovic, Z., Goulas, Y., Meyer, S., Lelarge, C., Prioul, J. L., Barbottin, A., Jeuffroy, M. H., Gate, P., Agati, G., & Moya, I. (2005). Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crops Res., 91(1), 35-49. doi:http://dx.doi.org/10.1016/j.fcr.2004.05.002
19. Cerovic, Z. G., Samson, G., Morales, F., Trembla, N., & Moya, I. (1999). Ultraviolet-induced fluorescence for plant monitoring: Present state and prospects. Agronomie, 19(7), 543-578. doi:http://dx.doi.org/10.1051/agro:19990701
20. Clark, S. D., Norman, R. J., Slaton, N. A., & Wilson Jr., C. E. (2001). Influence of nitrogen fertilizer source, application timing, and rate on grain yield of rice In B. R. Wells, R. J. Norman, & J. F. Meullenet (Eds.), Arkansas rice research studies 2000 (pp. 352-357). Fayetteville, Ark.: University of Arkansas Division of Agriculture.
21. Confalonieri, R., Stroppiana, D., Boschetti, M., Gusberti, D., Bocchi, S., & Acutis, M. (2006). Analysis of rice sample size variability due to development stage, nitrogen fertilization, sowing technique and variety using the visual jackknife. Field Crop. Res., 97(2-3), 135-141. doi:http://dx.doi.org/10.1016/j.fcr.2005.09.008
22. Crop Science Society of America. (1992). Glossary of crop science terms. Madison, Wis.: Crop Science Society of America.
23. Daughtry, C., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey III, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ., 74(2), 229-239. doi:http://dx.doi.org/10.1016/S0034-4257(00)00113-9
24. De Datta, S. K. (1995). Nitrogen transformations in wetland rice ecosystems. Fertil. Res., 42(1-3), 193-203. doi:http://dx.doi.org/10.1007/BF00750514
25. Dobermann, A., Witt, C., Dawe, D., Gines, H. C., Nagarajan, R., Satawathananont, S., Son, T. T., Tan, P. S., Wang, G. H., Chien, N. V., Thoa, V. T. K., Phung, C. V., Stalin, P., Muthukrishnan, P., Ravi, V., Babu, M., Chatuporn, S., Kongchum, M., Sun, Q., Fu, R., & Simba, G. C. (2002). Site-specific nutrient management for intensive rice cropping systems in Asia. Field Crop. Res., 74(1), 37-66. doi:http://dx.doi.org/10.1016/S0378-4290(01)00197-6
26. Dymond, J. R., & Trotter, C. M. (1997). Directional reflectance of vegetation measured by a calibrated digital camera. Appl. Optics, 36(18), 4314-4319. doi:http://dx.doi.org/10.1364/AO.36.004314
27. Eitel, J. U. H., Long, D. S., Gessler, P. E., & Hunt, E. R. (2008). Combined spectral index to improve ground-based estimates of nitrogen status in dryland wheat. Agron. J., 100(6), 1694-1702. doi:http://dx.doi.org/10.2134/agronj2007.0362
28. Eitel, J. U. H., Long, D. S., Gessler, P. E., & Smith, A. M. (2007). Using in-situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. Int. J. Remote Sens., 28(18), 4183-4190. doi:http://dx.doi.org/10.1080/01431160701422213
29. Eitel, J., Long, D. S., Gessler, P. E., Hunt, E. R., & Brown, D. J. (2009). Sensitivity of ground-based remote sensing estimates of wheat chlorophyll content to variation in soil reflectance. Soil Sci. Soc. Am. J., 73(5), 1715-1723. doi:http://dx.doi.org/10.2136/sssaj2008.0288
30. Esfahani, M., Abbasi, H. R. A., Rabiei, B., & Kavousi, M. (2007). Improvement of nitrogen management in rice paddy fields using chlorophyll meter (SPAD). Paddy Water Environ., 6(2), 181-188. doi:http://dx.doi.org/10.1007/s10333-007-0094-6
