Estimation of Nitrogen Content by Spectral Responses of Cabbage Seedlings Using Artificial Neural Network Approach

Abstract

Reflectance spectra of leaves are informative for non-destructive monitoring of thenutrition status. Nitrogen content of cabbage seedling leaves cultivated with five different fertilizationconditions was measured by Kjeldahl method, and was correlated with spectrum reflectance. Sincethe wavelength of the band-pass filters for silicon CCD multi-spectral imaging was shorter than1000nm, a selected band (450-950 nm) was considered in addition to the full band (400-2500 nm).Step-wise multi-linear regression (SMLR) and artificial neural network (ANN) were used to evaluatethe effectiveness of the wavelength in determination of nitrogen content. The analytical results ofSMLR calibration equations with four significant wavelengths (566, 574, 1396, and 1530 nm;rc2=0.82, SEC=9.85 mg/g, and SEV=12.35 mg/g) were significantly improved by an ANN model(rc2=0.89, SEC=8.27 mg/g, and SEV=8.84 mg/g) with cross-learning and random- samplingstrategies, in which the over-fitting was greatly reduced. For developing a practical multi-spectral imaging system with commercially available band-pass filters, the ANN model with four inputs of490, 570, 600, and 680 nm was trained to obtain a comparable result (rc2=0.87, SEC=8.73 mg/g,rv2=0.84, and SEV=9.60 mg/g) to the best calibration equation of SMLR with seven wavelengths inthe full band. The potential of using the ANN model developed in this study with multi-spectralimaging to monitor the nitrogen content of cabbage seedling is expected.