Management Of Tea Plantations Research Articles

Management of Insect Pests on Tea Plantations: Safety, Sustainability, and Efficiency.

Tea is the second most consumed beverage after water; thus, tea plants are economically important crops in many countries. The frequent application of chemical pesticides over large plantations of tea monoculture has led to pest outbreaks. In recent years, high amounts of highly water-soluble pesticides have been applied because of the proliferation of piercing-sucking insects; however, this method poses health hazards for humans and has negative environmental effects. This review outlines the effects of pesticide applications on the succession of tea pest populations, the risks posed by the use of highly water-soluble pesticides, and the principles of tea pest management. Various pest control techniques, including physical, biological, chemical-ecological, chemical pesticide, and cultural control methods, have been used in the last few decades. We discuss future prospects and challenges for the integrated pest management of tea plantations.

A dataset of spatial distribution of tea plantations at 10 m resolution in Fujian Province from 2000 to 2020

Being a major tea producing province in China, Fujian Province is badly in need of quick and accurate spatial distribution of tea plantation for decision-making in both the agricultural economic development and the ecological environment construction for the province. This study retrieved and processed Sentinel-1 (S1) radar data and Sentinel-2 (S2) multispectral data on the GEE cloud platform; and it extracted 98 features, such as spectral features, texture features, and terrain features from the terrain data. The recursive elimination support vector machine algorithm (SVM RFE) is used to screen the characteristic variables, resulting in the creation of 4 feature combination schemes. Using a support vector machine (SVM) classifier to extract the distribution data of tea plantations and assessing the accuracy of the 4 feature combination schemes, we obtained the spatial distribution data of tea plantations at 10 m resolution in Fujian Province in 2020. On this basis, we used the GEE cloud platform to access the vegetation disturbance information in Fujian Province from 2000 to 2020. We finally obtained a dataset of spatial distribution of tea plantations at 10 m resolution in Fujian Province from 2000 to 2020 by excluding the non-tea plantation areas from the images between 2000 and 2015 with the mask generated from the 2020 tea garden extraction results. This dataset has been manually validated using sample points from key tea-producing counties and townships. The results indicate an extraction accuracy of over 92% for tea plantations in 2020. The extraction accuracy of tea plantations obtained using interference data removal method in 2000, 2005, 2010, and 2015 was all above 80%. The dataset with a high accuracy in tea plantation extraction can provide support for relevant departments in tea plantation management.

Soil Microbial Community Characteristics and Their Effect on Tea Quality under Different Fertilization Treatments in Two Tea Plantations.

Fertilization is an essential aspect of tea plantation management that supports a sustainable tea production and drastically influences soil microbial communities. However, few research studies have focused on the differences of microbial communities and the variation in tea quality in response to different fertilization treatments. In this work, the soil fertility, tea quality, and soil microbial communities were investigated in two domestic tea plantations following the application of chemical and organic fertilizers. We determined the content of mineral elements in the soil, including nitrogen, phosphorus, and potassium, and found that the supplementation of chemical fertilizer directly increased the content of mineral elements. However, the application of organic fertilizer significantly improved the accumulation of tea polyphenols and reduced the content of caffeine. Furthermore, amplicon sequencing results showed that the different ways of applying fertilizer have limited effect on the alpha diversity of the microbial community in the soil while the beta diversity was remarkably influenced. This work also suggests that the bacterial community structure and abundance were also relatively constant while the fungal community structure and abundance were dramatically influenced; for example, Chaetomiaceae at the family level, Hypocreaceae at the order level, Trichoderma at the genus level, and Fusarium oxysporum at the species level were predominantly enriched in the tea plantation applying organic fertilizer. Moreover, the bacterial and fungal biomarkers were also analyzed and it was found that Proteobacteria and Gammaproteobacteria (bacteria) and Tremellomycetes (fungi) were potentially characterized as biomarkers in the plantation under organic fertilization. These results provide a valuable basis for the application of organic fertilizer to improve the soil of tea plantations in the future.

