Study on the Hyperspectral Discrimination Method of Lettuce Leaf
Greenness
GUO Jing-jing1, YU Hai-ye1, LIU Shuang2, XIAO Fei1, ZHAO Xiao-man1, YANG Ya-ping1, TIAN Shao-nan1, ZHANG Lei1*
1. School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
2. College of Horticulture, Jilin Agricultural University, Changchun 130118, China
Abstract：Lettuce leaf greenness is important in the physiological and sensory evaluation of crop quality. Based on the comparison of existing methods for greenness discrimination, combined with the application status and prospects of hyperspectral detection and analysis technology in the detection of plant physiological information, the research on the application method of hyperspectral technology in the greenness discrimination of lettuce leaves was carried out. The quantification of sensory evaluation of the vegetable quality and developing a multifunctional synchronous collection device for physiological information based on hyperspectral technology provide necessary theoretical support. Lettuce is the subject of study. Cultivation experiments were conducted under three different light environments, and relative chlorophyll content (SPAD) was used as a parameter to respond to greenness. Acquisition of dynamic hyperspectral and SPAD data throughout the life cycle of lettuce. Study of hyperspectral response characteristics to leaf greenness. The variation pattern of the hyperspectral curve was analyzed. Finally, a relationship model between hyperspectrum and SPAD was developed. The Savitzky-Golay convolution smoothing (SG) method was used to reduce the noise of the original hyperspectral data. The smoothed data was combined with the three preprocessing methods of multivariate scattering correction (MSC), standard normal variable transformation (SNV) and first derivative (FD), and finally adopted competitive adaptive reweighted sampling (CARS) and extraction effective vegetation index (VI) two methods for sensitive wavelength extraction. Combine the two methods of partial least squares (PLS) and least squares support vector machine (LSSVM) for modeling, and use the coefficient of determination (R2) and root mean square error (RMSE) as evaluation indicators to select the optimal greenness prediction model. The results showed that the hyperspectral curves of lettuce under different light environments showed a consistent overall trend but different reflectance values during the whole life cycle of lettuce at 10, 20 and 30 days. The lettuce reflectance values in the visible light range of 450～680 nm exhibited higher natural light exposure than the supplemental light treatment, while the hyperspectral response characteristics in the NIR range of 730～850 nm were exactly opposite to the visible light range. The combination of SG+FD pre-treatment and CARS sensitive wavelength extraction method based on SG+FD can achieve the most effective extraction of chlorophyll content feature information, and the extracted sensitive wavelengths accounted for 64.59% of the total wavelengths, which increased the number of extracted sensitive wavelengths by 63.34% compared with the original hyperspectrum (1.25%). The LSSVM method was identified as the optimal modeling method, and the model built based on the combined SG+FD+CARS+LSSVM method was the optimal lettuce greenness prediction model with the training set R2c=0.920 7, RMSEC=1.161 0, and the prediction set R2p=0.828 8, RMSEP=2.400 8, indicating that the model had high accuracy. The purpose of greenness judgment of lettuce leaves can be realized.
Key words：Lettuce;Greenness;Hyperspectrum;Chlorophyll content(SPAD);Predictive model
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