Efficient water-saving type intelligent drip irrigation system

Abstract

The invention relates to the technical field of agricultural irrigation, and discloses a high-efficiency water-saving intelligent drip irrigation system which comprises a soil humidity sensor used for monitoring the humidity level in soil in real time; the meteorological sensor is used for monitoring environment temperature, humidity, wind speed, wind direction and rainfall; the central controller is connected with the soil humidity sensor and the meteorological sensor and used for receiving and processing sensor data and dynamically adjusting the irrigation area and the irrigation amount based on the data; and the partition control valves are mounted on branch nodes of the irrigation pipe network, are connected with the central controller and are used for controlling the water flow of each irrigation area according to instructions of the central controller. By monitoring soil humidity and meteorological conditions in real time, the system can accurately control irrigation time and water volume according to actual requirements. According to the demand-driven irrigation strategy, waste of water resources is greatly reduced, it is ensured that irrigation is conducted at the most needed time, only the needed water is supplied, and the use efficiency of the water resources is further optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural irrigation, and particularly to an efficient water-saving intelligent drip irrigation system. Background Art

[0002] With the increasingly serious problem of global water shortage, how to effectively manage and utilize water resources has become a major challenge in the agricultural field. Against this background, the demand for intelligent irrigation systems is constantly growing, especially for achieving water conservation and optimization while ensuring crop growth. Intelligent drip irrigation systems utilize advanced sensing technologies and automatic control systems to monitor and adjust irrigation conditions in real time for the purpose of precise irrigation, which not only ensures the water requirements of crops but also greatly reduces water resource waste.

[0003] The intelligent irrigation systems on the market mainly rely on soil moisture sensors and meteorological sensors to collect data, analyze this data through a central control unit, and make irrigation decisions. These systems can automatically adjust the irrigation time and water volume according to the actual soil moisture conditions to ensure the efficiency and effectiveness of irrigation. In addition, some systems also include a remote monitoring platform that allows users to remotely view the irrigation status and adjust the irrigation plan through the Internet. However, these systems often lack more advanced data processing capabilities and prediction models, resulting in irrigation decisions being unable to make full use of the large amount of data collected.

[0004] Although the existing intelligent irrigation systems have improved the irrigation efficiency to a certain extent, there are still some deficiencies. For example, these systems often cannot dynamically process and analyze large-scale data, nor do they have effective prediction capabilities to cope with rapidly changing climate conditions. In addition, the user interfaces of existing systems are usually not intuitive enough and lack flexible user customization functions. Based on these problems, the new generation of efficient water-saving intelligent drip irrigation systems adopt more advanced machine learning algorithms and feedback control mechanisms, which not only improve the accuracy of data processing and prediction but also enhance the operation convenience of users and the security of the system through optimized user interfaces and data security measures. Through these improvements, the intelligent drip irrigation system can better adapt to different environmental conditions and user needs, significantly improving the utilization efficiency of water resources and the production efficiency of crops. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an efficient water-saving intelligent drip irrigation system, which solves the problems that some existing efficient water-saving intelligent drip irrigation systems cannot dynamically process and analyze large-scale data and lack effective prediction capabilities to cope with rapidly changing climate conditions.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An efficient water-saving intelligent drip irrigation system, comprising: A soil moisture sensor for real-time monitoring of the moisture level in the soil; A meteorological sensor for monitoring environmental temperature, humidity, wind speed and direction, and rainfall; A central controller connected to the soil moisture sensor and the meteorological sensor, for receiving and processing sensor data, and dynamically adjusting the irrigation area and irrigation volume based on the data; A zone control valve installed at the branch node of the irrigation pipe network, connected to the central controller, for controlling the water flow in each irrigation area according to the instructions of the central controller; A data transmission module for transmitting sensor data to the central controller, and transmitting the instructions of the central controller to the zone control valve; A remote monitoring platform communicatively connected to the central controller, for displaying real-time data, monitoring the irrigation status, and allowing users to adjust the irrigation strategy.

[0007] Preferably, the soil moisture sensor is a capacitive soil moisture sensor and is installed in the active layer of crop roots.

