Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

Abstract

Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 µg cm −2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.Ultraviolet and visible light (UV-vis) spectroscopy and high-performance liquid chromatography (HPLC) techniques have previously been used to quantify chlorophyll content. These are destructive methods and may enforce a limited sample size because they are expensive and labour-intensive. It is also not always possible to apply these methods in situ. Alternately, some portable chlorophyll content meters, such as CL-01, SPAD-502, Dualex, and CCM-200, have been used to estimate chlorophyll content [9]. However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15]. The reflectance from vegetation is characterised by the low reflectance over the blue (400-500 nm) and red (650-690 nm) spectral regions due to strong absorption by chlorophylls plus carotenoids in the blue wavelengths and chlorophyll a in the red wavelengths, and the steep rise in reflectance between 680 and 750 nm is caused by the chlorophylls increasing to absorb at wavelengths beyond 700 nm [16]. Based on these features, some empirical approaches, such as vegetation indices, which are generally based on a few narrow or broad spectral bands, are convenient for use to estimate chlorophyll content. Most vegetation indices for chlorophyll content use wavelengths ranging from 400 to 860 nanometres [17][18][19] or the red edge (680 to 750 nm) [20,21]. However, some datasets, such as LOPEX, CALMIT, ANGERS, and HAWAII, that have been used for the development of vegetation indi...

Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms

“…These results suggested that the inclusion of YL, which had the lowest Chl content in the dataset, functioned well at estimating the Chl content, but not the N content. Furthermore, these results indicated that the approach using hyperspectral reflectance and machine learning algorithm allowed not only the Chl content, as previously reported by Sonobe et al (2020) 54 , but also the N content to be estimated nondestructively in tea leaves. For green tea production, especially in Japan, N fertilizer is mainly applied in the autumn, winter, and at the bud-opening stage before the first crops in spring, to improve the quality 55 .…”

“…A stratified sampling approach for modelling was repeated 100 times to obtain robust results. tea leaves also indicated that the values of highest importance were confirmed at 700-750 nm (the red edge region) when OR and FDR were used 54 . For N content, the peaks of importance were observed at 675-725 and 1325-1575 nm, with the latter peak in the near-infrared region enhanced in DatasetB compared with DatasetA, especially for RF, Cubist, and SGB (Fig.…”

“…The flow of regression model generation was conducted as described by Sonobe et al (2020) 54 , with a slight modification. For modelling, all measurements were divided into three groups, a training dataset (50%), a validation dataset (25%), and a test data dataset (25%), using a stratified sampling approach 73 .…”

“…Data-based sensitivity analysis (DSA) is proposed as a visualization approach for extracting human-understandable knowledge from supervised learning black box data mining models 80,81 . A black box analysis of the fitted models was performed with their machine learning algorithms by querying the fitted models with sensitivity samples and recording their responses, as previously described by Sonobe et al (2020) 54 .…”

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

“…As such, the variations in the total chl:car ratio [8] or chl a:car ratio [9] have been used for evaluating environmental stress in plants. In agriculture, the controls of nutritional condition, pH, or light conditions have been conducted to improve the appearance or quality of products [10][11][12]. However, these treatments sometimes lead to the early mortality of plants due to excessive environmental stress [13].…”