In this study, a 400-1000nm hyperspectral camera was applied, and FS13, a product of Hangzhou Color Spectrum Technology Co., LTD., could be used for related research. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection).
Lettuce is rich in protein, carbohydrates, vitamins and other nutrients, and the planting area is wide. Nitrogen is one of the most important elements affecting the growth of lettuce. To establish a rapid, efficient and non-destructive method for nitrogen content detection of lettuce is convenient to guide the reasonable fertilization of lettuce. At present, there are few reports on the use of hyperspectral image technology to detect nitrogen content in lettuce leaves. In this study, hyperspectral image technology was applied to nondestructive detection of nitrogen content in lettuce leaves. By studying the effects of various spectral pretreatment methods on PLSB modeling, appropriate spectral pretreatment methods were selected for lettuce leaves, and sensitive wavelengths suitable for predicting nitrogen content in lettuce leaves were optimized. An attempt was made to establish the simplest and optimal prediction model of nitrogen content in lettuce leaves. This set of methods has not been reported, and it also provides a basis for the development of portable vegetable nutrient element detector, which has strong practical value.
The hyperspectral images of 60 lettuce leaves were collected by hyperspectral image technology, and the nitrogen content of the corresponding lettuce leaves was determined by AutoAnalyzer3 continuous flow analyzer. The average spectral data of 50×50 regions on the surface of the raw lettuce leaves was extracted by ENVI software. The extracted average spectral data were preprocessed (8 kinds of pretreatment methods). Finally, the original spectral data and 8 kinds of pretreatment spectral data were used as the input of PLSR to establish 9 prediction models for nitrogen content of lettuce. By comparing the results of these 9 prediction models, the optimal prediction model OSC+PLSR was selected, and the regression coefficient diagram of the OSC+PLSR model was analyzed. 13 sensitive wavelengths were selected, and then 13 sensitive wavelengths were taken as PLSR input. Finally, the prediction model of OSC+SW+PLSR lettuce nitrogen content was established. Compared with OSC+PLSR model, the prediction efficiency has been greatly improved, which can be used as an efficient, accurate and non-destructive new method for the prediction of nitrogen content in lettuce leaves, and can provide a reference for nitrogen nutrition diagnosis and economic and rational fertilization of lettuce.