Tea garden intelligent monitoring method and system based on Internet of Things

Abstract

The invention discloses a tea garden intelligent monitoring method and system based on the Internet of Things, and relates to the field of tea garden Internet of Things monitoring. Tea garden area division and single tea tree coding are carried out, gridding partition is carried out on the tea garden, and a unique identity code is allocated to each tea tree; internet of Things sensing equipment is deployed in each grid area of a tea garden and around a single tea tree, environment parameters of the tea garden are collected in real time, the collected environment data are transmitted to a cloud processing platform in real time, and the cloud processing platform analyzes, studies and judges the environment data; collecting growth state data of each tea tree in real time, and associatively storing the growth state data of each tea tree and the corresponding identity code in combination with the identity code of the tea tree; and constructing a tea garden tea tree growth knowledge graph, outputting a growth state report and targeted management suggestions of each tea tree according to tea tree identity codes based on a research and judgment result of the knowledge graph, and outputting a tea garden overall environment management and control report at the same time. According to the invention, the tea garden can be promoted to be upgraded towards digitization, intelligentization and standardization directions.

Description

Technical Field

[0001] This invention relates to the field of IoT tea garden monitoring, and more specifically to an IoT-based intelligent monitoring method and system for tea gardens. Background Technology

[0002] As an important economic crop in my country, tea's growth quality is closely related to tea garden environmental parameters and the growth of individual tea trees. Precise tea garden management is key to improving tea quality and reducing production costs. With the popularization of IoT technology in agriculture, existing tea gardens have gradually introduced IoT monitoring systems. By deploying sensors, cameras, and other equipment, these systems collect environmental parameters such as weather and soil conditions, and conduct preliminary monitoring of tea tree growth. This has, to some extent, replaced the traditional extensive management model of manual inspections, promoting the intelligent transformation of tea garden management.

[0003] However, current IoT-based intelligent monitoring technology for tea gardens still has many shortcomings and cannot meet the needs of precision planting. On the one hand, existing monitoring systems mostly adopt a regional centralized monitoring mode with large sensor spacing. This only obtains the average environmental parameters of the entire tea garden or a whole area, failing to accurately capture the micro-environmental differences in different areas and terrains. This results in a lack of targeted environmental management, and operations such as irrigation, fertilization, and pest and disease control remain somewhat blind, easily leading to resource waste or inadequate management, affecting tea tree growth and tea quality. On the other hand, existing technologies mostly focus on monitoring the overall growth of the tea garden, failing to achieve personalized management down to each individual tree. It is difficult to accurately identify abnormal growth or pest and disease infestations in individual tea trees, and it cannot formulate differentiated management strategies based on the growth needs of individual tea trees. This results in some weaker tea trees or those affected by pests and diseases not receiving timely intervention, thus affecting the overall yield and quality of the tea garden.

[0004] Furthermore, existing monitoring systems lack sufficient data processing capabilities, and the collected environmental data and tea tree growth data lack effective integration and analysis. This makes it difficult to dynamically adjust environmental control parameters based on the growth status of individual tea trees, thus failing to form a closed-loop management system of "monitoring-analysis-control-feedback." Therefore, how to overcome the limitations of existing technologies, achieve precise control of the tea garden environment, and simultaneously achieve personalized monitoring and management down to the level of each individual tree, solving the technical challenges of extensive regional monitoring and lack of individual tree control, has become a key issue that urgently needs to be addressed in the field of IoT-based intelligent tea garden monitoring. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for intelligent monitoring of tea gardens based on the Internet of Things, in order to solve the problems existing in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A smart monitoring method for tea gardens based on the Internet of Things includes the following steps: Tea garden area division and individual tea tree coding: The tea garden is divided into grid-based zones, and a unique identification code is assigned to each tea tree; IoT sensing devices are deployed in each grid area of ​​the tea garden and around individual tea trees to collect environmental parameters of the tea garden in real time. The collected environmental data is then transmitted to the cloud processing platform in real time, where the cloud processing platform analyzes and judges the environmental data. Based on IoT visual acquisition and sensing devices, the growth status data of each tea tree is collected in real time. Combined with the tea tree identification code, the growth status data of each tea tree is associated with and stored with the corresponding identification code. A knowledge graph of tea tree growth in a tea garden is constructed. Environmental parameters of the tea garden, growth status data of individual tea trees, and information from the tea tree coding database are entered into the knowledge graph in real time. The knowledge graph performs correlation analysis and fusion mining on data from various dimensions to determine the overall growth status of each tea tree, identify growth abnormalities, and trace the causes of abnormalities. Based on the analysis results of the knowledge graph, the cloud processing platform outputs a growth status report and targeted management suggestions for each tea tree according to its identity code, and also outputs an overall environmental management report for the tea garden.

