Estimation of Leaf Nitrogen Content using Artificial Neural Network with Cross-Learning Scheme and Significant Wavelengths

Abstract

Reflectance from crops provides spectral information for non-destructive monitoring of their nutrition status. In order to develop a multi-spectral imaging system for remote sensing of the nitrogen content of crops, the significant wavelengths and calibration models were carefully evaluated in this study. The significant wavelengths in full band (400-2500 nm) and a selected band (450-950 nm), which is suitable for silicon CCD cameras, were investigated. In this article, significant wavelengths for estimating nitrogen content of cabbage seedling leaves were first determined by SMLR (stepwise multi-linear regression) analysis. A proposed ANN (artificial neural network) model with cross-learning scheme (ANN-CL) was further developed to increase the prediction accuracy. To comply with the design of a practical multi-spectral imaging system using silicon CCD cameras and commercially available bandpass filters, an ANN-CL model with four inputs of spectral absorbance at 490, 570, 600, and 680 nm was developed. The calibration results (rc = 0.93, SEC = 0.873%, and SEV = 0.960%) reduced the SEV about 15% when compared with the SMLR method with four wavelengths (SEV = 1.099%). In addition, the results were comparable to that of SMLR with seven wavelengths (rc = 0.94, SEC = 0.806%, and SEV = 0.993%) in the full band. These results indicated that the ANN model with cross-learning using spectral information at 490, 570, 600, and 680 nm could be used to develop a practical remote sensing system to predict nitrogen content of cabbage seedlings.