Intelligent watering control method for alfalfa seed production

Abstract

The invention relates to the technical field of watering control, in particular to an intelligent watering control method for alfalfa seed production, which comprises the following steps: collecting humidity data of current soil through a soil humidity sensor, analyzing a soil humidity state and generating a soil humidity result; evaluating the required water quantity according to the soil humidity result, and adjusting the output quantity of a water pump to obtain an adjusted watering plan. The current soil humidity state of the farmland is obtained by monitoring the soil humidity in real time, so that the output quantity of the water pump is dynamically adjusted, intelligent regulation and control of the watering plan are achieved, the irrigation accuracy is improved, and waste and insufficiency of water resources in the watering process are avoided; meanwhile, the watering plan is optimized by means of air temperature and rainfall data, so that the irrigation action is closely combined with the actual weather condition, the irrigation timeliness is improved, and the negative effects caused by adverse weather conditions are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of watering control, and in particular to an intelligent watering control method for alfalfa seed production. Background Art

[0002] The field of watering control technology mainly involves the precise control of the watering process of crops through automation and intelligent means, including the regulation and management of water volume, watering time, frequency and area. The irrigation management of alfalfa seed cultivation stage requires the use of watering control methods.

[0003] In actual operation, the existing technology only controls the watering amount, time, frequency and area from the level of automation and intelligence, but lacks comprehensive consideration of multiple environmental factors such as changes in meteorological conditions, actual growth status of crops and sunshine intensity. As a result, it is difficult to accurately control the watering plan when facing sudden weather changes or large differences in crop growth requirements. Therefore, improvements are needed. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an intelligent watering control method for alfalfa seed production.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme, an intelligent watering control method for alfalfa seed production, comprising the following steps: The soil moisture sensor collects the current soil moisture data, analyzes the soil moisture status, and generates a soil moisture result; the required amount of water is evaluated according to the soil moisture result, the water pump output is adjusted, and an adjusted watering plan is obtained; Monitor the current temperature and expected rainfall conditions to generate temperature and rainfall results; use the temperature and rainfall results to optimize the adjusted watering plan to obtain an optimized watering plan; Using a camera mounted on a drone to regularly capture farmland images, calculate vegetation indexes, and generate vegetation growth status results; adjusting an optimized watering plan based on the vegetation growth status results to obtain an adjusted watering plan; Based on the adjusted watering plan, the sunshine intensity and duration are monitored, the light data are collected, and the watering frequency is adjusted in combination with the light data to obtain a watering strategy.

[0006] Preferably, the steps of obtaining the soil moisture result are: collecting the current soil moisture data, recording the soil moisture values ​​at different monitoring points, arranging the data in time series, formatting and storing the data, and eliminating invalid data to obtain a soil moisture data set; Based on the soil moisture data set, the soil moisture change trend is determined, the moisture change rate is calculated, and the soil moisture result is obtained.

[0007] Preferably, the steps of obtaining the adjusted watering plan are: judging the current soil moisture deficit according to the soil moisture result, and analyzing the soil type, infiltration capacity and surface runoff to obtain target water replenishment data; Based on the target water replenishment data, the water pump output is calculated using the following formula: ; in, is the pump output, is the irrigated area, is the evaporation rate, is the soil water deficit, is the soil permeability coefficient, is the rainfall influencing factor, For the estimated irrigation time, is the surface runoff ratio; Based on the water pump output, the water pump start and stop strategy and the water pump power are adjusted to generate an adjusted watering plan.

[0008] Preferably, the steps of obtaining the temperature and rainfall results are: collecting temperature data from different monitoring points, arranging them in chronological order, establishing a data storage structure, performing abnormal data elimination, and obtaining temperature data; Based on the temperature data, obtain the precipitation monitoring information in the weather forecast, determine the precipitation probability, precipitation level and precipitation duration, and obtain the temperature and rainfall results.

[0009] Preferably, the step of obtaining the optimized watering plan is: based on the temperature and rainfall results, calculating the adjusted irrigation amount, the calculation formula is: ; in, is the adjusted irrigation amount, is the initial irrigation plan amount, is the difference between the current temperature and the reference temperature, is the constant affecting the irrigation demand due to temperature change, is the rainfall, is the humidity index; Based on the adjusted irrigation amount, the pump output settings and irrigation schedule are configured to generate an optimized watering plan.

