Main content area

Design of a hyperspectral nitrogen sensing system for orange leaves

Min, Min, Lee, Won Suk, Burks, Thomas F., Jordan, Jonathan D., Schumann, Arnold W., Schueller, John K., Xie, Huikai
Computers and electronics in agriculture 2008 v.63 no.2 pp. 215-226
image analysis, nitrogen content, plant nutrition, infrared radiation, hyperspectral imagery, oranges, Citrus sinensis, equipment performance, leaves
The orange (Citrus sinensis) is one of the most important agricultural crops in Florida. Heavy reliance on agricultural chemicals and low fertilizer use efficiencies in citrus production have raised environmental and economic concerns. In this study, a nitrogen sensor was developed to predict nitrogen concentrations in orange leaves. Four design criteria were chosen to maximize the sensing efficiency and reliability. They were: (1) coverage of the spectral N sensing range, (2) no moving parts, (3) single leaf detection, and (4) diffuse reflectance measurement. Based on chlorophyll and protein spectral absorption bands, the sensor's wavelength ranges were chosen to be 620-950nm and 1400-2500nm. A reflectance housing was designed to block environmental noise and to ensure single leaf measurement. A halogen light source, two detector arrays, two linear variable filters, and data acquisition cards with 16-bit analog-to-digital converters were used to collect data. The designed N sensor had a spectral resolution less than 30nm. Test results showed that the nitrogen sensor had good linearity (r >0.99) and stability. With averaged signal-to-noise ratio (SNR) of 299, the system was able to predict N content with a root mean square difference (RMSD) of l.69gkg⁻¹ for the validation data set. Using the N sensor, unknown leaf samples could be classified into low, medium and high N levels with 70% accuracy.