Direction control system for intelligent precision irrigation of farmland based on internet of things

Abstract

The application discloses a farmland intelligent precision irrigation direction control system based on Internet of Things, and relates to the technical field of intelligent irrigation.The system comprises a data acquisition module, a data processing and analysis module, a data labeling and classification unit, a three-dimensional coordinate model construction module, a nozzle angle adjusting device, a nozzle water quantity adjusting unit and a control instruction executing unit.The system can make irrigation water more efficiently reach water demand areas by calculating the distance between the nearest nozzle and the water demand position, and can ensure that water can be quickly and accurately delivered to the required place by accurately calculating the distance, so that water loss and energy waste caused by long-distance water delivery are avoided.The system can effectively cope with the influence of natural wind on irrigation by adjusting the water spraying direction in combination with the wind direction and wind speed, and can make the sprayed water flow accurately reach the water demand position by real-time monitoring of the wind direction and wind speed information, and combining the information with the water spraying parameters of the nozzle, so that the system accurately calculates the horizontal and vertical angle adjustment values of the nozzle.

Description

Technical Field

[0001] This invention relates to the field of intelligent irrigation technology, and in particular to an intelligent precision irrigation directional control system for farmland based on the Internet of Things. Background Technology

[0002] With the continuous advancement of agricultural modernization, farmland irrigation, as a key link in agricultural production, is becoming increasingly important. Traditional farmland irrigation methods mainly rely on manual experience, which has many shortcomings. In terms of water demand monitoring, in the past, it mostly relied on farmers' subjective judgment or simple soil moisture testing tools, such as soil drills to observe soil moisture. This method is relatively crude and cannot obtain real-time and accurate moisture information at different soil depths, nor can it provide a visual understanding of the overall water demand and related needs of farmland crops.

[0003] Monitoring the condition of branches and leaves is often overlooked in traditional agriculture. The cleanliness, dryness, and pest status of crop branches and leaves have a significant impact on crop photosynthesis and healthy growth. For example, in some dusty areas, dust easily adheres to crop branches and leaves, affecting the efficiency of photosynthesis. However, it is difficult for farmers to discover and clean it in time. Early symptoms of pests are also not easily detected. Once discovered, they have often already caused significant damage to the crops. Traditional manual visual inspection is inefficient and prone to oversights for large areas of farmland.

[0004] In terms of sprinkler control, traditional irrigation systems typically have fixed sprinkler positions, spray ranges, and angles, making flexible adjustments impossible based on actual conditions. This can lead to over- or under-irrigation in irregularly shaped farmland. Furthermore, traditional irrigation systems largely fail to consider the impact of wind direction and speed on spraying effectiveness. In real farmland environments, wind can cause water droplets to deviate from the target area, reducing irrigation accuracy and uniformity. For example, if irrigation is performed as it would be in windless conditions, water droplets may be blown to areas that don't need irrigation, resulting in water waste.

[0005] In summary, these limitations of traditional farmland irrigation methods make it difficult to guarantee irrigation results, result in low water resource utilization, and lead to high labor intensity. They are increasingly unable to meet the development needs of modern agriculture for high efficiency, precision, and energy conservation. Therefore, it is necessary to propose an intelligent precision irrigation directional control system for farmland based on the Internet of Things to solve the above problems. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an IoT-based intelligent precision irrigation direction control system for farmland. This system solves the problems of neglecting the condition of crop branches and leaves, the impact of the cleanliness, dryness, and pest status of crop branches and leaves on crop photosynthesis, resulting in unhealthy plant growth, the fixed position of sprinklers in irrigation systems leading to over- or under-irrigation in some areas, and the presence of wind causing water droplets to deviate from the target area, thus reducing the accuracy and uniformity of irrigation.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An IoT-based intelligent precision irrigation direction control system for farmland includes:

[0009] Data acquisition module: includes soil moisture sensor, spectrometer and weather station. The spectrometer is used to analyze the condition of plant branches and leaves (cleanliness of branches and leaves, water content and pest situation). The weather station is equipped with wind speed sensor and wind direction sensor, and transmits data to central control equipment through wireless communication.

[0010] Data processing and analysis module: This includes a central control device and internal water demand calculation algorithms, optimal sprinkler calculation algorithms, and sprinkler angle adjustment algorithms. The central control device receives and processes data from the data acquisition module. The water demand calculation algorithm calculates the water demand for each water demand location based on a preset algorithm. The optimal sprinkler calculation algorithm calculates the distance between the nearest sprinkler and the water demand location. The sprinkler angle adjustment algorithm calculates the horizontal and vertical angles of the sprinkler when spraying water based on the water demand location.

[0011] Data labeling and classification unit: Based on the soil moisture measurement results of the soil moisture sensor and the branch and leaf status measured by the spectrometer, the water demand location and water demand are obtained, and classified and marked in the three-dimensional model, using different colors and symbols to represent water shortage location, cleaning demand location and pest location;

[0012] 3D coordinate model construction module: A 3D rectangular coordinate system is established with a corner of the farmland as the origin, and the coordinates of the soil moisture sensor, spectrometer and sprinkler module are marked in the 3D coordinate model;

[0013] Nozzle angle adjustment device: The nozzle is equipped with a device for adjusting the angle in both the horizontal and vertical directions;

[0014] Sprinkler head water volume adjustment unit: The sprinkler head is equipped with a flow sensor and a flow adjustment valve;

[0015] Control command execution unit: Based on the horizontal and vertical angles obtained by the nozzle angle adjustment algorithm, it sends control commands to the nozzle angle adjustment device and adjusts the water output of the nozzle according to the water demand.

[0016] Optionally, the data acquisition module uses the following method: Soil moisture measurement: Soil moisture sensors are distributed in a grid pattern in the farmland to measure soil moisture. In the xOy plane, a sensor is arranged at a distance Δx along the x-axis and at a distance Δy along the y-axis, forming several rectangular grids with side lengths Δx and Δy. Each grid point is (i,j). The data measured by the sensors is transmitted to the data processing center wirelessly.

[0017] Leaf and branch condition data collection: The spectrometer is mounted on a movable support and scans the plant leaves and branches along a direction perpendicular to the xOy plane. At each measurement location, the spectrometer acquires the spectral reflectance curve of the leaves and branches at that location. By comparing and analyzing the curve with a pre-established standard spectral database of leaves and branches in different states, the cleanliness, moisture content, and presence of pests at that location are determined.

[0018] Optionally, the three-dimensional rectangular coordinate system in the three-dimensional coordinate model construction module is constructed as follows: a three-dimensional rectangular coordinate system is established with one corner of the farmland as the origin, the x-axis is along the length of the farmland, the y-axis is along the width of the farmland, and the z-axis is perpendicular to the ground and upward.

