| 基于特征光谱参数的苹果叶片叶绿素含量估算 |
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| 引用本文： | 冯海宽,杨福芹,杨贵军,李振海,裴浩杰,邢会敏.基于特征光谱参数的苹果叶片叶绿素含量估算[J].农业工程学报,2018,34(6):182-188. |
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| 作者姓名： | 冯海宽 杨福芹 杨贵军 李振海 裴浩杰 邢会敏 |
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| 作者单位： | 1.北京农业信息技术研究中心，北京 100097； 2. 国家农业信息化工程技术研究中心，北京 100097； 3. 农业部农业信息技术重点实验室，北京 100097； 4. 北京市农业物联网工程技术研究中心，北京 100097；,1.北京农业信息技术研究中心，北京 100097； 2. 国家农业信息化工程技术研究中心，北京 100097； 5. 河南工程学院土木工程学院，郑州 451191,1.北京农业信息技术研究中心，北京 100097； 2. 国家农业信息化工程技术研究中心，北京 100097； 3. 农业部农业信息技术重点实验室，北京 100097； 4. 北京市农业物联网工程技术研究中心，北京 100097；,1.北京农业信息技术研究中心，北京 100097； 2. 国家农业信息化工程技术研究中心，北京 100097； 3. 农业部农业信息技术重点实验室，北京 100097； 4. 北京市农业物联网工程技术研究中心，北京 100097；,1.北京农业信息技术研究中心，北京 100097； 2. 国家农业信息化工程技术研究中心，北京 100097；,1.北京农业信息技术研究中心，北京 100097； 2. 国家农业信息化工程技术研究中心，北京 100097； |
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| 基金项目： | 国家自然科学基金（41601346）；北京市自然科学基金项目（4141001）；国家高技术研究发展计划863课题（2011AA100703）。 |
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| 摘 要： | 果树叶绿素含量的快速、无损、准确监测,可以及时掌握果树的营养水平,对指导果树管理具有重要意义。该文利用2012年和2013年山东省肥城市潮泉镇下寨村的苹果叶片叶绿素含量和叶片光谱数据,分析了叶绿素含量和苹果叶片原始光谱及其变换形式之间的相关性,筛选出较优光谱参数,并利用随机森林法、偏最小二乘法、BP神经网络和支持向量机回归法进行估算和验证。结果表明:1)叶绿素含量与叶片原始光谱及其变换形式之间的最优光谱参数分别为554和708 nm的原始光谱反射率,554和708 nm倒数之对数光谱,535、569、700和749 nm一阶微分光谱以及557和708 nm连续统去除光谱;2)随机森林、偏最小二乘法、BP神经网络和支持向量机估算模型的R2分别为0.94,0.61,0.66和0.60,RMSE分别为0.34,0.78,0.75和0.81 mg/dm2。说明随机森林算法模型用于估算苹果叶片叶绿素含量效果较好,为及时了解果树养分状况及果树营养诊断提供技术支持。
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| 关 键 词： | 叶绿素 光谱分析 支持向量机 苹果叶片 高光谱 随机森林 偏最小二乘法 BP神经网络 |
| 收稿时间： | 2017/9/30 0:00:00 |
| 修稿时间： | 2018/1/30 0:00:00 |
Estimation of chlorophyll content in apple leaves base on spectral feature parameters |
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| Feng Haikuan,Yang Fuqin,Yang Guijun,Li Zhenhai,Pei Haojie and Xing Huimin.Estimation of chlorophyll content in apple leaves base on spectral feature parameters[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(6):182-188. |
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| Authors: | Feng Haikuan Yang Fuqin Yang Guijun Li Zhenhai Pei Haojie and Xing Huimin |
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| Institution: | 1. Beijing Research Center for Information Technology In Agriculture, Beijing,100097, 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, 100097, China; 4.Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;,1. Beijing Research Center for Information Technology In Agriculture, Beijing,100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 5. College of Civil Engineering, Henan Institute of Engineering, Zhengzhou 451191, China,1. Beijing Research Center for Information Technology In Agriculture, Beijing,100097, 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, 100097, China; 4.Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;,1. Beijing Research Center for Information Technology In Agriculture, Beijing,100097, 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, 100097, China; 4.Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;,1. Beijing Research Center for Information Technology In Agriculture, Beijing,100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; and 1. Beijing Research Center for Information Technology In Agriculture, Beijing,100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; |
