Quantifying chlorophyll-a and b content in tea leaves using hyperspectral reflectance and deep learning

ABSTRACT

To improve the quality of green tea, low light stress has been used to increase the chlorophyll-a (chl-a) content of tea leaves, although shading treatments sometimes lead to early mortality of tea trees. Therefore, in situ measurement of chl-a and chlorophyll-b (chl-b), which are markers for evaluating light stress and response to changing environmental conditions, can be used to improve tea tree management. Chlorophyll content estimation is one of the most common applications of hyperspectral remote sensing, but most prior studies have evaluated samples grown under relatively low stress. Therefore, the results of prior studies are not applicable for estimating chl-a and chl-b contents of shade-grown tea. Machine learning algorithms have recently attracted attention as an approach for evaluating biochemical properties. In the present study, three different common machine learning algorithms were compared, including random forests, support vector machines and deep belief nets. The ratios of performance to deviation (RPDs) of deep belief nets (DBN) were always larger than 1.4 (the ranges of RPD were 1.49–4.92 and 1.48–5.10 for chl-a and chl-b, respectively), suggesting that DBN is a unique algorithm that can reliably be used for estimation of chl-a and chl-b contents.

Declaration of interest statement

The authors report no potential conflicts of interest.

Additional information

Funding

This research was supported by JSPS KAKENHI [grant number 19K06313] and Agriculture, Forestry and Fisheries Research Council [19191026].