[0008] Preferably, the meteorological sensor includes a temperature and humidity sensor, a wind speed and direction sensor, and a rainfall sensor, and the meteorological sensor is installed in an open area.

[0009] Preferably, the central controller includes an embedded system and a machine learning algorithm for analyzing sensor data and generating an optimal irrigation strategy, specifically including the following steps: Receiving real-time and historical data from the soil moisture sensor and the meteorological sensor; Cleaning the received data, including removing noise, identifying and removing outliers, and filling in missing data; Extracting soil moisture, environmental temperature, air humidity, wind speed, wind direction and rainfall from the cleaned data, and evaluating the influence of features on irrigation decisions; Using decision tree and random forest machine learning algorithms to train a model based on historical data, including model construction, parameter optimization and verification; Using the trained model, combined with real-time data to predict the irrigation strategy, including data input, feature processing and model application; According to the model prediction results, adjusting the water flow of the irrigation system through the control valve, and adjusting the irrigation time and water volume in real time to ensure precise irrigation; Implementing a feedback mechanism to optimize decisions, including monitoring irrigation effects and analyzing data feedback, and adjusting model parameters according to the results after irrigation to improve the accuracy of prediction and the efficiency of irrigation.

[0010] Preferably, the machine learning algorithm includes a decision tree algorithm or a random forest algorithm for training a model based on historical data and making real-time predictions, specifically including the following steps: Collect and prepare historical data, including soil moisture and meteorological conditions; Use decision tree or random forest algorithms to build a model, and train and validate the performance of the model through data partitioning and cross-validation; Apply the trained model to predict irrigation strategies based on real-time data, and adjust the irrigation plan to adapt to the current environment and soil conditions; Apply the prediction results in real time, adjust the irrigation control system, optimize the use of water resources, and continuously monitor the prediction effect.

[0011] Preferably, the zone control valve is an electric valve or a solenoid valve, which is used to dynamically adjust the water flow rate and irrigation duration of each irrigation zone according to the instructions of the central controller.

[0012] Preferably, the data transmission module uses wireless communication technologies, including LoRa, Zigbee, and Wi-Fi, to ensure the stability and reliability of data transmission.

[0013] Preferably, the remote monitoring platform includes a Web application and a mobile application, which provide real-time monitoring, data analysis, and a user interface, allowing users to remotely view and adjust irrigation strategies.

[0014] Preferably, the remote monitoring platform is equipped with an alarm system, which sends an alarm notification to the user when the soil moisture is lower than the preset value.

[0015] Preferably, the central controller further includes a feedback control module, which dynamically adjusts the irrigation strategy based on the real-time data of the soil moisture sensor and the meteorological sensor, generates an adjustment plan for irrigation zone division, and implements the adjustment through the zone control valve to ensure that the humidity of each irrigation zone is maintained within the optimal range.

[0016] The present invention provides an efficient water-saving intelligent drip irrigation system, which has the following beneficial effects: 1. By real-time monitoring of soil moisture and meteorological conditions, the system can accurately control the irrigation time and water volume according to actual needs. This demand-driven irrigation strategy greatly reduces water resource waste, ensures that each irrigation is carried out when it is most needed, and only supplies the required water volume. The system dynamically adjusts the irrigation strategy based on continuously collected data to adapt to weather changes and soil condition variations, further optimizing the water use efficiency.

[0017] 2. By maintaining the soil moisture within the optimal range, the system helps to create a more stable crop growth environment. Appropriate soil moisture can improve the crop growth rate and production quality, reduce crop diseases caused by improper irrigation. The automatic alarm system can timely remind users that the soil moisture is too low, preventing over-irrigation and resource waste, helping to maintain soil health and avoid over-exploitation of water resources.

