Prediction model of rice crude protein content, amylose content and actual yield under high temperature stress based on hyper-spectral remote sensing

Main Article Content

X.J. Xie
Y.H. Zhang
R.Y. Li
S.H. Shen
Y.X. Bao

Keywords

crude protein content, amylose content, actual yield, high temperature stress, spectral parameters

Abstract

Crude protein and amylose constitute two main representative components of rice quality. The non-destructive, quick assessment of grain crude protein content (GCPC), grain amylose content (GAC), and actual yield (AC) are necessary for quality and yield diagnosis in rice production. The objectives of this study were to determine the effects of high temperature stress on rice GCPC, GAC and AC, to define the relationships of GCPC, GAC, and AC to ground-based canopy hyper spectral reflectance and derivative parameters, and to establish quantitative models for real-time monitoring of rice GCPC, GAC, and AC using sensitive spectral parameters under high temperature stress. Two field warming experiments were performed in Nanjing in Jiangsu Province, China, to investigate the effects of high temperature ((treated for continuous 3 days from 9:00 am to 14:00 pm, average temperature was set at 35, 38 and 41 °C) and a control (CK)) at flowering stage in Liangyoupeijiu rice cultivar, using a free air temperature increase apparatus. Canopy hyper-spectral reflectance, GCPC, GAC, and AC were measured under high temperature treatments during different growth stages (flowering stage, grain-filling and ripening stages). The results showed that GCPC, AC (GAC) in Liangyoupeijiu were clearly reduced (increased) under high temperature stress in this study compared with the values of CK, and the reducing extent of GCPC and AC (the increasing extent of GAC) was increased with the increase of high temperature level. The hyper-spectral reflectance in different wavelength regions under high temperature stress was different. They increased in visible light region with the elevation of temperature, but reduced in near-infrared region. Among some selected spectral indices at three different growth stages used to estimate GCPC, GAC and AC, the optimum indices were difference vegetation index(810,450) and perpendicular vegetation index- Landsat multispectral scanner with high R2 when regressed against GCPC, GAC and AC. Furthermore, GCPC, GAC and AC prediction based on flowering stages were preferred than that on grain-filling and ripening stage by much bigger correlation coefficients. The six regression models developed in this study showed the agreement between the predicted and observed values when testing independent data under high temperature stress. Thus, the selected key hyper-spectral parameters can be reliably used to estimate GCPC, GAC and AC in rice under different high temperature treatments.