Assessing Pruning Intensity to Improve the Tea Productivity in Tropical Plantations by Multispectral Image Classification Model Using K-Means Algorithm

Tea (Camellia sinensis) plays a crucial role in the Indonesian economy, but its production has witnessed a decline over the last two years. The majority of tea plantations in Indonesia have historical roots from the Dutch East Indies colonial government, underscoring the importance of monitoring pruning practices in tea plantation management. However, manual monitoring methods prove ineffective, prompting the exploration of precision farming principles using satellite imagery and machine learning to address this challenge. This study was conducted on a 231-hectare tea plantation south of the Tangkuban Perahu Volcano in West Bandung Regency, Indonesia. Sentinel-2B imagery from June to October 2019-2023 was utilized to calculate Soil Adjusted Vegetation Index (SAVI) values and assess tea productivity. Employing the K-Means algorithm, productivity values were grouped, and pruning intensity was categorized into three classes, revealing spatial dynamics influencing tea tree productivity. Our results illustrate distinct spatial patterns of pruning intensity across the 231-hectare tea plantation, identifying three classes: less pruned, moderately intensive pruned, and intensively pruned. We show that proposed model of pruning intensity from K-Means algorithm has overall accuracy of 0.57, with highest precision in less pruned class, while the lowest in moderately intensive pruned class. Notably, consistently higher productivity was observed in less pruned tea trees, while an increase in productivity in intensively pruned trees occurred after the second pruning in 2022. These findings highlight the potential of satellite imagery and machine learning for enhancing precision monitoring in tea plantations, offering a practical approach for long-term plantation management. Emphasizing the significance of pruning strategies, our study suggests that optimizing tea productivity amidst environmental and management challenges is achievable through informed monitoring and strategic pruning practices.

Transcriptomic Analysis of the Effect of Pruning on Growth, Quality, and Yield of Wuyi Rock Tea.

Pruning is an important agronomic measure in tea plantation management. In this study, we analyzed the effect of pruning on gene expression in tea leaves from a transcriptomics perspective and verified the results of a transcriptomic analysis in terms of changes in physiological indicators of tea leaves. The results showed that pruning enhanced the gene expression of nine metabolic pathways in tea leaves, including fatty acid synthesis and carbohydrate metabolism, nitrogen metabolism, protein processing in the endoplasmic reticulum, and plant hormone signal transduction, thereby promoting the growth of tea plants and increasing tea yield. However, pruning reduced the gene expression of nine metabolic pathways, including secondary metabolites biosynthesis, flavonoid biosynthesis, phenylpropanoid biosynthesis, and sesquiterpenoid and triterpenoid biosynthesis, and lowered the content of caffeine, flavonoids, and free amino acids in tea plant leaves. In conclusion, pruning could promote the growth of tea plants and increase the yield of tea, but it was not conducive to the accumulation of some quality indicators in tea leaves, especially caffeine, flavonoids, and free amino acids, which, in turn, reduced the quality of tea. This study provides an important theoretical reference for the management of agronomic measures in tea plantations.

Effects of pruning on tea tree growth, tea quality, and rhizosphere soil microbial community.

Pruning is an important agronomic measure in tea plantation management. This study analyzed the effects of pruning on tea tree growth, tea quality, rhizosphere soil physicochemical indexes, microbial communities, and metabolic pathways. The results showed that pruning was beneficial for promoting tea tree growth and increasing tea yield, but not for the synthesis and accumulation of quality-related compounds in tea leaves. After pruning, organic matter, available phosphorus content and catalase, acid phosphatase, and sucrase activities in rhizosphere soil were significantly higher than those in unpruned tea trees, while total phosphorus, total potassium, and available nitrogen content were significantly lower than those in unpruned tea trees. The results of microbial community analysis of tea rhizosphere soil showed that the key changed characteristic microorganisms after pruning were Haliangium, Acidicaldus, Reyranella, Acidobacterium, Aquicella, and Granulicella, and the key changed characteristic microbial metabolic pathways were ko00072, ko00473, ko00750, ko01055, ko00521, and ko02040. Furthermore, the results found that pruning promoted Haliangium, Acidicaldus, and Reyranella abundances, ko00072, ko00473, and ko00750, respectively, microbial metabolic pathways in tea trees rhizosphere soil, and reduced Acidobacterium, Granulicella, and Aquicella abundance, ko01055, ko00521, and ko02040, respectively, microbial metabolic pathways, thereby increasing the activities of soil catalase, acid phosphatase, and sucrase, improving soil organic matter decomposition efficiency and available phosphorus content, and promoting tea yield, but not synthesis and accumulation of quality-related compounds in tea leaves. This study provides an important theoretical reference for the management of agronomic measures in tea plantations. IMPORTANCE Pruning is an important agronomic measure in tea cultivation and management. We found that pruning was beneficial to increase tea yield, but it would reduce tea quality, especially the content of polyphenols, theanine, flavonoids, and free amino acids in tea leaves was reduced. The reason for this phenomenon was that pruning promotes the enrichment of special functional microorganisms and the enhancement of special metabolic pathways in the soil, leading to changes in the nutrient cycle in the soil.