[0010] Preferably, the step of obtaining the vegetation growth condition result is: deploying a drone equipped with a camera to perform flight photography of the farmland and collect images of the farmland area; Based on the image of the farmland area, the vegetation index is calculated using the following formula: ; in, represents the vegetation index, is the reflectivity in the near-infrared band, is the reflectivity in the red light band, is the reflectivity of the green light band, is the reflectivity of the blue light band; Based on the vegetation index, the vegetation coverage rate and growth health status are analyzed, the vegetation index is compared with historical data, the growth trend of the vegetation is determined, and the vegetation growth status results are obtained.

[0011] Preferably, the step of obtaining the adjusted watering plan is: based on the vegetation growth condition result, calculating the adjusted optimized irrigation amount, the calculation formula is: ; in, To adjust and optimize irrigation volume, The basic irrigation amount, is the vegetation index, To grow, is the evaporation amount, is the rainfall; Based on the adjustment and optimization of irrigation amount, the water pump output is adjusted to generate an adjusted watering plan.

[0012] Preferably, the step of obtaining the watering strategy is: based on the adjusted watering plan, monitoring the sunshine intensity and duration, collecting real-time light data, and combining with historical light records, removing and normalizing abnormal light data to obtain a light data set; Based on the light data set, the optimized watering frequency is calculated using the formula: ; in, For the optimized watering frequency, is the sunlight intensity, is the duration of sunshine, is the average daily temperature, is the soil moisture content, is the wind speed; Based on the optimized watering frequency, the water pump start and stop cycle is set to generate a watering strategy.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: The present invention obtains the current soil moisture state of the farmland by real-time monitoring of soil moisture, and dynamically adjusts the output of the water pump to achieve intelligent control of the watering plan, thereby improving the accuracy of irrigation and avoiding waste and shortage of water resources during watering; at the same time, the watering plan is optimized with the help of temperature and rainfall data, so that the irrigation action is closely combined with the actual climatic conditions, the timeliness of irrigation is improved, and the negative impact caused by adverse weather conditions is reduced; in addition, the farmland is photographed by a drone and the vegetation index is calculated, and the irrigation strategy is optimized and adjusted again based on the changes in the vegetation growth conditions, ensuring that the irrigation decision is closely matched with the actual needs of crop growth, thereby improving the water resource utilization efficiency and seed yield in the alfalfa seed production process; in addition, the watering frequency is adjusted in time in combination with the light data to ensure that the crops obtain an appropriate amount of water under different sunlight conditions, thereby further improving the overall quality and yield of alfalfa seeds. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] See also Figure 1 The present invention provides a technical solution, an intelligent watering control method for alfalfa seed production, comprising the following steps: The soil moisture sensor collects the current soil moisture data, analyzes the soil moisture status, and generates soil moisture results; the required amount of water is evaluated based on the soil moisture results, the water pump output is adjusted, and the adjusted watering plan is obtained; Monitor the current temperature and expected rainfall conditions, and generate temperature and rainfall results; use the temperature and rainfall results to optimize the adjusted watering plan and obtain an optimized watering plan; Using drones equipped with cameras to regularly take farmland images, calculate vegetation indexes, and generate vegetation growth status results; based on the vegetation growth status results, adjust the optimized watering plan to obtain an adjusted watering plan; Based on the adjusted watering plan, the sunshine intensity and duration are monitored, the light data is collected, and the watering frequency is adjusted in combination with the light data to obtain the watering strategy.

[0017] The steps for obtaining soil moisture results are as follows: collect the current soil moisture data, record the soil moisture values ​​at different monitoring points, arrange the data in time series, format and store the data, and remove invalid data to obtain the soil moisture data set; Based on the soil moisture data set, the soil moisture change trend is determined, the moisture change rate is calculated, and the soil moisture results are obtained.

