摘要
现有光合预测模型主要考虑环境因子对植物光合作用的影响,模型只能对生理状况相似的叶片进行光合速率预测,本文面向不同生长状态叶片光合速率预测模型的建模需求,提出融合叶片暗荧光参数Fv/Fm的多环境因子光合速率预测模型构建方法。试验以不同生长状态的茄子叶片为样本,在获取暗荧光参数的同时,分别测量不同温度、CO2浓度和光照强度下的光合速率,构建建模样本集。在此基础上,利用遗传支持向量机算法,建立了光合速率统一预测模型,其训练集决定系数为0.8895,均方根误差为3.2679μmol/(m2·s)。采用异校验方式进行模型验证,结果表明,融合荧光参数后模型精度显著提高,光合速率预测值与实测值拟合斜率为0.9046,截距为0.3641,说明引入Fv/Fm后,模型可实现对不同生理状态叶片光合速率的精准预测。
The photosynthetic models now mainly consider the effects of environmental factors on plant photosynthesis. These models can only predict photosynthetic rate of leaves with similar physiological conditions. In order to meet the needs of modeling the models for leaf photosynthetic rate prediction in different growth states, a method for constructing a multi-environmental factors photosynthetic rate prediction model incorporating dark fluorescence parameters Fv/Fm was proposed. Taking eggplant leaves of different growth states as samples,the Fv/Fm were obtained,and the photosynthetic rates were measured at different temperatures,CO2 concentrations and light intensities to construct a set of modeling samples. Then a unified prediction model of photosynthetic rate was established by using genetic support vector regression. The determinant coefficient of the model was 0. 889 5,and the root mean square error was 3. 267 9 μmol/( m2·s). The results of XOR checkout showed that the accuracy of the model was improved remarkably by fusing the Fv/Fm. The fitting slope between the predicted and measured photosynthetic rates was 0. 904 6,the intercept was 0. 364 1,which showed that the model could predict an exact photosynthetic rate of leaves with different physiological conditions by leading in Fv/Fm.
作者
胡瑾
高攀
陈丹艳
李斌
荆昊男
张海辉
HU Jin;GAO Pan;CHEN Danyan;LI Bin;JIN Haonan;ZHANG Haihui(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling,Shaanxi 712100,China;Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services,Yangling,Shaanxi 712100,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第4期328-336,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(31671587)
陕西省重点研发计划项目(2018TSCXL-NY-05-02)
西安市科技计划项目(201806117YF05NC13(4))
中央高校基本科研业务费专项资金项目(2452017124)。
关键词
茄子叶片
生理状态
暗荧光参数
环境因子
光合速率
预测模型
eggplant leaves
physiological state
dark fluorescence parameters
environmental factors
photosynthetic rate
predicted model