[0019] The x-axis runs along the length of the farmland. A point along the length of the farmland is Δx away from the origin, and its coordinates on the x-axis are (Δx, 0, 0). The y-axis runs along the width of the farmland, and its coordinates on the y-axis are (0, Δy, 0). The z-axis is perpendicular to the ground and points upwards. When a point is Δz above the ground, its coordinates on the z-axis are (0, 0, Δz).

[0020] Optionally, the coordinate marking method for the soil moisture sensor, spectrometer, and sprinkler module in the three-dimensional coordinate model construction module is as follows:

[0021] Soil moisture sensor coordinate marking: The sensor's position within the farmland plane is known, and its distance from the origin along the longitudinal direction is Δx. s The distance in the width direction is Δy. s The z-axis coordinate is 0, and the coordinates of the soil moisture sensor in the three-dimensional coordinate model are (Δx). s ,Δy s ,0);

[0022] Spectrometer coordinate marking: The spectrometer is mounted on a movable support, and its position in the farmland plane is Δx, which is a distance along the longitudinal direction from the origin. p The distance in the width direction is Δy. p Since the spectrometer is above the ground, its coordinate in the z-axis direction is H, and its coordinates in the three-dimensional coordinate model are (Δx) p ,Δy p,H);

[0023] Sprinkler module coordinate marking: In a farmland with n sprinklers, for the a-th sprinkler (a = 1, 2, 3…n), its coordinates are (x... a ,y a ,z a ).

[0024] Optionally, the method for classifying and labeling the water demand location and water demand in the data labeling and classification unit is as follows: the water demand location and water demand are obtained based on the soil moisture measurement results of the soil moisture sensor and the branch and leaf status measured by the spectrometer.

[0025] Labeling of soil moisture measurement results in a three-dimensional coordinate model: For grid point (i,j), its coordinates in the three-dimensional coordinate system are (x,j ... i ,y j (z0), where z0 is the height of the soil surface;

[0026] Labeling of branch and leaf status in a 3D coordinate model: For a position (x, y) measured by the spectrometer, the corresponding position of the branch and leaf in 3D space is (x, y, z). t ), z t The value is determined by measuring the height of the instrument and the actual height of the plant branches and leaves;

[0027] Cleanliness labeling: Different icons or colors are used to indicate the cleanliness of branches and leaves;

[0028] Moisture content labeling: The moisture content of branches and leaves is indicated by the height or color of a cube;

[0029] Infested areas are marked: When an infestation is detected, it is indicated by a red skull icon or a different marking symbol at the corresponding branch or leaf location.

[0030] Optionally, the water demand calculation algorithm in the data processing and analysis module is as follows:

[0031] Soil moisture requirement calculation: Determine the target value of soil moisture. For each grid point (i,j), calculate the soil moisture deficit. Based on the soil moisture characteristic curve, convert the soil moisture deficit into soil water potential deficit. Then, combine the root distribution depth factor of the soil to calculate the irrigation water required for each grid point.

[0032] Water requirement calculation for leaf and branch moisture content: Set a leaf moisture threshold. When the leaf and branch moisture content is lower than the threshold, increase the water requirement.

[0033] Optionally, the classification labeling method for water demand location and water demand in the data labeling and classification unit is as follows:

[0034] Water Demand Location Marking: Based on the calculated water demand, water demand locations are distinguished in the 3D coordinate model. If the water demand at grid point (i,j) is greater than 0, this location is marked as a water-deficient location and indicated by a flashing red icon at coordinate (x,j). i ,y j The display at (x, y, z) shows the water requirement of the branches and leaves, corresponding to the branch and leaf positions (x, y, z). t The area marked with a red water droplet icon is shown.

[0035] Water demand label: The specific water demand value is displayed next to the marker icon at the water demand location.

[0036] Optionally, the optimal nozzle calculation algorithm in the data processing and analysis module is as follows:

[0037] Determine the coordinate representation of the sprinkler head and the water demand position: Assume the sprinkler head position in three-dimensional space is represented by coordinates (x, y, z). a ,y a ,z a ) represents the location of the sprinkler head, where a = 1, 2, ..., n, and n is the number of sprinklers; the location of water demand is represented by coordinates (x, y). w ,y w ,z w ) represents, where w = 1, 2, ..., q, and q is the number of locations requiring water;

[0038] Calculate the distance from it to all nozzles: the formula for the distance between two points is based on the Euclidean distance formula;

[0039] Find the shortest distance: Find the minimum distance by comparing all calculated nozzle distances.

[0040] Optionally, the nozzle angle adjustment algorithm in the data processing and analysis module is as follows:

[0041] Horizontal angle: In the absence of wind, the nozzle sprays directly at the location where water is needed, and the horizontal angle is 0 degrees; in the presence of wind, considering the influence of wind force on the water flow deviation, the wind speed is v, and the angle between the wind direction and the horizontal vector from the nozzle to the location where water is needed is α. The horizontal deviation acceleration a of the water flow is related to the wind speed and wind direction. According to Newton's second law: a = k·v·cosα, where k is a coefficient related to the water flow characteristics and air resistance.

[0042] Vertical Angle: Determine the coordinate system and relevant parameters. Establish a spatial rectangular coordinate system with the nozzle location as the origin. The x-axis is horizontal, the y-axis is vertically upward, and the z-axis is perpendicular to the xOy plane and points outward. Let the nozzle installation height be h1, the water demand position height be h2, and the nozzle lift angle be β1; the vertical component of the nozzle's water velocity is v. 0y =v0·sinβ1, with the horizontal component being v 0x=v0·cosβ1;

[0043] The effect of water pressure on range: Water pressure affects the nozzle range. Let the water pressure be P (unit: Pa) and the nozzle orifice cross-sectional area be A (unit: m²). 2 The density of the water flow is ρ (unit: kg / m³). 3 Based on Bernoulli's equation and the continuity equation, it is derived that the range L of the sprinkler is related to the water pressure. For a given sprinkler and piping system, the range L is expressed as... Where c is a coefficient related to the nozzle and pipe structure;

[0044] Calculate the vertical angle: The water flow undergoes a vertical upward projectile motion. The height difference from the nozzle to the water demand position is: Δh = h2 - h1. Based on the formula for the maximum height of vertical upward projectile motion... Where g is the acceleration due to gravity, if Δh ≤ h, then the location requiring water is within the trajectory of the vertical upward motion of the nozzle. In this case, the vertical angle can be calculated using kinematic formulas. Let t1 be the time it takes for the water to travel from the nozzle to the location requiring water. Then we have... At the same time, in the horizontal direction, d = v 0x t1, solve the two formulas simultaneously to find t1, and then obtain v. 0y ;based on get

[0045] Optionally, the central control device obtains the nearest nozzle based on the optimal nozzle calculation algorithm, then uses the angle sensor on the nozzle to obtain the horizontal and vertical angles of the nozzle at this time, and the horizontal and vertical angles of the nozzle when spraying water obtained by the nozzle angle adjustment algorithm, calculates the angle difference between the two, uses a PID control algorithm to calculate the adjustment amount based on the angle difference, and then controls the nozzle angle adjustment device, and adjusts the water output of the nozzle according to the water demand.