31. Fageria, N. K. (2009). The use of nutrients in crop plants. Boca Raton, Fla.: CRC Press.
32. Fageria, N. K., & Baligar, V. C. (2005). Enhancing nitrogen use efficiency in crop plants. Adv. Agron., 88, 97-185. doi:http://dx.doi.org/10.1016/S0065-2113(05)88004-6
33. Fageria, N. K., Santos, A. B., & Cutrim, V. A. (2007). Yield and nitrogen use efficiency of low land rice genotypes as influenced by nitrogen fertilizeration. Pesq. Agropec. Braz., 42, 1029-1034. doi:http://dx.doi.org/10.1590/S0100-204X2007000700016
34. Fairhurst, T., Witt, C., Buresh, R., & Dobermann, A. (2007). Rice: A Practical Guide to Nutrient Management (2nd ed.). International Rice Research Institute and International Plant Nutrition Institute and International Potash Institute. Retrieved from http://www.eseap.org/ppiweb/seasia.nsf/$webindex/article=872632A9482570760008A05FC76C1
35. Fan, M. S., Jiang, R. F., & Liu, X. J. (2005). Interactions between non-flooded mulching cul- tivation and varying nitrogen inputs in rice-wheat rotations. Field Crop Res., 91(2-3), 307-318. doi:http://dx.doi.org/10.1016/j.fcr.2004.08.006
36. FAOSTAT. (2009). FAO statistic division. Retrieved from http://faostat.fao.org.site/575default.aspx#ancor
37. Follett, R. H., Follett, R. F., & Halvorson, A. D. (1992). Use of a chlorophyll meter to evaluate the nitrogen status of dry land winter wheat. Commun. Soil Sci. Plant Anal., 23(7-8), 687-697. doi:http://dx.doi.org/10.1080/00103629209368619
38. Freney, J. R., Trevitt, A. C., De Datta, S. K., Obcemea, W. N., & Real, J. G. (1990). The interdependence of ammonia volatilization and denitrification as nitrogen loss processes in flooded rice fields in the Philippines. Biol. Fertil. Soils, 9(1), 31-36. doi:http://dx.doi.org/10.1007/BF00335858
39. Furuya, S. (1987). Growth diagnosis of rice plants by means of leaf colour Jpn. Agric. Res. Q., 20(3), 147-153
40. Gaudin, R., & Dupuy, J. (1999). Ammonical nutrition of transplanted rice fertilized with large urea granules. Agron. J., 91(1), 33-36. doi:http://dx.doi.org/10.2134/agronj1999.00021962009100010006x
41. Gholizadeh, A., Amin, M. S. M., Anuar, A. R., Aimrun, W., & 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.
42. Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ., 80(1), 76-87. doi:http://dx.doi.org/10.1016/S0034-4257(01)00289-9
43. Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops Geophys. Res. Lett., 32(L08403), 1-4.
44. Goulas, Y., Cerovic, Z. G., Cartelat, A., & Moya, I. (2004). Dualex: A new instrument for field measurements of epidermal ultraviolet absorbance by chlorophyll fluorescence. Appl. Opt., 43(23), 4488-4496. doi:http://dx.doi.org/10.1364/AO.43.004488
45. Graeff, S. J., Fenning, P., Claupein, W., & 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., 2008, 1-8. doi:http://dx.doi.org/10.1155/2008/359760
46. Haboudane, D., Miller, J. R., & Tremblay, N. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ., 81(2-3), 416-426. doi:http://dx.doi.org/10.1016/S0034-4257(02)00018-4
47. Haboudane, D., Miller, J., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ., 90(3), 337-352. doi:http://dx.doi.org/10.1016/j.rse.2003.12.013
48. Hairuddin, A. M., MdTahir, N., & Shah Baki, S. R. (2011). Overview of image processing approach for nutrient deficiencies detection in Elaeis Guineensis. Paper presented at The 2011 IEEE International Conference on System Engineering and Technology (ICSET). Shah Alam, Malaysia: IEEE Malaysia Chapter. doi:http://dx.doi.org/10.1109/ICSEngT.2011.5993432
49. Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ., 86(4), 542-553. doi:http://dx.doi.org/10.1016/S0034-4257(03)00131-7
50. Hatfield, J. L., Gitelson, A. A., Schepers, J. S., & Walthall, C. L. (2008). Application of spectral remote sensing for agronomic decisions. Agron. J., 100(Supplement 3), S117-S131. doi:http://dx.doi.org/10.2134/agronj2006.0370c
51. Heffer, P. (2009). Assessment of fertilizer use by crop at the global level 2006/07-2007/08. Paris, France: International Fertilizer Industry Association - IFA.
