A Model for Predicting Nitrogen of Lettuce Leaves Based on Hyperspectral Imaging

The goal of this study was to study on the model of hyperspectral imaging for detecting the nitrogen of crops nondestructively. Lettuce, as the research object, was cultivated in different levels of nitrogen in soilless cultivation method. In rosette stage, the hyperspectral images of lettuce leaves (390-1050 nm) were collected, and nitrogen content of the corresponding lettuce leaves were determined by Kjeldahl method. Then the average spectral data of region of interest of lettuce leaves were extracted by ENVI software, and Smoothing, Multiplicative scatter correction (Multiplication scatter correction, MSC), Standard normal variate transformation combined detrending (Standard normalized variable + detrending, SNV + detrending), First derivative, Second derivative, Orthogonal signal correction (Orthogonal signal correction, OSC) were used for pretreating the extracted raw spectral data respectively. Partial least squares regression (PLSR) was used to correlate the reflectance spectral of whole wavelengths with nitrogen content of lettuce leaves respectively, and the pretreatment methods above were studied on the influence of the models. The results showed that the model using OSC pretreatment method was the best. In addition, according to the regression coefficient of OSC+ PLSR model, sensitive wavelengths were selected to simplify the model. The spectral data at sensitive wavelengths in calibration set were reconstructed for the model, and prediction set was used to test the model. The results showed that determination coefficients (R-c(2), R-p(2)) from calibration set and prediction set were 0. 89, 0. 81, and the root mean. square error of calibration (RMSEC) and root mean. square error of prediction (RMSEP) were 0. 33 and 0. 45, respectively. An easier model with good performance was developed in this study, and it could provide an effective modeling method for predicting the nitrogen content in lettuce leaves.