[0018] Specifically, based on the humidity measurement equipment pre-placed at different monitoring points, each record is compared with the effective interval set by experience, for example, the humidity value is compared with the range of 0% to 100%. If some records are found to be less than 0% or more than 100%, they are regarded as invalid data and deleted. After that, the humidity records retained by the comparison are rearranged in order according to the timestamp, and read and formatted one by one in the software environment in this order, including unified correction of possible numerical precision and storage with fixed decimal places. In order to check whether there are still abnormal records, the processed humidity data will be compared again with the results of other monitoring points in the same period to check whether there are any cases exceeding the intra-domain empirical threshold. The intra-domain empirical threshold here is the upper and lower bounds formed by the mean and standard deviation calculated by combining the local soil moisture observation data for many years. For example, when the sample size is 300 observations, the humidity mean is first calculated. And calculate the standard deviation of the data set , and then set the upper and lower floating range As a reference threshold for identifying anomalies, if an entry is found to fall outside this range during the inspection process, it is determined that it may have extreme deviations and is eliminated. Finally, the remaining data are integrated and summarized according to the specified time series to obtain the soil moisture dataset.

[0019] Based on the soil moisture data set obtained above, the humidity records of each monitoring point in a continuous time period are selected and arranged in chronological order. Then, the humidity differences at adjacent sampling moments are accumulated to determine the change trend of soil moisture. If the observed cumulative change in humidity in a short period of time is greater than or equal to the change threshold determined by multiple experiments, for example, if the total change calculated after 48 consecutive measurements a day and half an hour interval each time exceeds 5%, it is summarized as a significant upward or downward trend. This threshold of 5% is determined by multiple comparative measurements of the same monitoring points under similar climatic conditions in the past year. When the system identifies the humidity change trend of a single monitoring point, it will compare all monitoring points as a whole, use differential analysis to determine the overall trend, and calculate the humidity change rate in combination with the set time step. In order to intuitively represent the change rate, the formula can be used. Quantify, where Representative moments Humidity readings, represents the time difference between adjacent sampling moments, is the humidity change rate, The humidity reading at time t-1 is combined with the analysis results of all monitoring points to complete the final statistical summary and obtain the soil moisture result.

[0020] The steps to obtain the adjusted watering plan are as follows: based on the soil moisture results, determine the current soil moisture deficit, analyze the soil type, infiltration capacity and surface runoff conditions, and obtain the target water replenishment data; Based on the target water replenishment data, calculate the water pump output using the following formula: ; in, is the pump output, is the irrigated area, is the evaporation rate, is the soil water deficit, is the soil permeability coefficient, is the rainfall influencing factor, For the estimated irrigation time, is the surface runoff ratio; Based on the water pump output, the water pump start and stop strategy and water pump power are adjusted to generate an adjusted watering plan.

[0021] Specifically, based on the soil moisture results obtained above and referring to the soil classification table clearly defined in the local agricultural technical data, clay loam, sandy loam or loam were selected as the basis for determining the soil type. Six sampling points were set up in different areas and the texture ratio of each sample was quantitatively measured. After determining that the area was mainly sandy loam, the permeability test was continued for each sample. One liter of water was evenly sprayed in a fixed area and the time taken for the water to completely infiltrate was recorded. The permeability detailed table was established in combination with the soil texture information to determine the range of the permeability coefficient of the soil. For example, if the collected infiltration rate is higher than 0.6 cm per minute, it is considered to have strong permeability, if it is between 0.4 and 0.6 cm per minute, it is considered to have medium permeability, and if it is lower than 0.4, it is considered to have high permeability. Centimeters per minute is considered to have weak infiltration capacity. The above values ​​are based on the average of soil moisture and infiltration rate monitoring data from the local agricultural science and technology extension station for 180 consecutive days. The judgment interval is then formed, and the soil surface runoff is taken into consideration. The specific method is to monitor the flow count values ​​of the drainage channel at the edge of the site after the rain. For example, a flow rate in the range of 0 to 2 liters per hour is classified as low runoff, 2 to 4 liters per hour as medium runoff, and more than 4 liters per hour as high runoff. The corresponding runoff category is matched in subsequent calculations. The collected soil types, infiltration capacities and surface runoff conditions are analyzed one by one in the above manner, and each value is recorded in the soil parameter table. Finally, the soil moisture deficit obtained previously is compared with the items in the soil parameter table to obtain the target water replenishment data.

[0022] The benefit of the formula is that by introducing multiple parameters such as soil permeability coefficient, rainfall influencing factor and surface runoff ratio, it comprehensively quantifies the regional area, evaporation rate and soil moisture deficit, thereby taking into account multiple factors such as evaporation, rainfall compensation and soil characteristics within the same mathematical structure.