52. Hinzman, L. D., Bauer, M. E., & Daughtry, C. S. (1986). Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat. Remote Sens. Environ., 19(1), 47-61. doi:http://dx.doi.org/10.1016/0034-4257(86)90040-4
53. Huber, D. M., & Thompson, I. A. (2007). Nitrogen and plant disease In L. E. Datnoff, W. H. Elmer, & D. M. Huber (Eds.), Mineral nutrition and plant disease (pp. 31-44). St. Paul, Minn.: The American Psychopathological Society.
54. Hunt Jr., E. R., Cavigelli, M., Daughtry, C. S., McMurtrey, J. E., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric., 6(4), 359-378. doi:http://dx.doi.org/10.1007/s11119-005-2324-5
55. Hunt Jr., E. R., Daughtry, C. S., Eitel, J. U., & Long, D. S. (2011). Remote sensing leaf chlorophyll content using a visible band index. Agron. J., 103(4), 1090-1099. doi:http://dx.doi.org/10.2134/agronj2010.0395
56. Hunt Jr., E. R., Daughtry, C. S., McMurtrey, J. E., Walthall, C. L., Baker, J. A., Schroeder, J. C., & Liang, S. (2002). Comparison of remote sensing imagery for nitrogen management. In P. C. Robert, R. H. Rus, & W. E. Larson (Ed.), Proc. of the Sixth International Conference on Precision Agriculture and Other Precision Resources Management. Madison, Wis.: ASA-CSSASSSA.
57. Hunt Jr., E. R., Daughtry, C. S., Walthall, C. L., McMurtrey, J. E., & Dulaney, W. P. (2003). Agricultural remote sensing using radio-controlled aircraft In T. VanToai, D. Major, M. McDonald, J. Schepers, & L. Tarpley (Eds.), Digital image and spectral techniques: applications to precision agriculture and crop physiology. ASA special publications number 66 (pp. 197-205). Madison, Wis.: American Society of Agronomy.
58. Hunt Jr., E. R., Doraiswamy, P., McMurtrey, J. E., Daughtry, C. S., Perry, E. M., & Akhmedov, B. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Applied Earth Obser. Geoinform., 21, 103-112. doi:http://dx.doi.org/10.1016/j.jag.2012.07.020
59. Inoue, Y., Sakaiya, E., Zhu, Y., & Takahashi, W. (2012). Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sens. Environ., 126, 210-221. doi:http://dx.doi.org/10.1016/j.rse.2012.08.026
60. IRRI (International Rice Research Institute) . (2006). Bringing hope, improving lives-strategies. Plan 2007-2015 International Research Institute, (p. 61). Los Banos, Philippines.
61. IRRI (International Rice Research Institute). (1996). Use of leaf colour chart (LCC) for N management in rice. Crop and Resource Management Network Technology Brief No. 1. Los Banos, Philippines: IRRI.
