Tea garden irrigation method and system suitable for high temperature and drought stress

Abstract

The invention discloses a tea garden irrigation method and system suitable for high temperature and drought stress, and relates to the technical field of agricultural automation, and the method comprises the following steps: laying an irrigation pipe network for a tea garden, and connecting the irrigation pipe network with an irrigation system; an internet-of-things acquisition system is adaptively installed on the irrigation pipe network; collecting tea garden soil moisture real-time data through an Internet of Things collection system, and transmitting the data to a central processing unit; the central processing system compares the collected soil moisture data with a preset threshold value, and outputs a control instruction to automatically control the start and stop of the irrigation system; and data acquisition is repeated through the Internet of Things acquisition system to carry out intelligent precise irrigation under high temperature and drought stress of the tea garden. The system integrates informatization, digitization and intellectualization, and accurately supplements tea tree growth water deficit under seasonal drought stress in the subtropical red soil hilly area, so that the adverse effect of high-temperature drought on tea tree growth can be eliminated, tea garden drought is avoided, the tea garden irrigation efficiency can be improved, and water resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural automation, and more particularly to a method and system for irrigating a tea garden under high temperature and drought stress. Background Art

[0002] Against the backdrop of global warming, extreme heat and drought in summer and autumn are showing a trend of increasing frequency, wider range and greater harm. It is reported that the probability of occurrence of two typical seasonal droughts in the subtropical red soil hilly areas, namely, high temperature drought in July and August and continuous drought in September and October, is over 85%, and the degree of harm is 1960s < 1970s < 1980s < 1990s < 21st century.

[0003] Tea trees are perennial evergreen plants that like warmth and humidity. They have special requirements for water. The suitable annual precipitation is 1200-1500mm, and the water demand varies greatly in different stages of the year. In spring, tea trees have vigorous physiological metabolism and require more water and nutrients. The average daily water consumption is about 3mm. In the hot summer and autumn tea stage, the light is strong, the temperature is high, and the evaporation is fast. The average daily water consumption of tea trees is 5.8-7mm. After entering late autumn, the temperature drops, the evaporation decreases, the tea trees enter a dormant state, the water demand decreases, and the average daily water consumption is 1-3mm. In terms of the water demand during the growth period of tea trees, the seasonal distribution of precipitation is not completely consistent with the water demand of tea trees. The seasonal distribution of rain is uneven. There is more rain in spring, and the soil in the tea garden is sufficient. In summer and autumn, there are frequent regional and seasonal droughts with little rain, which seriously affect the normal growth and development of tea trees. When tea trees are affected by drought, they have an internal mechanism to deal with water stress by sacrificing photosynthesis. The stomata of tea leaves are closed, the photosynthetic rate per unit leaf area is reduced, and the carbon and nitrogen synthesis metabolism of leaves is weakened, which will not only cause the reduction of summer and autumn tea production and value, but also affect the yield and quality of spring tea in the following year, and even cause the death of tea trees. Therefore, timely watering of tea gardens in summer and autumn has become an important technical measure to ensure high yield and quality of tea and achieve sustainable and healthy development of the tea industry.

[0004] Irrigation is one of the fastest and most effective ways for tea-growing areas to resist high temperatures and droughts. Common irrigation methods for tea gardens include pouring irrigation, flow irrigation, sprinkler irrigation, and drip irrigation. Sprinkler irrigation is widely used for its advantages of high irrigation uniformity, high water-saving rate, labor saving, difficulty in forming surface runoff, adaptability to various terrain conditions, and conduciveness to improving the microclimate of tea gardens. However, as far as tea garden sprinkler irrigation is concerned, there is no consensus on technical parameters such as the best irrigation period and appropriate irrigation amount. Tea farmers often rely on experience in their operations, and lack advanced technologies such as informatization and automation.

