Abstract
Abstract
Chlorophyll, as an important factor for the normal growth and development of plants, is of great significance for the management of agricultural water and fertilizer. In this study, the chlorophyll content of maize leaves was taken as the research object, and the chlorophyll content prediction model was quantitatively studied. ASD FieldSpec Pro spectrometer was used to measure the spectral reflectance of the leaf samples, and the spectral curve characteristics of different contents were analyzed. The results show that: (1) The reflection spectrum undergoes the nine-point smoothing of Savitzky-Golay and combines with the MSC, NOR, and SNV transforms to significantly increase the signal-to-noise ratio of the reflection spectrum. The combination band with higher correlation can significantly improve the stability and prediction of the model ability. (2) In the PLSR model, MSC processing is performed on the smoothed spectrum, and the model established after the second-order differential transformation has the best effect, Rc2 = 0.95, RMSEC = 2.32, SEC = 1.35.
Reference8 articles.
1. Estimating chlorophyll content and bathymetry of lake tahoe using AVIRIS data [J];Hamilton;Remote Sensing of Environment,1993
2. Spectral Ratio of Reflectance for Estimating Chlorophyll Content of Leaf [J];Inada;Japanese Journal of Crop Science,1985
3. Estimation of spring wheat chlorophyll content based on hyperspectral features and PLSR model [J];Nijat;Transactions of the Chinese Society of Agricultural Engineering,2017
4. Hyperspectral models for estimating vegetation chlorophyll content based on red edge parameter [J];Yao;Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering,2009
5. Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations [J];Lamb;International Journal of Remote Sensing,2002