[0007] Optionally, it also includes a tea garden data intelligent memory management step, which adopts a personal data intelligent memory management method to store all monitoring data of the tea garden throughout its entire life cycle. The monitoring data includes tea tree code association data, environmental monitoring data, single tea tree growth status data, knowledge graph analysis data, and control operation data. Memory archives are established according to data type and tea tree identity code to achieve accurate data traceability and historical retrieval.

[0008] Optionally, the intelligent memory management of tea garden data has data update and completion functions. When monitoring data is missing or abnormal, based on the associative memory characteristics of personal data intelligent memory, combined with historical data in the knowledge graph and monitoring data of similar tea trees, it automatically completes missing data and corrects abnormal data, while recording the data update trajectory to form a complete memory update archive, ensuring the accuracy and integrity of the memory data.

[0009] Optionally, the knowledge graph integrates tea tree growth cycle data, environmental impact data, pest and disease control data, tea tree variety characteristic data, and relevant tea-related knowledge from the Lu Yu Tea Industry Big Data Model to form a knowledge system covering the entire tea tree growth process.

[0010] Optionally, the environmental parameters include air temperature and humidity, light intensity, soil moisture, soil nutrients, and pest and disease-related parameters. The collected environmental data is transmitted to the cloud processing platform in real time. The cloud processing platform analyzes and judges the environmental data. When the environmental parameters exceed the preset threshold, the corresponding control command is automatically triggered to achieve precise real-time control of the tea garden environment.

[0011] Optionally, the tea garden tea tree growth knowledge graph adopts a heterogeneous data storage architecture for data storage and management, constructing a knowledge graph storage system adapted to multiple types of data in tea gardens. It supports the unified access and storage of structured data, semi-structured data, and unstructured data related to tea tree growth. The structured data includes tea tree codes and environmental parameter thresholds, the semi-structured data includes tea tree growth logs, and the unstructured data includes tea tree leaf images, pest and disease images, and tea-related literature.

[0012] Optionally, the cloud processing platform supports dual access via mobile terminals and PC terminals. Managers can view the growth status of each tea tree, tea garden environmental parameters and control records in real time through the terminal, receive abnormal early warning information, and manually adjust environmental control parameters and management strategies to achieve a combination of intelligent monitoring and manual intervention, adapting to the needs of large-scale tea garden management.

[0013] Optionally, the tea tree identification code is attached to the tea tree branch in the form of a QR code or RFID radio frequency tag. By scanning the code with a mobile terminal, all related information of the tea tree can be quickly retrieved, realizing rapid traceability and query of tea tree information, which meets the needs of digital management of tea gardens.

