Tea Plucking Research Articles

Climate Smart Tea (Camellia sinensis) Cultivation Practices in Rathnapura District


 Tea (Camellia sinensis) cultivation in Sri Lanka, which plays a key role in the nation's economy, is facing unprecedented challenges due to the impacts of climate change. Climatic changes led to adverse outcomes, including deteriorating soil conditions marked by erosion, hindered tea plant growth, reduced quality, yield and market value of the tea. For many small-scale tea farmers, tea cultivation is their primary source of income. Climate-related challenges make their livelihoods more vulnerable, with poverty, especially extreme rain led to high maintenance costs and some cultivators to abandon tea plucking, hindering maintain efforts due to insufficient facilities and high expenses. Therefore, mitigating climate challenges is important. Objectives of this study is to find out the effects of climate change on tea cultivation, identify awareness levels and explore climate smart mitigation practices employed in Ratnapura District. Primary data was collected using in- depth interviews and field observations. Secondary data was collected from Tea Research Institute (TRI) databases. The region faces challenges from climate change, with high rainfall, occasional temperature fluctuations, droughts, and flood events. Terracing techniques are utilized to create level surfaces, effectively controlling soil erosion by slowing the flow of rainwater down slopes, cover cropping with grasses and legumes being planted between tea rows to shield the soil from the erosive force of raindrops, efficient drainage systems, such as trenches and channels, redirect excess rainwater away from the fields, preventing waterlogging, shade management, through strategic tree planting, and the use of mulch, tea pruning practices, aid in protecting tea bushes from heavy rainfall, planting high rainfall resilience varieties and preserving soil structure. Tea cultivators lack awareness regarding climate-smart practices and have limited knowledge about TRI recommendations. The research findings important to empower local tea farmers with innovative adaptation, thereby enhancing the adaptive capacity of the tea industry.
 
 Keywords: Climate smart, Tea cultivation, Climate changes, High rainfall, Innovative adaptation

Detection and Localization of Tea Bud Based on Improved YOLOv5s and 3D Point Cloud Processing

Currently, the detection and localization of tea buds within the unstructured tea plantation environment are greatly challenged due to their small size, significant morphological and growth height variations, and dense spatial distribution. To solve this problem, this study applies an enhanced version of the YOLOv5 algorithm for tea bud detection in a wide field of view. Also, small-size tea bud localization based on 3D point cloud technology is used to facilitate the detection of tea buds and the identification of picking points for a renowned tea-picking robot. To enhance the YOLOv5 network, the Efficient Channel Attention Network (ECANet) module and Bi-directional Feature Pyramid Network (BiFPN) are incorporated. After acquiring the 3D point cloud for the region of interest in the detection results, the 3D point cloud of the tea bud is extracted using the DBSCAN clustering algorithm to determine the 3D coordinates of the tea bud picking points. Principal component analysis is then utilized to fit the minimum outer cuboid to the 3D point cloud of tea buds, thereby solving for the 3D coordinates of the picking points. To evaluate the effectiveness of the proposed algorithm, an experiment is conducted using a collected tea image test set, resulting in a detection precision of 94.4% and a recall rate of 90.38%. Additionally, a field experiment is conducted in a tea experimental field to assess localization accuracy, with mean absolute errors of 3.159 mm, 6.918 mm, and 7.185 mm observed in the x, y, and z directions, respectively. The average time consumed for detection and localization is 0.129 s, which fulfills the requirements of well-known tea plucking robots in outdoor tea gardens for quick identification and exact placement of small-sized tea shoots with a wide field of view.

CFD Simulation and Optimization of the Leaf Collecting Mechanism for the Riding-Type Tea Plucking Machine

