Determination of Pear Leaf Nitrogen Content Based on Multi-Spectral Imaging Technology and Multivariate Calibration
Fast determination of mineral nutrition contents of fruit trees is essential for orchard precision fertilizing management. A multi-spectral imaging system was developed and tested for the measurement of leaf nitrogen content of fruit trees in the study. Images taken using this system included visible images(R-G-B) and near-infrared image(NIR). These images were further processed into several indices such as RVI, NDVI, GNDVI, -log(R) and –log(G). Total 185 leaf samples were picked from Huang-hua pear trees which were planted in three orchards with different nitrogen fertilizing levels. Among them, 135 samples were randomly sorted out as calibration set with the remaining 50 as prediction set. A SPAD-502 chlorophyll meter was used for nitrogen reference measurement. In calibration modeling, leaf front and back faces were photographed respectively. Calibration models were developed based on single variant as well as multiple variants. The result shows that calibration models based on leaf front face are better than those based on leaf back face. Among others, R and G are the most important factors for nitrogen determination with less contribution of B and NIR. Based on the images of leaf front face, R, G, RVI, NDVI, GNDVI, -log(R) and –log(G) were found significantly correlated with nitrogen content with correlation coefficients of prediction (r_pre^2) of 0.7516, 0.7396, 0.7332, 0.7220, 0.7588, 0.7598 and 0.7379 respectively. The linear combinations of R-G-B-NIR, RVI-NDVI-GNDVI and NDVI-GNDVI achieved better prediction accuracy with r_pre^2 of 0.8157, 0.7775 and 0.7661 respectively. To further improve the prediction accuracy, a three-layer BP-ANN was developed with the three combinations as its input data. The result shows that BP-ANN has an excellent performance to predict nitrogen contents. BP-ANN with the input of R-G-B-NIR performs best with r_pre^2 of 0.9386 and maximum error of 3.52(SPAD). The study suggests that multi-spectral imaging system integrated with prediction model of BP-ANN with original reflectance intensity of R-G-B-NIR channels as its input data is promising for in situ measurement of nitrogen content of fruit tree.
H. Q. Yang and G. Lv, "Determination of Pear Leaf Nitrogen Content Based on Multi-Spectral Imaging Technology and Multivariate Calibration", Key Engineering Materials, Vols. 467-469, pp. 718-724, 2011