基于特征波长优化的便携式作物叶绿素检测仪研究

Development of Handheld Chlorophyll Detector Based on Characteristic Wavelengths Optimization

为了满足田间作物长势快速检测与指导变量管理的需求,基于作物叶绿素光谱响应特征波长筛选与优化,开发了一款便携式作物叶绿素检测仪。首先,采用高光谱仪采集玉米冠层325~1075 nm反射光谱,并采样萃取叶片叶绿素含量真值,开展叶绿素敏感响应波长筛选。经蒙特卡洛无信息变量消除(MC-UVE)算法在10~100个特征波长范围内进行变量筛选,表明采用50个特征波长时具有最优的叶绿素含量检测能力。其次,选择AS7265x型光谱传感器,以半峰宽20 nm的12个区间覆盖筛选的50个波长,设计的叶绿素检测仪包括传感器、主控制器、显示和控制等模块,实现作物冠层反射光数据采集、处理、显示和存储功能。开展传感器反射率标定与田间应用测试,基于传感器获取的反射率构建叶绿素含量偏最小二乘检测模型验证集决定系数为0.628;进一步组合归一化红边植被指数(NDRE:730、900 nm)和绿光归一化差值植被指数(GNDVI:535、900 nm),检测模型精度提高到0.69,模型嵌入系统最终实现了田间叶绿素含量快速检测,为作物长势高效分析提供了技术支持。

In order to meet the needs of rapid detection of crop growth and guide variable management,a portable crop chlorophyll detector was developed based on the selection and optimization of characteristic wavelength of crop chlorophyll spectral response.Firstly,the reflectance spectra of field leaves were collected by ASD spectrometers,and the true chlorophyll content of leaves was extracted to screen the wavelength of chlorophyll sensitive response based on hyperspectral reflectance.The Monre Carlo uninformative variables elimination(MC-UVE)algorithm was used to select 10~100 characteristic wavelengths,which showed that the optimal chlorophyll content detection ability was achieved when 50 characteristic wavelengths were used.Secondly,the AS7265x spectral sensor was selected to cover 50 wavelength positions screened in 12 intervals with FWHM(full width at half maximum)of 20 nm.The chlorophyll detector was designed to include modules such as sensor,main controller,display and control,and realize the functions of collection,processing,display and storage of reflected spectral data of crop canopy.Sensor reflectivity calibration and field application tests were carried out,based on the reflectivity of 12 bandwidths,a partial least squares regression detection model of chlorophyll content was constructed,and the coefficient of determination of the verification set was 0.628.The normalized difference red edge index(NDRE:730 nm,900 nm)and the green normalized difference vegetative index(GNDVI:535 nm,900 nm)were further combined,and the accuracy of the detection model was improved to be 0.69.By embedding the model into the system,the rapid detection of chlorophyll content in the field was realized,which provided technical support for the efficient analysis of crop growth.