[0018] 3. The present invention enables remote viewing of real-time data, adjustment of irrigation strategies, and receipt of alert notifications through the Web and mobile applications. This not only provides great convenience for farmers but also enables them to respond promptly to environmental changes, thereby making more reasonable irrigation decisions. The data analysis and trend prediction functions provided by the system help users understand long-term data patterns and influencing factors, support more scientific decision-making, and enhance the overall efficiency and economic benefits of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is the system module diagram of the present invention; Figure 2 is the meteorological sensor module diagram of the present invention; Figure 3 is the partial system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0021] Please refer to the attached Figure 1 - attached Figure 3 , the embodiment of the present invention provides an efficient water-saving intelligent drip irrigation system, including: A soil humidity sensor for real-time monitoring of the humidity level in the soil; Detailed description of the deployment and function of the soil humidity sensor: Installation depth and location: The soil humidity sensor is usually installed in the active root area of the main crops, with a depth set at 20 - 30 cm. This can accurately monitor the key water layer affecting crop absorption.

[0022] A meteorological sensor for monitoring environmental temperature, humidity, wind speed, wind direction, and rainfall; Configuration and monitoring functions of the meteorological sensor: Configuration location: The meteorological sensor is placed at a height of 1.5 to 2 meters above the ground in an unobstructed position to avoid the influence of ground reflection and other interference factors on data accuracy.

[0023] Monitoring parameters: The sensor continuously monitors temperature, humidity, wind speed, wind direction, and rainfall, and these data are crucial for calculating evaporation and adjusting irrigation strategies.

[0024] The central controller, connected to the soil moisture sensor and the meteorological sensor, is used to receive and process sensor data and dynamically adjust the irrigation area and irrigation volume based on the data; Functions and processing flow of the central controller: Data processing capabilities: The central controller is equipped with an advanced microprocessor and sufficient storage space to process a large amount of data from all sensors. The controller runs advanced algorithms for data cleaning, including outlier detection and missing value imputation.

[0025] Decision-making and execution: The controller has built-in machine learning algorithms that not only analyze current data but also consider historical weather and soil moisture patterns to automatically determine the most suitable irrigation timing and water volume.

[0026] The zone control valve, installed at the branch nodes of the irrigation pipe network and connected to the central controller, is used to control the water flow in each irrigation area according to the instructions of the central controller; Precise control: Each irrigation area is equipped with an independent electric control valve that can precisely adjust the water flow according to the commands of the central controller. This allows for irrigation according to the specific needs of each area, avoiding overwatering or water shortage problems.

[0027] Emergency response: Under extreme weather conditions, such as predicting heavy rain, the control system can quickly respond by closing all control valves to prevent over-irrigation and resource waste.

[0028] The data transmission module is used to transmit sensor data to the central controller and the instructions of the central controller to the zone control valve; Efficient connection: The data transmission module uses advanced communication technologies to ensure real-time and stable connection between the sensor and the controller. The module supports multi-band operation to ensure communication reliability in complex environments.

[0029] Measurement and transmission technology: The sensor measures the soil moisture at regular intervals and uses low-power wireless communication technology to transmit the data to the central controller in real time. The sensor is designed to be waterproof to adapt to various climate conditions.

[0030] The remote monitoring platform, communicatively connected to the central controller, is used to display real-time data, monitor the irrigation status, and allow users to adjust the irrigation strategy.

[0031] Multi-platform access: The remote monitoring platform supports access from various devices such as smartphones, tablets to computers. Users can view real-time data, historical data, and irrigation status at any time.

[0032] User-friendly interface: The interface is designed intuitively, enabling users to easily set irrigation plans, adjust irrigation parameters, and receive system-generated information.

[0033] The soil moisture sensor is a capacitive soil moisture sensor and is installed in the active layer of crop roots.

[0034] The meteorological sensors include temperature and humidity sensors, wind speed and direction sensors, and rainfall sensors, and the meteorological sensors are installed in open areas.

[0035] Please refer to the appendix Figure 1 - Appendix Figure 3 , in a preferred embodiment of the present invention, the central controller includes an embedded system and a machine learning algorithm for analyzing sensor data and generating an optimal irrigation strategy, specifically including the following steps: 1. Receive real-time and historical data from the soil moisture sensor and the meteorological sensors; Data collection: Source: The central controller receives real-time and historical data from the soil moisture sensor and the meteorological sensors. This data includes soil moisture, ambient temperature, air humidity, wind speed, wind direction, and rainfall.

[0036] Data transmission: Wireless communication technologies such as LoRa, Zigbee, and Wi-Fi are used to ensure stable and real-time data transmission from the sensors to the controller.

