Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative

Area of Science:

• Agricultural Science
• Remote Sensing
• Spectroscopy

Background:

• Leaf nitrogen concentration (LNC) is a key indicator for assessing crop growth status.
• Accurate LNC estimation is crucial for precision agriculture and remote sensing applications.
• Laser-induced fluorescence offers a promising technology for non-destructive LNC monitoring.

Purpose of the Study:

• To evaluate the performance of fluorescence spectrum (FS) and first-derivative fluorescence spectrum (FDFS) for estimating LNC in paddy rice.
• To investigate the combined application of FS and FDFS for improved LNC monitoring.
• To assess the robustness and stability of different spectral analysis methods for LNC estimation.

Main Methods:

• Analysis of fluorescence spectral information from paddy rice (Yangliangyou 6 and Manly Indica).
• Application of artificial neural networks for LNC estimation using FS and FDFS.
• Multivariate analysis and principal component analysis (PCA) on combined FS + FDFS data.
• Comparison of LNC estimation accuracy (R^2) and standard deviation (s.d.) for different methods.

Main Results:

• Individual FS and FDFS showed comparable performance for LNC estimation (R^2 ≈ 0.78).
• The combined FS + FDFS approach improved LNC estimation accuracy (R^2 = 0.813, s.d. = 0.051).
• PCA-enhanced FS + FDFS demonstrated superior robustness and stability (R^2 = 0.851, s.d. = 0.032) compared to individual spectral methods.

Conclusions:

• Combining FS and FDFS enhances the accuracy of LNC estimation in paddy rice.
• The integrated spectral approach offers a more robust and stable method for remote sensing-based crop monitoring.
• This study provides a valuable technique for precise agricultural management through accurate LNC assessment.