[0005] Therefore, how to propose a tea garden irrigation method and system suitable for high temperature drought stress, integrate advanced technologies such as informatization and intelligence, accurately supplement the water deficit under drought stress, thereby realizing intelligent and precise water-saving irrigation, and successfully resolving the drought problem in tea tree production and cultivation is an issue that technicians in this field urgently need to solve. Summary of the invention

[0006] In view of this, the present invention provides a method and system for tea garden irrigation under high temperature drought stress, which integrates advanced technologies such as informatization and intelligence, and organically combines engineering technology with Internet of Things technology to accurately supplement water deficit under drought stress, thereby realizing intelligent and precise water-saving irrigation, successfully resolving the drought problem in tea tree production and cultivation, aiming to eliminate the adverse effects of seasonal drought on tea tree growth, avoid the occurrence of drought in tea gardens and improve irrigation efficiency. In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A tea garden irrigation method suitable for use under high temperature and drought stress, comprising:

[0008] Step 1: Lay the irrigation pipe network in the tea garden and connect the irrigation pipe network to the irrigation system;

[0009] Step 2: Adapt and install the Internet of Things collection system on the irrigation network;

[0010] Step 3: Collect the real-time data of soil moisture in the tea garden through the Internet of Things collection system and transmit the data to the central processor;

[0011] Step 4: The central processing system compares the collected soil moisture data with the preset moisture threshold for tea tree drought, the fixed irrigation time period, and the optimum moisture threshold for tea tree growth, and outputs control instructions to automatically control the start and stop of the irrigation system;

[0012] Step 5: Repeat data collection through the Internet of Things collection system to carry out intelligent precision irrigation under high temperature and drought stress in the tea garden.

[0013] Optionally, the intelligent precision irrigation under high temperature and drought conditions in the tea garden by repeatedly collecting data through the Internet of Things collection system includes: collecting initial soil moisture data at the roots of the tea trees, judging whether the collected initial soil moisture data meets preset start conditions, and starting sprinkler irrigation when the preset start conditions are met; collecting real-time soil moisture data at the roots of the tea trees, and judging whether the real-time soil moisture data meets preset stop conditions, and stopping sprinkler irrigation when the preset stop conditions are met, so as to achieve precision irrigation.

[0014] Optionally, the collecting of initial soil moisture data at the roots of the tea trees includes installing a soil moisture sensor at a location where the absorbing roots of the tea trees are concentrated (at a 40 cm soil layer) to collect soil moisture data in real time.

[0015] Optionally, the preset starting conditions include: a preset moisture threshold for tea tree drought (60% of the maximum field water holding capacity) and a preset fixed sprinkler irrigation time period (from 18:00 p.m. to 6:00 a.m. the next day).

[0016] Optionally, the preset stop conditions include: a preset optimum moisture threshold for tea tree growth (80% of the maximum field water holding capacity) and outside a preset fixed sprinkler irrigation time period.

[0017] Optionally, the central processing system also includes comparing the collected initial soil moisture data with the moisture threshold for occurrence of tea tree drought and a preset fixed sprinkler irrigation time period preset in the central processing system. When the comparison result is met, the central processing system automatically starts and stops the sprinkler irrigation system.

[0018] Optionally, the output control instruction automatically controls the start and stop of the irrigation system, including: transmitting the collected soil moisture data to the central processing system, and comparing the moisture threshold for drought occurrence of tea trees preset by the central processing system. When the initial soil moisture content is lower than the moisture threshold for drought occurrence and at the same time meets the preset fixed sprinkler irrigation time period, the central processing system automatically starts the sprinkler irrigation system.

[0019] Optionally, the output control instruction to automatically control the start and stop of the irrigation system also includes: when the sprinkler irrigation continues until the real-time soil moisture detection data reaches the optimal moisture threshold for tea tree growth preset by the central processing system or exceeds the preset fixed sprinkler irrigation time period, the central processing system automatically stops the sprinkler irrigation.

[0020] Optionally, a tea garden irrigation system suitable for use under high temperature and drought stress comprises:

[0021] Irrigation module: used to lay the irrigation pipe network in the tea garden and connect the irrigation pipe network to the irrigation system;

[0022] Internet of Things information collection module: used to adapt and install the Internet of Things collection system for the irrigation network;

[0023] Data transmission module: used to collect real-time soil moisture data in tea gardens through the Internet of Things collection system and transmit the data to the central processor;

[0024] Irrigation control module: It is used to collect soil moisture data through the central processing system, compare and determine the preset moisture threshold for tea tree drought, the fixed irrigation time period value, and the optimum moisture threshold for tea tree growth, and output control instructions to automatically control the start and stop of the irrigation system;

[0025] Intelligent module: used for intelligent precision irrigation under high temperature and drought stress in tea gardens through repeated data collection through the Internet of Things collection system.