In the process of tea plucking and leaf gathering, the structure optimization design of the leaf collecting mechanism is the key element responsible for collecting fresh leaves. The unreasonable design and manufacture of leaf collecting mechanisms will cause the smooth collection of fresh leaves, the quality of the collected fresh leaves will be damaged, and the commodity value will be reduced. In order to further study the structural characteristics of the leaf collecting mechanism, an air outlet model of the leaf collecting mechanism was established for the phenomena of internal vortex rotation and impact in the leaf collecting mechanism process. The internal flow field of the leaf collecting mechanism, the movement trajectory of fresh leaves, and the non-homogeneous flow are calculated using computational fluid dynamics (CFD). Based on Box-Behnken’s central combinatorial design theory, the velocity inlet and outlet air structure factors are taken as the influencing factors to carry out response surface test research. The effect of different parameters such as engine rotation, shape of the blowing cavity and air outlet parts, and velocity on the flow is determined. The optimal parameter combination is as follows: the height of the outlet end, the length of the inlet end, and the velocity inlet are 0.01 m, 0.03 m, and 25 m/s, respectively. Furthermore, it was found that when the number of plates increases from 1 to 4, the non-homogeneity decreases all the time, and the distribution of blowing air is improved without a sharp decrease in velocity. The average velocity outlet was larger than the velocity inlet, which meets the requirements of blade gathering. Considering comprehensively, the flow field simulation of the blade collecting mechanism with four baffles was consistent with the test results of the velocity outlet. The validation results showed that the model can successfully simulate the air flow inside the leaf-collecting mechanism, and the reasonable structure design was conducive to reducing the number of collisions between tea buds and improving the quality of tea buds. This research has certain theoretical and practical implications for the accurate plucking of high-quality tea.

Development and field evaluation of a robotic harvesting system for plucking high-quality tea

The critical issue of the robotic harvesting high-quality tea is to realize tea shoot detection, plucking point localization, and motion planning. In addition, the accuracy and efficiency of the robotic plucking of high-quality tea in the field are essential. Therefore, a robotic harvesting system is proposed in this paper by combining deep learning, point cloud processing, and spatial path planning. First, the deep learning method and the compressed YOLOv3 network are used to quickly and accurately identify tea shoots. Second, an efficient point cloud processing-based 3D localization algorithm for high-quality tea plucking points was proposed. The genetic algorithm is then used to shorten the end-effector's motion path by optimizing the plucking sequences. Eventually, a harvester robot with a parallel manipulator was developed to conduct field plucking experiments and evaluate the effectiveness of the proposed harvesting system. All experimental results demonstrate that the success rates of detection, localization, and motion plucking are 85.16 %, 78.90 %, and 80.23 %, respectively. Furthermore, the overall process harvesting success rate is 53.91 %, and the average plucking time for a single shoot is 2.233 s. Therefore, the proposed harvesting approach can provide technical support for the precise and rapid harvesting of high-quality tea.

SPATIAL MACHINE LEARNING FOR MONITORING TEA LEAVES AND CROP YIELD ESTIMATION USING SENTINEL-2 IMAGERY, (A Case of Gunung Mas Plantation, Bogor)

Indonesia's tea production and export volume have fluctuated with a downward trend in the last five years, partly due to the increasingly competitive world tea quality. Crop yield estimation is part of the management of tea plucking, affecting tea quality and quantity. The constraint in estimating crop yields requires technology that can make the process more effective and efficient. Remote sensing technology and machine learning have been widely used in precision agriculture. Recently, big data processing, especially remote sensing data, machine learning, and deep learning have been carried out using a cloud computing platform. Therefore, we propose using GeoAI, a combination of Sentinel-2A imagery, machine learning, and Google Collaboratory, to predict ready for plucking tea leaves at optimal plucking time at Gunung Mas Plantation Bogor. We used selected bands of Sentinel-2A and extracted more features (i.e., NDVI) as a training set. Then we utilized the tea blocks boundary and tea plucking data to generate labels using Random Forest (RF) and Support Vector Machine (SVM). The classification results were further used to estimate the production of crop tea yield. The RF classifier is able to achieve overall accuracy at 51% and SVM at 54%. Meanwhile, accuracy at optimally aged tea blocks is able to achieve at 75.62% for RF and 52.88% for SVM. Thus, the SVM classifier is better in terms of overall accuracy. Meanwhile, the RF classifier is superior in predicting ready for plucking tea at optimally aged tea blocks.

