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 Classification of nitrogen application level…
Author:ZHOU Qiong1 2  YANG Hong-yun2 3*  YANG Jun2 3  SUN Yu-ting1 2 YANG Wen-ji2 3   SHI Qiang-qiang1 
Unit:ZHOU Qiong1 2  YANG Hong-yun2 3*  YANG Jun2 3  SUN Yu-ting1 2  
Keyword:rice  nitrogen application level  parameter optimization  
Classification:S511
Year,volume(Issue):page number:2017,48(8):524-1528
Summary:

【Objective】Support vector machine optimized by parameters was applied to predict classification of nitrogen application level for rice in order to provide scientific basis for accurate fertilization and high yield management of rice. 【Method】Four nitrogen application levels(from high to low, the amount of pure nitrogen was 225, 150, 75 and 0 kg/ha respectively) were set, and rice cultivar Jinyou 458 was used as experiment material. The SPAD values of the 6th to 9th phyllotaxis rice leaves were obtained by chlorophyll meter SPAD-502(SPAD value of leaf top, leaf middle and leaf bottom). The SPAD values of rice leaves under four nitrogen application levels were trained and predicted by using support vector machine optimized by particle swarm optimization and grid search algorithm. 【Result】For the 7th and 8th phyllotaxis leaf combination, the 7th, 8th and 9th phyllotaxis leaf combination and the 6th, 7th and 8th phyllotaxis leaf combination, the rice nitrogen application rate classification detected by support vector machine optimized by particle swarm optimization was better than support vector machine optimized by  grid search algorithm, its accuracy was 75.000% higher. Moreover, its accuracy on the 7th and 8th phyllotaxis normalized leaf combination was the highest(88.889%). 【Conclusion】Support vector machine optimized by particle swarm optimization is suitable for predict the classification of rice nitrogen application levels and meets the needs of agricultural research.

Foundation item:
国家自然科学基金项目(61562039,61363041,61462038);江西省教育厅科技项目(GJJ160374)
About The Author:
*为通讯作者,杨红云(1975-),副教授,主要从事农业信息技术研究工作,E-mail: nc_yhy@163.com。周琼(1995-),研究方向为机器学习及数据挖掘,E-mail: zhou_qiongqiong@163.com
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