[0026] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for tea garden irrigation under high temperature and drought stress, which has the following beneficial effects:

[0027] The invention proposes a tea garden irrigation method suitable for high temperature drought stress, comprising: step one: laying an irrigation pipe network in a tea garden, and connecting the irrigation pipe network to an irrigation system; step two: adapting and installing an Internet of Things acquisition system on the irrigation pipe network; step three: collecting real-time soil moisture data in the tea garden through the Internet of Things acquisition system, and transmitting the data to a central processing unit; step four: comparing and judging a preset tea tree drought occurrence moisture threshold, a fixed sprinkler irrigation time period value, and a tea tree growth optimum moisture threshold with the collected soil moisture data through the central processing system, and outputting a control instruction to automatically control the start and stop of the irrigation system; and step five: performing intelligent and precise irrigation under high temperature drought stress in the tea garden by repeatedly collecting data through the Internet of Things acquisition system. The present invention integrates advanced technologies such as informatization and intelligence, and organically combines engineering technology with Internet of Things technology to accurately supplement water deficit under drought stress, thereby realizing intelligent and precise water-saving irrigation, and successfully resolving the drought problem in tea tree production and cultivation; through the organic combination of Internet of Things technology and engineering technology, the water deficit of tea tree growth under seasonal drought stress in subtropical red soil hilly areas is accurately supplemented, thereby achieving the elimination of the adverse effects of high temperature drought on the growth of tea trees, avoiding the occurrence of drought in tea gardens, and improving the irrigation efficiency of tea gardens, reducing water resource waste, and at the same time having full-process intelligent and automated control, saving time, effort, labor, and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0029] Figure 1 The present invention provides a schematic flow chart of a tea garden irrigation method suitable for use under high temperature and drought stress.

[0030] Figure 2 This is a structural framework diagram of a tea garden irrigation system suitable for high temperature and drought stress provided by the present invention.

[0031] Figure 3 This is a schematic structural diagram of a tea garden irrigation device suitable for use under high temperature and drought stress provided by the present invention.

[0032] Among them, 1-first interface, 2-forked interface, 3-first valve, 4-second interface, 5-third interface, 6-second valve, 7-fourth interface. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] The embodiment of the present invention discloses a tea garden irrigation method suitable for high temperature drought stress, such as Figure 1 As shown, including:

[0035] Step 1: Lay the irrigation pipe network in the tea garden and connect the irrigation pipe network to the irrigation system;

[0036] Step 2: Adapt and install the Internet of Things collection system on the irrigation network;

[0037] Step 3: Collect the real-time data of soil moisture in the tea garden through the Internet of Things collection system and transmit the data to the central processor;

[0038] Step 4: The central processing system compares the collected soil moisture data with the preset tea tree drought moisture threshold, the fixed irrigation time period value, and the tea tree growth optimum moisture threshold, and outputs control instructions to automatically control the start and stop of the irrigation system;

[0039] Step 5: Repeat data collection through the Internet of Things collection system to carry out intelligent precision irrigation under high temperature and drought stress in the tea garden.

[0040] Furthermore, the intelligent precision irrigation under high temperature and drought conditions in the tea garden by repeatedly collecting data through the Internet of Things collection system includes: collecting initial soil moisture data at the roots of the tea trees, judging whether the collected initial soil moisture data meets the preset start conditions, and starting sprinkler irrigation when the preset start conditions are met; collecting real-time soil moisture data at the roots of the tea trees, and judging whether the real-time soil moisture data meets the preset stop conditions, and stopping sprinkler irrigation when the preset stop conditions are met, thereby achieving precision irrigation.

[0041] Furthermore, the collection of initial soil moisture data at the roots of tea trees includes installing a soil moisture sensor at the concentrated location of the absorbing roots of the tea trees to collect soil moisture data in real time. Specifically, a soil moisture sensor is installed at 40 cm of the soil layer at the root of the tea garden (the concentrated location of the absorbing roots of the tea trees) to collect soil moisture data in real time. Specifically, the model of the soil moisture sensor is Unisplendour Hongyuan FR-SMS.