[0023] The steps to obtain the parameter are as follows: This parameter represents the irrigation area. For example, if a farmland is 250 meters long and 120 meters wide, the approximate rectangular area is square meters.

[0024] The steps for obtaining the parameter are as follows: this parameter represents the evaporation rate. First, an evaporation dish is set up in the monitoring area and ensure that there are no trees blocking the placement area. The evaporation rate is obtained by recording the water level change in the evaporation dish every hour. If the initial water level in the dish is 0.08 meters, and the water level drops to 0.0797 meters after one hour, the evaporation drop is 0.0003 meters per hour.

[0025] The steps to obtain the parameter are as follows: This parameter represents the soil moisture deficit. First, compare the soil moisture results obtained above with the suitable moisture content recommended by the local agricultural technology department. If the sampling test shows that the measured soil moisture content is 0.18, and the suitable moisture content is defined as 0.25, then the soil moisture deficit value is .

[0026] The steps to obtain the parameter are as follows: the parameter represents the soil permeability coefficient. The soil infiltration rate is obtained by conducting a standard infiltration test in the field, and then the ZR value is obtained by calculating the ratio of the actual measured infiltration rate to the reference infiltration rate. The infiltration rate is determined by setting an infiltration ring in a certain area and injecting one liter of water into the ring and recording the time required for absorption. If the test shows that the stable infiltration rate of the soil is 0.5 cm / min, and the reference infiltration rate is defined as 0.4 cm / min, then .

[0027] The steps to obtain the parameter are as follows: the parameter represents the rainfall impact factor. First, the total rainfall and rainfall type distribution in the local area in the past three days need to be recorded to observe whether there is local heavy rainfall. By evaluating the change in soil moisture during the rainfall period, the degree of compensation for the current irrigation demand is calculated. If the cumulative rainfall in an area reaches 20 mm during the observation and the soil moisture value during the measurement increases by 0.03, then combined with the reference rainfall impact map, it can be summarized as =1.15. The specific value is obtained by comparing the measured humidification amplitude with the benchmark rainfall humidification curve. If the benchmark rainfall humidification curve shows that 20 mm corresponds to a humidity increase of 0.026, then , and then get .

[0028] The steps for obtaining the parameter are as follows: the parameter represents the expected irrigation time. If an area can be irrigated from 2 pm to 6 pm for 4 hours, the T value is 4.

[0029] The steps for obtaining the parameter are as follows: the parameter represents the surface runoff ratio. After rainfall, if the total precipitation is 1000 liters, of which 70 liters finally flow into the drainage outlet, then the surface runoff ratio is .

[0030] Calculation process: To demonstrate the application process of this calculation formula, a set of representative input parameter example values ​​are selected for calculation. This set of values ​​is intended to illustrate the complete process, which is different from the examples used to explain individual calculation steps or parameter acquisition. Step 1: Substitute the example values ​​and let Square meters, Meters per hour, , , , Hour, ; The second step is to Perform the calculation: ; Step 3, the multiplication part: middle; They are: , 9 and multiply it by , 2.3814 and multiply by , 2.9768 and multiply by ; Step 4, denominator part: ; Step 5, Final Cubic meters per hour. The results show that under the above example parameters, the pump output is 0.92 cubic meters per hour, which matches the required irrigation water volume and related conditions.

[0031] According to the water pump output calculated previously, combined with the pump flow level parameters confirmed in the monitoring platform, the water pump start-stop strategy adjustment process is implemented. First, the water pump start-up time and shutdown time are set in the software interface, and the integrity of the power supply line and water supply pipeline is checked before the water pump is started. If the instantaneous pressure of the water pipeline falls within the pre-established range of 0MPa to 2MPa and the current is within the range of 0A to 5A, the water pump is allowed to start, and the running water pump power is set to match the flow value obtained previously. The estimated volume flow value is compared with the previously obtained 0.92 cubic meters per hour by successively detecting the pipeline outlet flow rate. If the volume flow value is found to be in the range of 0.9 cubic meters per hour to 1.0 cubic meters per hour, the established water pump power is maintained; if it exceeds this range, the operating frequency of the motor is adjusted immediately, and a new round of collection values ​​of the soil moisture sensor is synchronously called for comparison. The reference threshold can be set at a relative moisture content of not less than 80%. The above threshold is obtained by the local agricultural department through statistics of alfalfa seed breeding experience over the years and observation of the planting yield per hectare. If the collected data shows that the soil moisture is still on the low side, the water pump is allowed to continue to operate until the end of the set irrigation period, and finally an adjusted watering plan is generated.