[0037] 2. Clean the received data, including removing noise, identifying and removing outliers, and filling in missing data; Removing noise: Filtering algorithms are used to remove noise generated by the environment and equipment.

[0038] Outlier handling: Statistical methods are used to identify and remove outliers in the data.

[0039] Filling in missing data: Interpolation methods are used to fill in the missing data points.

[0040] 3. Extract soil moisture, ambient temperature, air humidity, wind speed, wind direction, and rainfall from the cleaned data, and evaluate the impact of the features on irrigation decisions; Feature engineering: Key features affecting irrigation decisions are extracted from the preprocessed data, and new features may be created to enhance the prediction ability of the model.

[0041] 4. Use decision tree and random forest machine learning algorithms to train the model based on historical data, including model construction, parameter optimization, and validation; 5. Use the trained model to predict the irrigation strategy in combination with real-time data, including data input, feature processing, and model application; Algorithm selection: Decision tree or random forest algorithms are selected to build a prediction model according to irrigation requirements and environmental factors.

[0042] Cross-validation: Optimize model parameters using cross-validation and verify the performance of the model on unseen data to ensure the generalization ability of the model.

[0043] 6. According to the model prediction results, adjust the water flow of the irrigation system through the control valve, and adjust the irrigation time and water volume in real time to ensure precise irrigation; Irrigation strategy prediction: Real-time prediction: Input real-time data into the trained model to predict the immediate irrigation requirements, including water volume and time.

[0044] Decision-making: Adjust the irrigation plan according to the model output to adapt to the current environmental and soil conditions.

[0045] Irrigation control execution: Control valve operation: Dynamically adjust the water flow rate and irrigation duration of each irrigation area through electric valves or solenoid valves to implement precise irrigation.

[0046] Implement a feedback mechanism to optimize decision-making, including monitoring irrigation effects and analyzing data feedback, and adjusting model parameters according to the results after irrigation to improve the prediction accuracy and irrigation efficiency.

[0047] System feedback: Implement a feedback control module to dynamically adjust the irrigation strategy according to real-time data and prediction results, and optimize water resource utilization.

[0048] The central controller regularly receives real-time and historical data from soil moisture sensors and meteorological sensors. This includes key parameters such as soil moisture, ambient temperature, air humidity, wind speed, wind direction, and rainfall.

[0049] Machine learning algorithms include decision tree algorithms or random forest algorithms, which are used to train models based on historical data and make real-time predictions. The specific steps are as follows: a. Collect and prepare historical data, involving soil moisture and meteorological conditions; Data sources: Regularly collect data on soil moisture, ambient temperature, air humidity, wind speed, wind direction, and rainfall using soil moisture sensors and meteorological sensors.

[0050] Data storage: The data is stored in a central database, which may include cloud storage to ensure the integrity and security of the data.

[0051] b. Use decision tree or random forest algorithms to build a model, and train and verify the performance of the model through data partitioning and cross-validation; Model development: Use decision tree or random forest algorithms to build a prediction model on the historical dataset. This includes selecting appropriate features and handling any possible missing values or outliers.

[0052] Data Partitioning and Cross-Validation: The data is partitioned into a training set and a test set, and cross-validation techniques are usually used to evaluate the performance and generalization ability of the model. c. Apply the trained model to predict the irrigation strategy based on real-time data, and adjust the irrigation plan to adapt to the current environment and soil conditions. Real-time Data Processing: The central controller receives sensor data in real time and immediately analyzes this data using the trained model.

[0053] Predict Irrigation Strategy: Based on the model output, generate an irrigation strategy, including determining the optimal time and amount of irrigation water.

[0054] d. Apply the prediction results in real time, adjust the irrigation control system, optimize the use of water resources, and continuously monitor the prediction effect.

[0055] Adjustment of Irrigation Control System: Automatically adjust the irrigation control system according to the prediction results, and use zone control valves to precisely control the water flow in each irrigation zone.

[0056] Continuous Monitoring and Optimization: Continuously monitor the comparison between the actual irrigation effect and the prediction results, and adjust and optimize the model as needed.

