Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

Area of Science:

• Agricultural Science
• Remote Sensing
• Plant Physiology

Background:

• Accurate estimation of plant biochemical content is crucial for crop management.
• Spectral reflectance techniques offer a non-destructive method for assessing plant health and composition.

Purpose of the Study:

• To develop and evaluate methods for remotely sensing nitrogen (N), crude fat (EE), and crude fiber (CF) concentrations in corn leaves.
• To compare the effectiveness of raw spectral reflectance versus first derivative reflectance for biochemical estimation.

Main Methods:

• Spectral reflectance measurements were taken on fresh corn leaves.
• Curve-fitting analyses were used to establish estimation models correlating reflectance data with biochemical concentrations.
• Model performance was evaluated using coefficient of determination (R2), root mean square error (RMSE), and relative error of prediction (REP).

Main Results:

• Estimation models were developed for N, EE, and CF concentrations with R2 values of 0.891, 0.698, and 0.480, respectively.
• First derivative reflectance provided superior estimation accuracy compared to raw spectral reflectance for all three variables.
• Optimal estimation wavelengths were identified at 759 nm for N, 1954 nm for EE, and 2370 nm for CF.

Conclusions:

• First derivative spectral reflectance is a promising technique for non-destructively estimating key biochemical components in corn leaves.
• The developed models, particularly for nitrogen, show high potential for application in precision agriculture and crop monitoring.