[0032] The steps for obtaining temperature and rainfall results are as follows: collect temperature data from different monitoring points, establish a data storage structure after arranging them in chronological order, perform abnormal data elimination, and obtain temperature data; Based on the temperature data, obtain the precipitation monitoring information in the weather forecast, determine the precipitation probability, precipitation level and precipitation duration, and obtain the temperature and rainfall results.

[0033] Specifically, the temperature data of different monitoring points are collected, and the monitoring time and measurement location indicated in each record are used as the index basis. During the implementation process, the temperature value of each sample is compared with the pre-established valid range. For example, the temperature is compared with the interval of 0℃ to 60℃. If the record falls outside this interval, it is marked as an abnormal record. Then, the retained normal temperature records are arranged one by one in the order of monitoring time and a data storage structure is established in the software environment. In order to refine the execution process, it is necessary to pay attention to whether the temperature of each monitoring point at the same time deviates too much from that of other monitoring points. If the temperature of a monitoring point is 10℃ higher or lower than that of the nearby monitoring point, it is regarded as a potential abnormality and a second comparison is performed. The 10℃ threshold is calculated by statistically averaging the observation values ​​of all measuring points in the past 30 days. The difference is 5℃ and multiplied by 2. At the same time, the value will be appropriately corrected with reference to the terrain, altitude and building obstruction around the monitoring site. If an abnormality is confirmed, the corresponding record will be deleted and the abnormal situation at that time point will be marked in the data structure. If it is normal, the temperature value will continue to be stored in the arranged sequence, and then these continuous temperature records will be read again to observe whether there is a discontinuous phenomenon. If data missing for certain time periods is detected, adjacent time interpolation will be performed based on the actual situation or the time period will be directly marked as an invalid interval. All data will be considered as the final normal record after such a second check. Finally, the above normal records will be classified and managed according to the monitoring points in the data storage structure. Each monitoring point has a separate temperature sequence. If horizontal comparison of different monitoring points is required, they can be matched and merged by time at any time. After completing all steps, the temperature data is obtained.

[0034] Based on the temperature data, the forecast information of the local meteorological department is compared and the precipitation monitoring information within the past 24 to 72 hours is extracted. First, the cloud coverage, wind direction and speed, and humidity level given by the meteorological department are checked item by item according to the actual observed temperature change trend. If the cloud coverage is between 60% and 90% and the wind direction is consistent with the geographical environment, it is preliminarily judged that there is a certain possibility of precipitation, and further subdivided according to the actual forecast issued by the local meteorological station. If the forecast indicates that the probability of precipitation in this period exceeds 50% and the previous atmospheric humidity is higher than 70%, the rainfall probability is marked as high in the system, and the precipitation level can be judged in combination with the temperature data. If the forecast indicates that the maximum temperature of the day is less than 25°C and there is continuous cloudy coverage, the rainfall level is set to moderate rain or heavy rain, corresponding to the reference range Between 10 mm and 50 mm, if the temperature is above 25°C and the cloud cover is only about 40%, the rainfall level is set to light rain, and then the duration of precipitation is estimated in combination with the observed cloud band movement speed. For example, if the meteorological department predicts that the cloud band will cross the area within three hours, the duration is 3 hours. If the cloud band moves slowly and can cover more than 12 hours, the duration is extended accordingly and the probability of rainfall is set to not less than 60% within this time range. All these judgment processes are recorded one by one and compared with the previously arranged temperature data. If it is found that the temperature gradually drops during the day and can still maintain a relatively low level at night, it is consistent with the forecast information, so as to confirm the precipitation probability, precipitation level and precipitation duration in the record and summarize them, and finally obtain the temperature and rainfall results.

[0035] The steps to obtain the optimized watering plan are: based on the temperature and rainfall results, calculate the adjusted irrigation amount, the calculation formula is: ; in, is the adjusted irrigation amount, is the initial irrigation plan amount, is the difference between the current temperature and the reference temperature, is the constant affecting the irrigation demand due to temperature change, is the rainfall, is the humidity index; Configure pump output settings and irrigation schedules to generate an optimized watering plan based on the adjusted irrigation volumes.