Soil organic carbon stock in China’s tea plantations and their great potential of carbon sequestration

China is the biggest tea producer in the world, and soils in the tea plantations play important roles in the carbon cycle, and regional and national carbon neutrality. The knowledge regarding the variation and the key drivers of soil organic carbon (SOC) stock in tea plantations could provide a scientific basis for assessing the potential of regional and national carbon sequestration and neutrality. This is also helpful for the sustainable management of tea plantations. However, little information is available on soil organic carbon (SOC) stock and influential factors in tea plantations. Thereby, SOC density (SOCD) and stock were estimated based on 1723 datasets collected from 322 published studies. Subsequent estimations reveal that the total SOC stock in China’s tea plantations is 207.13 Tg with 124.58 Tg SOC stored in top soil layer (0–20 cm), and the average SOCD is 59.17 Mg ha−1 ranging from 0.9 Mg ha−1 to 589.54 Mg ha−1. The SOCD showed a significant three-dimension zonal pattern and was significantly influenced by managerial practices. Tea plantations managed in ecological ways and/or with long stand age even showed higher SOCD than that in neighbored forests. The lower average but wide range of SOCD in tea plantations indicates their great potential in carbon sequestration. This makes tea plantations critically important in regional and national carbon accounting and carbon neutrality, and cannot be ignored in future research and policymaking initiatives.

Improved SVM-Based Soil-Moisture-Content Prediction Model for Tea Plantation.

Accurate prediction of soil moisture content in tea plantations plays a crucial role in optimizing irrigation practices and improving crop productivity. Traditional methods for SMC prediction are difficult to implement due to high costs and labor requirements. While machine learning models have been applied, their performance is often limited by the lack of sufficient data. To address the challenges of inaccurate and inefficient soil moisture prediction in tea plantations and enhance predictive performance, an improved support-vector-machine- (SVM) based model was developed to predict the SMC in a tea plantation. The proposed model addresses several limitations of existing approaches by incorporating novel features and enhancing the SVM algorithm's performance, which was improved with the Bald Eagle Search algorithm (BES) method for hyper-parameter optimization. The study utilized a comprehensive dataset comprising soil moisture measurements and relevant environmental variables collected from a tea plantation. Feature selection techniques were applied to identify the most informative variables, including rainfall, temperature, humidity, and soil type. The selected features were then used to train and optimize the SVM model. The proposed model was applied to prediction of soil water moisture in a tea plantation in Guangxi State-owned Fuhu Overseas Chinese Farm. Experimental results demonstrated the superior performance of the improved SVM model in predicting soil moisture content compared to traditional SVM approaches and other machine-learning algorithms. The model exhibited high accuracy, robustness, and generalization capabilities across different time periods and geographical locations with R2, MSE, and RMSE of 0.9435, 0.0194 and 0.1392, respectively, which helps to enhance the prediction performance, especially when limited real data are available. The proposed SVM-based model offers several advantages for tea plantation management. It provides timely and accurate soil moisture predictions, enabling farmers to make informed decisions regarding irrigation scheduling and water resource management. By optimizing irrigation practices, the model helps enhance tea crop yield, minimize water usage, and reduce environmental impact.

Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves.

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700-1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.

