关键词: 多光谱图像, 叶绿素含量, 表型信息, 粒子群优化算法, 支持向量回归

Abstract:

Objective: Rapid and accurate detecting chlorophyll content in plant leaves is an important step to explore the photosynthesis, nitrogen nutrition, stress status and yield prediction. In this study, multispectral image spectral information was used to quickly and accurately detect plant chlorophyll content, in order to provide reference and technical guidance for plant growth monitoring, stress diagnosis, and precise management. Method: In this study, the 1-year-old seedlings of Ilex dabieshanensis and Ilex verticillata were targeted. A multispectral camera, RedEdge-MX, was used to construct a system for extracting plant phenotypic information. The system collected multispectral images of the leaves of the two species at maturity and growth stages in five bands, including blue (B), green (G), red (R), near infrared (NIR), and red edge (RedEdge). The multispectral images were processed to obtain the spectral reflectance at each band of each leaf. Correlation analysis was conducted between the obtained spectral reflectance and the relative chlorophyll content (SPAD value) obtained by using a hand-held chlorophyll content analyzer. The traditional support vector regression (SVR) algorithm and the grid search (GS) algorithm, genetic algorithm (GA) and particle swarm optimization (PSO) were used for inversion modeling, respectively. The inversion models were compared, and a model with the highest fitting degree was obtained and it was able to quickly and accurately predict plant chlorophyll content using multispectral image spectral information. Result: The results showed that the correlation between the spectral reflectance and SPAD was obtained with the inversion model selected by comparing the traditional SVR algorithm with the optimized GS-SVR algorithm, GA-SVR algorithm and the PSO-SVR algorithm, the model fitting degrees were $ {R}_{1}^{2} $ =0.24 and (root mean-square error) $ {\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{1} $ =0.160; $ {R}_{2}^{2} $ =0.72, $ {\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{2} $ =0.097; $ {R}_{3}^{2} $ =0.84, $ {\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{3} $ =0.073; $ {R}_{4}^{2} $ =0.91, $ {\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{4} $ =0.066, respectively. Secondly, (adjusted R-squared) ${R}_{\mathrm{adjusted1}}^{2}$ =0.23, (mean absolute error) $ {\mathrm{M}\mathrm{A}\mathrm{E}}_{1} $ =0.119; ${R}_{\mathrm{adjusted2}}^{2}$ =0.71, $ {\mathrm{M}\mathrm{A}\mathrm{E}}_{2} $ =0.069; ${R}_{\mathrm{adjusted3}}^{2}$ =0.83, $ {\mathrm{M}\mathrm{A}\mathrm{E}}_{3} $ =0.050; ${R}_{\mathrm{adjusted4}}^{2}$ =0.87, $ {\mathrm{M}\mathrm{A}\mathrm{E}}_{4} $ =0.044. Conclusion: After comprehensive comparison, it is found that the optimized PSO-SVR algorithm has the best inversion prediction effect. In this study, by collecting multispectral images of plants, the optimized PSO-SVR algorithm is used to obtain the optimal inversion model of spectral reflectance and SPAD in five bands, which can realize the rapid detection of chlorophyll content in plant leaves. Combined with the multispectral imaging and machine learning algorithms, the accuracy and robustness of the models have been improved. Meanwhile, the results of this study can be extended to remote sensing level to realize the inversion of vegetation chlorophyll in a large area, providing theoretical basis and technical support for accurate forest seedling growth monitoring, stress diagnosis and dynamic regulation.

Key words: multispectral image, chlorophyll content, phenotypic information, particle swarm optimization algorithm, support vector regression