| 光谱学与光谱分析 |
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| Estimation of Winter Wheat Protein Content Based on New Indexes |
| JIN Xiu-liang1, 2, 3, XU Xin-gang2, 3, LI Zhen-hai2, 3, WANG Qian2, 3, WANG Yan1, 2, 3, LI Cun-jun2, 3, WANG Ji-hua2* |
1. Key Laboratory of Crop Genetics and Physiology of Jiangsu Province/Key Laboratory of Crop Physiology, Ecology and Cultivation in Middle and Lower Reaches of Yangtse River of Ministry of Agriculture, Yangzhou University, Yangzhou 225009, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097, China |
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Abstract Wheat protein content is an important indicator often employed in wheat sale price. Spectral indexes and concurrent winter wheat protein content (WWPC) samples were obtained across three years. Data from 2008/2009 and 2009/2010 were utilized to build the new ratio indexes and product indexes, and then selected grey relational method and partial least squares method were used to improve the estimation accuracy of WWPC, data from 2011/2012 was utilized to validate model. The results showed that the correlation coefficients between ratio indexes and WWPC were better than that between single indexes and WWPC. The r of single indexes and ratio indexes were 0.726 and 0.751, respectively, and the product indexes were used to improve the parts of single indexes. The estimation accuracy of WWPC was improved by using GRA-PLS, the determination coefficients (R2) of single indexes, ratio indexes and product indexes were 0.537, 0.631 and 0.521, respectively, and corresponding root mean square errors (RMSE) were 0.665%, 0.564% and 0.574%, respectively. The results indicated that it was feasible to estimate WWPC by building new ratio indexes and product indexes, and then applying the GRA-PLS.
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Received: 2013-01-05
Accepted: 2013-03-12
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Corresponding Authors:
WANG Ji-hua
E-mail: w-jihua@263.net
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[1] Daniel C, Triboi E. European Journal of Agronomy, 2002, 16: 1.
[2] MacDonald G K. Australian Journal of Agricultural Research, 1992, 43: 949.
[3] WANG Yue-fu, JIANG Dong, YU Zhen-wen, et al(王月福，姜 东，于振文，等). Scientia Agricultura Sinica (中国农业科学), 2003, 36: 513.
[4] Bausch W C, Duke H R. Transactions of the American Society Agriculture Engineers, 1996, 32: 1869.
[5] Chen P F, Haboudane D, Tremblay N, et al. Remote Sensing of Environment, 2010, 114: 1987.
[6] Feng W, Yao X, Zhu Y, et al. European Journal of Agronomy, 2008, 28：394.
[7] Lee T, Reddy K R, Sassenrath-Cole G F. Crop Science, 2000, 40: 1814.
[8] WANG Ji-hua, HUANG Wen-jiang, ZHAO Chun-jiang, et al(王纪华，黄文江，赵春江，等). Journal of Remote Sensing(遥感学报), 2003, 7(4): 277.
[9] Wang Z J, Wang J H, Liu L Y, et al. Field Crops Research, 2004, 90: 311.
[10] LI Ying-xue, ZHU Yan, TIAN Yong-chao, et al(李映雪，朱 艳，田永超，等). Scientia Agricultura Sinica (中国农业科学), 2005, 38(7): 1332.
[11] XIAO Chun-hua, LI Shao-kun, LU Yan-li, et al(肖春华，李少昆，卢艳丽，等). Acta Agronomica Sinica (作物学报), 2007, 33(9): 1468.
[12] LU Yan-li, LI Shao-kun, WANG Ke-ru, et al (卢艳丽，李少昆，王克如，等). Acta Agronomica Sinica(作物学报), 2006, 32(2): 232.
[13] Li W G, Wang J H, Zhao C J, et al. Journal of Remote Sensing, 2008, 12: 506.
[14] Zhao C J, Liu L Y, Wang J H, et al. International Journal of Applied Earth Observation and Geoinformation，2005, 7(1): 1.
[15] Liu L Y, Wang J H, Bao Y, et al. International Journal of Remote Sensing, 2006, 27(4): 737.
[16] HUANG Wen-jiang, WANG Ji-hua, LIU Liang-yun, et al(黄文江，王纪华，刘良云，等). Remote Sensing Technology and Application(遥感技术与应用), 2004, 19(3): 143.
[17] HUANG Wen-jiang, WANG Ji-hua, LIU Liang-yun, et al(黄文江，王纪华，刘良云，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2004, 20(4): 203.
[18] CHEN Peng-fei, WANG Ji-shun, PAN Peng, et al(陈鹏飞，王吉顺，潘 鹏，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(9): 75.
[19] JIN Xiu-liang, XU Xin-gang, WANG Ji-hua, et al(金秀良，徐新刚，王纪华，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2012，32(11): 3103.
[20] Broge N H, Leblanc E. Remote Sensing of Environment, 2000, 76: 156.
[21] Rondeaux G, Steven M, Baret F. Remote Sensing of Environment, 1996, 55: 95.
[22] Haboudane D, Miller J R, Tremblay N, et al. Remote Sensing of Environment, 2002, 81: 416.
[23] Dash J，Curran P J. International Journal of Remote Sensing, 2004, 25：5403.
[24] Gitelson A A, Merzlyak M N. Journal of Plant Physiology, 1996, 148: 493.
[25] Penuelas J, Filella I, Gamon J A. New Phytologist, 1995, 131: 291.
[26] Metternicht G. International Journal of Remote Sensing, 2003, 24: 2855.
[27] Rouse J W, Haas R H, Schell J A, et al. 1974. Monitoring the Vernal Advancement of Retrogradation (Green Wave Effect) of Natural Vegetation. NASA/GSFC, Type Ⅲ, Final Report, Greenbelt, MD, USA, 1. |
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