[0046] This invention provides an IoT-based intelligent precision irrigation direction control system for farmland, which has the following beneficial effects:

[0047] 1. This invention constructs a three-dimensional rectangular coordinate system with the corner or center of the farmland as the origin, which can accurately determine the coordinate information of each location in the farmland. This allows the positions of soil moisture sensors, spectrometers and sprinklers to be accurately marked in three-dimensional space, realizing the visual management of farmland irrigation facilities and monitoring equipment. The needs for cleaning, wetting and pest control are also intuitively displayed in the coordinate system with different areas or marks. This intuitive visualization method helps to quickly understand the overall water requirements and related needs of farmland crops.

[0048] 2. This invention utilizes a spectrometer to monitor the cleanliness of branches and leaves, enabling timely detection of dirt, dust, or other deposits on the branches and leaves that may affect photosynthesis. By issuing timely warnings of low branch and leaf cleanliness, the device takes corresponding cleaning measures to ensure normal photosynthesis of crops, promote crop growth, and monitor the dryness of branches and leaves in real time. This helps prevent drought stress caused by water shortage and ensures water supply during critical growth periods. Monitoring of pest-prone areas can also provide early warning of pests.

[0049] 3. This invention calculates the distance between the nearest sprinkler and the water demand location, enabling irrigation water to reach the water demand area more efficiently. By accurately calculating the distance, the system can select the sprinkler closest to the water demand location for spraying water, reducing water transmission loss and ensuring that water can be delivered quickly and accurately to the place where it is needed, avoiding water loss and energy waste caused by long-distance water transportation.

[0050] 4. This invention combines wind direction and speed to adjust the water spray direction, which can effectively cope with the impact of natural wind on irrigation. In windy conditions, if wind direction and speed are not considered, the sprayed water may be blown off course by the wind, resulting in uneven irrigation or deviation from the target area. By monitoring wind direction and speed information in real time and combining it with the spray parameters of the nozzle, the system can accurately calculate the horizontal and vertical angle adjustment values ​​of the nozzle, so that the sprayed water can accurately reach the water demand location against the wind or with the wind. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the modules of the intelligent precision irrigation directional control system for farmland of the present invention;

[0052] Figure 2 This is a schematic diagram of the working process of the intelligent precision irrigation directional control system for farmland of the present invention. Detailed Implementation

[0053] 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.

[0054] Example 1

[0055] Please see Figure 1 A smart precision irrigation directional control system for farmland based on the Internet of Things, comprising:

[0056] Data acquisition module: includes multiple soil moisture sensors deployed in different locations in the farmland, and a spectrometer installed in the farmland area. The spectrometer is used to analyze the condition of plant branches and leaves (cleanliness of branches and leaves, moisture content and pest situation), as well as a weather station set up around the farmland. The weather station is equipped with wind speed sensor and wind direction sensor, and transmits data to the central control equipment via wireless communication.

[0057] Data processing and analysis module: This includes a central control device and internal water demand calculation algorithms, optimal sprinkler calculation algorithms, and sprinkler angle adjustment algorithms. The central control device receives data from soil moisture sensors and spectrometers, and performs data analysis, noise reduction, and synchronization processing to ensure data accuracy and timeliness. The water demand calculation algorithm calculates the water demand for each water demand location based on a preset algorithm. The optimal sprinkler calculation algorithm calculates the nearest sprinkler and the distance between the sprinkler and the water demand location. The sprinkler angle adjustment algorithm calculates the horizontal and vertical angles of the sprinkler when spraying water based on the water demand location.

[0058] Data labeling and classification unit: In the three-dimensional coordinate model, based on the calculated water demand and the detection results of soil moisture sensors and spectrometers, the water demand locations and water demand are classified and marked, and different colors and symbols are used to represent water-deficient locations, cleanliness demand locations, and pest locations, which facilitates the formulation of subsequent irrigation strategies and sprinkler control.

[0059] 3D coordinate model construction module: Using a corner of the farmland as the origin, a 3D rectangular coordinate system is established, with the x-axis along the length of the farmland, the y-axis along the width of the farmland, and the z-axis perpendicular to the ground and upward. The coordinates of the soil moisture sensor, spectrometer, and sprinkler module are marked in the 3D coordinate model.

[0060] Sprinkler module: The installation position and spacing of the sprinklers are rationally planned according to factors such as the shape and size of the farmland, irrigation needs, and the range of the sprinklers to ensure the uniformity and effectiveness of irrigation coverage. The position and number of each sprinkler are precisely marked in the three-dimensional coordinate model.

[0061] Nozzle angle adjustment device: The nozzle is equipped with a device for adjusting the angle in the horizontal and vertical directions, such as a stepper motor driven rotation mechanism, which controls the rotation of the motor to realize the lifting and turning functions of the nozzle, thereby changing the spray angle of the nozzle, as well as nozzle angle sensors, including horizontal and vertical angle sensors, for measuring the real-time angle of the nozzle.

[0062] Sprinkler head water volume regulation unit: Each sprinkler head is equipped with an electrically controlled flow regulation valve to regulate the water output of the sprinkler head. The opening and closing degree of the valve is precisely controlled according to the water demand and irrigation strategy to meet the water requirements of different regions and different crops. A flow sensor is installed in the sprinkler head pipeline to monitor the actual water output in real time and feed the data back to the central control equipment. When there is a deviation between the actual water output and the set value, the central control equipment automatically adjusts the valve opening and closing degree to ensure the accuracy of irrigation water volume.

[0063] Control command execution unit: Based on the horizontal and vertical angles calculated by the nozzle angle adjustment algorithm in the central control equipment, it sends control commands to the nozzle angle adjustment device. The commands include rotation direction, angle value and speed information to ensure that the nozzle can be accurately and quickly adjusted to the target angle. It also controls the nozzle water volume adjustment unit according to the water demand of the water demand location to adjust the water output of the nozzle.

[0064] Server Unit: The server is responsible for storing and managing various types of data from the entire smart irrigation system for farmland, including historical irrigation data, sensor data, and meteorological data. It uses a database management system (such as MySQL) to classify, store, index, and query the data, making it convenient for users to retrieve and analyze the data at any time.

[0065] Remote monitoring and control platform: A remote monitoring interface is built, and users can log in to the server through a mobile APP or web browser to view the irrigation status of farmland, sensor data, and sprinkler working status information in real time. At the same time, users can manually adjust irrigation parameters and start or stop irrigation tasks on the remote platform to achieve remote control of farmland irrigation.

[0066] Communication network unit: Construct a wireless sensor network within the farmland area to ensure stable and efficient data transmission between sensors, nozzles and other devices. The network topology is optimized based on the farmland terrain and device distribution to improve communication reliability and coverage.