Effects of UAV-LiDAR and Photogrammetric Point Density on Tea Plucking Area Identification

High-cost data collection and processing are challenges for UAV LiDAR (light detection and ranging) mounted on unmanned aerial vehicles in crop monitoring. Reducing the point density can lower data collection costs and increase efficiency but may lead to a loss in mapping accuracy. It is necessary to determine the appropriate point cloud density for tea plucking area identification to maximize the cost–benefits. This study evaluated the performance of different LiDAR and photogrammetric point density data when mapping the tea plucking area in the Huashan Tea Garden, Wuhan City, China. The object-based metrics derived from UAV point clouds were used to classify tea plantations with the extreme learning machine (ELM) and random forest (RF) algorithms. The results indicated that the performance of different LiDAR point density data, from 0.25 (1%) to 25.44 pts/m2 (100%), changed obviously (overall classification accuracies: 90.65–94.39% for RF and 89.78–93.44% for ELM). For photogrammetric data, the point density was found to have little effect on the classification accuracy, with 10% of the initial point density (2.46 pts/m2), a similar accuracy level was obtained (difference of approximately 1%). LiDAR point cloud density had a significant influence on the DTM accuracy, with the RMSE for DTMs ranging from 0.060 to 2.253 m, while the photogrammetric point cloud density had a limited effect on the DTM accuracy, with the RMSE ranging from 0.256 to 0.477 m due to the high proportion of ground points in the photogrammetric point clouds. Moreover, important features for identifying the tea plucking area were summarized for the first time using a recursive feature elimination method and a novel hierarchical clustering-correlation method. The resultant architecture diagram can indicate the specific role of each feature/group in identifying the tea plucking area and could be used in other studies to prepare candidate features. This study demonstrates that low UAV point density data, such as 2.55 pts/m2 (10%), as used in this study, might be suitable for conducting finer-scale tea plucking area mapping without compromising the accuracy.

Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll

Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines.

A Collaborative Robot for Tea Harvesting with Adjustable Autonomy

A collaborative mobile robot has been developed for tea plucking in narrow terrain tea gardens. The robot executes stable side-by-side motions and carries most of the load of the tea harvesting machine. The robot is controlled with adjustable autonomy so that the human can provide a supervisory role and adjust the optimal height and forward speed of the cutting tool. During tea plucking, the robot can autonomously detect the tree rows and avoid colliding with the trees. The experimental results show that the proposed collaborative robot can stably co-work with humans and significantly improve working efficiency and comfort.

Efisiensi Kinerja dan Hasil Panen Teh Menggunakan Gunting dan Mesin pada Beragam Kemiringan Lahan

<p><em>Tea leaf plucking is an important activity that can determine the quantity and quality of tea production. Tea plantations characteristically have variations of slopes that affect the performance of the picking process. This study aimed to analyze the performance efficiency of tea plucking activities using plucking machines and scissors. Field data were collected from several blocks in the South Gambung and North Gambung afdeling. Data collected consisted of the elevation of the place, the slope of the land, the plucking area, plucking time, the number of pluckers, the plots length of the plucking activity, the width of the plots, and the harvested tea's weight. The data collected were processed into field capacity and tea plucking performance efficiency, which were then analyzed descriptively. Results showed that the slope of the land has an effect on work efficiency of 3.00%-8.68%. The effective and theoretical field capacity of tea plucking machines is 0.14 hectares per hour and 0.20 hectares per hour, respectively, while the scissors is 0.015 hectares per hour and 0.020 hectares per hour. The quality of tea harvested with machines that meet the requirements is 44.10%, better than scissors harvested, which is only 28.09%.</em></p>

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.

Structural changes, increased hypoxia, and oxidative DNA damage in placenta due to maternal smokeless tobacco use.

Smokeless tobacco (SLT) consumption during pregnancy is a well-recognized health risk that causes placental damage including hypoxia and oxidative damage. Although consumption of SLT by women varies from region to region, majority of tea leave pluckers consume SLT for relieving stress and pain. Still, the effects of SLT consumption have not been evaluated in tea garden workers (TGW). While previous studies have attempted to report effects of cigarette smoke using in vitro model, hypoxia-inducible factor (HIF)-1α expression in human placentae from pregnant women exposed to SLT has not been previously studied. This study was aimed to explore the effects of SLT consumption on placental structure, expression of HIF-1α and oxidative DNA damage in sample population of TGW. A total of 51 placentae were collected from SLT users and nonusers (n=30 and 21, respectively) with full-term normal delivery, who were involved in the plucking of tea leaves during pregnancy in tea plantation. Low birth weight (LBW, i.e., weight <2,500 g) and normal birth weight (NBW) groups among both SLT user and nonuser were compared for the stated parameters. Placental tissues were processed for transmission electron microscopy (TEM) study and immunohistochemical analysis for the expression of HIF-1α and 8-hydroxy-2'-deoxyguanosine (8-OHdG). Altered ultrastructural characteristics were observed in the tertiary villi of LBW group among SLT users which included endothelial cells protrusion into capillary lumen, degenerated nuclei, significant thickening of trophoblast basement membrane and vasculo-syncytial membrane, abnormalities of the microvilli, swollen or damaged mitochondria, and dilatation in endoplasmic reticulum cisternae. Furthermore, significant reduction in the perimeter, area, and number of the stromal capillary of the tertiary villi of placenta were found in LBW group as compared with NBW group from the SLT users. Enhanced expression for HIF-1α and oxidative DNA damage (8-OHdG) biomarker was observed in SLT users as compared with nonusers. Maternal SLT exposure during pregnancy may be associated with villus hypoxia and consequently oxidative DNA damage. It is presumed that deleterious effect of SLT exposure on placenta could result in impairment of placental barrier, and restrict nutrient and oxygen supply from mother to fetus, and thus could be a cause of fetal growth restriction.

