| 采用GA-ELM的寒地水稻缺氮量诊断方法 |
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| 引用本文： | 许童羽,郭忠辉,于丰华,徐博,冯帅.采用GA-ELM的寒地水稻缺氮量诊断方法[J].农业工程学报,2020,36(2):209-218. |
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| 作者姓名： | 许童羽 郭忠辉 于丰华 徐博 冯帅 |
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| 作者单位： | 沈阳农业大学信息与电气工程学院,沈阳 110161;辽宁省农业信息化工程技术中心,沈阳110161;沈阳农业大学信息与电气工程学院,沈阳,110161 |
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| 基金项目： | 十三五国家重点研发计划（2016YFD0200600），农业部光谱检测重点实验室开放课题基金 |
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| 摘 要： | 为快速、准确、无损实现寒地水稻缺氮量的诊断。该文基于田间试验系统采集的资料,研究东北粳稻氮素含量的亏损或富余与光谱反射率差值之间的关系,并建立东北粳稻氮素含量差值的反演模型。该文采用高光谱反演水稻的缺氮量,并为实施精准施肥提供参考依据,达到减肥不减产的目的,采用产量最高的原则来构建标准氮素含量与标准光谱反射率,并在此基础上,将获取的水稻叶片氮素含量和叶片光谱反射率分别与标准氮素含量和标准光谱反射率做差,得到氮素含量差值和光谱反射率差值,然后对光谱反射率差值分别采用离散小波多尺度分解、连续投影法(successive projections algorithm,SPA)和构建植被指数的方法进行降维处理,将处理后的结果分别作为偏最小二乘(partial least squares regression,PLSR)、极限学习机(extreme learning machine,ELM)和遗传算法优化极限学习机(genetic algorithm-extreme learning machine,GA-ELM)的建模输入,构建东北粳稻氮素含量差值的反演模型。结果分析表明:采用离散小波多尺度分解的结果建立的GA-ELM反演模型预测效果最好,训练集与验证集的R2均在0.7062以上,均方根误差(root mean square error,RMSE)均低于0.51mg/g以下,在预测能力、稳定性和泛化性上比PLSR和ELM的预测模型有明显提高。
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| 关 键 词： | 光谱分析 模型 高光谱 离散小波多尺度分解 遗传优化算法 极限学习机 |
| 收稿时间： | 2019/8/29 0:00:00 |
| 修稿时间： | 2019/12/30 0:00:00 |
Genetic algorithm combined with extreme learning machine to diagnose nitrogen deficiency in rice in cold region |
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| Xu Tongyu,Guo Zhonghui,Yu Fenghu,Xu Bo and Feng Shuai.Genetic algorithm combined with extreme learning machine to diagnose nitrogen deficiency in rice in cold region[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(2):209-218. |
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| Authors: | Xu Tongyu Guo Zhonghui Yu Fenghu Xu Bo and Feng Shuai |
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| Institution: | 1.College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang ,110161, China; 2.Liaoning Agricultural Information Technology Center, Shenyang ,110161, China,1.College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang ,110161, China;,1.College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang ,110161, China; 2.Liaoning Agricultural Information Technology Center, Shenyang ,110161, China,1.College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang ,110161, China; and 1.College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang ,110161, China; |
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| Abstract: | Nitrogen is a key plant nutrient and its deficiency or surplus could inhibit plant growth and reduce crop yield.Over the past decade,remote sensing has been increasingly used to diagnosis nitrogen deficiency in crop.Taking rice grown in cold region in northeast China as an example,this paper studies the relationship between nitrogen content in japonica rice and the difference between spectral reflectance based on data measured from field.This relationship was used to inversely estimate nitrogen deficiency in the rice based on hyperspectral images.In our analysis,the nitrogen content producing the highest yield was defined as standard nitrogen content and its associated spectral reflectance was defied as standard spectral reflectance.The difference between real nitrogen content and the standard nitrogen content,as well as the difference between the real spectral reflectance the standard spectral reflectance,were calculated respectively.The difference in spectral reflectance was dimensionally reduced using the discrete wavelet multi-scale decomposition,continuous projection method(successive projections algorithm,SPA)and vegetation index construction.The characteristic bands screened by SPA were 459、460、475、671、723、874 and 996 nm.Analysis showed that when the discrete wavelet multi-scale decomposition was used to reduce the dimension,the Sym8 wavelet mother function worked best when it was decomposed at the seventh layer.Comparing DVI,NDVI and RVI vegetation index found that the determination coefficient of the DVI index and nitrogen deficiency was significantly higher than that of NDVI and RVI index.The three indexes were used as input to the partial least squares(PLSR),the extreme learning machine(ELM)and genetic algorithm optimization extreme learning machine(GA-ELM).The GA-ELM model was most accurate with the R2 being 0.7062 for the training set and 0.7594 for the verification set;their associated RMSE was 0.5099mg/g and 0.4276mg/g respectively.The GA-ELM model based on the optimal vegetation index was least accurate,with the R2 for the training set and the verifying set being 0.6615 and 0.6509 respectively;their associated RMSE was 0.4415mg/g and 0.5312mg/g.Overall,GA-ELM improved stability and predictability of the model compared with PLSR and ELM.It can thus be used as a new method to detect nitrogen content in rice leaf,and has important implication in precision fertilization. |
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| Keywords: | spectrum analysis models hyperspectral discrete wavelet multiscale decomposition genetic optimization algorithm extreme learning machine |
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