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| Abstract: | Abstract: Chlorophyll content is an important parameter for evaluating the growth status using spectral reflectance feature. The rapid, non-destructive and accurate monitoring of chlorophyll content using hyperspectral reflectance has become an important research content for monitoring the growth of fruit trees. The object of this study was to analyze the relevance of chlorophyll content and the original spectrum of apple leaves and its transformation forms and to select optimum spectral parameters. Chlorophyll content model was built and verified by random forest (RF), partial least square (PLS), back propagation (BP) neural network and support vector machine (SVM). Parameters of samples including spectral reflectance of leaves and the concurrent apple leaves chlorophyll content were acquired in Tai''an, Shandong, China during apple growth seasons in 2012 and 2013. The result showed: 1) The optimum spectral parameters between chlorophyll content and the original spectrum reflectance (R) of apple leaves were 554 and 708 nm, and the correlation coefficients of that were ?0.46 and ?0.66 respectively. The optimum spectral parameters between the chlorophyll content and the logarithm of reciprocal of spectra of apple leaves were 554 and 708 nm, and the correlation coefficients of that were 0.46 and 0.66 respectively. The optimum spectral parameters between chlorophyll content and the first order differential (D) reflectance spectra of apple leaves were 535 (trough), 569 (peak), 700 (trough) and 749 nm (peak), and the correlation coefficients of that were ?0.66, 0.64, ?0.69 and 0.76 respectively. The optimum spectral parameters between chlorophyll content and the continuum removal (CR) reflectance spectra of apple leaves were 557 (trough) and 708 nm (trough), and the correlation coefficients of that were ?0.35 and ?0.73, respectively. 2) The out-of-bag importance between chlorophyll content and reflectance spectra was analyzed using out-of-bag data of RF, the size order of out-of-bag data was D749 > CR708 > D569 > D700>D535 > CR557 > log(1/708) > log(1/554) > R554 > R708, the maximum and minimum were D749 and R708, respectively, and the corresponding values were 166.28 and 7.34, respectively. Based on out-of-bag data analysis, the D749, CR708, D569, D700 and D535 were chosen to build chlorophyll content estimation model using RF, PLS, BP, and SVM. The result showed that the R2, RMSE (root mean square error) and RE (relative error) were 0.94, 0.34 mg/dm2 and 0.08% respectively for RF-estimation model; the R2, RMSE and RE were 0.61, 0.78 mg/dm2 and 0 respectively for PLS-estimation model; the R2, RMSE and RE were 0.66, 0.75 mg/dm2 and 0.25% respectively according to BP-estimation model; the R2, RMSE and RE were 0.60, 0.81 mg/dm2 and 0.70% respectively according to SVM-estimation model. The accuracies of RF, PLS, BP and SVM validation model were compared. The R2 of RF, PLS, BP and SVM model was 0.86, 0.91, 0.60 and 0.66, respectively; the RMSE of RF, PLS, BP and SVM model was 0.79, 0.75, 1.18 and 1.20 mg/dm2, respectively; the RE of RF, PLS, BP and SVM model was 1.31%, 6.68%, 3.19% and 0.46%, respectively. The study showed that the accuracy of RF estimation model is much higher than PLS, BP and SVM. The stability of the RF validation model is also higher than that of the PLS and BP validation model, which is close to the PLS regression. Overall, the RF algorithm has better performance than PLS, BP and SVM algorithm. Therefore, using hyperspectral technology with RF algorithm can estimate apple leaf chlorophyll content more rapidly and accurately and provide a theoretical basis for rapid nutrition diagnosis and growth monitoring. |
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| Keywords: | chlorophyll spetrum analysis support vector machines apple leaves hyperspectral random forest partial least squares BP neural network |
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