[0042] In a specific implementation, the step of installing a soil moisture sensor at a concentrated location of the absorbing roots of tea trees to collect soil moisture data in real time includes:

[0043] A data acquisition module, which is used to collect initial soil moisture data, regional moisture content difference, and high temperature and drought data information at different measuring points;

[0044] A data cleaning module, which is used to analyze and calculate the original data collected by the data collection module to identify and remove abnormal values;

[0045] The data reconstruction module is used to interpolate missing values ​​of the data sequence processed by the data cleaning module and / or the original missing data sequence, specifically including: setting a total of n high temperature and drought measurement points in the tea garden, drawing the topological relationship between the measurement points according to the location distribution of the high temperature and drought measurement points in the tea garden, and constructing the corresponding spatial weight matrix S. Each element S in the matrix ij The expression is as follows:

[0046]

[0047] Where i and j represent the measurement point numbers, i∈[1,n], j∈[1,n];

[0048] The G index is calculated based on the high temperature and drought data of the measuring points to test the spatial autocorrelation between the measuring points; at time t, the expression of the G index is as follows:

[0049]

[0050] In the formula, and Respectively represent the monitoring data of the i-th and j-th measuring points at time t;

[0051] And judge the relationship between the G index and the preset threshold Q. When |G|>Q, it is considered that the overall high temperature and drought in the tea garden has a spatial positive autocorrelation effect, and the following missing data interpolation model is used; otherwise, the traditional method is used to construct the missing data interpolation model;

[0052] Retrieve the time points or time periods marked as missing values ​​in the original database, and retrieve the missing values ​​generated by removing abnormal data in the data cleaning module, determine the missing data set that needs to be interpolated, select the complete monitoring data before and after the missing data set as the training set, and use the following formula for fitting:

[0053]

[0054] In the formula, s i =[s i1 ,s i2 , ..., s in ], that is, the vector corresponding to the i-th row of the spatial weight matrix S; That is, the monitoring data of different measuring points at time t; β is the regression coefficient, which is used to measure the pure dynamic effect; α is the spatial autoregression coefficient, which is used to measure the spatial effect between measuring points; γ is the lagged first-order spatial autoregression coefficient, which is used to measure the spatiotemporal effect between measuring points; is the intercept term;

[0055] Then, the complete monitoring data before and after the missing data set are substituted to solve the regression parameters before and after the missing data set, and the average value of the two sets of regression parameters is used as the model parameter of the missing data set, which is recorded as and

[0056] Substitute the model parameters obtained above into the above formula to obtain missing data, and interpolate them into the corresponding sequence; then store the interpolated data in the data storage module;

[0057] The data storage module includes an original database and a pre-processing database. The original database is used to store the original data collected by the collection module; the pre-processing database is used to store the data after data cleaning and reconstruction.

[0058] Furthermore, the collection of initial soil moisture data at the roots of tea trees includes installing a soil moisture sensor at the concentrated root area of ​​the tea tree (at the 40 cm soil layer) to collect soil moisture data in real time. The preset start conditions include: a preset moisture threshold for tea tree drought (60% of the maximum field water holding capacity) and a preset fixed sprinkler irrigation time period (from 18:00 p.m. to 6:00 a.m. the next day). The preset stop conditions include: a preset optimum moisture threshold for tea tree growth (80% of the maximum field water holding capacity) and outside the preset fixed sprinkler irrigation time period.

[0059] Furthermore, the central processing system also includes comparing the collected initial soil moisture data with the moisture threshold for tea tree drought and the preset fixed sprinkler irrigation time period preset in the central processing system. When the comparison result is met, the central processing system automatically starts and stops the sprinkler irrigation system.

[0060] Furthermore, the output control instructions automatically control the start and stop of the irrigation system, including: transmitting the collected soil moisture data to the central processing system, and comparing the moisture threshold for drought occurrence of tea trees preset by the central processing system. When the initial soil moisture content is lower than the moisture threshold for drought occurrence and at the same time meets the preset fixed sprinkler irrigation time period, the central processing system automatically starts the sprinkler irrigation system.