[0014] An IoT-based intelligent monitoring system for tea gardens includes: Tea Garden Grid Coding Module: Used for dividing tea garden areas and coding individual tea trees, dividing the tea garden into grid zones, and assigning a unique identification code to each tea tree; Tea garden data parameter acquisition module: used to deploy IoT sensing devices in each grid area of ​​the tea garden and around individual tea trees to collect tea garden environmental parameters in real time, and transmit the collected environmental data to the cloud processing platform in real time. The cloud processing platform analyzes and judges the environmental data. Tea Tree Data Encoding and Identity Correspondence Module: This module is used to collect the growth status data of each tea tree in real time based on IoT visual acquisition devices and sensing devices, and to associate and store the growth status data of each tea tree with the corresponding identity code in combination with the tea tree identity code. Tea Garden Tea Tree Growth Knowledge Graph Construction Module: This module is used to construct a knowledge graph of tea garden tea tree growth. It inputs tea garden environmental parameters, individual tea tree growth status data, and information from the tea tree coding database into the knowledge graph in real time. The knowledge graph performs correlation analysis, fusion mining, and analysis on the data from various dimensions to determine the overall growth status of each tea tree, identify growth anomalies, and trace the causes of the anomalies. Tea Garden Monitoring Report Generation Module: This module is used to process the knowledge graph-based analysis results from the cloud platform, output a growth status report and targeted management suggestions for each tea tree according to its identity code, and also output an overall environmental management report for the tea garden.

[0015] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for intelligent monitoring of tea gardens based on the Internet of Things (IoT). Through grid-based zoning and unique coding of individual tea trees, it achieves refined and individualized management of tea gardens, solving the problems of extensive management and difficulty in accurately controlling individual tea trees in traditional tea gardens. By using IoT sensors and visual devices to collect environmental and growth status data in real time and upload it to the cloud, it can comprehensively and in real-time grasp the condition of the tea garden, improving data collection efficiency and accuracy. By associating and storing multi-dimensional data and constructing a knowledge graph of tea garden and tea tree growth, it can deeply mine data correlations, accurately analyze the growth status of tea trees, promptly identify growth anomalies and trace their causes, providing a scientific basis for pest and disease control, water and fertilizer regulation, etc. Finally, it outputs personalized growth reports and management suggestions according to the tea tree coding, while simultaneously forming an overall environmental management plan for the tea garden. This achieves intelligent decision-making and precise operation across the entire chain from the overall situation to the individual tree, significantly improving tea garden management efficiency, reducing labor costs, ensuring stable tea quality and yield, and promoting the upgrading of tea gardens towards digitalization, intelligence, and standardization. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the method flow provided by the present invention; Figure 2 This is a schematic diagram of the system structure provided by the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention discloses an intelligent monitoring method for tea gardens based on the Internet of Things (IoT), such as... Figure 1 As shown, it includes the following steps: Tea garden area division and individual tea tree coding: The tea garden is divided into grid-based zones, and a unique identification code is assigned to each tea tree; IoT sensing devices are deployed in each grid area of ​​the tea garden and around individual tea trees to collect environmental parameters of the tea garden in real time. The collected environmental data is then transmitted to the cloud processing platform in real time, where the cloud processing platform analyzes and judges the environmental data. Based on IoT visual acquisition and sensing devices, the growth status data of each tea tree is collected in real time. Combined with the tea tree identification code, the growth status data of each tea tree is associated with and stored with the corresponding identification code. A knowledge graph of tea tree growth in a tea garden is constructed. Environmental parameters of the tea garden, growth status data of individual tea trees, and information from the tea tree coding database are entered into the knowledge graph in real time. The knowledge graph performs correlation analysis and fusion mining on data from various dimensions to determine the overall growth status of each tea tree, identify growth abnormalities, and trace the causes of abnormalities. Based on the analysis results of the knowledge graph, the cloud processing platform outputs a growth status report and targeted management suggestions for each tea tree according to its identity code, and also outputs an overall environmental management report for the tea garden.