[0067] The data acquisition module uses the following acquisition method:

[0068] Soil moisture measurement: Soil moisture sensors are distributed in a grid pattern in the farmland to measure soil moisture. In the xOy plane, a sensor is placed at intervals Δx along the x-axis and at intervals Δy along the y-axis, forming several rectangular grids with side lengths Δx and Δy. Each grid point is (i,j), where i = 1, 2, ..., n, j = 1, 2, ..., m, and n and m are the number of grid points along the x and y axes, respectively. The sensor data is transmitted wirelessly to a data processing center for calibration. The raw soil moisture data is then calibrated to eliminate sensor errors and the influence of environmental factors (such as temperature and soil texture) on the measurement results. The calibration coefficient can be obtained experimentally or by referring to relevant literature. Let the calibrated soil moisture data be θ. ij Its value range is generally between 0 and 1, where 0 indicates that the soil is completely dry and 1 indicates that the soil is saturated with moisture.

[0069] Leaf and branch condition data collection: Plant leaves and branches exhibit different reflectance characteristics to different wavelengths of light under different conditions (clean, dirty, dry, normal moisture content, pest-damaged, etc.). For example, in the visible light band (wavelength range of approximately 380-780 nm), healthy green plant leaves, due to the presence of chlorophyll, absorb more blue light (wavelength approximately 380-495 nm) and red light (wavelength approximately 620-780 nm), and reflect green light (wavelength approximately 495-570 nm), thus appearing green. When leaves and branches are dirty, their spectral reflectance changes, usually decreasing and stabilizing across all wavelengths. Dry leaves and branches may experience changes in cell structure due to water loss, thereby altering their spectral reflectance characteristics. Pest-damaged leaves and branches may show lesions or tissue damage, which will also cause specific changes in their spectral reflectance.

[0070] The spectrometer is mounted on a movable stand and scans the plant branches and leaves along a direction perpendicular to the xOy plane. At each measurement position (x,y) (corresponding to (x,y,z) in three-dimensional coordinates), the measurement is recorded. t ), z t (The height of the branches and leaves) The spectrometer obtains the spectral reflectance curve of the branches and leaves at that location. By comparing and analyzing it with a pre-established standard spectral database of branches and leaves in different states, the cleanliness, moisture content and presence of pests of the branches and leaves at that location are determined.

[0071] The three-dimensional rectangular coordinate system in the three-dimensional coordinate model construction module is constructed as follows: a three-dimensional rectangular coordinate system is established with a corner of the farmland as the origin. The x-axis is along the length of the farmland, the y-axis is along the width of the farmland, and the z-axis is perpendicular to the ground and pointing upwards. This corner corresponds to the starting position in the length direction and the starting position in the width direction. Therefore, the x-coordinate of the origin is x0 = 0, and the y-coordinate is y0 = 0. Taking the plane where the ground is located as the z = 0 plane, the vertical coordinate of the origin is z0 = 0.

[0072] The x-axis runs along the length of the farmland, starting from the origin and moving to the right as the positive direction. A point along the length of the farmland is Δx away from the origin, and its coordinates on the x-axis are (Δx, 0, 0). The y-axis runs along the width of the farmland, starting from the origin and moving forward as the positive direction. A point along the width of the farmland is Δy away from the origin, and its coordinates on the y-axis are (0, Δy, 0). The z-axis is perpendicular to the ground and moves upward, starting from the ground, and moving upward as the positive direction. When a point is Δz above the ground, its coordinates on the z-axis are (0, 0, Δz).

[0073] The coordinate marking method for the soil moisture sensor, spectrometer, and sprinkler module in the 3D coordinate model construction module is as follows:

[0074] Soil moisture sensor coordinate marking: The soil moisture sensor is buried underground, and its position within the farmland plane is known. Its distance from the origin along the longitudinal direction is Δx. s The distance in the width direction is Δy. s Although the sensor is underground, irrigation using sprinklers can only spray water onto the soil surface. Therefore, the z-axis coordinate is 0, and the soil moisture sensor's coordinates in the three-dimensional coordinate model are (Δx). s ,Δy s ,0);

[0075] Spectrometer coordinate markings: The spectrometer is mounted on a movable support at a height of H (meters). Its position within the farmland plane is Δx, a distance along the longitudinal direction from the origin. p The distance in the width direction is Δy. p Since the spectrometer is above the ground, its coordinate in the z-axis direction is H, and its coordinates in the three-dimensional coordinate model are (Δx) p ,Δy p ,H);

[0076] Sprinkler module coordinate marking: In a farmland with n sprinklers, for the a-th sprinkler (a = 1, 2, 3…n), its coordinates are (x... a ,y a ,z a ), where x ay is the sprinkler head's coordinate along the x-axis, z is the sprinkler head's coordinate along the y-axis (the sprinkler head's position within the farmland plane is known), and z is the sprinkler head's coordinate along the y-axis. a It is the coordinate of the nozzle in the z-axis direction (because the nozzle has height and is not on the ground level).

[0077] In this embodiment, a three-dimensional rectangular coordinate system is constructed with the corner or center of the farmland as the origin, which can accurately determine the coordinate information of each location in the farmland. This allows the positions of soil moisture sensors, spectrometers, and sprinklers to be accurately marked in three-dimensional space, realizing the visual management of farmland irrigation facilities and monitoring equipment. Through the three-dimensional model, farmers or agricultural managers can intuitively see the distribution of sensors and sprinklers throughout the farmland, which facilitates equipment maintenance and layout planning. When it is necessary to operate on a specific area, such as checking the data of a certain sensor or adjusting the angle of a certain sprinkler, its position in three-dimensional space can be quickly located, saving the time of searching and locating and improving management efficiency.

[0078] A three-dimensional Cartesian coordinate system can clearly determine the spatial information of water demand locations. For soil water demand, it can accurately mark the water demand of soil at different depths, such as the differences in position of topsoil (0-10cm), middle soil (10-30cm), and deep soil (30-50cm or deeper) in the coordinate system. This allows managers to clearly understand the drought or over-watering status of different soil layers. For foliage water demand, the location of foliage water demand can be precisely located in the coordinate system according to the different heights and layers of the canopy. For example, the water demand of foliage in the upper, middle, and lower parts of the canopy can be clearly distinguished vertically, facilitating targeted irrigation or other water supplementation measures. Cleaning, moisturizing, and pest control needs are also visually displayed in the coordinate system using different areas or markers. This intuitive visualization method helps to quickly understand the overall water demand status and related needs of farmland crops.

[0079] Example 2

[0080] This embodiment is a further optimization based on embodiment 1. Specifically, the method for classifying and marking the water demand location and water demand in the data labeling and classification unit is as follows: the water demand location and water demand are obtained based on the soil moisture measurement results of the soil moisture sensor and the branch and leaf status measured by the spectrometer.