[0061] Furthermore, the output control instruction automatically controls the start and stop of the irrigation system and also includes: when the sprinkler irrigation continues until the real-time soil moisture detection data reaches the optimal moisture threshold for tea tree growth preset by the central processing system or exceeds the preset fixed sprinkler irrigation time period, the central processing system automatically stops the sprinkler irrigation.

[0062] In a specific implementation, the steps for determining the optimal moisture threshold are:

[0063] Get the historical data of the optimal moisture threshold within the set time or distance before the current time;

[0064] Extract training data from the above historical data according to the set training step and prediction step, the training data takes into account both feature dimension and time dimension, that is, the input data is a sequence formed by each feature value of the set training step, and the output data is an output sequence of the set prediction step;

[0065] Using the above training data to train the hybrid neural network model to the optimal state;

[0066] According to the set training step, historical data before the current time node is extracted as sample data to be predicted, and input into the hybrid neural network model to realize the prediction of the optimal moisture threshold within the future set prediction step; when the drought situation in the tea garden is derived to the set prediction step, the above steps are repeated; wherein the hybrid neural network model includes an input layer, a one-dimensional convolutional layer, a time series algorithm unit and a fully connected layer, the one-dimensional convolutional layer is a convolutional layer of a one-dimensional convolutional neural network, and the time series algorithm unit is a GRU unit or an LSTM unit.

[0067] In a specific implementation, a hybrid neural network model is constructed by one-dimensional convolution and GRU, and the model is trained to an optimal model. The specific steps are as follows:

[0068] (1) Randomly initialize the one-dimensional convolution kernel matrix and the corresponding bias term, and perform convolution operation on the training input sequence and the one-dimensional convolution kernel matrix. The calculation formula is as follows:

[0069] x i ′=X*K i +b i ;

[0070] Among them, * represents the convolution operation, K i represents the i-th convolution kernel, b i represents the bias term corresponding to the i-th convolution kernel, X represents the training data, and x i ′ represents the output result of the i-th feature sequence after convolution. The training input sequence will generate new sequence data after convolution processing;

[0071] (2) Randomly initialize the relevant weight matrix W of the update gate in the GRU model xz , W hz and the bias term b z , reset the gate's associated weight matrix W xr , W hr and the bias term b r , hidden layer (g t )’s related weight matrix W xg , W hg and the bias term b g , the weight matrix W0 and bias term b0 of the fully connected layer, and use the following formula for forward propagation of the network:

[0072] z (t) =σ(W xz T x ' (t) +W hz T h (t-1) +b z );

[0073] r (t) =σ(W xr T x′ (t) +W hr T h (t-1) +b r );

[0074]

[0075] y (t) =W0 T h (t) +b0;

[0076] Where T represents the transpose of the matrix, Represents the dot product of the matrix, that is, multiplying the elements at corresponding positions, x ( ' t) represents sequence data, σ represents Sigmoid, i.e., the logical activation function, tanh represents the hyperbolic tangent activation function, and h t-1 、h t The distribution represents the output information of the hidden layer at the previous moment and the current moment, y (t) Represents the final output result;

[0077] (3) Establish the following loss function:

[0078]

[0079] Among them, y d and tThey correspond to the true value of each parameter and the forward propagation output value in step (2);

[0080] (4) Use the gradient descent algorithm to continuously update all weights and bias parameters in steps (1) and (2) so that the loss function in step (3) is small enough, that is, the model is trained to the optimal level.

[0081] When using the trained hybrid neural network model to predict the optimal moisture threshold for a future set step length, first extract the optimal moisture threshold data for the current time and the training step length set before, and perform data cleaning and normalization. The processed data is input as input value into the trained hybrid neural network model to realize the prediction of the optimal moisture threshold for a certain step length in the future. Finally, the prediction result is denormalized, which is the actual predicted value of the optimal moisture threshold. The denormalization is performed according to the following formula:

[0082]

[0083] in, is the direct output value of the model, is the predicted value after denormalization.

[0084] Furthermore, the preset moisture threshold for occurrence of tea tree drought is 60% of the maximum water holding capacity of the field, and the preset fixed sprinkler irrigation time period is from 18:00 every afternoon to 6:00 the next morning.