[0020] Specifically, this embodiment discloses the following steps: Step 1: Tea garden area division and individual tea tree coding. The tea garden is divided into grid-based areas, and each tea tree is assigned a unique identification code. The identification code is associated with the geographical location information, variety information and planting time of the tea tree, forming a tea tree coding database to achieve accurate identification and positioning of each tea tree. Step 2: Real-time monitoring and control of the tea garden environment. IoT sensing devices are deployed in each grid area of ​​the tea garden and around individual tea trees to collect environmental parameters in real time. These environmental parameters include air temperature and humidity, light intensity, soil moisture, soil nutrients, and pest and disease-related parameters. The collected environmental data is transmitted to the cloud processing platform in real time. The cloud processing platform analyzes and judges the environmental data. When the environmental parameters exceed the preset threshold, the corresponding control command is automatically triggered to achieve precise real-time control of the tea garden environment. Step 3: Monitoring the growth status of individual tea trees. Based on IoT visual acquisition devices and sensing devices, the growth status data of each tea tree is collected in real time. The growth status data includes plant height, crown width, leaf color, leaf growth and pest and disease infection status. Combined with the tea tree identification code, the growth status data of each tea tree is associated with the corresponding identification code and stored to realize the individual tracking and recording of the growth status of each tea tree. Step 4: Monitor the overall status of individual tea trees based on knowledge graphs, and construct a knowledge graph of tea tree growth in the tea garden. The knowledge graph integrates tea tree growth cycle data, environmental impact data, pest and disease control data, tea tree variety characteristic data, and relevant tea-related knowledge from the Lu Yu Tea Industry Big Data Model to form a knowledge system covering the entire tea tree growth process. The environmental data collected in Step 2, the individual tea tree growth status data collected in Step 3, and the information from the tea tree coding database are entered into the knowledge graph in real time. The knowledge graph performs correlation analysis and fusion mining on the data from each dimension to determine the overall growth status of each tea tree, identify growth anomalies, and trace the causes of anomalies. Step 5: Monitoring results output and feedback. Based on the judgment results of the knowledge graph, the cloud processing platform outputs a growth status report and targeted management suggestions for each tea tree according to the tea tree identity code. At the same time, it outputs an overall environmental management report for the tea garden, realizing the coordinated promotion of precise environmental management and personalized management of individual tea trees.

[0021] Furthermore, this embodiment also includes a tea garden data intelligent memory management step, which adopts a personal data intelligent memory management method to store all monitoring data of the tea garden throughout its entire life cycle. The monitoring data includes tea tree code association data, environmental monitoring data, single tea tree growth status data, knowledge graph analysis data, and control operation data. Memory archives are established according to data type and tea tree identity code to achieve accurate data traceability and historical retrieval.

[0022] Furthermore, the intelligent memory management of tea garden data has data update and completion functions. When monitoring data is missing or abnormal, based on the associative memory characteristics of personal data intelligent memory, combined with historical data in the knowledge graph and monitoring data of similar tea trees, it automatically completes missing data and corrects abnormal data, while recording the data update trajectory to form a complete memory update archive, ensuring the accuracy and integrity of the memory data.

[0023] Furthermore, the tea garden tea tree growth knowledge graph adopts a heterogeneous data storage architecture for data storage and management, constructing a knowledge graph storage system adapted to various types of data in tea gardens. It supports unified access and storage of structured, semi-structured, and unstructured data related to tea tree growth. The structured data includes tea tree codes and environmental parameter thresholds; the semi-structured data includes tea tree growth logs; and the unstructured data includes tea tree leaf images, pest and disease images, and tea-related literature. In addition, the knowledge graph can be dynamically invoked and linked with Lu Yu Tea Industry's large-scale model via an HTTP interface, enabling precise question answering and reasoning based on the knowledge graph. It supports intelligent querying and recommendation of causes of abnormal tea tree growth and control solutions, achieving closed-loop management from data to knowledge to intelligent decision-making. Moreover, the knowledge graph can be dynamically updated and optimized based on newly added tea tree growth data, environmental data, and tea-related literature knowledge, improving the accuracy and comprehensiveness of the analysis.

[0024] Furthermore, the cloud processing platform supports dual access via mobile and PC terminals. Managers can view the growth status of each tea tree, tea garden environmental parameters, and control records in real time through the terminal, receive abnormal early warning information, and manually adjust environmental control parameters and management strategies to achieve a combination of intelligent monitoring and manual intervention, adapting to the needs of large-scale tea garden management.

[0025] Furthermore, the tea tree identification code is attached to the tea tree branch in the form of a QR code or RFID radio frequency tag. By scanning the code with a mobile terminal, all related information of the tea tree can be quickly retrieved, realizing rapid traceability and query of tea tree information, which meets the needs of digital management of tea gardens.