[0081] Labeling of soil moisture measurement results in a three-dimensional coordinate model: For grid point (i,j), its coordinates in the three-dimensional coordinate system are (x,j ... i ,y j ,z0), where,x i = (i-1)Δx, y j = (j-1)Δy, z0 is the height of the soil surface; in the three-dimensional coordinate model, it is represented by a cube in the coordinate (xi ,y j Soil moisture information is marked at z0, and the height of the cube changes dynamically according to the soil moisture level. For example, when θ ij When θ = 0.8, the height is h1; when θ = 0.8, the height is h1. ij When h = 0.5, the height is h2 (h1>h2), which visually shows the spatial distribution of soil moisture. Color is used as one dimension for labeling, with blue indicating moist (high soil moisture), green indicating moderate, and brown indicating dry (low soil moisture).

[0082] Labeling of branch and leaf status in a 3D coordinate model: For a position (x, y) measured by the spectrometer, the corresponding position of the branch and leaf in 3D space is (x, y, z). t ), z t The value is determined by measuring the height of the instrument and the actual height of the plant branches and leaves; if the spectrometer is installed at a height of H p At this location, the average height of the plant branches and leaves is h. f Then z t =H p -h f ;

[0083] Cleanliness labeling: Different icons or colors are used to represent the cleanliness of branches and leaves. A green circle icon indicates high cleanliness (above the cleanliness threshold), a yellow triangle icon indicates medium cleanliness, and a red square icon indicates low cleanliness (with dirt). These icons can be mapped to the corresponding branch and leaf positions (x, y, z) in the 3D coordinate model. t Displayed at );

[0084] Moisture content labeling: The moisture content of branches and leaves is represented by the height of a cube or its color. Branches and leaves with high moisture content correspond to taller cylinders and are bright green, while branches and leaves with low moisture content correspond to shorter cylinders and are dark green. The labeling location is also within (x, y, z). t ) place;

[0085] Infested areas are marked: When an infestation is detected, it is indicated by a red skull icon or a different marking symbol at the corresponding branch or leaf location.

[0086] The water demand calculation algorithm in the data processing and analysis module is as follows:

[0087] Soil moisture requirement calculation: Determine the target soil moisture value θ tar get This value is determined based on the different growth requirements and growth stages of various crops. For example, for a certain crop during its growth period, θ... tar get =0.7, then, for each grid point (i,j), calculate the soil moisture deficit Δθ. ij =θ tar get-θ ij Based on the soil moisture characteristic curve (a curve describing the relationship between soil water potential and soil moisture), the soil moisture deficit is converted into a soil water potential deficit. Combined with the root distribution depth d in the soil r Factors were considered to calculate the required irrigation water W for each grid point. ij The calculation formula is: Where, ρ w It is the density of water (taken as 1000 kg / m³). 3 );

[0088] Calculation of water requirement based on leaf and branch moisture content: Set a leaf moisture threshold. When the leaf and branch moisture content is below the threshold, appropriately increase the calculated water requirement value. Assume the leaf and branch moisture content is W. l When W l <w lmin , (w lmin When the threshold value is the lower limit of leaf moisture content, the adjustment coefficient is k. l >1; when w lmin ≤W l ≤W lmax (W lmax When k is the upper limit threshold of leaf and branch moisture content, l >1; when W l >W lmax At that time, k l <1. Then the adjusted water demand W' ij =k l ×W ij .

[0089] The classification labeling method for water demand location and water demand in data annotation and classification units is as follows:

[0090] Water demand location labeling: Based on the calculated water demand, the water demand locations are distinguished in the three-dimensional coordinate model. If the water demand W' at grid point (i,j) is... ij If the value is greater than 0, then the location is marked as a water-deficient location and indicated by a flashing red icon (such as a red water droplet icon) at the coordinate (x...). i ,y j The display at (x, y, z) indicates that this location needs irrigation. Similarly, the water requirement for branches and leaves is indicated at the corresponding branch and leaf locations (x, y, z). t The area marked with a red water droplet icon is shown.

[0091] Water demand labeling: The specific water demand value is displayed next to the marker icon at the water demand location. For example, in (x i ,y j Next to the red water droplet icon marked at z0) is displayed "Water Requirement: Xm". 3(X is the water demand value calculated for this grid point). For the water demand of branches and leaves, the water demand value is displayed at the corresponding marked position so as to accurately understand the water demand of each position and provide a basis for precision irrigation.

[0092] In this embodiment, the location and amount of water required are marked in the three-dimensional coordinate model, which enables more accurate analysis of the water requirements of different locations in farmland. Traditional irrigation methods often rely on experience or general regional divisions to determine the amount of irrigation, while the marking in the three-dimensional coordinate system allows for accurate calculation of the water requirements for each specific water requirement point. This precise water requirement analysis helps to realize personalized irrigation strategies, improve the efficiency of water resource utilization, and reduce water waste. At the same time, through long-term monitoring and data analysis of water requirements in different regions, it can also provide a scientific basis for farmland soil improvement and crop planting structure adjustment.

[0093] By using a spectrometer to monitor the cleanliness of branches and leaves, dirt, dust, or other deposits that may affect photosynthesis can be detected in a timely manner. For example, in a dusty environment, dust easily adheres to the surface of crop branches and leaves, affecting light absorption and gas exchange, thereby reducing photosynthetic efficiency. By issuing an early warning of low branch and leaf cleanliness, the device can take corresponding cleaning measures to ensure normal photosynthesis of crops and promote crop growth.

[0094] Real-time monitoring of the dryness of branches and leaves helps prevent crops from suffering from drought stress due to water shortage. In the early stages of drought, branches and leaves will show slight signs of wilting and drying. At this time, by monitoring the system and replenishing water in time, the serious impact of drought on crop growth, such as slow growth and reduced yield, can be avoided. At the same time, for areas with high water demand but poor soil water retention capacity, irrigation can be prioritized based on the monitoring results of branch and leaf dryness to ensure water supply during the critical growth period of crops.

[0095] Monitoring the affected areas can enable early warning of pests. Many pests initially infest only localized areas, and if not detected and treated in time, they can easily spread over a large area, causing devastating damage to crops. Spectrometers can detect early symptoms of pests on branches and leaves, such as slight changes in leaf color and minor damage to the leaf surface, which is more sensitive and accurate than visual observation. For example, during the early hatching stage of corn borer larvae, they will first feed on the heart leaves of corn. At this time, monitoring with a spectrometer can detect abnormalities in the spectral characteristics of the heart leaves, issue an early warning in time, and spray water to remove the pests. By accurately identifying the type and location of pests, more effective control measures can be adopted.