[0085] Furthermore, the preset optimum moisture threshold for tea tree growth is 80% of the maximum water holding capacity in the field.

[0086] In a specific embodiment, a tea garden irrigation system suitable for use under high temperature and drought stress, such as Figure 2 As shown, including:

[0087] Irrigation module: used to lay the irrigation pipe network in the tea garden and connect the irrigation pipe network to the irrigation system;

[0088] Internet of Things information collection module: used to adapt and install the Internet of Things collection system for the irrigation network;

[0089] Data transmission module: used to collect real-time soil moisture data in tea gardens through the Internet of Things collection system and transmit the data to the central processor;

[0090] Irrigation control module: It is used to collect soil moisture data through the central processing system, compare and determine the preset moisture threshold for tea tree drought, the fixed irrigation time period value, and the optimum moisture threshold for tea tree growth, and output control instructions to automatically control the start and stop of the irrigation system;

[0091] Intelligent module: used for intelligent precision irrigation under high temperature and drought stress in tea gardens through repeated data collection through the Internet of Things collection system

[0092] In a specific embodiment, a tea garden irrigation method suitable for use under high temperature and drought stress comprises:

[0093] Irrigation decision: The collected soil moisture data is transmitted to the central processing system. By comparing the moisture threshold for tea tree drought preset by the central processing system (60% of the maximum water holding capacity in the field), when the soil moisture content is lower than the moisture threshold for tea tree drought and at the same time meets the preset fixed sprinkler irrigation time period (from 18:00 every afternoon to 6:00 the next morning), the central processing system automatically starts the sprinkler irrigation system.

[0094] Automated sprinkler irrigation: After the sprinkler irrigation system is turned on, the tea garden is automatically sprinkled through the irrigation pipe network. When the sprinkler irrigation continues until the real-time soil moisture detection data reaches the optimal moisture threshold for the growth of tea trees in the tea garden preset by the central processing system (80% of the maximum field water holding capacity), or exceeds the fixed sprinkler irrigation time period, the central processing system automatically shuts down and stops the sprinkler irrigation, and repeats the data collection to the automated sprinkler irrigation steps to achieve precise irrigation, so as to ensure that the tea trees obtain appropriate water supply, avoid the occurrence of drought in the tea garden and improve irrigation efficiency.

[0095] In a specific embodiment, a tea garden irrigation device suitable for use under high temperature and drought stress, such as Figure 3 As shown, it includes: a water pump, a water outlet pipeline, a flow meter, a water inlet pipeline, a water inlet pool, a sprinkler device, a central processing system and a soil moisture detection device. One end of the water pump is connected to the water inlet pool through the water inlet pipeline, and the other end is connected to the flow meter and the sprinkler device in sequence through the water outlet pipeline. The water inlet pipeline is composed of a first interface 1, a bifurcated interface 2, a first valve 3 and a second interface 4 connected in sequence, and the water outlet pipeline is composed of a third interface 5, a second valve 6 and a fourth interface 7 connected in sequence. The flow meter, the water pump and the soil moisture probe are connected to the central processing system signal. The soil moisture detection device is installed at 40 cm of the soil layer at the root of the tea garden, and also includes a power supply device. The power supply device is electrically connected to the flow meter, the water pump, the soil moisture detection device and the central processing system for providing electrical energy. The central processing system controls the operation of the water pump according to the data collected by the soil moisture detection device and the flow meter data, and sprinkler irrigation is achieved through the sprinkler device.

[0096] The invention has the function of automatically detecting soil moisture content, and the detection data is automatically transmitted to the central processing system in real time and automatically stored, and supports the query, export and printing of various real-time data and historical data in any time period; it has an intelligent sprinkler irrigation function in a fixed time period. From 18:00 in the afternoon to 6:00 in the morning of the next day, when the soil moisture is lower than a preset value, the valve automatically opens for sprinkler irrigation, and when the preset value is reached, the valve automatically closes to stop sprinkler irrigation, and no sprinkler irrigation is performed in other time periods; it has a sprinkler irrigation data statistics function, and can real-timely count the start time, end time and irrigation amount of each sprinkler irrigation in the sprinkler irrigation time period, and supports data query, export and printing.