[0057] The zone control valves are electric valves or solenoid valves, which are used to dynamically adjust the water flow rate and irrigation duration in each irrigation zone according to the instructions of the central controller.

[0058] Selection of Valve Type: Select a suitable electric valve or solenoid valve to ensure reliable operation and sufficient flow control.

[0059] Installation and Configuration: The valves are installed at key branch nodes of the irrigation system, and the central controller is configured to control these valves.

[0060] The data transmission module uses wireless communication technologies, including LoRa, Zigbee, and Wi-Fi, to ensure the stability and reliability of data transmission.

[0061] Selection of Wireless Communication Technology: Select wireless communication technologies such as LoRa, Zigbee, and Wi-Fi, considering their transmission distance, interference resistance ability, and energy consumption.

[0062] System Integration: Integrate the communication module into the central controller and sensors to ensure stable and real-time data transmission between all devices.

[0063] Security Measures: Implement encryption measures and security protocols to protect the transmitted data from unauthorized access.

[0064] Please refer to the appendix Figure 1 - Appendix Figure 3, in a preferred embodiment of the present invention, the remote monitoring platform includes a Web application and a mobile application, which provide real-time monitoring, data analysis, and a user interface, allowing users to remotely view and adjust irrigation strategies.

[0065] The remote monitoring platform is equipped with an alarm system that sends an alarm notification to the user when the soil humidity is below a preset value.

[0066] The central controller also includes a feedback control module that dynamically adjusts the irrigation strategy based on the real-time data from the soil humidity sensor and the weather sensor, generates an adjustment plan for the irrigation area division, and implements the adjustment through the zone control valve to ensure that the humidity in each irrigation area remains within the optimal range.

[0067] Specifically, 1. Construction and functions of the remote monitoring platform a. Platform architecture Web and mobile application development: Develop cross-platform Web applications and mobile applications to ensure that users can access the system regardless of the device. The applications should support real-time data display, historical data analysis, irrigation strategy adjustment, and receiving alarm notifications.

[0068] User interface design: Design an intuitive user interface, including a dashboard, charts, and a control panel, enabling users to easily monitor key metrics and adjust irrigation settings.

[0069] b. Real-time monitoring and data analysis Real-time data display: Display real-time data from the soil humidity sensor and the weather sensor, including soil humidity, weather conditions, etc.

[0070] Data analysis function: Provide data trend analysis, prediction model output, and irrigation effect evaluation to help users understand irrigation requirements and adjust strategies.

[0071] c. Alarm system Alarm setting: Allow users to set alarm thresholds according to specific needs, such as too low soil humidity.

[0072] Notification mechanism: Send alarms to users in real time via email, SMS, or in-app push notifications.

[0073] 2. Central controller and feedback control module a. Central controller functions Data integration and processing: Receive data from various sensors and perform necessary preprocessing such as denoising, outlier handling, and data interpolation.

[0074] Implementation of decision algorithms: Use machine learning algorithms such as decision trees and random forests to train models based on the processed data and use them to predict the optimal irrigation strategy.

[0075] b. Feedback control module Dynamically adjust irrigation strategy: Based on real-time monitoring data and the output of the prediction model, dynamically generate and adjust the irrigation area division plan to ensure that the soil moisture in each area is maintained in the optimal state.

[0076] Control valve management: Through electric valves or solenoid valves, adjust the water flow rate and irrigation duration according to the control strategy to accurately execute the irrigation plan.

[0077] 3. Data communication and security Stable data transmission: Adopt wireless technologies such as LoRa, Zigbee, and Wi-Fi to ensure stable and real-time data transmission from sensors to the central controller.

[0078] Data security measures: Implement encryption measures to protect the security of data during transmission and storage, and avoid data leakage.

[0079] 4. User support and system maintenance User support service: Provide a detailed user manual and online support to help users solve technical problems encountered during use.

[0080] System maintenance and update: Regularly update the software and hardware systems, introduce new functions, improve system performance, and fix known defects.