[0036] Specifically, the benefit of the formula lies in that, by integrating the initial irrigation plan with the impact of temperature changes and rainfall in the numerator and introducing the logarithmic function of the humidity index in the denominator, multiple factors such as temperature, precipitation and soil moisture are included in the same quantifiable structure, forming a calculation method that can simultaneously reflect environmental changes and soil moisture conditions. It not only emphasizes the intensified evaporation factor caused by rising temperatures, but also takes into account the corrective significance of rainfall and humidity indicators on irrigation demand. In addition, the formula is constructed by combining exponents and logarithms, so that a relatively smooth irrigation adjustment result can be obtained in the case of abnormally high temperature or sudden increase in precipitation. Therefore, it can better match the demand for irrigation water in different periods under changing climate conditions.

[0037] The steps for obtaining the parameter are as follows: this parameter represents the initial irrigation plan. The value comes from the comprehensive calculation process of the planting cycle, crop type and total field area. Generally, it is necessary to first accumulate the daily water demand of the target crop, and derive a periodic benchmark based on the local soil water storage capacity and evaporation. The specific method is to estimate the initial amount by counting the water consumption of the same crop in a similar growth period, and then make multiple records and exclude abnormal periods to obtain a relatively stable value, and then accumulate in different time periods to obtain the planned amount. For example, first calculate the average daily water consumption level m based on the existing 60-day continuous water consumption observation records, and then multiply m by 30 days to obtain the total water demand for the month, and then make appropriate additions and subtractions based on the actual area percentage of the field to obtain a more accurate irrigation plan, such as 20 cubic meters or 25 cubic meters.

[0038] The steps for obtaining the parameter are as follows: this parameter represents the difference between the current temperature and the reference temperature. The real-time temperature monitored in the previous process must be continuously recorded and compared with the standard temperature in the historical reference library. The standard temperature generally selects an average temperature that is more suitable for crop growth and relatively stable evaporation as a reference. The acquisition method is to extract observation data of the same season for 10 consecutive years or longer in the local meteorological data, and calculate its average value as the reference temperature. Then, the reference value is subtracted from the real-time collected temperature records for each period to obtain the reference temperature. For example, when the base temperature is 20 degrees Celsius and the current measured temperature is 26 degrees Celsius, then .

[0039] The steps to obtain the k parameter are as follows. This parameter represents the constant that affects the irrigation demand due to temperature changes. It is a dimensionless value. It is necessary to perform regression analysis on the data such as the evaporation increment and soil moisture change of local crops under different temperature conditions, so as to find a calibration coefficient that can reflect the increase in water demand due to temperature increase. The specific method is to record the maximum temperature, the evaporation amount and the soil water loss rate of each day in segments during the monitoring period, and then construct a mapping relationship. The slope term is fitted by linear regression, and the slope is corrected after multiple observations to obtain the numerical range of k. Finally, a fixed value that meets statistical significance is selected. For example, by regressing the 180-day experimental field data, the slope is between 0.015 and 0.025. After comprehensive analysis, k is determined to be 0.02.

[0040] The steps to obtain the P parameter are as follows. This parameter represents the rainfall. For example, a standard rain gauge is placed at the monitoring site and the cumulative rainfall within 72 hours is read to be 18 mm. If the current plot area is 5,000 square meters, then the total rainfall of 18 mm corresponding to 5,000 square meters is calculated. cubic meter.

[0041] The steps for obtaining the H parameter are as follows: this parameter represents the humidity index. When obtaining it, the atmospheric relative humidity and the soil surface moisture content data need to be combined to produce a quantitative value that can reflect the mutual influence of atmospheric humidity and soil humidity. It is necessary to continuously record the relative humidity on site, and then determine the soil moisture content at multiple sampling points in the field. The two data are then combined according to the weighted rule. Usually, the weighted coefficient can be set according to the degree of atmospheric influence and the importance of the soil, such as the atmosphere accounts for 0.4 and the soil accounts for 0.6. Then, a mathematical model is constructed to realize the mapping from relative humidity and moisture content to H. For example, the relative humidity is set to The soil moisture content is , you can set ,in ,like and , The collected value is 0.65. The determination is 0.32, then , at this time H=0.452.