62. Jensen, T., Apan, A., Young, F., & Zeller, L. (2007). Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform. Comput. Electron. Agric., 59(1-2), 66-77. doi:http://dx.doi.org/10.1016/j.compag.2007.05.004
63. Jia, L., Chen, X., Zhang, F., Buerkert, A., & Roemheld, V. (2007). Optimum nitrogen fertilization of winter wheat based on colour digital camera images. Commun. Soil Sci. Plant Anal., 38(11-12), 1385-1394. doi:http://dx.doi.org/10.1080/00103620701375991
64. Jia, L., Chen, X., Zhang, F., Buerkert, A., & Römheld, V. (2004). Use of digital camera to assess nitrogen status of winter wheat in the Northern China plain. J. Plant Nutr., 27(3), 441-450. doi:http://dx.doi.org/10.1081/PLN-120028872
65. Johanson, G. V., Raun, W. R., Solie, J., & Marvin, S. (2002). Managing nitrogen fertilizer using a nitrogen rich strip: Projected profitability. Departments of plant and soil science and biosystems and agriculture engineering, Stillwater, Okla.: Oklahoma State University. Retrieved from http://www.nue.okstate.edu/Extensio/Managing-N-Fert.htm
66. Jongschaap, R. E., & Booij, R. (2004). Spectral measurements at different spatial scales in potato: relating leaf, plant and canopy nitrogen status. Int. J. Appl. Earth Obs. Geoinf., 5(3), 205-218. doi:http://dx.doi.org/10.1016/j.jag.2004.03.002
67. Lailiberte, A. S., Rango, A., Herrick, J. E., Fredrickson, L., & Burkett, L. (2007). An object based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. J. Arid Environ., 69(1), 1-14. doi:http://dx.doi.org/10.1016/j.jaridenv.2006.08.016
68. Lamb, D. W., & Brown, R. B. (2001). Remote sensing and mapping of weeds in crops. J. Agric. Eng. Res., 78(2), 117-125. doi:http://dx.doi.org/10.1006/jaer.2000.0630
69. Lee, K. J., & Lee, B. W. (2013). Estimation of rice growth and nitrogen nutrition status using colour digital camera image analysis. Eur. J. Agron., 48, 57-65. doi:http://dx.doi.org/10.1016/j.eja.2013.02.011
70. Lee, Y. J., Yang, C. M., Chang, K. W., & Shen, Y. (2008). A simple spectral index using reflectance of 735 nm to assess nitrogen status of rice canopy. Agron. J., 100(1), 205-212. doi:http://dx.doi.org/10.2134/agrojnl2007.0018
71. Lee, Y. J., Yang, C. M., Chang, K. W., & Shen, Y. (2011). Effects of nitrogen status on leaf anatomy, chlorophyll content and canopy reflectance of paddy rice Bot. stud., 52, 295-303.
72. Lelong, C. C., Burger, P., Jubelin, G., Roux, B., Labbe, S., & Barett, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8, 3557-3585. doi:http://dx.doi.org/10.3390/s8053557
73. Li, F., Miao, Y. X., Chen, X. P., Zhang, H. L., Jia, L. L., & Bareth, G. (2010). Estimating winter wheat biomass and nitrogen status using an active crop sensor Intell. autom. soft comput., 16(6), 1221-1230.
74. Li, F., Miao, Y., Zhang, F., Cui, Z., Li, R., Chen, X., Zhang, H., Schroder, J., Raun, W. R. & Jia, L. (2009). In-season optical sensing improves nitrogen use efficiency for winter wheat. Soil Sci. Soc. Am. J., 73(5), 1566-1574. doi:http://dx.doi.org/10.2136/sssaj2008.0150
75. Li, Y., Chen, D., Walke, C. N., & Angus, J. F. (2010). Estimating the nitrogen status of crops using a digital camera. Field Crop. Res., 118(3), 221-227. doi:http://dx.doi.org/10.1016/j.fcr.2010.05.011
76. Link, A., & Reusch, S. (2006). Implementation of site-specific nitrogen application status and development of the YARA nsensor. NJF seminar 390, precision technology in crop production implementation and benefits (pp. 37-41). Stockholm, Sweden: Norsk Jernbaneforbund.