Deep Learning Model for Soil Environment Quality Classification of Pu-erh Tea

Pu-erh tea, Camellia sinensis is a traditional Chinese tea, one of the black teas, originally produced in China’s Yunnan Province, named after its origin and distribution center in Pu-erh, Yunnan. Yunnan Pu-erh tea is protected by geographical Indication and has unique quality characteristics. It is made from Yunnan large-leaf sun-green tea with specific processing techniques. The quality formation of Pu-erh tea is closely related to the soil’s environmental conditions. In this paper, time-by-time data of the soil environment of tea plantations during the autumn tea harvesting period in Menghai County, Xishuangbanna, Yunnan Province, China, in 2021 were analyzed. Spearman’s correlation analysis was conducted between the inner components of Pu’er tea and the soil environmental factor. The analysis showed that three soil environmental indicators, soil temperature, soil moisture, and soil pH, were highly significantly correlated. The soil environmental quality evaluation method was proposed based on the selected soil environmental characteristics. Meanwhile, a deep learning model of Long Short Term Memory (LSTM) Network for the soil environmental quality of tea plantation was established according to the proposed method, and the soil environmental quality of tea was classified into four classes. In addition, the paper also compares the constructed models based on BP neural network and random forest to evaluate the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the indicators for comparative analysis. This paper innovatively proposes to introduce the main inclusions of Pu’er tea into the classification and discrimination model of the soil environment in tea plantations, while using machine learning-related algorithms to classify and predict the categories of soil environmental quality, instead of relying solely on statistical data for analysis. This research work makes it possible to quickly and accurately determines the physiological status of tea leaves based on the establishment of a soil environment quality prediction model, which provides effective data for the intelligent management of tea plantations and has the advantage of rapid and low-cost assessment compared with the need to measure the intrinsic quality of Pu-erh tea after harvesting is completed.

Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data

Tea height, leaf area index, canopy water content, leaf chlorophyll, and nitrogen concentrations are important phenotypic parameters to reflect the status of tea growth and guide the management of tea plantation. UAV multi-source remote sensing is an emerging technology, which can obtain more abundant multi-source information and enhance dynamic monitoring ability of crops. To monitor the phenotypic parameters of tea canopy more efficiently, we first deploy UAVs equipped with multispectral, thermal infrared, RGB, LiDAR, and tilt photography sensors to acquire phenotypic remote sensing data of tea canopy, and then, we utilize four machine learning algorithms to model the single-source and multi-source data, respectively. The results show that, on the one hand, using multi-source data sets to evaluate H, LAI, W, and LCC can greatly improve the accuracy and robustness of the model. LiDAR + TC data sets are suggested for assessing H, and the SVM model delivers the best estimation (Rp2 = 0.82 and RMSEP = 0.078). LiDAR + TC + MS data sets are suggested for LAI assessment, and the SVM model delivers the best estimation (Rp2 = 0.90 and RMSEP = 0.40). RGB + TM data sets are recommended for evaluating W, and the SVM model delivers the best estimation (Rp2 = 0.62 and RMSEP = 1.80). The MS +RGB data set is suggested for studying LCC, and the RF model offers the best estimation (Rp2 = 0.87 and RMSEP = 1.80). On the other hand, using single-source data sets to evaluate LNC can greatly improve the accuracy and robustness of the model. MS data set is suggested for assessing LNC, and the RF model delivers the best estimation (Rp2 = 0.65 and RMSEP = 0.85). The work revealed an effective technique for obtaining high-throughput tea crown phenotypic information and the best model for the joint analysis of diverse phenotypes, and it has significant importance as a guiding principle for the future use of artificial intelligence in the management of tea plantations.

일제강점기 일본인의 다원 경영에 대한 고찰

The purpose of this thesis is to organize and analyze the records of Japanese tea plantation management in Korea during the Japanese colonial period, and to reveal the hidden goals of Japan and the Japanese Government-General of Korea. This study reveals the intention of the Japanese government and the Japanese government-general to manage tea and tea culture through the management of tea gardens by Japanese living in Korea during the Japanese colonial period. It was confirmed as follows that the intentions of the Japanese and the Japanese Government-General of Korea had an impact on the Japanese tea plantation management. We collected data on Ogawa tea fields in Jeongeup and Ozaki tea fields in Gwangju to review tea field operations and compare the two tea fields. The tea fields in Ogawa are tea fields grown from Japanese tea seeds, and the tea fields in Ozaki are wild tea fields native to Korea. This was revealed in the newspapers promoting the Ogawa tea plantations in Jeongeup and the Ozaki tea plantations receiving warnings not to interfere with the tea business in mainland Japan. Japan and the Japanese Government-General of Korea revealed that there was an intention and purpose to manage Korean tea culture by spreading the lie that Japan started tea culture and tea industry in Korea during the Japanese colonial period.