Abstract 67 | PDF Downloads 50

References

Bahar, B., Yildirim, M., Barutcular, C. and Brahim, G., 2008. Effect of canopy temperature depression on grain yield and yield components in bread and durum wheat. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 36: 34.
Cropscan, 2003. Data logger controller. User’s guide and technical reference. Cropscan, Rochester, NY, USA.
Dong, W.J., Deng, A.X., Zhang, B., Tian, Y.L., Chen, J., Yang, F. and Zhang, W.J., 2011a. An experimental study on the effects of different diurnal warming regimes on single cropping rice with free air temperature increased (FATI) facility. Acta Ecologica Sinica 8: 2169-2177.
Dong, W.J., Tian, Y.L., Zhang, B., Chen, J. and Zhang, W.J., 2011b. Effects of asymmetric warming and grain quality and related key enzymes activities for Japonica rice (Nanjing44) under FATI facility. Acta Agronomica Sinica 37: 832-841.
Endo, M., Tohru, T., Kazuki, H., Shingo, K., Kentaro, Y., Masahiro, O., Atsushi, H., Masao, W. and Makiko, K.K., 2009. High temperatures cause male sterility in rice plants with transcriptional alterations during pollen development. Plant and Cell Physiology 50: 1911-1922.
Fan, X.M., Jiang, D., Dai, T.B., Jing, Q. and Cao, W.X., 2005. Effects of nitrogen rates on activities of key regulatory enzymes for grain starch and protein accumulation in wheat grown under drought and waterlogging from anthesis to maturity. Scientia Agricultura Sinica 38: 1132-1141.
Feng, W., Yao, X., Zhu, Y., Tian, Y. C. and Cao, W.X., 2008. Monitoring leaf nitrogen status with hyper-spectral reflectance in wheat. European Journal of Agronomy 28: 394-404.
Foster, A.J., Kakani, V.G. and Mosali, J., 2017. Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression. Precision Agriculture 8: 192-209.
Fox, G. and Manley, M., 2014. Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals. Journal of the Science of Food and Agriculture 94: 174-179.
Gitelson, A.A. and Merzlyak, M.N., 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing18: 291-298.
Hansen, P.M., Jorgensen, J.R. and Thomsen, A., 2002. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. Journal of Agricultural Science 139: 307-318.
Hong, Y., Gu, Z.B. and Liu, X.X., 2004. Extraction and determination of pure amylose and amylopection. Science and Technology of Food Industry 25: 86-88.
Iizumi, T., Hayashi, Y. and Kimura, F., 2007. Influence on rice production in Japan from cool and hot summers after global warming. Journal of Agricultural Meteorology 63: 11-23.
Johnson, L.F., Hlawka, C. A. and Peterson, D.L., 1994. Multivariate analysis of AVRIS data for canopy biochemistry estimation along the oregon transect. Remote Sensing of Environment47: 216-230.
Labus, M., Nielsen, G., Lawrence, R., Engel, R. and Long, D., 2002. Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal of Remote Sensing 23: 4169-4180.
Laza, M.R.C., Sakai, H., Cheng, W.G., Tokida, T., Peng, S.B. and Hasegawa, T., 2015. Differential response of rice plants to high night temperatures imposed at varying developmental phases. Agricultural and Forest Meteorology 209: 69-77.
Li, W.G., Wang, J.H., Zhao, C.J., Liu, L.Y. and Tong, Q.X., 2008. Estimating rice yield based on quantitative remote sensing inversion and growth model coupling. Transactions of the CSAE 24: 128-131.
Li, Y.X., Zhu, Y., Tian, Y.C., You, X.T., Zhou, D.Q. and Cao, W.X., 2005. Relationship of grain protein content and relevant quality traits to canopy reflectance spectra in wheat. Scientia Agricultura Sinica 38: 1332-1338.
Liu, M.B., Li, X.L., Liu, L.Y., Huang, J.F. and Tang, Y.L., 2014. Detection of crude protein, crude starch, and amylose for rice by hyper-spectral reflectance. Spectroscopy Letters 47: 101-106.
Lu, Y.M., Tan, W.P., Xiao, C.L., Fan, M.R. and Liao, Y.L., 2014. Effects of high temperature on starch formation of grain and activities of enzymes related to starch synthesis of quality rice varieties. Acta Agriculturae Boreali-Sinica 29: 135-139.
Lyon, J.G., Yuan, D., Lunetta, R.S. and Elvidge, C.D., 1998. A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing 64: 143-150.
Mason, R.E. and Singh, R.P., 2014. Considerations when deploying canopy temperature to select high yielding wheat breeding lines under drought and heat stress. Agronomy 4: 191-201.
Matsui, T., Omasa, K. and Horie. T., 2001.The difference in sterility due to high temperatures during the flowering period among japonica rice varieties. Plant Production Science 4: 90-93.
Nicola, C., Whitworth, M.B. and Fisk, I.D., 2018. Protein content prediction in single wheat kernels using hyper-spectral imaging, Food Chemistry 240: 32-42.
Nijs, I., Kockelbergh, F., Teughels, H., Blum, H., Hendrey, G. and Impens, I., 1996. Free air temperature increase (FATI): a new tool to study global warming effects on plants in the field. Plant Cell and Environment 19: 495-502.
Onoyama, H., Ryu, C., Suguri, M. and Iida, M., 2015. Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyper-spectral imaging: growing degree-days integrated model. Precision Agriculture 16: 558-570.
Richardson, A.J. and Wiegang, C.L., 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43: 1541-1552.
Shi, C.L., Luo, Z.Q., Jiang, M., Shi, Y.L., Li, Y.X., Xuan, S.L., Liu, Y., Yang, S.B. and Yu, G.K., 2017. An quantitative analysis of high temperature effects during meiosis stage on rice grain number per panicle. Chinese Journal of Rice Science 3: 658-664.
Tian, Y.L., Zheng, J.C., Zhang, B., Chen, J., Dong, W.J., Yang, F. and Zhang, W.J., 2010. Design of free air temperature increasing (FATI) system for upland with three diurnal warming scenarios and their effects. Scientia Agricultura Sinica 18: 3724-3731.
Xie, X.J., Li, B.B. and Zhu, H.X., 2012. Estimating contents of crude protein and amylose content in rice grain by hyper-spectral under different high temperature stress. Research of Agricultural Modernization 33: 481-484.
Xie, X.J., Zhang, Y.H., Wang, L., Yang, X.H., Yu, Q. and Bao, Y.X., 2017. Effect of asymmetric warming on rice (Oryza sativa) growth characteristics and yield components under a free air temperature increase apparatus. Indian Journal of Agricultural Sciences 87: 1384-1390.
Xie, X.J., Li, R.Y., Zhang, Y.H., Liu, L., Shen, S.H. and Bao, Y.X., 2016. Effect of elevated [CO2] on the leaf photosynthetic physiological characteristics in rice and maize. Jiangsu Agricultural Sciences 44: 120-123.
Xie, X.J., Li, Y.X., Li, R.Y., Zhang, Y.H., Huo, Y.T., Bao, Y.X. and Shen, S.H., 2013. Hyper-spectral characteristics and growth monitoring of rice (Oryza sativa) under asymmetric warming.International Journal of Remote Sensing 34: 8449-8462.
Xie, X.J., Shen, S.H., Li, Y.X. and Li, B.B., 2011. Prediction model of rice (Oryza sativa) yield under high temperature stress based on hyper-spectral remote sensing.Indian Journal of Agricultural Sciences81: 935-940.
Xie, X.J., Shen, S.H., Li, Y.X., Li, B.B., Cheng, G.F. and Yang, S.B., 2010. Red edge characteristics and monitoring SPAD and LAI for rice with high temperature stress. Transactions of the CSAE3: 183-190.
Xie, X.J., Zhang, Y.H., Li, R.Y., Shen, S.H. and Bao, Y.X., 2018. Asymmetric warming affects N dynamics and productivity of rice (Oryza sativa L.). Communications in Soil Science and Plant Analysis 49: 1032-1044.
Yang, Z., Li, Y.X., Xu, D.F. and Liu, S.D., 2009. Relationships of canopy reflectance spectra with wheat yield and yield components. Chinese Journal of Agrometeorology 29: 338-343.
Yuan, M.M., Zhu, J.G., Liu, G. and Wang, W.L., 2018. Response of diurnal variation in photosynthesis to elevated atmospheric CO2concentration and temperature of rice between cloudy and sunny days: a free air CO2 enrichments study. Acta Ecologica Sinica 38: 273-280.
Zhang, H., Song, T.Q., Wang, K.L., Wang, G.X., Hu, H. and Zeng, F.P., 2012. Prediction of crude protein content in rice grain with canopy spectral reflectance. Plant Soil and Environment 58: 514-520.