Potential of Fluorescence Index Derived from the Slope Characteristics of Laser-Induced Chlorophyll Fluorescence Spectrum for Rice Leaf Nitrogen Concentration Estimation
Source abstract
Abstract
Leaf nitrogen concentration (LNC) is a major biochemical parameter for estimating photosynthetic efficiency and crop yields. Laser-induced fluorescence, which is a promising potential technology, has been widely used to estimate the growth status of crops with the help of multivariate analysis. In this study, a fluorescence index was proposed based on the slope characteristics of fluorescence spectrum and was used to estimate LNC. Then, the performance of different fluorescence characteristics (proposed fluorescence index, fluorescence ratios, and fluorescence characteristics calculated by principal component analysis (PCA)) for LNC estimation was analyzed based on back-propagation neural network (BPNN) model. The proposed fluorescence index exhibited more stability and reliability for LNC estimation than fluorescence ratios and characteristics calculated by PCA. In addition, the effect of different kernel functions and hidden layer sizes of BPNN model on the accuracy of LNC estimation was discussed for different fluorescence characteristics. The optimal train functions “trainrp,” “trainbr,” and “trainlm” were then selected with higher R2 and lower standard deviation (SD) values than those of other train functions. In addition, experimental results demonstrated that the hidden layer size has a smaller impact on the accuracy of LNC estimation than the kernel function of the BPNN model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Leaf Properties and Growth Measurement · Remote Sensing in Agriculture · Water Quality Monitoring and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, Hubei, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, Hubei, China
Funding
National Key Research and Development Program of China · National Natural Science Foundation of China · Natural Science Foundation of Hubei Province · Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University · Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)
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