Soil Moisture Content Prediction Model for Tea Plantations Based on a Wireless Sensor Network

<p>Suitable soil moisture content (SMC) can not only increase the ability of tea tree roots to absorb and utilize nutrients but also improve the utilization rate of soil nutrients, which can ensure a continuous and stable yield and tea leaf quality. Traditional methods for predicting soil water content generally have low accuracy and efficiency problems. A real-time soil information collection system based on a wireless sensor network was built, and a new predicting SMC Model (AO-SVM) for tea plantations using support vector machine optimized (SVM) by Aquila Optimizer (AO) was constructed and evaluated. The SMC prediction model was established using weather data, soil temperature (ST), soil electrical conductivity (SEC), and PH value (pH), and soil water potential (SWP), and so on. First, the correlation between individual SMC, ST, SEC, pH, SWP was analyzed and parameters with high correlation with soil water content were subsequently identified. The AO-SVM model was utilized to predict the soil moisture content. The experiments showed that the R2 of AO-SVM model proposed in this paper is 0.925. It indicates that the AO-SVM model is effective and feasible and achieves advantageous performance over long short term memory (LSTM), generalized regression neural network (GRNN), the opposition-based chaotic salp swarm algorithm optimized SVM (OCSSA-SVM), sparrow search algorithm optimized SVM (SSA-SVM), particle swarm optimization SVM (PSO-SVM), and the whale algorithm optimized SVM (WOA-SVM) model, which can help guide the irrigation and fertilization management of tea plantations.</p> <p> </p>

Influence of multi-timescale precipitation indices on primary tea production in Baoshan, Yunnan, China

The regional economy in Yunnan, Southwest China, relies heavily on tea production. Both the quality and amount of tea production are sensitive to extreme climate events, but exactly how different timescale climate events influence tea production remains unknown. In this study, we explore the dynamic responses of tea production indices in Baoshan, Yunnan to 6 multi-timescale precipitation indices (MPIs) at lags of up to 20 years. The results are as follows: (1) Wet injury caused by precipitation during summer and autumn is a prominent threat to annual tea production in Baoshan, which leads to negative impacts on annual tea yield and raw tea price, with the impact on the latter mainly reflected in tea quality during the primary processing for black tea and green tea. (2) Annual tea yield and raw tea price are negatively correlated with CDD (consecutive dry days) and R20 (very heavy precipitation days) at lags greater than one year. (3) Spring tea yield and raw tea price are negatively correlated with spring precipitation, but positively correlated with days in the winter-spring dormancy period of tea plant (WSDP) and precipitation in WSDP; the positive correlation of days in WSDP is the most prominent with spring tea production of the 3 MPIs. (4) Unlike other famous tea areas, lower R20 frequency in Baoshan is not associated with high rates of soil erosion, but the lack of drainage ditches in the tea garden still means that continuous precipitation or heavy rainfall are a risk to Baoshan tea production. The insights provided by this study will help farmers and other decision-makers to understand the mechanism of MPIs’ influence on tea production, inform regional responses in tea plantation management to the observed different precipitation trends, and improve future management decisions under a changing climate.

Research on Tea Tree Growth Monitoring Model Using Soil Information.

Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achieved remarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frost damage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden is very important for tea plantation management and economic values. Aiming at the problems existing in current meteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, large amount of calculation for predicted models and incomplete information on frost damage occurrence, this paper proposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmental data, and temperature trend results from a nearest local weather station for accurate frost damage occurrence level determination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Time-series meteorological data collected from a small weather station was used for testing and parameterization of a two-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was further used to validate the capability of a two-fold model for frost damage forecasting. Results showed that compared with the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR), the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model (K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage, and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed method accurately predicted three times frost damage occurrences, including onset time, duration and occurrence level. These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.

Developing a guiding and growth status monitoring system for riding-type tea plucking machine using fully convolutional networks

Tea (Camellia sinensis) is one of the most popular beverage crops worldwide. As tea production is labor-intensive, riding-type tea plucking machines have been employed to improve harvest efficiency. High level of operating skills are essential to maneuver these machines. Improper operation may cause damage to tea plants and reduce the quality of harvested tea leaves. Besides harvest, monitoring the growth status of tea canopies is another essential task of tea plantation management. Monitoring conducted through conventional manual observation is time consuming. Therefore, this study attempted to solve these two issues by developing a system that guided an operator to steer a riding-type tea plucking machine in real-time and provided growth status of tea plants after each scouting trip. Fully convolutional networks (FCNs) and image processing techniques were applied to develop the guiding subsystem. This study compared two FCNs, namely FCN-8 s and ENet. The results showed that ENet outperformed FCN-8 s in generating a guiding line for the operator. Statistical analysis indicated that, with the guiding subsystem, the deviation between the driving direction and tea row center was significantly reduced. The guiding subsystem also could detect obstacles on the tea row where it was operated on. When in operation, the riding-type tea plucking machine was used as a platform to collect images of tea canopies for tea plantation management. The monitoring subsystem provided a graphical user interface consisting of a route recording, a growth status map, a canopy image of tea plants, and growth status ratings of the tea plants. FCN-8 s performed better than ENet in quantifying the growth status of tea plants, with a mean IU of 0.964 and a mean accuracy of 0.989.