[0096] Example 3

[0097] This embodiment is a further optimization based on Embodiment 1. Specifically, the optimal nozzle calculation algorithm in the data processing and analysis module is as follows:

[0098] Determine the coordinate representation of the sprinkler head and the water demand position: Assume the sprinkler head position in three-dimensional space is represented by coordinates (x, y, z). a ,y a ,z a ) represents the location of the sprinkler head, where a = 1, 2, ..., n, and n is the number of sprinklers; the location of water demand is represented by coordinates (x, y). w ,y w ,z w ) represents, where w = 1, 2, ..., q, and q is the number of locations requiring water (usually the number of grid points or the number of locations requiring water for branches and leaves);

[0099] Calculate the distance from it to all nozzles: The formula for the distance between two points is based on the Euclidean distance formula. Where, d aw This represents the distance from the a-th nozzle to the w-th water-requiring location;

[0100] Find the shortest distance: by comparing all calculated d aw (For each water demand location w), find the minimum d. mirw This is the distance from the water-requiring location to the nearest sprinkler head, and the index a' of the corresponding nearest sprinkler head is also recorded.

[0101] In this embodiment, calculating the distance between the nearest sprinkler and the water demand location enables irrigation water to reach the water demand area more efficiently. In traditional irrigation, there may be a mismatch between the irrigation location and the actual water demand location, resulting in excess water in some areas and insufficient water in others. By accurately calculating the distance, the system can select the sprinkler closest to the water demand location to spray water, reducing water transmission loss and ensuring that water can be delivered to the place where it is needed quickly and accurately. For example, in an irregularly shaped farmland, when crops in the edge area need irrigation, the system can find the sprinkler closest to that area, avoiding water loss and energy waste caused by long-distance water transport.

[0102] This precise selection of sprinklers also helps to optimize the layout of sprinklers. By analyzing the distance relationship between different water demand locations and sprinklers, agricultural engineers can identify unreasonable sprinkler layouts, such as sprinklers being too dense in some areas and sprinklers not covering other areas. Based on this information, the sprinklers can be rearranged or adjusted to improve the uniformity and effectiveness of the entire farmland irrigation system.

[0103] When a soil moisture sensor detects a water shortage in a certain area, it quickly calculates the nearest sprinkler head and starts spraying water, enabling a rapid response to water demand. Under extreme weather conditions, such as high temperatures and dry weather, the water demand of crops increases dramatically. If irrigation is not carried out in time, crops may wither or even die. By quickly finding the nearest sprinkler head and starting irrigation, water can be replenished to crops in a short time, alleviating drought and ensuring normal crop growth. For example, during the hot afternoon in summer, if a crop area is detected to be water deficient, the system can immediately command the nearest sprinkler head to start working, so that the crops can receive water supply in the shortest possible time and reduce the damage of high temperatures to crops.

[0104] Example 4

[0105] This embodiment is a further optimization based on Embodiment 1. Specifically, the nozzle angle adjustment algorithm in the data processing and analysis module is as follows:

[0106] Horizontal angle: In windless conditions, the nozzle sprays directly at the water-receiving location, with a horizontal angle of 0 degrees; in windy conditions, considering the influence of wind force on water flow deviation, the wind speed is v (unit: m / s), and the angle between the wind direction and the horizontal vector from the nozzle to the water-receiving location is α. The horizontal deviation acceleration a of the water flow is related to the wind speed and wind direction. According to Newton's second law: a = k·v·cosα, where k is a coefficient related to water flow characteristics and air resistance (this coefficient needs to be determined through experiments or empirical data).

[0107] Let the initial velocity of the water jet from the sprinkler head be v0 (unit: m / s). In the horizontal direction, the water flow is a uniformly accelerated linear motion under the action of wind. Let the horizontal distance from the sprinkler head to the location where water is needed be d (unit: m). According to the kinematic equations: Where t is the time it takes for the water to travel from the nozzle to the desired location. Since the height difference between the nozzle and the desired location is relatively small compared to the horizontal distance (this is a general approximation), time t is approximately calculated by dividing the horizontal distance by the initial velocity of the nozzle's spray. Horizontal displacement: The actual horizontal angle θ1 is calculated using the arctangent function. Let Δx be the actual horizontal offset of the water flow: but

[0108] Vertical Angle: Determine the coordinate system and relevant parameters. Establish a spatial rectangular coordinate system with the nozzle location as the origin. The x-axis is horizontal (pointing to the water demand location), the y-axis is vertical upward, and the z-axis is perpendicular to the xOy plane and points outward. Let the nozzle installation height be h1 (unit: m), the water demand location height be h2 (unit: m), and the nozzle lift angle be β1 (i.e., the angle between the nozzle and the horizontal plane). The vertical component of the nozzle's water velocity is v. 0y =v0·sinβ1, with the horizontal component being v 0x =v0·cosβ1;

[0109] The effect of water pressure on range: Water pressure affects the nozzle range. Let the water pressure be P (unit: Pa) and the nozzle orifice cross-sectional area be A (unit: m²). 2 The density of the water flow is ρ (unit: kg / m³). 3 Based on Bernoulli's equation and the continuity equation, it is derived that the range L of the sprinkler is related to the water pressure. For a given sprinkler and piping system, the range L is expressed as... Where c is a coefficient related to the nozzle and pipe structure;

[0110] Calculate the vertical angle: The water flow undergoes a vertical upward projectile motion. The height difference from the nozzle to the water demand position is: Δh = h2 - h1. Based on the formula for the maximum height of vertical upward projectile motion... Where g is the acceleration due to gravity, if Δh ≤ h, then the location requiring water is within the trajectory of the vertical upward motion of the nozzle. In this case, the vertical angle can be calculated using kinematic formulas. Let t1 be the time it takes for the water to travel from the nozzle to the location requiring water. Then we have... At the same time, in the horizontal direction, d = v 0x t1, solve the two formulas simultaneously to find t1, and then obtain v. 0y ;based on get

[0111] In this embodiment, the precise calculation of the spray direction by combining water pressure, wind direction, and wind speed can improve irrigation accuracy and save energy and water resources. Considering the water pressure factor can ensure that the water flow from the nozzles can reach the expected distance and range. Different nozzles have different ranges and spray radii under different water pressures. By accurately calculating the impact of water pressure on the spray distance, the system can adjust the outlet pressure of the nozzles or select the appropriate nozzle type according to the actual situation, so that the water flow can accurately cover the water-requiring area. For example, when irrigating a distant area, the system can automatically increase the water pressure or select a nozzle with a longer range. In areas close to the nozzles, appropriately reducing the water pressure can avoid over-irrigation. This can avoid problems such as insufficient irrigation area due to insufficient water pressure or water waste and ground erosion due to excessive water pressure.