[0026] Furthermore, in this embodiment, the heterogeneous data storage of the knowledge graph is equipped with a data fault tolerance and recovery mechanism. Multiple verifications and backups are performed on the stored tea garden-related data. When a certain type of data in the knowledge graph is stored abnormally or lost, the backup data can be quickly called for recovery. At the same time, abnormal data is marked and an early warning is triggered to ensure the stable operation of the knowledge graph and the integrity of the data.

[0027] and Figure 1 Corresponding to the method shown, the present invention also discloses an IoT-based intelligent monitoring system for tea gardens. Figure 1 The implementation of the method, specifically its structure, is as follows: Figure 2 As shown, it includes: Tea Garden Grid Coding Module: Used for dividing tea garden areas and coding individual tea trees, dividing the tea garden into grid zones, and assigning a unique identification code to each tea tree; Tea garden data parameter acquisition module: used to deploy IoT sensing devices in each grid area of ​​the tea garden and around individual tea trees to collect tea garden environmental parameters in real time, and transmit the collected environmental data to the cloud processing platform in real time. The cloud processing platform analyzes and judges the environmental data. Tea Tree Data Encoding and Identity Correspondence Module: This module is used to collect the growth status data of each tea tree in real time based on IoT visual acquisition devices and sensing devices, and to associate and store the growth status data of each tea tree with the corresponding identity code in combination with the tea tree identity code. Tea Garden Tea Tree Growth Knowledge Graph Construction Module: This module is used to construct a knowledge graph of tea garden tea tree growth. It inputs tea garden environmental parameters, individual tea tree growth status data, and information from the tea tree coding database into the knowledge graph in real time. The knowledge graph performs correlation analysis, fusion mining, and analysis on the data from various dimensions to determine the overall growth status of each tea tree, identify growth anomalies, and trace the causes of the anomalies. Tea Garden Monitoring Report Generation Module: This module is used to process the knowledge graph-based analysis results from the cloud platform, output a growth status report and targeted management suggestions for each tea tree according to its identity code, and also output an overall environmental management report for the tea garden.

[0028] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0029] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent monitoring of tea gardens based on the Internet of Things, characterized in that, Includes the following steps: Tea garden area division and individual tea tree coding: The tea garden is divided into grid-based zones, and a unique identification code is assigned to each tea tree; IoT sensing devices are deployed in each grid area of ​​the tea garden and around individual tea trees to collect environmental parameters of the tea garden in real time. The collected environmental data is then transmitted to the cloud processing platform in real time, where the cloud processing platform analyzes and judges the environmental data. Based on IoT visual acquisition and sensing devices, the growth status data of each tea tree is collected in real time. Combined with the tea tree identification code, the growth status data of each tea tree is associated with and stored with the corresponding identification code. A knowledge graph of tea tree growth in a tea garden is constructed. Environmental parameters of the tea garden, growth status data of individual tea trees, and information from the tea tree coding database are entered into the knowledge graph in real time. The knowledge graph performs correlation analysis and fusion mining on data from various dimensions to determine the overall growth status of each tea tree, identify growth abnormalities, and trace the causes of abnormalities. Based on the analysis results of the knowledge graph, the cloud processing platform outputs a growth status report and targeted management suggestions for each tea tree according to its identity code, and also outputs an overall environmental management report for the tea garden.

2. The intelligent monitoring method for tea gardens based on the Internet of Things according to claim 1, characterized in that, It also includes a tea garden data intelligent memory management step, which adopts a personal data intelligent memory management method to store all monitoring data of the tea garden throughout its entire life cycle. The monitoring data includes tea tree code association data, environmental monitoring data, single tea tree growth status data, knowledge graph analysis data, and control operation data. Memory archives are established according to data type and tea tree identity code to achieve accurate data traceability and historical retrieval.

