| 基于高光谱图像的茶树LAI与氮含量反演 |
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| 引用本文： | 吴伟斌,李佳雨,张震邦,凌彩金,林贤柯,常星亮.基于高光谱图像的茶树LAI与氮含量反演[J].农业工程学报,2018,34(3):195-201. |
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| 作者姓名： | 吴伟斌 李佳雨 张震邦 凌彩金 林贤柯 常星亮 |
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| 作者单位： | 1. 华南农业大学工程学院，广州 510642；,2. 华南农业大学林学与风景园林学院，广州 510642；,1. 华南农业大学工程学院，广州 510642；,3. 广东省农业科学院茶叶研究所，广州 510642；,4. 华南农业大学资源环境学院，广州 510642,1. 华南农业大学工程学院，广州 510642； |
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| 基金项目： | 广东省现代农业产业技术体系创新团队-茶叶产业创新团队设施与机械化岗位专家（2017LM1093，2017LM1094，2016LM1119）. |
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| 摘 要： | 为了对茶树进行实时、快速、无损的叶面积指数LAI和氮含量检测,该文以英红九号茶树为试验对象,利用便携式高光谱成像仪采集光谱数据、人工破坏性采摘叶片进行叶面积指数的计算以及传统化学方法测量叶片氮含量,比较不同高光谱特征变换形式与LAI和氮含量之间的相关性,并选择其中相关系数较高的高光谱特征变量作为自变量,分别采用线性、指数、对数和抛物线表达式建立LAI和氮含量的回归模型。结果显示:在多种高光谱数据变量建立的模型中,以绿峰反射率R_g为自变量的对数拟合模型最佳,其拟合样本的决定系数R~2和验证样本的均方根误差RMSE值分别为0.9和0.087 6。以植被指数变量VI_4(红边面积/黄边面积)与氮含量建立的指数模型为最佳建模效果,拟合样本的决定系数R~2和验证样本的均方根误差RMSE值分别为0.830 3和0.102 9,研究结果可为茶树叶面积指数LAI和营养成分的无损检测提供参考。
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| 关 键 词： | 作物 氮素 模型 叶面积指数 高光谱图像 茶树 |
| 收稿时间： | 2017/7/14 0:00:00 |
| 修稿时间： | 2018/1/15 0:00:00 |
Estimation model of LAI and nitrogen content in tea tree based on hyperspectral image |
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| Wu Weibin,Li Jiayu,Zhang Zhenbang,Ling Caijin,Lin Xianke and Chang Xingliang.Estimation model of LAI and nitrogen content in tea tree based on hyperspectral image[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(3):195-201. |
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| Authors: | Wu Weibin Li Jiayu Zhang Zhenbang Ling Caijin Lin Xianke and Chang Xingliang |
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| Institution: | 1. College of Engineering, South China Agricultural University, Guangzhou 510642, China;,2. College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642,1. College of Engineering, South China Agricultural University, Guangzhou 510642, China;,3. Tea Research Institute, Guangdong Academy of Agricultural Science, Guangzhou 510642, China;,4. College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China and 1. College of Engineering, South China Agricultural University, Guangzhou 510642, China; |
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| Abstract: | Abstract: Leaf area index (LAI), the total area of plant leaves on the unit land area, is an important vegetation characteristic of plant canopy, which can reflect the growth status of vegetation. The effective detection of leaf nitrogen, a significant chemical element which could promote the growth of plant leaves, is beneficial to the precision fertilization and nutrient management of tea plantations, while it is also of great importance to the improve of quality and yield of tea leaves. In order to improve the production of Yinhong 9th tea, rational fertilization and protect the tea garden ecological environment, in this study, we used hyperspectral nondestructive testing technology to detect the LAI and nitrogen content with hyperspectral camera. Nitrogen is an important element that makes up tea chlorophyll, its content directly affects the synthesis of organic matter in tea tree, and it can affect leaf area index. Therefore, leaf area index and nitrogen content have certain correlation. At present, information on research of plant leaf area index for hyperspectral detection is limited. By using hyperspectral inversion tea nondestructive detection of two parameters, it can solve the problem of tea plant nutrition diagnosis, which may has positive impact on quality of the Yinhong 9th tea. Although the detection of nitrogen content of leaf is direct and accurate using the traditional chemical method, its complex operation, sample damaging and incapable to detect large area orchard in real-time, fast and nondestructive make it not the best method. To achieve real-time, fast and nondestructive detection of leaf area index (LAI) and nitrogen content of tea leaves, in this paper, a portable hyperspectral imager had been used to gather spectral data. Destructive leaf picking had been used to calculate leaf area index and traditional chemical method had been used to calculate the leaf nitrogen content. Using the Yinghong 9th tea tree as test subject, correlation analysis was done among hyperspectral characteristic variable data, LAI and leaf nitrogen content. Estimated model was built using high relevant hyperspectral characteristic parameter data, LAI and nitrogen content by linear, index, logarithm, parabola and etc. After that, evaluation of model performance and model accuracy test by root mean square error (RMSE) were conducted. After comparing different transformation of spectral data between the correlation of LAI and nitrogen content, the pretreated spectral parameters were used as independent variables to build the regression model of LAI and nitrogen content, respectively. The results showed that the logarithmic optimal fitting model was built between Green peak reflectance Rg and leaf area index, from which the regression coefficient value R2 and test samples RMSE value was 0.900 0 and 0.087 6, respectively, while the best modeling result was the model built between vegetation index VI4 and the nitrogen content, from which the regression coefficient value R2 and test samples RMSE value was 0.830 3 and 0.102 9, respectively. According to the result above, a better estimated model was developed using LAI detected by traditional chemical method, nitrogen content and hyperspectral data, which produced the theoretical basis for fast and nondestructive detection of LAI and nitrogen content of tea leaves in the possible future. However, sample process and modeling comparison of different growing season had not been done. The further research should be continued to study the relationship between LAI and the nutritional status, growth index and nutrient management of tea tree in different cultivation measures and ecological conditions. These results can provide reference for the nondestructive detection of LAI and the nutrient component of tea tree. |
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| Keywords: | crops nitrogen model leaf area index hyperspectral image tea canopy |
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