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.

This study was conducted to assess the contributions of nitrous oxide (N₂O) emissions from soil between rows and soil under the canopy of tea plants (Camellia sinensis (L.) Kuntze) to the entire N₂O emissions from tea fields and to determine the factors responsible for changes in N₂O emissions. The study was performed in an experimental tea field in which fertilizer application rate was 510 kg nitrogen (N) ha⁻¹yr⁻¹. N₂O flux and soil properties including temperature, water filled pore space (WFPS), ammonium-nitrogen ( -N) and nitrate-nitrogen ( -N) concentrations, and soil pH were measured over a period of two years. Total N₂O emissions from the entire tea field were 10.6 and 14.8 kg ha⁻¹yr⁻¹ in 2008 and 2009, respectively. Total N₂O emissions from soil under the canopy of tea plants were approximately 35% of the total N₂O emissions from the entire tea field, suggesting that the contribution of N₂O emission from soil under the canopy of tea plants to overall N₂O emission was not negligible. A high N₂O flux from soil under the canopy was observed when there was heavy rainfall. The key factors for high N₂O fluxes observed in soil under the canopy were considered to be increases in soil temperature and WFPS. On the other hand, high N₂O fluxes from the soil between rows was observed after fertilizer application in spring and autumn, and there was a significant positive correlation between N₂O flux and -N concentration in the soil between rows.

The aim of this study was to assess the mitigating effects of lime nitrogen (calcium cyanamide) and dicyandiamide (DCD) application on nitrous oxide (N₂O) emissions from fields of green tea [Camellia sinensis (L.) Kuntze]. The study was conducted in experimental tea fields in which the fertilizer application rate was 544 kg nitrogen (N) ha⁻¹ yr⁻¹ for 2 years. The mean cumulative N₂O flux from the soil between the canopies of tea plants for 2 years was 7.1 ± 0.9 kg N ha⁻¹ yr⁻¹ in control plots. The cumulative N₂O flux in the plots supplemented with lime nitrogen was 3.5 ± 0.1 kgN ha⁻¹, approximately 51% lower than that in control plots. This reduction was due to the inhibition of nitrification by DCD, which was produced from the lime nitrogen. In addition, the increase in soil pH by lime in the lime nitrogen may also be another reason for the decreased N₂O emissions from soil in LN plots. Meanwhile, the cumulative N₂O flux in DCD plots was not significantly different from that in control plots. The seasonal variability in N₂O emissions in DCD plots differed from that in control plots and application of DCD sometimes increased N₂O emissions from tea field soil. The nitrification inhibition effect of lime nitrogen and DCD helped to delay nitrification of ammonium-nitrogen (NH₄⁺-N), leading to high NH₄⁺-N concentrations and a high ratio of NH₄⁺-N /nitrate-nitrogen (NO₃^–-N) in the soil. The inhibitors delayed the formation of NO₃^–-N in soil. N uptake by tea plants was almost the same among all three treatments.

Quantifying carotenoid content in agriculture is essential for assessing crop nutritional value, improving crop quality, promoting human health, understanding plant stress responses, and facilitating breeding and genetic improvement efforts. Hyperspectral reflectance imaging is a nondestructive and rapid tool for estimating the carotenoid content. In spectrometer reflectance measurements, there are various sources of noise that can compromise the accuracy of carotenoid content estimations. Recently, various machine learning algorithms have been identified as robust against various types of noise, eliminating the need for denoising processes. Specifically, Cubist and the one-dimensional convolutional neural network (1D-CNN) have been used in evaluating vegetation properties based on reflectance data. We used regression models based on Cubist and 1D-CNN to estimate carotenoid content from reflectance data (the spectral resolution was resampled in 5 nm bands across the entire wavelength domain from 400 to 850 nm) with various degrees of Gaussian and spike noise added. The Cubist-based model was the most robust for this purpose: it achieved a ratio of performance to deviation of 1.41, a root mean square error of 1.11 µg/cm², and a coefficient of determination (R²) of 0.496 when applied to reflectance data with a combination of Gaussian (mean: 0; variance: 0.04) and spike noise (density: 0.05; amplitude: 0.05).

Tea is second only to water as the world’s most popular drink and it is consumed in various forms, such as black and green teas. A range of cultivars has therefore been developed in response to customer preferences. In Japan, farmers may grow several cultivars to produce different types of tea. Leaf chlorophyll content is affected by disease, nutrition, and environmental factors. It also affects the color of the dried tea leaves: a higher chlorophyll content improves their appearance. The ability to quantify chlorophyll content would therefore facilitate improved tea tree management. Here, we measured the hyperspectral reflectance of 38 cultivars using a compact spectrometer. We also compared various combinations of preprocessing techniques and 14 variable selection methods. According to the ratio of performance to deviation (RPD), detrending was effective at reducing the influence of additive interference of scattered light from particles and then regression coefficients was the best variable selection method for estimating the chlorophyll content of tea leaves, achieving an RPD of 2.60 and a root mean square error of 3.21 μg cm⁻².

A laboratory incubation experiment is conducted for 90 days under controlled conditions where either pruning residue or its biochar is applied to determine which application generates the lowest amount of greenhouse gas from tea plantation soil. To study the effect of incorporation depth on soil N₂O and CO₂ emissions, experiment 1 is performed with three treatments: (1) control; (2) tea pruning residue; and (3) residue biochar mixed with soil from two different depths (0–5 cm and 0–10 cm layers). In experiment 2, only the 0–10 cm soil layer is used to study the effect of surface application of tea pruning residue or its biochar on soil N₂O and CO₂ emissions compared with the control. The results show that biochar significantly increases soil pH, total C and C/N ratio in both experiments. The addition of pruning residue significantly increases soil total C content, cumulative N₂O and CO₂ emissions after 90 days of incubation. Converting pruning residue to biochar and its application significantly decreases cumulative N₂O emission by 17.7% and 74.2% from the 0–5 cm and 0–10 cm soil layers, respectively, compared to their respective controls. However, biochar addition increases soil CO₂ emissions for both the soil layers in experiment 1. Surface application of biochar to soil significantly reduces both N₂O and CO₂ emissions compared to residue treatment and the control in experiment 2. Our results suggest that converting pruning residue to biochar and its addition to soil has the potential to mitigate soil N₂O emissions from tea plantation.