In-field tea shoot detection and 3D localization using an RGB-D camera

Tea shoot detection and localization are highly challenging tasks because of varying illumination, inevitable occlusion, tiny targets, and dense growth. To achieve the automatic plucking of tea shoots in a tea garden, a reliable algorithm based on red, green, blue-depth (RGB-D) camera images was developed to detect and locate tea shoots in fields for tea harvesting robots. In this study, labeling criteria were first established for the images collected for multiple periods and varieties in the tea garden. Then, a “you only look once” (YOLO) network was used to detect tea shoot (one bud with one leaf) regions on RGB images collected by an RGB-D camera. Additionally, the detection precision for tea shoots was 93.1% and the recall rate was 89.3%. To achieve the three-dimensional (3D) localization of the plucking position, 3D point clouds of the detected target regions were acquired by fusing the depth image and RGB image captured by an RGB-D camera. Then, noise was removed using point cloud pre-processing and the point cloud of the tea shoots was obtained using Euclidean clustering processing and a target point cloud extraction algorithm. Finally, the 3D plucking position of the tea shoots was determined by combining the tea growth characteristics, point cloud features, and sleeve plucking scheme, which solved the problem that the plucking point may be invisible in fields. To verify the effectiveness of the proposed algorithm, tea shoot localization and plucking experiments were conducted in the tea garden. The plucking success rate for tea shoots was 83.18% and the average localization time for each target was about 24 ms. All the results demonstrate that the proposed method could be used for robotic tea plucking.

Prevalence of musculoskeletal disorders and their association with ergonomic physical risk factors among women working in tea gardens of Darjeeling district of West Bengal, India

Introduction: Work related musculoskeletal disorders (WRMSDs) is common among tea-plucking folks and may be attributed to the long duration of work in same posture, the load they carry while plucking and transporting the leaves to the nearest depot, improper job rotations and the difficult hilly terrains.&#x0D; Objective: To determine the prevalence and pattern of musculoskeletal disorders among tea garden workers and to ascertain the risk factors for the same.&#x0D; Methods: A cross-sectional study was conducted among 210 female tea garden workers employed in tea gardens of Darjeeling district using 30 cluster sampling design. Data on musculoskeletal morbidities were collected using Standard Nordic Musculoskeletal Questionnaire and ergonomic risk was assessed by Rapid Entire Body Assessment (REBA) worksheet.&#x0D; Results: Proportion of musculoskeletal disorders in past 12 months was 92.4% and in past 7 days was 71.4%. Upper extremities were the most commonly affected body part. Higher altitude, age, BMI, more duration in present job, history of work related injuries and increased REBA risk assessment score were found to be significantly associated with musculoskeletal disorders.&#x0D; Conclusion: Musculoskeletal morbidities among female tea garden workers of Darjeeling were found to be considerably high. Ergonomic mechanised assistance in tea plucking can reduce this proportion of MSD and thus may go a long way in reducing sickness absenteeism and increasing productivity.&#x0D; Financial support and sponsorship - Nil.&#x0D; Conflicts of interest - There are no conflicts of interest.

