Abstract
Precision nitrogen fertilizer application depends on accurate estimation of plant nitrogen content. However, the assessment of plant nitrogen content at early growth stages of paddy rice through remote sensed images is complicated by the compound effects of backgrounds (e.g. flood water, bare soil, algae, etc.) on the band reflectance. The rapid changing of plant nitrogen content during the vegetative phase makes the development of an operational prediction model very difficult. In this study, aerial images acquired by a quadcopter unmanned aerial vehicle (UAV) equipped with a multispectral sensor were used to estimate plant nitrogen content at vegetative phase of rice crops. The experiments were conducted at the experimental farm of Taiwan Agricultural Research Institute (TARI) from 2018 to 2020. A variable, N-index (ratio between N content of plants to be evaluated and plants not receiving N fertilizers), was introduced to resolve the issues related to rapid changing of plant N content during the vegetative phase. After removing the interference on band reflectance by background from the aerial images, the most appropriate vegetation indices and period that can capture the variations of N-index of rice plants were identified. It was found that a normalized difference red edge index (NDRI) and red edge chlorophyll index (RECI) based model correlated well with the N-index values from c.a. 30 days after transplanting (DAT) to 55 DAT (i.e., the most crucial period for rice yield and grain quality). The developed model was then used to display the spatial and temporal heterogeneity in plant nitrogen status within an experimental field as an example to illustrate how to use the model. In the example, soil plant analysis development (SPAD) meter values at locations of various levels of estimated N-index were collected as surrogates of plant nitrogen content at various DATs to build relationships for converting N-index maps to SPAD maps for potential variable rate fertilizer application management.
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References
Arai, K., Shigetomi, O., Sakashita, M., & Miura, Y. (2014). Estimation of protein content in rice crop and nitrogen content in rice leaves through regression analysis with NDVI derived from camera mounted radio-control helicopter. International Journal of Advanced Research in Artificial Intelligence, 3, 12–19. https://doi.org/10.14569/IJARAI.2014.030303
Barnes, E. M., Clarke, T. R., Richards, S. E., Colaizzi, P. D., 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 Fifth International Conference on Precision Agriculture, ASA/SSSA/CSSA, (Vol. 1619).
Blackmer, T. M., Schepers, J. S., & Varvel, G. E. (1994). Light reflectance compared with other nitrogen stress measurements in corn leaves. Agronomy Journal, 86, 934–938. https://doi.org/10.2134/agronj1994.00021962008600060002x
Bremner, J. M., et al. (1996). Nitrogen-total. In D. L. Sparks (Ed.), Methods of soil analysis. Pt 3. Chemical methods. SSSA Book Ser. 5, (pp. 1085–1122). SSSA and ASA.
Buresh, R. J., Castillo, E. G., & De Datta, S. K. (1993). Nitrogen losses in puddle soils as affected by timing of water deficit and nitrogen fertilizer. Plant and Soil, 157, 197–206. https://doi.org/10.1007/BF00011048
Cabangon, R. J., Castillo, E. G., & Tuong, T. P. (2011). Chlorophyll meter-based nitrogen management of rice grown under alternate wetting and drying irrigation. Field Crops Research, 121, 136–146. https://doi.org/10.1016/j.fcr.2010.12.002
Cassman, K. G., Dobermann, A., & Walters, D. T. (2002). Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio, 31, 132–140. https://doi.org/10.1579/0044-7447-31.2.132
Chang, K. W., Shen, Y., & Lo, J. C. (2005). Predicting rice yield using canopy reflectance measured at booting stage. Agronomy Journal, 97, 872–878. https://doi.org/10.2134/agronj2004.0162
Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20, 2741–2759. https://doi.org/10.1080/014311699211778
Galloway, J. N., & Cowling, E. B. (2002). Reactive nitrogen and the world: 200 years of change. Ambio, 31, 64–71. https://doi.org/10.1579/0044-7447-31.2.64
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7
Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271–282. https://doi.org/10.1078/0176-1617-00887
Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., & Derry, D. (2002). Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23, 2537–2562. https://doi.org/10.1080/01431160110107806
Guyot, G. (1990). Optical properties of vegetation canopies. In M. D. Steven & J. A. Clark (Eds.), Applications of remote sensing in agriculture (pp. 19–43). Butterworths.
Ju, X. T., Kou, C. L., Zhang, F. S., & Christie, P. (2006). Nitrogen balance and groundwater nitrate contamination: Comparison among three intensive cropping systems on the North China plain. Environmental Pollution, 143, 117–125. https://doi.org/10.1016/j.envpol.2005.11.005
Kalra, Y. P. (1998). Hand book of reference methods for plant analysis (pp. 75–92). CRC Press. https://doi.org/10.1201/9780367802233
Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine, 19, 44–57. https://doi.org/10.1109/79.974727
Knyazikhin, Y., Schull, M. A., Stenberg, P., Mõttus, M., Rautiainen, M., Yang, Y., et al. (2013). Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences, 110, 185–192. https://doi.org/10.1073/pnas.1210196109
Lee, Y. J., Chang, K. W., Shen, Y., Huang, D. M., & Tsay, H. L. (2007). A handy imaging system for precision agriculture studies. International Journal of Remote Sensing, 28, 4867–4876. https://doi.org/10.1080/01431160601075566
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. Agronomy Journal, 100, 205–212. https://doi.org/10.2134/agronj2007.0018
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. Botanical Studies, 52, 295–303.
