基于数字图像处理的油茶生物量估测模型研究

发布时间：2018-07-12 11:40

本文选题：油茶 + 生物量　；参考：《湖南农业大学》2016年硕士论文

【摘要】：针对传统方法在获取植物生物量时繁琐、复杂且具有破坏性的缺点,本文以油茶为主要研究对象,以数字图像处理技术为研究基础,对三种不同树龄的油茶种植片区,通过采用数字图像处理技术,对其不同成长阶段和不同阶段的部分器官的生物图像特征和对应的生物量估测模型进行了探讨,主要工作和主要结果包括：(1)依据数字图像照片的非接触特性,对油茶植株进行无损拍照,通过数字图像处理软件提取油茶植株的冠幅直径(D)、植株高度(H)以及侧面面积(S)等特征数据。(2)依据油茶的幼苗期、始果期、盛果期等不同生长时期的图像特征,分析不同时期油茶生物量与各特征变量以及各种组合变量之间的相关性。选择相关系数最高的冠幅直径(D)、植株高度(H)、植株高度与冠幅平方乘积(HD2)和侧面积(S)四个因子作为油茶生物量估测模型的自变量。(3)以单一特征数据为自变量、以油茶生物量为因变量,通过线性回归、稳健线性回归、多项式回归、随机森林和支持向量回归的建模方法,构建油茶不同时期生物量估测模型,并检验了各估测模型的精度。结果表明,油茶幼林期随机森林模型估测效果较好,始果期多项式回归模型估测效果较好,盛果期线性回归模型估测效果较好。(4)以多个特征数据作为自变量,分别建立不同时期的油茶生物量估测模型。通过分析比较构建的向量回归、神经网络、逐步回归、多元线性回归、和随机森林模型,验证各模型精度,比较各种建模方法的预测效果,结果表明,在油茶三个生长时期多元线性回归模型的预测精度都较高,模型性能优秀。
[Abstract]:Aiming at the disadvantages of traditional methods in obtaining plant biomass, which are complicated, complicated and destructive, this paper takes Camellia oleifera as the main research object, and takes digital image processing technology as the research basis, to three different tree age Camellia oleifera planting areas. By using digital image processing technology, the biological image characteristics and biomass estimation models of some organs in different growth stages and different stages were discussed. The main work and results are as follows: (1) according to the non-contact characteristics of the digital image, the Camellia oleifera plants were photographed without damage. The characteristic data such as crown diameter (D), plant height (H) and side area (S) of Camellia oleifera were extracted by digital image processing software. The correlation of Camellia oleifera biomass with each characteristic variable and various combination variables in different periods was analyzed. Four factors with highest correlation coefficient, crown diameter (D), plant height (H), plant height and crown square product (HD2) and lateral area (S) were selected as independent variables of Camellia oleifera biomass estimation model. (3) single characteristic data was used as independent variable. Taking Camellia oleifera biomass as dependent variable, the models of biomass estimation in different periods of Camellia oleifera were constructed by linear regression, robust linear regression, polynomial regression, stochastic forest and support vector regression, and the accuracy of each estimation model was tested. The results showed that the estimation effect of random forest model in young stage of Camellia oleifera was better, that of polynomial regression model in early fruiting stage was better, and that of linear regression model in fruiting stage was better. (4) multiple characteristic data were used as independent variables. The biomass estimation models of Camellia oleifera were established in different periods. By analyzing and comparing the constructed vector regression, neural network, stepwise regression, multivariate linear regression, and stochastic forest model, the accuracy of each model is verified, and the prediction results of various modeling methods are compared. In the three growing periods of Camellia oleifera, the multiple linear regression models have high prediction accuracy and excellent performance.
【学位授予单位】：湖南农业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：S794.4;TP391.41

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