Computer vision-based high-quality tea automatic plucking robot using Delta parallel manipulator

Aiming to reduce the labour cost and improve the competitiveness of the tea production, this paper presents a complete solution, including the mechanical structure, the visual recognition system (VRS) and the motion control system of the high-quality tea automatic plucking robot. The design objectives involve high reliability and stability, ease of maintenance, cost-effectiveness, accurate recognition and localization of tea shoots, and high-efficient plucking manipulation. The VRS is established using the computer vision together with corresponding machine learning algorithms, where the Particle swarm optimization-Support vector machine algorithm and the deep convolutional neural YOLOv3 model are developed to recognize the tender tea shoots and localize the plucking point, according to the images captured by the global and local cameras, respectively. The Delta parallel manipulator equipped with a shear cutter is adopted to execute the trajectory tracking and tea plucking operations. The improved ant colony algorithm is utilized for path planning and trajectory smoothing of the manipulator. Simulation and laboratory platform experiment results verify the feasibility and effectiveness of the presented solution.

Design and experiments of 4CJ-1200 self-propelled tea plucking machine

As agricultural labor is decreasing, famous tea production is nearly hard to sustain and is getting more and more expensive in China. To improve the mechanization of tea plucking, this paper designs a self-propelled track-type tea plucking machine that integrates mechanic, hydraulic and electronics. The main parameters of chassis were optimized based on conditions of tea garden. And, three different kinds of cutter blades were designed and analyzed about vibration performance with finite element software. Also, a rigid-flex mixed motion model was established. Based on the model and plots, it is explicit that: 1) the blades move in a sine law; 2) the blades take a symmetrical deform from middle to end while moving; 3) the deformation increases along the blade from the center to sides. The single factor experiment revealed that both travel speed and speed ratio of cutting and travelling had a significant effect on quality indexes (integrity rate, unpicking rate, stubble unevenness) of plucked tea leaf. By universal rotary combination experiment of quadratic regression, the regression equations of three indexes about two experimental factors were established. And, the comprehensive optimization of three indexes was conducted with software Design expert. The optimal operating parameters of machine are as follow: travel speed is 0.41 m/s, speed ratio of cutting and travelling is 1.06; prediction of three indexes are that integrity rate could be reach to 78.26%, unpicking rate reduced to 0.82%, stubble unevenness reduced to 1.30 mm. The results of verification experiment are that: integrity rate could be reach to 77.41%, unpicking rate reduced to 0.87%, stubble unevenness reduced to 1.23 mm. These test indexes that are very close to the predicted values, all surpass the requests specified in the standard of tea plucking with machine. In all, the optimized parameter set is indeed synthetically optimal one that enables the wide use of this tea plucking machine, which can be used in the tea garden that has a lengthways slope less than 18°, a crosswise slope less than 20°, and a height of tea trees between 50-120 cm. From above, the tea plucking machine investigated in this paper provides an effective solution to present tea picking problem that is the bottleneck of tea industry. Further studies need to do for making this machine more automated and intelligent. Keywords: agricultural machine, tea, harvesting, reciprocating cutter, model, design, experiment, optimization DOI: 10.25165/j.ijabe.20211406.5519 Citation: Han Y, Xiao H R, Song Z Y, Chen Q M, Ding W Q, Mei S. Design and experiments of 4CJ-1200 self-propelled tea plucking machine. Int J Agric & Biol Eng, 2021; 14(6): 75–84.

The effect of root application of meso- and microfertilizers on tea cv. Kolkhida’s yield under stressful hydrothermal conditions during tea plucking season

The effect of root application of meso- and microfertilizers on tea cv. Kolkhida’s yield under stressful hydrothermal conditions during tea plucking season

Predicting risk of low birth weight offspring from maternal features and blood polycyclic aromatic hydrocarbon concentration

Prenatal exposure to organic pollutants increases the risk of low birth weight (LBW) offspring. Women involved in the plucking of tea leaves can be exposed to polycyclic aromatic hydrocarbons (PAHs) during pregnancy through inhalation and diet. Therefore, the aim of the study was to investigate the association of maternal socio-demographic features and blood PAH concentration with LBW; also to develop a model for predicting LBW risk. The study was performed by recruiting 55 women who delivered LBW and 120 women with NBW (normal birth weight) babies from Assam Medical College. The placental tissue, maternal and cord blood samples were collected. A total of sixteen PAHs and cotinine were analysed by HPLC and GC-MS. Association of PAH concentration with weight was determined using correlation and multiple logistic regression analyses. Predictive model was developed using SVMlight and Weka software. Maternal features such as age, education, food habits, occupation, etc. were found to be associated with LBW deliveries (p-value<0.05). Overall, 9 PAHs and cotinine were detected in the samples. A multiple logistic regression depicted an increased likelihood of LBW by exposure to PAHs (pyrene, di-benzo (a,h) anthracene, fluorene and fluoranthene) and cotinine. Models based on the features and PAHs/ cotinine predicted LBW offspring with 84.35% sensitivity and 74% specificity. LBW prediction models are available at http://dev.icmr.org.in/plbw/ webserver. With machine learning gaining more importance in medical science; our webserver could be instrumental for researchers and clinicians to predict the state of the fetus.