Lin, F. F., Qiu, L. F., Deng, J. S., Shi, Y. Y., Chen, L. S., & Wang, K. (2010). Investigation of SPAD meter-based indices for estimating rice nitrogen status. Computers and Electronics in Agriculture, 71, S60–S65. https://doi.org/10.1016/j.compag.2009.09.006
Muñoz-Huerta, R. F., Guevara-Gonzalez, R. G., Contreras-Medina, L. M., Torres-Pacheco, I., Prado-Olivarez, J., & Ocampo-Velazquez, R. V. (2013). A review of methods for sensing the nitrogen status in plants: Advantages, disadvantages and recent advances. Sensors, 13, 10823–10843. https://doi.org/10.3390/s130810823
Meyer, G. E., & Neto, J. C. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 63, 282–293. https://doi.org/10.1016/j.compag.2008.03.009
Meyer, G.E., Hindman, T.W. & Lakshmi, K. (1998). Machine vision detection parameters for plant species identification. In: Meyer, G.E., & DeShazer, J.A. (Eds.), Precision agriculture and biological quality (vol. 3543, pp. 327–335). Proceedings of SPIE. https://doi.org/10.1117/12.336896
Nguyen, H. T., & Lee, B. W. (2006). Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy, 24, 349–356. https://doi.org/10.1016/j.eja.2006.01.001
Perez, A. J., Lopez, F., Benlloch, J. V., & Christensen, S. (2000). Color and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture, 25, 197–212. https://doi.org/10.1016/S0168-1699(99)00068-X
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126. https://doi.org/10.1016/0034-4257(94)90134-1
Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., et al. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94, 815–820. https://doi.org/10.2134/agronj2002.0815
Steele, M. R., Gitelson, A. A., Rundquist, D. C., & Merzlyak, M. N. (2009). Nondestructive estimation of anthocyanin content in grapevine leaves. American Society for Enology and Viticulture, 60, 87–92.
Stroppiana, D., Boschetti, M., Brivio, P. A., & Bocchi, S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111, 119–129. https://doi.org/10.1016/j.fcr.2008.11.004
Wang, Y. P., Chang, K. W., Chen, R. K., Lo, J. C., & Shen, Y. (2010). Large area rice yield forecasting using satellite imageries. International Journal of Applied Earth Observation and Geoinformation, 12, 27–35. https://doi.org/10.1016/j.jag.2009.09.009
Wang, Y. P., Chen, S. H., Chang, K. W., & Shen, Y. (2012). Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps. Precision Agriculture, 13, 553–567. https://doi.org/10.1007/s11119-012-9266-5
Wang, Y. P., & Shen, Y. (2015). Identifying and characterizing yield limiting soil factors with the aid of remote sensing and data mining techniques. Precision Agriculture, 16, 99–118. https://doi.org/10.1007/s11119-014-9365-6
Wang, Z., Zhang, W., Beebout, S. S., Zhang, H., Liu, L., Yang, J., et al. (2016). Grain yield, water and nitrogen use efficiencies of rice as influenced by irrigation regimes and their interaction with nitrogen rates. Field Crops Research, 193, 54–69. https://doi.org/10.1016/j.fcr.2016.03.006
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38, 259–269. https://doi.org/10.13031/2013.27838
Ye, Y., Liang, X., Chen, Y., Liu, J., Gu, J., Guo, R., et al. (2013). Alternate wetting and drying irrigation and controlled-release nitrogen fertilizer in late-season rice. Effects on dry matter accumulation, yield, water and nitrogen use. Field Crops Research, 144, 212–224. https://doi.org/10.1016/j.fcr.2012.12.003
Zheng, H., Cheng, T., Li, D., Zhou, X., Yao, X., Tian, Y., et al. (2018). Evaluation of RGB, color-infrared and multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sensing, 10, 824. https://doi.org/10.3390/rs10060824
Zhu, Y., Yao, X., Tian, Y., Liu, X., & Cao, W. (2008). Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation, 10, 1–10. https://doi.org/10.1016/j.jag.2007.02.006
Acknowledgements
This work was financially supported by the “Innovation and Development Center of Sustainable Agriculture” from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education, and Ministry of Science and Technology, Taiwan, ROC [Grant Number MOST 106-2313-B-005-013-MY3].
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Wang, YP., Chang, YC. & Shen, Y. Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery. Precision Agric 23, 1–17 (2022). https://doi.org/10.1007/s11119-021-09823-w
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DOI: https://doi.org/10.1007/s11119-021-09823-w