[0112] Meanwhile, by adjusting the spray direction based on wind direction and speed, the system can effectively address the impact of natural wind on irrigation. In windy conditions, if wind direction and speed are not considered, the sprayed water may be blown off course, resulting in uneven irrigation or deviation from the target area. By monitoring wind direction and speed in real time and combining this information with the spray parameters of the sprinkler head, the system can accurately calculate the horizontal and vertical angle adjustment values ​​of the sprinkler head, ensuring that the sprayed water reaches the required location accurately, either against or with the wind. For example, in light winds, the sprinkler head can be slightly tilted upwind to spray water, allowing the wind to blow the water mist towards the target area; in windy conditions, it may be necessary to increase the spray angle of the sprinkler head or adjust the rotation speed of the sprinkler head to resist wind interference and ensure the accuracy of irrigation.

[0113] Precisely calculating the direction of water spraying can reduce unnecessary energy consumption. When the water flow can be directly aimed at the area requiring water, no additional energy is needed to change the direction of the water flow or compensate for energy loss caused by wind deviation. This means that the water pump can operate at a lower power, thereby reducing energy consumption. For example, in a large farm, if each sprinkler can avoid increased energy consumption due to wind problems by accurately calculating the direction of water spraying, the irrigation energy consumption of the entire farm will be significantly reduced. From the perspective of water resources, accurate water spraying direction helps to avoid unnecessary water loss. If the water spraying direction is inappropriate, water may be sprayed into unwanted areas, such as open spaces outside farmland or roads, resulting in water waste. By combining multiple factors to accurately calculate the water spraying direction, water can be sprayed to the roots of crops and other places that need water to the maximum extent, improving the utilization rate of water resources and reducing water waste and the impact on the surrounding environment.

[0114] Example 5

[0115] This embodiment is a further optimization based on embodiment 1. Specifically, the central control device obtains the nearest nozzle based on the optimal nozzle calculation algorithm, and then uses the angle sensor on the nozzle to obtain the horizontal and vertical angles of the nozzle at this time, as well as the horizontal and vertical angles of the nozzle when spraying water obtained by the nozzle angle adjustment algorithm. The difference between the two angles is calculated, and the adjustment amount is calculated based on the angle difference using a PID control algorithm, thereby controlling the nozzle angle adjustment device and adjusting the water output of the nozzle according to the water demand.

[0116] In this embodiment, the central control equipment automates the irrigation process by controlling the sprinkler angles. Farmers or agricultural managers no longer need to manually adjust the sprinkler angles in the field, greatly saving manpower and time costs. For example, in large-scale farmland, manually adjusting the angle of each sprinkler is impractical. However, with the intelligent control system, all sprinkler angles can be remotely controlled by simply inputting relevant instructions or parameters on the server. This greatly improves the efficiency and convenience of irrigation management for farmland with vast areas and complex terrain. The automated control can also be integrated with other agricultural management systems. For example, it can be connected to a weather station system. When weather data shows that rain is imminent, the server can automatically adjust the sprinkler spraying plan, suspend or reduce the amount of irrigation water, and avoid water waste caused by repeated irrigation. At the same time, it can also be integrated with a soil fertility monitoring system to automatically adjust the amount of fertilizer added to the irrigation water according to the soil nutrient status and crop needs, realizing integrated management of precision fertilization and irrigation.

[0117] Intelligent control relies on servers to collect and analyze large amounts of data, including soil moisture, leaf condition, weather conditions, and sprinkler operating parameters. This data can provide a scientific basis for irrigation decisions, making irrigation management more precise and optimized. For example, through the analysis of historical data and the application of machine learning algorithms, the server can predict the water requirements of different crops at different growth stages and adjust irrigation strategies in advance. If it is found that the water requirements of a certain crop change regularly within a specific growth cycle, the system can automatically increase or decrease the irrigation amount in the corresponding time period to achieve the best irrigation effect.

[0118] like Figure 2 As shown, the workflow of this invention is as follows:

[0119] S1. Establish a three-dimensional rectangular coordinate system with one corner of the farmland as the origin. The x-axis is along the length of the farmland, the y-axis is along the width of the farmland, and the z-axis is perpendicular to the ground and pointing upwards. Construct a three-dimensional coordinate model and mark the soil moisture sensor, spectrometer, and sprinkler in the three-dimensional coordinate model.

[0120] S2. Use a soil moisture sensor to measure soil moisture, and use a spectrometer to measure and monitor the cleanliness of branches and leaves, moisture content, and insect-infested parts; calculate water requirements, and mark the water requirement locations and water requirements in a three-dimensional coordinate model. The water requirement locations are marked as water shortage, cleanliness, and insect infestation.

[0121] S3. Calculate the distance between the nearest sprinkler head and the location where water is needed;

[0122] S4. Analyze the horizontal and vertical angles of the nozzle by combining water pressure, wind speed and wind direction;

[0123] S5. Use the central control equipment to control the angle of the nozzles and the amount of water sprayed.

