关键词: 烤烟, 叶绿素含量估测, 无人机, 光谱参数, 随机森林回归, 多元线性回归, 偏最小二乘回归, 支持向量机回归

Abstract:

[Objective] Leaf chlorophyll content (LCC) of flue-cured Tobacco is an important indicator for characterizing the photosynthesis, nutritional status, and growth of the crop. Tobacco is an important economic crop with leaves as the main harvest object, it is crucial to monitor its LCC. Hyperspectral data can be used for the rapid estimation of LCC in flue-cured tobacco leaves, making it of great significance and application value. The purpose of this study was to efficiently and accurately estimate the LCC of flue-cured tobacco during different growth stages. [Methods] Zhongyan 100 was chose as the research object, five nitrogen fertilization levels were set. In each plot, three plants were randomly and destructively sampled, resulting in a total of 45 ground samples for each data collection. After transplanting, the reflectance data of the flue-cured tobacco canopy at six growth stages (32, 48, 61, 75, 89, and 109 d ) were collected using a UAV equipped with a Resonon Pika L hyperspectral. Spectral indices for the LCC estimation model of flue-cured tobacco were screened in two ways: (1) based on 18 published vegetation indices sensitive to LCC of crop leaves; (2) based on random combinations of any two bands in the wavelength range of 400‒1000 nm. The Difference Spectral Index (DSI), Ratio Spectral Index (RSI), and Normalized Spectral Index (NDSI) were calculated and plotted against LCC. The correlations between the three spectral indices and leaf LCC were calculated and plotted using contour maps. Five regression models, unary linear regression (ULR), multivariable linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR), were used to estimate the chlorophyll content. A regression estimate model of LCC based on various combinations of spectral indices was eventually constructed by comparing the prediction accuracies of single spectral index models multiple spectral index models at different growth stages. Results and Discussions] The results showed that the LCC range for six growth stages was 0.52‒2.95 mg/g. The standard deviation and coefficient of variation values demonstrated a high degree of dispersion in LCC, indicating differences in fertility between different treatments at the test site and ensuring the applicability of the estimation model within a certain range. Except for 109 d after transplanting, most vegetation indices were significantly correlated with LCC (p<0.01). Compared with traditional vegetation indices, the newly combined spectral indices significantly improved the correlation with LCC. The sensitive bands at each growth stage were relatively concentrated, and the spectral index combinations got high correlation with LCC were mainly distributed between 780‒940 nm and 520‒710 nm. The sensitive bands for the whole growth stages were relatively dispersed, and there was little difference in the position of sensitive band between different spectral indices. For the univariate LCC estimation model, the highest modeling accuracy was achieved using the newly combined Normalized Spectral Index and Red Light Ratio Spectral Index at 75 d after transplanting. The coefficients of determination (R² ) and root mean square errors (RMSE) for the modeling and validation sets were 0.822, 0.814, and 0.226, 0.230, respectively. The prediction results of the five resgression models showed that the RFR algorithm based on multivariate data performed best in LCC estimation. The R² and RMSE of the modeling set using data at 75 d after transplanting were 0.891 and 0.205, while those of the validation set reached 0.919 and 0.146. In addition, the estimation performance of the univariate model based on the whole growth stages dataset was not ideal, with R² of 0.636 and 0.686, and RMSE of 0.333 and 0.304 for the modeling and validation sets, respectively. However, the estimation accuracy of the model based on multiple spectral parameters was significantly improved in the whole growth stages dataset, with R² of 0.854 and 0.802, and RMSE of 0.206 and 0.264 for the modeling and validation sets of the LCC-RFR model, respectively. In addition, in the whole growth stages dataset, the estimation accuracy of the LCC-RFR model was better than that of the LCC-MLR, LCC-PLSR, and LCC-SVR models. Compared with the modeling set, R² increased by 19.06%, 18.62%, and 29.51%, while RMSE decreased by 31.93%, 29.51%, and 28.24%. Compared with the validation set, R² increased by 8.21%, 12.62%, and 8.17%, while RMSE decreased by 3.76%, 9.33%, and 4.55%. [Conclusions] The sensitivity of vegetation indices (VIs) to LCC is closely connected to the tobacco growth stage, according to the results this study, which examined the reaction patterns of several spectral indices to LCC in flue-cured tobacco. The sensitivity of VIs to LCC at various growth stages is critical for crop parameter assessment using UAV hyperspectral photography. Five estimation models for LCC in flue-cured tobacco leaves were developed, with the LCC-RFR model demonstrating the greatest accuracy and stability. The RFR model is less prone to overfitting and can efficiently decrease outlier and noise interference. This work could provide theoretical and technological references for LCC estimate and flue-cured tobacco growth monitoring.

Key words: flue-cured tobacco, chlorophyll content monitoring, unmanned aerial vehicles, spectral parameters, random forest regression, multivariable linear regression, partial least squares regression, support vector regression