摘要
以不同截形叶螨(Tetranychus truncatus Ehara)危害等级下枣叶片高光谱和叶绿素含量数据为基础,分析不同截形叶螨危害等级(0级、1级、2级、3级、4级)下枣叶片高光谱特征,构建基于一阶微分光谱的不同截形叶螨危害等级枣叶片叶绿素含量高光谱线性回归估测模型。结果表明:截形叶螨危害造成叶片中叶绿素含量减少,导致光谱反射率降低,表现为随危害等级的增加叶绿素含量呈逐级减少趋势。在不同截形叶螨危害等级枣叶片叶绿素估测模型中,危害等级为0级时,模型拟合度最好,达到0.810,表明利用高光谱数据构建不同危害等级枣叶片叶绿素含量估算模型具有一定的潜力,对危害植被叶片的虫害诊断意义重大。
The chlorophyll content of plants is one of important indicators to reflect the physiological functions,health status and other nutrients of plant leaves.Real-time dynamic estimation of the chlorophyll content of Ziziphus jujuba Mill leaves has scientifically guiding significance for jujube cultivation and management.Based on the hyperspectral and chlorophyll content data of jujube leaves under different Tetranychus truncatus Ehara damage grades,hyperspectral characteristics of jujube leaveswere analyzed under different Tetranychus truncatus Ehara damage grades(grade 0,grade 1,grade 2,grade 3,grade 4),high spectral linear regression estimation model of chlorophyll content in jujube leaves was constructed based on the sensitive band of first order differential data.The spectral reflectance of jujube leaves reduced with decrease of blade chlorophyll content.The established linear regression model was effective,and the model had the highest determination coefficient R2 of 0.810 when the damage level was 0.The results also suggested that the spectrum technology could be applied widely in monitoring blade chlorophyll content of jujube and important for the pest diagnosis in damages of vegetation leaves.
作者
高亚利
王振锡
连玲
师玉霞
杨勇强
GAO Yali;WANG Zhenxi;LIAN Ling;SHI Yuxia;YANG Yongqiang(College of Forestry and Horticulture, Xinjiang Agricultural University, Urumqi 830052, China;Key Laboratory of Forestry Ecology and Industrial Technology in Arid Area of Xinjiang Education Department,Urumqi 830052,China)
出处
《西北农业学报》
CAS
CSCD
北大核心
2020年第4期613-621,共9页
Acta Agriculturae Boreali-occidentalis Sinica
基金
新疆高校科研计划(XJEDU2017M013)。
关键词
高光谱
枣叶片
截形叶螨
危害等级
叶绿素含量
估算模型
Hyperspectral
Jujube leaves
Tetranychus truncatus Ehara
Damage grades
Chlorophyll content
Estimation model
