Estimation of Leaf and Canopy Scale Tea Polyphenol Content Based on Characteristic Spectral Parameters
DUAN Dan-dan1, 2, LIU Zhong-hua1*, ZHAO Chun-jiang2, 3, ZHAO Yu2, 3, WANG Fan2, 3
1. College of Horticulture, Hunan Agricultural University，Changsha 410128，China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097，China
3. Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097，China
Abstract：The content of tea polyphenols has strong physiological activity and antioxidant properties, which are important attributes of tea quality, and play an important role in human body fat metabolism and scavenging free radicals. Compared with the assay method of tea polyphenol content, although monitoring the content of tea polyphenols based on remote sensing technology has the advantages of high efficiency, accuracy and real-time, there are few studies on how to use remote sensing data to monitor the content of tea polyphenols. This study took tea leaves from five tea gardens in Yingde City, Guangdong Province, as the research object, and measured the content of tea polyphenols and the corresponding hyperspectral data at two scales of leaf and canopy of spring tea, summer tea and autumn tea. Standard normal variate transformation (SNV) was used to preprocess leaf and canopy hyperspectral reflectance data; then, Successive projections algorithm (SPA) and competitive adaptive weighted sampling algorithm (CARS) to select the sensitive bands of tea polyphenols at two scales of leaf and canopy in different growing seasons; Finally, the tea polyphenol content models in different periods were constructed and verified by partial least squares (PLS), random forest (RF) and multiple linear regression (MLR). The results showed that: (1) The tea polyphenols content increased significantly with the passage of seasons, the content of tea polyphenols in spring tea (15.37%) was the lowest, the content of tea polyphenols in summer tea was the second (18.29%), and the content of tea polyphenols in autumn tea (20.77% in autumn tea) was the highest; (2) The characteristic bands of tea polyphenols are mainly concentrated in the short-wave near-infrared band (around 2 100~2 200 nm), near-infrared (around 1 300~1 400 nm), red wave-red edge band and green band; (3) The CARS-PLS, SPA-MLR and CARS-PLS have the highest precision among the tea polyphenol models constructed based on the spectral characteristics from spring，summer and autumn canopy, with R2 of 0.56，0.45 and 0.52 respectively，and RMSE of 1.15，1.68 and 1.77 respectively；The validation set R2 was 0.43，0.40 and 0.41 respectively，and the RMSE was 1.60，1.91 and 1.91 respectively；The SPA-PLS，CARS-PLS and SPA-MLR models based on the spectral characteristics of spring tea，summer tea and autumn tea canopy leaves have the highest precision，with R2 of 0.50，0.42 and 0.42 respectively，and RMSE of 1.25，1.70 and 1.66 respectively；The validation set R2 was 0.43，0.36 and 0.38 respectively，and the RMSE was 1.44, 1.96 and 2.49 respectively. The results showed that it is feasible to measure the content of tea polyphenols at two scales of leaf and canopy in different seasons based on remote sensing data and has great potential for real-time monitoring of tea quality characteristics in large areas.
Key words：Tea polyphenols; Hyperspectral data; Partial least squares; Random forest; Multiple linear regression
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