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Using portable RapidSCAN active canopy sensor for rice nitrogen status diagnosis

Published online by Cambridge University Press:  01 June 2017

J. Lu ,
Y. Miao ,
W. Shi ,
J. Li ,
J. Wan ,
X. Gao ,
J. Zhang  and
H. Zha
Y. Miao*
Affiliation:
International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environment Sciences, China Agricultural University, Beijing, 10093, China
W. Shi
Affiliation:
International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environment Sciences, China Agricultural University, Beijing, 10093, China
J. Li
Affiliation:
International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environment Sciences, China Agricultural University, Beijing, 10093, China
J. Wan
Affiliation:
International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environment Sciences, China Agricultural University, Beijing, 10093, China
X. Gao
Affiliation:
International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environment Sciences, China Agricultural University, Beijing, 10093, China
J. Zhang
Affiliation:
International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environment Sciences, China Agricultural University, Beijing, 10093, China
H. Zha
Affiliation:
International Center for Agro-Informatics and Sustainable Development (ICASD), College of Resources and Environment Sciences, China Agricultural University, Beijing, 10093, China
*
†E-mail: ymiao@cau.edu.cn or ymiao2007@gmail.com
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Abstract

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

Keywords

nitrogen nutrition indexnitrogen sufficiency indexnitrogen status diagnosisprecision nitrogen managementvegetation index
Type
Precision Nitrogen
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 349 - 352
Copyright
© The Animal Consortium 2017 

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References

Barnes, E, Clarke, T, Richards, S, Colaizzi, PD, Haberland, J, Kostrzewski, M, et al. 2000. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In: Proceedings of the 5th International Conference on Precision Agriculture, Edited by PC Robert, Bloomington, Minnesota, USA, pp. 16–19.Google Scholar
Cao, Q, Miao, Y, Wang, H, Huang, S, Cheng, S, Khosla, R, et al. 2013. Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research 154, 133–144.CrossRefGoogle Scholar
Cao, Q, Miao, Y, Feng, G, Gao, X, Li, F, Liu, B, et al. 2015. Active canopy sensing of winter wheat nitrogen status: an evaluation of two sensor systems. Computers and Electronics in Agriculture 112, 54–67.Google Scholar
Huang, S, Miao, Y, Zhao, G, Yuan, F, Ma, X, Tan, C, et al. 2015. Satellite remote sensing-based in-season diagnosis of rice nitrogen status in Northeast China. Remote Sensing 7, 10646–10667.CrossRefGoogle Scholar
Jasper, J, Reusch, S and Link, A 2009. Active sensing of the N status of wheat using optimized wave-length combination: impact of seed rate, variety and growth stage. In: Precision Agriculture 09: Papers from the 7th European Conference on Precision Agriculture, edited by EJ Van Henten, D Goense and C Lokhorst, Wageningen Academic Publishers, Wageningen, Netherlands, pp. 23–30.Google Scholar
Jordan, CF 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50 (4), 663–666.CrossRefGoogle Scholar
Lemaire, G, Jeuffroy, MH and Gastal, F 2008. Diagnosis tool for plant and crop N status in vegetative stage: theory and practices for crop N management. European Journal of Agronomy 28 (4), 614–624.Google Scholar
Rouse, JW, Haas, JRH, Schell, JA and Deering, DW 1974. Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of Thid Earth Resources Technology Satellite-1 Symposium, NASA Special Publication 351, NASA, Washington, DC, USA, pp. 309–317.Google Scholar
Sripada, RP, Heiniger, RW, White, JG and Meijer, AD 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal 98, 968–977.Google Scholar
Tremblay, N, Fallon, E and Ziadi, N 2011. Sensing of crop nitrogen status: opportunities, tools, limitations, and supporting information requirements. HortTechnology 21, 274–281.Google Scholar
Tucker, CJ 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127–150.Google Scholar
Xia, T, Miao, Y, Wu, D, Shao, H, Khosla, R and Mi, G 2016. Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sensing 8, 605.CrossRefGoogle Scholar
Yao, Y, Miao, Y, Cao, Q, Wang, H, Gnyp, ML, Bareth, G, et al. 2014. In-season estimation of rice nitrogen status with an active crop canopy sensor. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4403–4413.Google Scholar
Yao, Y, Miao, Y, Huang, S, Gao, L, Ma, X, Zhao, G, et al. 2012. Active canopy sensor-based precision N management strategy for rice. Agronomy for Sustainable Development 32, 925–933.CrossRefGoogle Scholar