77. Lukina, E., Stone, M., & Raun, W. (1999). Estimating vegetation coverage in wheat using digital images. J. Plant Nutr., 22(2), 341-350. doi:http://dx.doi.org/10.1080/01904169909365631
78. Madeira, A. C., Mendonca, A., Ferriera, M. E., & De Taborda, M. L. (2000). Relationship between spectroradiometric and chlorophyll measurements in green beans. Commun. Soil Sci. Plant Anal., 31(5-6), 631-643. doi:http://dx.doi.org/10.1080/00103620009370465
79. Marenco, R. A., Antezana-Vera, S. A., & Nascimento, H. C. S. (2009). Relationship between specific leaf area, leaf thickness, leaf water content and SPAD-502 readings in six amazonian tree species. Photosynthetica, 47(2), 184-190. doi:http://dx.doi.org/10.1007/s11099-009-0031-6
80. Martin, K., Raun, W., & Solie, J. (2012). By-plant prediction of corn grain yield using optical sensor readings and measured plant height. J. Plant Nutr., 35(9), 1429-1439. doi:http://dx.doi.org/10.1080/01904167.2012.68413
81. McMurtrey, J. E., Chappelle, E. W., Kim, M. S., Meisinger, J. J., & Corp, L. A. (1994). Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sens. Environ., 47(1), 36-44. doi:http://dx.doi.org/10.1016/0034-4257(94)90125-2
82. Mengel, K., & Viro, M. (1978). The significance of plant energy status for the uptake and incorporation of NH4-N by young rice plants Soil Sci. Plant Nutr., 24(3), 407-416. doi:http://dx.doi.org/10.1080/00380768.1978.10433119
83. Mercado-Luna, A., Rico-Garcia, E., Lara-Herrera, A., Soto-Zarazua, G., Ocampo-Velazquez, R., Guevara-Gonzalez, R., & Herrera-Ruiz, G. (2010). Nitrogen determination on tomato (Lycopersicon esculentum Mill.) seedlings by colour image analysis (RGB). Afr. j. biotechnol., 9(33), 5326-5332.
84. Mohanty, S. (2010). The global rice market: Where is it going? Rice today, 9(1), 42-43.
85. Nguyen, H. T., & Lee, B. W. (2006). Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur. J. Agron., 24(4), 349-356. doi:http://dx.doi.org/10.1016/j.eja.2006.01.001
86. Ning, H. F., Qiao, J. F., Liu, Z. H., Lin, Z. M., Li, G. H., Wang, Q. S., Wang, S. H. & Ding, Y. F. (2010). Distribution of proteins and amino acids in milled and brown rice as affected by nitrogen fertilization and genotype. J. Cereal Sci., 52(1), 90-95. doi:http://dx.doi.org/10.1016/j.jcs.2010.03.009
87. Norman, R. J., Wilson, C. E., & Slaton, N. A. (2003). Soil fertilization and mineral nutrition in US mechanized rice culture. In C. W. Smith (Ed.), Rice: origin, history, technology and production (pp. 331-411). Hoboken, N.J.: John Wiley and Sons Inc.
88. Olfs, H. W., Blankenau, K., Brentrup, F., Jasper, J., Link, A., & Lammel, J. (2005). Soil and plant-based nitrogen fertilizer recommendations in arable forming. J. Plant Nutr. Soil Sci., 168(4), 414-431. doi:http://dx.doi.org/10.1002/jpln.200520526
89. Pagola, M., Ortiz, R., Irigoyen, I., Bustince, H., Barrenechea, E., & Aparicio-Tejo, P. (2009). New method to assess barley nitrogen nutrition status based on image colour analysis. Comput. Electron. Agric., 65(2), 213-218. doi:http://dx.doi.org/10.1016/j.compag.2008.10.003
90. Pampolino, M. F., Laureles, E. V., Gines, H. C., & Buresh, R. J. (2008). Soil carbon and nitrogen changes in long-term continuous lowland rice cropping. Soil Sci. Soc. Am. J., 72(3), 798. doi:http://dx.doi.org/10.2136/sssaj2006.033
91. Peng, S., Cassman, K. G., & Kropff, M. J. (1995). Relationship between leaf photosynthesis and nitrogen content of field-grown rice in the tropics. Crop Sci, 35(6), 1627-1630. doi:http://dx.doi.org/10.2135/cropsci1995.0011183X003500060 018x
92. Perez, A. J., Lopez, F., Benlloch, J. V., & Christensen, S. (2000). Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric., 25(3), 197-212. doi:http://dx.doi.org/10.1016/S0168-1699(99)00068-X
93. Peterson, T. A., Blackmer, T. M., Francis, D. D., & Schepers, J. S. (1993). Using a chlorophyll meter to improve N management. nebguide g93-1171a. coop. ext. serv. Lincoln, Neb.: Univ. of Nebraska.
