| 基于高光谱的双季稻分蘖数监测模型 |
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| 引用本文： | 曹中盛,李艳大,叶春,舒时富,孙滨峰,黄俊宝,吴罗发.基于高光谱的双季稻分蘖数监测模型[J].农业工程学报,2020,36(4):185-192. |
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| 作者姓名： | 曹中盛 李艳大 叶春 舒时富 孙滨峰 黄俊宝 吴罗发 |
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| 作者单位： | 江西省农业科学院农业工程研究所,江西省智能农机装备工程研究中心,江西省农业信息化工程技术研究中心,南昌 330200 |
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| 基金项目： | 国家重点研发计划项目（2016YFD0300608）;国家青年拔尖人才支持计划项目;江西省科技计划项目（20182BCB22015,20161BBI90012,20192ACB80005,20192BBF60050）;江西省"双千计划"项目和江西省农业科学院创新基金博士启动项目（20182CBS001）联合资助 |
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| 摘 要： | 旨在阐明双季稻分蘖数与冠层反射高光谱间的定量关系,构建基于高光谱的双季稻分蘖数监测模型。基于不同早、晚稻品种和施氮水平的田间试验,于关键生育期(分蘖期、拔节期和孕穗期)测定早、晚稻分蘖数,同步使用FieldSpec HandHeld 2型高光谱仪采集早、晚稻冠层反射高光谱数据,分别利用光谱指数法和连续小波变换构建新型光谱指数和敏感小波特征对双季稻分蘖数进行监测,建立双季稻分蘖数光谱监测模型,并用独立试验数据进行检验。结果表明,新型光谱指数和敏感小波特征对双季稻分蘖数的监测效果优于其他类型光谱参数(植被指数和“三边”参数),其中位于红边区域的小波特征db7(s9,w735)监测早稻分蘖数时表现最优,监测模型R2为0.754,模型检验相对均方根误差RRMSE为0.128;位于红边区域的小波特征mexh(s6,w714)监测晚稻分蘖数时表现最优,监测模型R2为0.837,模型检验RRMSE为0.112。研究结果可为双季稻分蘖数快速无损监测和群体质量精确调控提供理论基础与技术支持。
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| 关 键 词： | 双季稻 分蘖数 高光谱 小波特征 模型 |
| 收稿时间： | 2019/12/11 0:00:00 |
| 修稿时间： | 2020/1/19 0:00:00 |
Model for monitoring tiller number of double cropping rice based on hyperspectral image |
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| Cao Zhongsheng,Li Yand,Ye Chun,Shu Shifu,Sun Binfeng,Huang Junbao and Wu Luofa.Model for monitoring tiller number of double cropping rice based on hyperspectral image[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(4):185-192. |
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| Authors: | Cao Zhongsheng Li Yand Ye Chun Shu Shifu Sun Binfeng Huang Junbao and Wu Luofa |
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| Institution: | Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China,Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China,Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China,Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China,Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China,Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China and Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, China |
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| Abstract: | The fast,real-time and non-destructive monitoring of double-cropping rice tiller number has important practical significance for growth diagnosis and yield prediction.Hyperspectral sensing has been proved effective to estimate the rice growth parameters,such as the chlorophyll content,leaf area index and biomass,yet few investigations pay attention to the tiller number.The objective of this study was to establish a regulation model for estimating double-cropping rice tiller number based on the hyperspectral reflectance across a wide range of growth stages(tillering stage,jointing stage,and booting stage).In the presented study,the tiller number and hyperspectral reflectance data were firstly obtained from two double-cropping rice field experiments,which encompassed variations in two years,four cultivars and five nitrogen application rates.Then the sensitive spectral indices and wavelet features were extracted from the hyperspectral reflectance data through spectral indices approach and continuous wavelet analysis,respectively.Finally,the regression models for tiller number estimation based on sensitive spectral indices and wavelet features were developed and validated using independent field experiment datasets.The results suggested that the newly developed spectral indices and sensitive wavelet features with red-edge bands performed better than the published vegetation indices and‘three edge’parameters.The normalized different spectral index named NDSI(ρ975,ρ714)was strongly related to the early rice tiller number.It had a determination coefficient(R2)of 0.724 in calibration and relative root mean square error(RRMSE)of 0.151 in validation.The ratio spectral index RSI(ρ788,ρ738)strongly related to the late rice tiller number with R2 of 0.792 and RRMSE of 0.142 in calibration and validation,respectively.Compared with the published vegetation indices,‘three edge’parameters and newly developed spectral indices,the sensitive wavelet features observed in the red-edge region with high scales(29 and 26)performed best in the double-cropping rice tiller number estimation.The wavelet feature named db7(s9,w735)was strongest related to the early rice tiller number.It had R2 of 0.754 in calibration and RRMSE of 0.128 in validation.The wavelet feature named mexh(s6,w714)was strongest related to the late rice tiller number.It had R2 of 0.837 in calibration and RRMSE of 0.112 in validation.Additionally,the sensitive spectral indices and wavelet features also could reduce the saturation effect with low noise equivalent(NE).It meant that in the condition the optical sensors equip few bands,the spectral indices NDSI(ρ975,ρ714)and RSI(ρ788,ρ738)could be used to monitor the early rice and late rice tiller number.Furthermore,the wavelet features db7(s9,w735)and(s6,w714)could improve the accuracy for monitoring double-cropping rice tiller number based on the hyperspectral reflectance data with monitoring models of TNearly=3.632×db7(s9,w735)+7.318 and TNlate=-15.351×mexh(s6,w714)+8.173,respectively. |
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| Keywords: | mechanization machine vision navigation combine harvester region growing algorithm multi-peak Gaussian fitting least squares |
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