Exploring the Potential of Unmanned Aerial Vehicle (UAV) Remote Sensing for Mapping Plucking Area of Tea Plantations

Mapping plucking areas of tea plantations is essential for tea plantation management and production estimation. However, on-ground survey methods are time-consuming and labor-intensive, and satellite-based remotely sensed data are not fine enough for plucking area mapping that is 0.5–1.5 m in width. Unmanned aerial vehicles (UAV) remote sensing can provide an alternative. This paper explores the potential of using UAV-derived remotely sensed data for identifying plucking areas of tea plantations. In particular, four classification models were built based on different UAV data (optical imagery, digital aerial photogrammetry, and lidar data). The results indicated that the integration of optical imagery and lidar data produced the highest overall accuracy using the random forest algorithm (94.39%), while the digital aerial photogrammetry data could be an alternative to lidar point clouds with only a ~3% accuracy loss. The plucking area of tea plantations in the Huashan Tea Garden was accurately measured for the first time with a total area of 6.41 ha, which accounts for 57.47% of the tea garden land. The most important features required for tea plantation mapping were the canopy height, variances of heights, blue band, and red band. Furthermore, a cost–benefit analysis was conducted. The novelty of this study is that it is the first specific exploration of UAV remote sensing in mapping plucking areas of tea plantations, demonstrating it to be an accurate and cost-effective method, and hence represents an advance in remote sensing of tea plantations.

Effects of different tea plantation management systems on arthropod assemblages and network structure

AbstractTea plantations are intensive monocultures and vulnerable to various pests due to a low diversity of natural enemies. In ancient tea plantations, the trees have grown for hundreds of years in mixed stands with many native trees, resulting in a landscape deemed as a “forest tea plantation.” However, no empirical studies have explicitly examined the effects of forest tea plantations on arthropod communities. Here, we addressed the difference of arthropod assemblages and ecological networks between the forest tea plantations and monoculture tea plantations, specifically focusing on the natural enemies and insect herbivores. Our results showed that the diversity of all arthropods, insect herbivore, and their natural enemies was higher in forest tea plantations compared to the monoculture tea plantations. In contrast, monoculture tea plantations had a higher abundance of all arthropods and insect herbivores due to a sharp increase in the abundance of the pest leafhopper, Empoasca vitis. Furthermore, forest tea plantation had higher co‐occurrences among natural enemies and insect herbivores than a randomized network, which suggests that natural enemies may co‐occur with some specific herbivore species. These results allow us to predict that forest tea plantations enhance insect herbivore control by increasing diversity of natural enemies and network structure between natural enemies and herbivores. Our study suggests that the ecological network approach opens up new possibilities for improving our prediction and understanding of pest suppression in agroecosystems.

Estimation of Sensible and Latent Heat Fluxes Using Surface Renewal Method: Case Study of a Tea Plantation

An experiment of sensible and latent heat flux measurement was conducted in a tea plantation near the Yangtze River within Danyang of Jiangsu Province, China. High-frequency (~10 Hz) air temperature measurement with fine-wire thermocouples (⌀ = 50 μm) was used for the estimation of sensible heat flux (H), and latent heat flux (LE) was extracted as a residual of the energy balance equation using additional measurements of net radiation (Rn) and soil heat flux (G). Results were compared against the eddy covariance (EC) system under unstable conditions only, and days with high precipitation were excluded from further analysis. Half-hourly datasets of the sensible heat flux estimated using the surface renewal method (SR) (HSR) and measured by the EC system (HEC) were analyzed. Results showed good agreement with R2 = 0.80, root mean square error (RMSE) = 27.87 W m−2, relative error (RE) = 9.02%, and a regression slope of 0.68—this slope was used for the calibration of the uncalibrated HSR estimated by SR. On the other hand, the half-hourly dataset of LESR was regressed against EC, and it showed good agreement with relatively high R2 = 0.93, RMSE = 32.99 W·m−2, and RE = 5.67%. Hence, the SR method may estimate the surface fluxes at a relatively low cost, ultimately improving calculations of evapotranspiration. Thus, the SR method could provide an economical tool for improving crop water management of tea plantations.