94. Primicerio, J., Gennaro, S. F., Fiorillo, E., Genesio, L., Lugato, E., & Matese, A. (2012). A flexible unmanned aerial vehicle for precision agriculture. Precis. Agric., 13(4), 517-523. doi:http://dx.doi.org/10.1007/s11119-012-9257-6
95. Quilter, M. C., & Anderson, V. J. (2000). Low altitude/large scale aerial photographs: A tool for range and resource managers Rangelands, 22(2), 13-17.
96. Richardson, A. D., Duigan, S. P., & Berlyn, G. P. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol., 153, 185-194. doi:http://dx.doi.org/10.1046/j.0028-646X.2001.00289.x
97. Richardson, M. D., & Karcher, D. E. (2003). Quantifying turf-grass colour using digital image analysis. Crop Sci., 43(3), 943-951. doi:http://dx.doi.org/10.2135/cropsci2003.9430
98. Rorie, R. L., Purcell, L. C., Mozaffari, M., Karcher, D. E., King, C. A., Marsh, M. C., & Longer, D. E. (2011). Association of "greenness" in corn with yield and leaf nitrogen concentration. Agron. J., 103(2), 529-535. doi:http://dx.doi.org/10.2134/agronj2010.0296
99. Rorie, R. L., Purcell, L. C., Karcher, D. E., & King, C. A. (2011). The assessment of leaf nitrogen in corn from digital images. Crop Sci., 51(5), 2174. doi:http://dx.doi.org/10.2135/cropsci2010.12.0699
100. Saberioon, M. M., Amin, M. S. M., Aimrun, W., Anuar, A. R., & Gholizadeh, A. (2013). Multi-spectral images tetracam agriculture digital camera to estimate nitrogen and grain yield of rice at different growth stages Phillip. agric. sci., 96(1), 108-112.
101. Sakamoto, T., Gitelson, A. A., Nguy-Robertson, A. L., Arkebauer, T. J., Wardlow, B. D., & Suyker, A. E. (2012). An alternative method using digital cameras for continuous monitoring of crop status. Agric. For. Meteorol., 154-155, 113-126. doi:http://dx.doi.org/10.1016/j.agrformet.2011.10.014
102. Samseemoung, G., Soni, P., Jayasuriya, H. P. W., & Salokhe, V. M. (2012). Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation. Precis. Agric., 13(6), 611-627. doi:http://dx.doi.org/10.1007/s11119-012-9271-8
103. Savant, N. K., & De Datta, S. K. (1982). Nitrogen transformation in wetland rice soil. Advanc. Agron., 35, 241-302. doi:http://dx.doi.org/10.1016/S0065-2113(08)60327-2
104. Scharpf, P. C., & Lory, J. A. (2002). Calibrating corn colour from aerial photographs to predict side-dress nitrogen need. Agron. J., 94(3), 397-404. doi:http://dx.doi.org/10.2134/agronj2002.0397
105. Schepers, J. S., & Raun, W. R. (2008). Crop nitrogen requirement and fertilization In Nitrogen in agricultural systems (pp. 563-612). Madison, Wis.: ASA, CSSA, and SSSA.
106. Schroeder, J. J., Neetson, J. J., Oenema, O., & Struik, P. C. (2000). Does the crop or the soil indicate how to save nitrogen in maize production? Reviewing the state of the art. Field Crops Res., 66(2), 151-164. doi:http://dx.doi.org/10.1016/S0378-4290(00)00072-1
107. Seang, T. P. (2006). Geo-referenced balloon digital aerial photo technique: a low-cost high-resolution option for developing countries. Proceeding of 5th Annual Conferences on Geographic Information Technology and Application. Bangkok, Thailand.