Association of maternal determinants with low birth weight babies in tea garden workers of Assam.

This study was aimed to evaluate the association of maternal determinants with birth weight (BW) of babies in tea garden workers (TGW) and housewives (HW). A total of 175 subjects were recruited from Assam Medical College, Dibrugarh, India. In this cross-sectional study, maternal determinants, BW of babies and placental weight were explored in TGW (n = 102) and HW (n = 73). These factors were assessed and correlated by logistic regression models. A higher incidence of low birth weight (LBW) was found in mothers working in the tea garden (48.04%) as compared to HW (10.96%). Activity of plucking of leaves in tea garden by women had a higher risk for LBW babies (adjusted odd ratio [AOR] 4.33, 95% confidence interval [CI] 1.38-13.57, P = 0.012) and decreased placental weight (AOR 11.42, 95% CI 1.18-126.02, P = 0.036) as compared to HW considered as reference group. Women who worked continuously in the tea garden during 9 months of pregnancy also revealed an elevated risk for LBW (AOR 5.32, 95% CI 1.34-21.09, P = 0.017). This study suggests the activity of plucking of tea leaves by women is associated with LBW of babies and decreased placental weight. Particularly, if mothers worked continuously in the tea garden during 9 months of pregnancy, it also increased the risk of delivering LBW babies. This exploratory study provides an important platform for further prospective studies, which could be focused on the potential consequences of maternal occupational exposures during pregnancy on fetal development.

Total Polyphenol, Rehydration Ratio, and Liquor Color of Different Grade Green Tea

Tea is a non-alcoholic beverage that is widely consumed after water with potential health benefits. In general, tea processing divided into three types, non-enzymatic oxidation process (green tea), semi-oxidation (oolong tea), and with enzymatic oxidation (black tea). The sorting and grading is a stage in controlling the quality of green tea. This study aims to determine the characteristics of different grades of green tea on the total polyphenols, liquor color, and rehydration ratio. The results showed that the pekoe/pecco grade had the highest total polyphenol and rehydration ratio compared to other grades. Pekoe grade has a greenish yellow and brighter liquor color. Application of good agriculture practice (GAP) and plucking of fresh tea leaves should be considered to obtain more grade pekoe in the green tea processing.

Musculoskeletal Disorders of Garo Women Workers Engaged in Tea-Plucking Activity: An Ergonomic Analysis.

Background:The tea-plucking activity in Garo Hills, Meghalaya, India is performed in a traditional way making the majority of women workers, especially those who have spent more years in tea-plucking activity prone to musculoskeletal disorders.Materials and Methods:The present study was conducted on a sample of 40 women workers who had the highest field experience in tea leaf plucking. Pain as a musculoskeletal disorder was recorded using a 5-point scale ranging from very mild pain (1) to very severe discomfort (5) to quantify the stress on muscles used in work. The coefficient of correlation was used to explore the relationship between age, years of involvement, BMI of women, and their musculoskeletal problem. The analysis of discomfort in upper extremity was done by using a rapid upper limb assessment (RULA) technique. Analysis of discomfort in entire body parts was carried out using a rapid entire body assessment (REBA) technique.Results and Discussion:During tea plucking, women workers reported severe discomfort in the head (4.5), neck (4.3), both fingers (4.2), upper and lower back (4.3 and 4.4), and feet (4.3). The RULA grand score was observed seven indicating the need for immediate investigation and changes. REBA result was 11 for entire body parts leading to conclude that workers were working under high physical strain.Conclusion:Workers with severe musculoskeletal disorders can face permanent disability that prevents them from returning to their jobs or handling simple everyday tasks. Therefore, some rest periods, ergonomic intervention, and personal protective equipment are needed to minimize the discomfort of women workers in the tea-plucking activity.