Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves.

Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

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

Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325-1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.

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

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.

Assessing hyperspectral indices for tracing chlorophyll fluorescence parameters in deciduous forests.

Chlorophyll fluorescence can be used to quantify the efficiency of photochemistry and heat dissipation. While several instruments such as Pulse-Amplitude-Modulation (PAM) fluorometers are available for taking direct measurements of parameters related to chlorophyll fluorescence, large-scale instantaneous ecosystem monitoring remains difficult. Several hyperspectral indices have been claimed to be closely related to some chlorophyll fluorescence parameters (e.g. photosystem II quantum yield (Yield), qP, NPQ), which may pave a way for efficient large-scale monitoring of fluorescence parameters. In this study, we have examined 30 published hyperspectral indices for their possible use in tracing chlorophyll fluorescence parameters. The comparison is based on a series of unique datasets with synchronous measurements of reflected hyperspectra and seven fluorescence parameters (i.e., Fm, F0, Fs, Fm', Yield, qP and NPQ) from leaves of Fagus crenata and other six broadleaf species sampled in Mt. Naeba, Japan. Among them, the first dataset is composed of seasonal canopy field measurements of Fagus crenata leaves, while the second is composed of field measurements of other deciduous species including Lindera umbellate, Clethra barbinervis, Viburnum furcatum, Eleutherococcus sciadophylloides, Quercus crispula and Acer japonicum. Furthermore, an additional dataset composed of data resulting from various controlled experiments using inhibitors has been applied for improving physiological interpretations of indices. Results revealed that PRI had higher coefficients of determination and lower root mean square errors than other indices evaluated with a set of chlorophyll fluorescence parameters. However, this pattern was seen only for beech leaves and performed poorly across other species. As a result, no specific indices that are currently available are recommended for tracing fluorescence parameters.

Assessing the xanthophyll cycle in natural beech leaves with hyperspectral reflectance.

The xanthophyll cycle is critical for protecting the photosynthetic apparatus from light-induced oxidative stress. A clear view of the xanthophyll cycle is thus important for understanding abiotic stresses that are closely related to plant growth and reproduction. The epoxidation state (EPS) is well correlated with the photosynthetic radiation use efficiency, and is widely used for assessing the xanthophyll cycle. The hyperspectral index, photochemical reflectance index (PRI), has been claimed to be closely related with the EPS, and offers instantaneous information of photosynthetic activity: its applications are, however, largely limited to herbaceous and coniferous species, and few studies have ever focussed on both sunlit and shaded leaves of deciduous plants. In the present study, we examined the possibility of applying PRI for tracing the xanthophyll cycle in a typical deciduous species (Fagus crenata Blume) as well as other species in a cold-temperate mountainous area. This is based on a series of experiments with only light stress and other inhibited treatments. Furthermore, searching for new hyperspectral indices has also been attempted based on both original and first derivative spectra. Results revealed that PRI had low correlations with the EPS of deciduous leaves, especially for sunlit leaves. As a comparison, the newly identified dD677, 803, a differential type of index using reflectance derivatives at 677 and 803nm, had a much better performance. The robustness of the newly identified index has been confirmed from both inhibitor-treatments and an additional dataset from other deciduous species. The proposed index is hence applicable for tracing the xanthophyll cycle in deciduous species.