108. Shanahan, J. F., Roberts, D., Ferguson, R. B., & Schepers, J. S. (2008). Procedures for using active sensors to direct in-season n application on corn. Proceeding of the 9th International Conference on Precision Agriculture. Denver, Col.: Precision Farming Centre.
109. Shou, L., Jia, L., Cui, Z., Chen, X., & Zhang, F. (2007). Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat. J. Plant Nutr., 30(10), 1669 -1680. doi:http://dx.doi.org/10.1080/01904160701615533
110. Singh, B., Singh, Y., Ladha, J. K., Bronson, K. F., Balasubramanian, V., Singh, J., & Khind, C. S. (2002). Chlorophyll meter-and leaf colour chart-based nitrogen management for rice and wheat in Northwestern India. Agron. J., 94(4), 821-829. doi:http://dx.doi.org/10.2134/agronj2002.0821
111. Song, S., Gong, W., Zhu, B., & Huang, X. (2011). Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance. ISPRS J. Photogramm. Remote Sens., 66(5), 672-682. doi:http://dx.doi.org/10.1016/j.isprsjprs.2011.05.002
112. StaCruz, P. C., Simbahan, G. C., Hill, J. E., Dobermann, A., Zeigler, R. S., Du, P. V., dela Pena, F. A.; Samiayyan, K.; Suparyono, N. V. & Zhong, Z. (2001). Pest profiles at varying nutrient input levels. In S. Peng, & B. Hardy (Eds.), Rice Research for Food Security and Poverty Alleviation (pp. 431-440). Los Banos: International Rice Research Institute.
113. Swain, K. C., Jayasuriya, H. P., & Salokhe, V. M. (2007). Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption. J. Appl. Remote Sens., 1(1), 013547. doi:http://dx.doi.org/10.1117/1.2824287
114. Swain, K. C., Thomso, H. P., & Jayasuriya, H. P. (2010). Adoption of an unmanned helicopter for low altitude remote sensing to estimate yield and total biomass. Trans. ASABE, 53(1), 21-27. doi:http://dx.doi.org/10.13031/2013.29493
115. Takebe, M., Yoneyama, T., Inad, K., & Murakami, T. (1990). Spectral reflectance ratio of rice canopy for estimating crop N status. Plant Soil., 122(2), 295-297. doi:http://dx.doi.org/10.1007/BF02851988
116. Takenaga, H. (1995). Science of Rice Plant: Physiology. In Internal Factors in Relation to Nutrient Absorption (Vol. 2, pp. 294-309). Tokyo: Food and Agricultural Policy Research Center.
117. Teoh, C. C., Hassan, D. A., Radzali, M. M., & Jafni, J. J. (2012). Prediction of SPAD chlorophyll meter readings using remote sensing technique. J. trop. agric. food sci., 40(1), 127-136.
118. Tremblay, N., Fallon, E., Bélec, C., Tremblay, G., & Thibault, E. (2007). Growing season and soil factors related to predicting corn nitrogen fertilization in Quebec. Managing crop nitrogen for weather. Proc. of the Symp. Integrating Weather Variability into Nitrogen Recommendations. Norcross, Ga.: Int. Plant Nutrition Inst.