[0124] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart precision irrigation directional control system for farmland based on the Internet of Things, characterized in that: include: Data acquisition module: includes a soil moisture sensor, a spectrometer and a weather station. The spectrometer is used to analyze the condition of branches and leaves, the weather station is used to measure wind speed and direction, and the data is transmitted to the central control equipment via wireless communication. Data processing and analysis module: includes central control equipment, water demand calculation algorithm, optimal sprinkler head calculation algorithm and sprinkler head angle adjustment algorithm. The central control equipment receives and processes the data. The optimal nozzle calculation algorithm is as follows: Determine the coordinate representation of the sprinkler head and water demand position: Assume the sprinkler head position is represented by coordinates in three-dimensional space. It means that, among them This refers to the number of sprinkler heads; the water demand locations are indicated by coordinates. It means that, among them , It refers to the number of locations requiring water; Calculate the distance from it to all nozzles: the formula for the distance between two points is based on the Euclidean distance formula; Find the shortest distance: Find the minimum distance by comparing all calculated nozzle distances; The nozzle angle adjustment algorithm is as follows: Horizontal Angle: In calm conditions, the nozzle sprays water directly at the location requiring it, with a horizontal angle of 0 degrees; in windy conditions, the wind speed is [missing value]. The angle between the wind direction and the horizontal vector from the nozzle to the water demand location is... According to Newton's second law: ,in, It is a coefficient related to water flow characteristics and air resistance; Let the initial velocity of the water sprayed from the nozzle be... In the horizontal direction, the horizontal distance from the nozzle to the location where water is needed is... (Unit: m), according to the kinematic formula: Where t is the time it takes for the water to travel from the nozzle to the desired location, the formula is: Horizontal displacement: The actual horizontal angle Calculate using the arctangent function, let This is the actual horizontal offset of the water flow: , then = , ; Vertical angle: Determine the coordinate system and relevant parameters. Establish a spatial rectangular coordinate system with the nozzle location as the origin. The axis is horizontal. The axis is vertically upward. Axis perpendicular to With the plane facing outwards, let the installation height of the nozzle be... The height of the water demand location is The nozzle is raised at an angle of 100 degrees. The vertical component of the water velocity from the nozzle is: The horizontal component is ; The effect of water pressure on range: Let the water pressure be... (unit: The nozzle cross-sectional area is (unit: The density of the water flow is (unit: The range L is represented as ,in, It is a coefficient related to the nozzle and pipe structure; Calculate the vertical angle: The water flow undergoes a vertical upward projectile motion in the vertical direction. The height difference from the nozzle to the water demand position is: According to the formula for vertical upward projectile motion ,in It is the acceleration due to gravity. If the water demand location is within the trajectory of the nozzle's vertical upward motion, then the vertical angle can be calculated using kinematic formulas. Let the time it takes for the water flow to travel from the nozzle to the water demand location be... Then there is At the same time, there is in the horizontal direction Solve the two formulas simultaneously to find And thus obtain ;based on ,get ; The central control equipment uses the optimal sprinkler calculation algorithm to obtain the nearest sprinkler, and then uses the angle sensor on the sprinkler to obtain the horizontal and vertical angles of the sprinkler at this time, as well as the horizontal and vertical angles of the sprinkler when spraying water obtained by the sprinkler angle adjustment algorithm. It calculates the angle difference between the two, and uses a PID control algorithm to calculate the adjustment amount based on the angle difference. Data labeling and classification unit: Based on soil moisture measurement results and the condition of branches and leaves, the location and amount of water required are obtained and classified and labeled in the three-dimensional model; The method for classifying and labeling water demand location and water demand is as follows: the water demand location and water demand are obtained based on the soil moisture measurement results of the soil moisture sensor and the branch and leaf status measured by the spectrometer; Labeling of soil moisture measurement results in a 3D coordinate model: for grid points Its coordinates in the three-dimensional coordinate system are , It is the height of the soil surface; Labeling of branch and leaf condition in a 3D coordinate model: for the location measured by the spectrometer The corresponding branches and leaves are located in three-dimensional space as follows: , The value is determined by measuring the height of the instrument and the actual height of the plant branches and leaves; Cleanliness labeling: Different icons or colors are used to indicate the cleanliness of branches and leaves; Moisture content labeling: The moisture content of branches and leaves is indicated by the height or color of a cube; Infested areas are marked: When an infestation is detected, it is indicated by a red skull icon or a different marking symbol at the corresponding branch or leaf location; 3D coordinate model construction module: Using a corner of the farmland as the origin, a 3D rectangular coordinate system is established, and the coordinates of the soil moisture sensor, spectrometer, and sprinkler module are marked in the 3D model; Nozzle angle adjustment device: The nozzle is equipped with a device for adjusting the angle in both the horizontal and vertical directions; Sprinkler head water volume adjustment unit: The sprinkler head is equipped with a flow sensor and a flow adjustment valve; Control command execution unit: Based on the horizontal and vertical angles obtained by the nozzle angle adjustment algorithm, it sends control commands to the nozzle angle adjustment device and adjusts the water output of the nozzle according to the water demand.

2. The IoT-based intelligent precision irrigation direction control system for farmland according to claim 1, characterized in that: The data acquisition method of the data acquisition module is as follows: Soil moisture measurement: Soil moisture sensors are arranged in a grid pattern in the farmland to measure soil moisture. In the plane, along Δ along the axis Deploy a sensor along Δ along the axis Deploy a sensor to form several sides with length ∆ and ∆ A rectangular grid, where each grid point is The data measured by the sensors is transmitted to the data processing center wirelessly. Leaf and branch condition data acquisition: The spectrometer is mounted on a movable bracket, along a path perpendicular to... The plane is used to scan and measure the branches and leaves of the plant. At each measurement location, the spectrometer acquires the spectral reflectance curve of the branches and leaves at that location. By comparing and analyzing the curve with a pre-established standard spectral database of branches and leaves in different states, the cleanliness, moisture content, and presence of pests at that location are determined.

3. The IoT-based intelligent precision irrigation direction control system for farmland according to claim 2, characterized in that: The three-dimensional coordinate system in the three-dimensional coordinate model construction module is constructed as follows: a three-dimensional rectangular coordinate system is established with one corner of the farmland as the origin. The axis runs along the length of the farmland. The axis runs along the width of the farmland. The axis is perpendicular to the ground and points upwards; The axis runs along the length of the farmland, and the distance from a point along the length of the farmland to the origin is... This point is at The coordinates on the axis are ; The axis runs along the width of the farmland, and it is located in... The coordinates on the axis are The z-axis is perpendicular to the ground and points upwards. When a certain position is at a height of [missing information] above the ground... At that time, the point was The coordinates on the axis are .

4. The IoT-based intelligent precision irrigation direction control system for farmland according to claim 2, characterized in that: The coordinate marking method for the soil moisture sensor, spectrometer, and sprinkler module in the three-dimensional coordinate model construction module is as follows: Soil moisture sensor coordinate marking: The sensor's position within the farmland plane is known, and its distance from the origin along the longitudinal direction is... The distance in the width direction is The z-axis coordinate is 0, and the coordinates of the soil moisture sensor in the three-dimensional coordinate model are... ; Spectrometer coordinate markings: The spectrometer is mounted on a movable support, and its position within the farmland plane is a distance along the longitudinal direction from the origin. The distance in the width direction is Because the spectrometer is above the ground, it is in The coordinates in the axial direction are The coordinates of the spectrometer in the three-dimensional coordinate model are: ; Sprinkler module coordinate markings: present in farmland The first nozzle, for the first One nozzle Its coordinates are .

5. The IoT-based intelligent precision irrigation direction control system for farmland according to claim 2, characterized in that: The water demand calculation algorithm in the data processing and analysis module is as follows: Soil moisture requirement calculation: Determine the target value for soil moisture for each grid point. The soil moisture deficit is calculated, and the soil moisture deficit is converted into soil water potential deficit based on the soil moisture characteristic curve. Then, combined with the root distribution depth factor, the irrigation water required for each grid point is calculated. Water requirement calculation for leaf and branch moisture content: Set a leaf moisture threshold. When the leaf and branch moisture content is lower than the threshold, increase the water requirement.

6. The IoT-based intelligent precision irrigation direction control system for farmland according to claim 2, characterized in that: The classification labeling method for water demand location and water demand in the data labeling and classification unit is as follows: Water demand location marking: Based on the calculated water demand, the water demand locations are distinguished in the 3D coordinate model. (If grid points...) The water demand at the location is greater than Then mark that location as a water-scarce location and use a flashing red icon at the coordinates. The display shows the water requirements of the branches and leaves, indicated at the corresponding branch and leaf locations. The area is marked with a red water droplet icon; Water demand label: The specific water demand value is displayed next to the marker icon at the water demand location.

7. The IoT-based intelligent precision irrigation direction control system for farmland according to claim 1, characterized in that: This allows for the control of the nozzle angle adjustment device, and the adjustment of the nozzle's water output according to the water demand.

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