3. The intelligent monitoring method for tea gardens based on the Internet of Things according to claim 1, characterized in that, The intelligent memory management system for tea garden data has data update and completion functions. When monitoring data is missing or abnormal, based on the associative memory characteristics of personal data intelligent memory, combined with historical data in the knowledge graph and monitoring data of similar tea trees, it automatically completes missing data and corrects abnormal data. At the same time, it records the data update trajectory to form a complete memory update archive, ensuring the accuracy and integrity of the memory data.

4. The intelligent monitoring method for tea gardens based on the Internet of Things according to claim 1, characterized in that, The knowledge graph integrates data on tea tree growth cycle, environmental impact, pest and disease control, tea tree variety characteristics, and related tea-related knowledge from the Lu Yu Tea Industry Big Data Model, forming a knowledge system covering the entire tea tree growth process.

5. The intelligent monitoring method for tea gardens based on the Internet of Things according to claim 1, characterized in that, The environmental parameters include air temperature and humidity, light intensity, soil moisture, soil nutrients, and pest and disease-related parameters. The collected environmental data is transmitted to the cloud processing platform in real time. The cloud processing platform analyzes and judges the environmental data, and when the environmental parameters exceed the preset threshold, it automatically triggers the corresponding control command to achieve precise real-time control of the tea garden environment.

6. The intelligent monitoring method for tea gardens based on the Internet of Things according to claim 1, characterized in that, The tea garden tea tree growth knowledge graph adopts a heterogeneous data storage architecture for data storage and management, and constructs a knowledge graph storage system adapted to multiple types of data in tea gardens. It supports the unified access and storage of structured data, semi-structured data and unstructured data related to tea tree growth. The structured data includes tea tree codes and environmental parameter thresholds, the semi-structured data includes tea tree growth logs, and the unstructured data includes tea tree leaf images, pest and disease images and tea-related literature.

7. The intelligent monitoring method for tea gardens based on the Internet of Things according to claim 1, characterized in that, The cloud processing platform supports dual access via mobile and PC terminals. Managers can view the growth status of each tea tree, tea garden environmental parameters, and control records in real time through the terminal, receive abnormal early warning information, and manually adjust environmental control parameters and management strategies. This achieves a combination of intelligent monitoring and manual intervention, adapting to the needs of large-scale tea garden management.

8. The intelligent monitoring method for tea gardens based on the Internet of Things according to claim 1, characterized in that, The tea tree identification code is attached to the tea tree branch in the form of a QR code or RFID radio frequency tag. By scanning the code with a mobile terminal, all related information of the tea tree can be quickly retrieved, realizing rapid traceability and query of tea tree information, which meets the needs of digital management of tea gardens.

9. A smart monitoring system for tea gardens based on the Internet of Things, characterized in that, include: Tea Garden Grid Coding Module: Used for dividing tea garden areas and coding individual tea trees, dividing the tea garden into grid zones, and assigning a unique identification code to each tea tree; Tea garden data parameter acquisition module: used to deploy IoT sensing devices in each grid area of ​​the tea garden and around individual tea trees to collect tea garden environmental parameters in real time, and transmit the collected environmental data to the cloud processing platform in real time. The cloud processing platform analyzes and judges the environmental data. Tea Tree Data Encoding and Identity Correspondence Module: This module is used to collect the growth status data of each tea tree in real time based on IoT visual acquisition devices and sensing devices, and to associate and store the growth status data of each tea tree with the corresponding identity code in combination with the tea tree identity code. Tea Garden Tea Tree Growth Knowledge Graph Construction Module: This module is used to construct a knowledge graph of tea garden tea tree growth. It inputs tea garden environmental parameters, individual tea tree growth status data, and information from the tea tree coding database into the knowledge graph in real time. The knowledge graph performs correlation analysis, fusion mining, and analysis on the data from various dimensions to determine the overall growth status of each tea tree, identify growth anomalies, and trace the causes of the anomalies. Tea Garden Monitoring Report Generation Module: This module is used to process the knowledge graph-based analysis results from the cloud platform, output a growth status report and targeted management suggestions for each tea tree according to its identity code, and also output an overall environmental management report for the tea garden.

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