119. Tubana, B., Harrell, D., Walker, T., Teboh, J., Lofton, J., Kanke, Y., & Phillips, S. (2011). Relationships of spectral vegetation indices with rice biomass and grain yield at different sensor view angles. Agron. J., 103(5), 1405-1413. doi:http://dx.doi.org/10.2134/agronj2011.0061
120. Uddling, J., Gelang-Alfredsson, J., Piikki, K., & Pleijel, H. (2007). Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth Res., 91(1), 37-46. doi:http://dx.doi.org/10.1007/s11120-006-9077-5
121. Van den Berg, A. K., & Perkins, T. D. (2004). Evaluation of a portable chlorophyll meter to estimate chlorophyll and nitrogen contents in sugar maple (Acer saccharum Marsh.) leaves. Forest Eco. Manag., 200(1-3), 113-117. doi:http://dx.doi.org/10.1016/j.foreco.2004.06.005
122. Varinderpal-Singh, Bijay-Singh, Yadvinder-Singh, Thind, H. S., & 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. doi:http://dx.doi.org/10.1007/s10705-010-9363-7
123. Vericat, D., Brasington, J., Wheaton, J., & Cowie, M. (2008). Accuracy assessment of aerial photographs acquired using lighter-than-air blimps: Low-cost tools for mapping river corridors. River Res. Appl., 25(8), 985-1000. doi:http://dx.doi.org/10.1002/rra.1198
124. Wood, C. W., Reeves, D. W., Duffield, R. R., & Edminsten, K. L. (1992). Field chlorophyll measurements for evaluation of corn nitrogen status. J. Plant Nutr., 15(4), 487-500. doi:http://dx.doi.org/10.1080/01904169209364335
125. Wright, D. L., Richie, G., Rasmussen, V. P., Ramsey, R. D., & Baker, D. (2003). Managing grain protein in wheat using remote sensing. Online Journal of Space Communication. Retrieved from http://satjournal.tcom.ohiou.edu/pdf/wright.pdf
126. Wu, J. (2006). Validation and application of high resolution remote sensing in agricultural fields. Unpublished Doctoral Dissertation. Minneapolis, Minn.: University of Minnesota.
127. Xue, L., Cao, W., Zhu, Y., & Dai, T. (2004). Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agron. J., 96(1), 135-142. doi:http://dx.doi.org/10.2134/agronj2004.0135
128. Yang, W. H., Peng, S., Huang, J., Sanico, A. L., Bures, R. J., & Witt, C. (2003). Using leaf colour charts to estimate leaf nitrogen status of rice Agron. J., 95, 212-217.
129. Yao, X., Du, W., Feng, S., & Zou, J. (2010). Image-based plant nutrient status analysis: An overview. Proc. of IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS). Xiamen, Taiwan: IEEE.
130. Yao, Y., Miao, Y., Huang, S., Gao, L., Ma, X., Zhao, G., Jiang, R., Chen, X., Zhang, F., Yu, K., Gnyp, M. L., Bareth, G., Liu, C., Zhao, L., Yang, W., & Zhu, H. (2012). Active canopy sensorbased precision N management strategy for rice. Agron. Sustain. Dev., 32(4), 925-933. doi:http://dx.doi.org/10.1007/s13593-012-0094-9
131. Yi, Q. X., Huang, J. F., Wang, F. M., Wang, X. Z., & Lu, Z. Y. (2007). Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environ. Sci. Technol., 41(19), 6770-6775. doi:http://dx.doi.org/10.1021/es070144e
132. Yu, K., Li, F., Gnyp, M. L., Miao, Y., Bareth, G., & Chen, X. (2013). Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS J. Photogram. Rem. Sens., 78, 102-115. doi:http://dx.doi.org/10.1016/j.isprsjprs.2013.01.008
133. Zhang, J. H., Wang, K., Bailey, J. S., & Wang, R. C. (2006). Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 16(1), 108-117. doi:http://dx.doi.org/10.1016/S1002-0160(06)60032-5
134. Zhu, J., Wang, K., Deng, J., & Harmon, T. (2009). Quantifying nitrogen status of rice using low altitude uav-mounted system and object-oriented segmentation methodology. Presented at the ASME International Design Engineering Technical Conference and Computers and Information in Engonering Conference. San Diego, Calif.
135. Zhu, Y., Zhou, D., Yao, X., Tian, Y., & Cao, W. (2007). Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice. Aust. J. Agric. Res., 58(11), 1077-1085. doi:http://dx.doi.org/10.1071/AR06413
136. Zillman, E., Graeff, S., Link, J., Batchelor, W. D., & Claupein, W. (2006). Assessment of cereal nitrogen requirements derived by optical on the go sensors on heterogeneous soils. Agron. J., 98(3), 682-690. doi:http://dx.doi.org/10.2134/agronj2005.0253