[0097] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0098] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tea garden irrigation method suitable for use under high temperature and drought stress, characterized in that: include: Step 1: Lay the irrigation pipe network in the tea garden and connect the irrigation pipe network to the irrigation system; Step 2: Adapt and install the Internet of Things collection system on the irrigation network; Step 3: Collect the real-time data of soil moisture in the tea garden through the Internet of Things collection system and transmit the data to the central processor; Step 4: The central processing system compares the collected soil moisture data with the preset tea tree drought moisture threshold, the fixed irrigation time period value, and the tea tree growth optimum moisture threshold, and outputs control instructions to automatically control the start and stop of the irrigation system; Step 5: Repeat data collection through the Internet of Things collection system to carry out intelligent and precise irrigation under high temperature and drought stress in the tea garden.

2. A tea garden irrigation method suitable for high temperature and drought stress according to claim 1, characterized in that: The intelligent precision irrigation under high temperature and drought conditions in the tea garden by repeatedly collecting data through the Internet of Things collection system includes: collecting initial soil moisture data at the roots of tea trees, judging whether the collected initial soil moisture data meets preset start conditions, and starting sprinkler irrigation when the preset start conditions are met; collecting real-time soil moisture data at the roots of tea trees, and judging whether the real-time soil moisture data meets preset stop conditions, and stopping sprinkler irrigation when the preset stop conditions are met, thereby achieving precision irrigation.

3. A tea garden irrigation method suitable for high temperature drought stress according to claim 2, characterized in that: The method of collecting the initial soil moisture data at the roots of the tea trees includes installing a soil moisture sensor at the concentrated location of the absorbing roots of the tea trees to collect the soil moisture data in real time.

4. A tea garden irrigation method suitable for high temperature and drought stress according to claim 2, characterized in that: The preset starting conditions include: a preset moisture threshold for tea tree drought occurrence and a preset fixed sprinkler irrigation time period.

5. A tea garden irrigation method suitable for high temperature and drought stress according to claim 2, characterized in that: The preset stop conditions include: a preset optimum moisture threshold for tea tree growth and outside a preset fixed sprinkler irrigation time period.

6. A tea garden irrigation method suitable for high temperature and drought stress according to claim 1, characterized in that: The central processing system also includes comparing the collected initial soil moisture data with the moisture threshold for tea tree drought and the preset fixed sprinkler irrigation time period in the central processing system. When the comparison results are met, the central processing system automatically starts and stops the sprinkler irrigation system.

7. The tea garden irrigation method suitable for high temperature and drought stress according to claim 1, characterized in that: The output control instructions automatically control the start and stop of the irrigation system, including: transmitting the collected soil moisture data to the central processing system, and comparing the drought moisture threshold of the tea trees preset by the central processing system. When the initial soil moisture content is lower than the drought moisture threshold and the preset fixed sprinkler irrigation time period is met, the central processing system automatically starts the sprinkler irrigation system.

8. A tea garden irrigation method suitable for high temperature and drought stress according to claim 7, characterized in that: The output control instruction automatically controls the start and stop of the irrigation system and also includes: when the sprinkler irrigation continues until the real-time soil moisture detection data reaches the optimal moisture threshold for tea tree growth preset by the central processing system or exceeds the preset fixed sprinkler irrigation time period, the central processing system automatically stops the sprinkler irrigation.

9. A tea garden irrigation system suitable for use under high temperature and drought stress, characterized in that: include: Irrigation module: used to lay the irrigation pipe network in the tea garden and connect the irrigation pipe network to the irrigation system; Internet of Things information collection module: used to adapt and install the Internet of Things collection system for the irrigation network; Data transmission module: used to collect real-time soil moisture data in tea gardens through the Internet of Things collection system and transmit the data to the central processor; Irrigation control module: It is used to collect soil moisture data through the central processing system, compare and determine the preset moisture threshold for tea tree drought, the fixed irrigation time period value, and the optimum moisture threshold for tea tree growth, and output control instructions to automatically control the start and stop of the irrigation system; Intelligent module: used for intelligent precision irrigation under high temperature and drought stress in tea gardens through repeated data collection through the Internet of Things collection system.

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