[0081] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient and water-saving intelligent drip irrigation system, characterized in that: include: Soil moisture sensor, used to monitor moisture levels in the soil in real time; Meteorological sensors to monitor ambient temperature, humidity, wind speed and direction, and rainfall; a central controller connected to the soil moisture sensor and the meteorological sensor, for receiving and processing sensor data, and dynamically adjusting the irrigation area and irrigation amount based on the data; A zone control valve, installed at a branch node of the irrigation pipe network and connected to the central controller, for controlling the water flow of each irrigation zone according to the instructions of the central controller; A data transmission module, used for transmitting sensor data to a central controller, and transmitting instructions from the central controller to the zone control valve; The remote monitoring platform is connected to the central controller for displaying real-time data, monitoring irrigation status and allowing users to adjust irrigation strategies.

2. The high-efficiency water-saving intelligent drip irrigation system according to claim 1 is characterized in that: The soil moisture sensor is a capacitive soil moisture sensor and is installed in the active root layer of the crop.

3. The high-efficiency water-saving intelligent drip irrigation system according to claim 1 is characterized in that: The meteorological sensor comprises a temperature and humidity sensor, a wind speed and direction sensor and a rainfall sensor, and the meteorological sensor is installed in an open area.

4. The high-efficiency water-saving intelligent drip irrigation system according to claim 1, characterized in that: The central controller includes an embedded system and a machine learning algorithm for analyzing sensor data and generating an optimal irrigation strategy, specifically including the following steps: Receive real-time and historical data from soil moisture sensors and meteorological sensors; Cleaning received data, including removing noise, identifying and removing outliers, and filling in missing data; Extract soil moisture, ambient temperature, air humidity, wind speed, wind direction, and rainfall from the cleaned data and evaluate the impact of the features on irrigation decisions; Use decision tree and random forest machine learning algorithms to train models based on historical data, including model construction, parameter optimization, and validation; Use the trained model and real-time data to predict irrigation strategies, including data input, feature processing, and model application; According to the model prediction results, the water flow of the irrigation system is adjusted through the control valve, and the irrigation time and water volume are adjusted in real time to ensure accurate irrigation; Implement feedback mechanisms to optimize decision-making, including monitoring irrigation effects and analyzing data feedback, and adjusting model parameters based on post-irrigation results to improve prediction accuracy and irrigation efficiency.

5. The high-efficiency water-saving intelligent drip irrigation system according to claim 4, characterized in that: The machine learning algorithm includes a decision tree algorithm or a random forest algorithm, which is used to train a model based on historical data and perform real-time prediction, and specifically includes the following steps: Collect and prepare historical data on soil moisture and meteorological conditions; Build models using decision tree or random forest algorithms, and train and validate model performance through data partitioning and cross-validation; Apply trained models to predict irrigation strategies based on real-time data and adjust irrigation plans to suit current environmental and soil conditions; Apply forecast results in real time to adjust irrigation control systems, optimize water use, and continuously monitor forecast results.

6. The high-efficiency water-saving intelligent drip irrigation system according to claim 1, characterized in that: The zone control valve is an electric valve or a solenoid valve, which is used to dynamically adjust the water flow and irrigation duration of each irrigation zone according to the instructions of the central controller.

7. The high-efficiency water-saving intelligent drip irrigation system according to claim 1, characterized in that: The data transmission module uses wireless communication technologies, including LoRa, Zigbee and Wi-Fi, to ensure the stability and reliability of data transmission.

8. The high-efficiency water-saving intelligent drip irrigation system according to claim 1, characterized in that: The remote monitoring platform includes a web application and a mobile application that provides real-time monitoring, data analysis, and a user interface, allowing users to remotely view and adjust irrigation strategies.

9. The high-efficiency water-saving intelligent drip irrigation system according to claim 1, characterized in that: The remote monitoring platform is equipped with an alarm system, which sends an alarm notification to the user when the soil moisture is lower than a preset value.

10. The high-efficiency water-saving intelligent drip irrigation system according to claim 1, characterized in that: The central controller also includes a feedback control module, which dynamically adjusts the irrigation strategy based on real-time data from soil moisture sensors and meteorological sensors, generates an irrigation area division adjustment plan, and implements the adjustment through the zoning control valve to ensure that the humidity of each irrigation area remains within the optimal range.

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