[0042] Calculation process: To demonstrate the application process of this calculation formula, a set of representative input parameter example values ​​are selected for calculation. This set of values ​​is intended to illustrate the complete process, which is different from the examples used to explain individual calculation steps or parameter acquisition. Step 1: Enter the example values, for example: =20, =5, k=0.02, P=10, H=0.6; The second step is to calculate the exponential term first: ; The third step is to calculate the molecular part: ; Step 4: Calculate the denominator: ; and then ; Step 5: Divide the numerator by the denominator: ; Step 6. Take the square root of 21.84: ;therefore .

[0043] The results show that under this example condition, the adjusted irrigation amount is about 4.68, which corresponds to the multiple environmental impacts involved in the previous steps. If the value is greater than 10, it means that the superposition effect of environmental factors on water demand is stronger. If the value is less than 2, it means that the influence of factors such as temperature increase is weak. The higher it is, the greater the crop's current water demand is.

[0044] Based on the adjusted irrigation volume, combined with the irrigation priority determined in the previous process and the dispatchable water pump equipment information, the setting steps for the water pump output need to first extract the power and maximum water flow rate of each water pump that can be allocated on that day from the irrigation management system, and then compare the 4.68 cubic meters or other corresponding values ​​obtained previously, and set the start time and end time of the water pump in the software interface. If the water output of a certain water pump is observed to be between 0.5 cubic meters per hour and 1.0 cubic meters per hour and the instantaneous pressure of the site pipe network is maintained in the range of 1MPa to 1.5MPa, it is determined that the unit can work normally and meet the current irrigation needs. Next, the operation plan of the water pump is arranged in the time period table, and then the other The output capacity of the available water pump is compared with the irrigation period. If it is found that there is a site that needs to increase the irrigation volume and there is still a gap in the previously determined irrigation time, the output power of the corresponding water pump will be increased to the next level and the pipe diameter of the on-site pipeline will be checked. For example, it is determined whether the pipe diameter is greater than 50 mm to ensure that the flow rate does not exceed the safe range. The current and voltage should also be compared item by item from 0A to 5A and 0V to 24V respectively to eliminate abnormal conditions. When the power and flow configuration of all water pumps is completed, the corresponding irrigation schedule will be automatically generated. If multiple fields need to be irrigated, the time periods will be distinguished one by one to avoid overlapping interference of the pipeline network and time slots will be scheduled. When the scheduling of all plots is completed and correct, an optimized watering plan will be generated.

[0045] The steps for obtaining the vegetation growth status results are as follows: deploying a drone equipped with a camera to fly over the farmland and collect images of the farmland area; Based on the image of the farmland area, the vegetation index is calculated using the following formula: ; in, represents the vegetation index, is the reflectivity in the near-infrared band, is the reflectivity in the red light band, is the reflectivity of the green light band, is the reflectivity of the blue light band; Based on the vegetation index, analyze the vegetation coverage and growth health status, compare the vegetation index with historical data, determine the growth trend of vegetation, and obtain the results of vegetation growth status.

[0046] Specifically, deploy drones and carry cameras to conduct aerial photography of farmland. First, determine the route and flight altitude and set up several safety warning signs around the farmland. Then define the flight path and shooting time period in the software interface. Before each flight, check whether the battery power is maintained within the rated range, for example, between 70% and 100%, and verify the working status of the propeller. If the battery capacity is found to be less than 70% or the motor current exceeds 5A during the above inspection, replace the battery or check the motor. After confirmation, start the flight photography at the ground remote control end. During the flight, cruise at a pre-set flight altitude of 20 to 30 meters to ensure that the lens framing range can cover the entire farmland. During the flight process The attitude of the drone must also be monitored. If the tilt angle is greater than 15 degrees, it will return immediately and perform flight correction operations. In order to obtain a continuous image sequence, the shooting frequency must be set in the shooting software and one picture must be recorded every second or every two seconds. The shooting frequency can be appropriately adjusted for different plots according to the complexity of the terrain. For example, the shooting frequency in flat areas can be kept low, while the frequency in slopes or ditch areas can be appropriately increased. All images are sent back to the ground receiving end one by one through air-to-ground transmission. If the file size of an image is significantly smaller or larger than other images and exceeds the set range of 50%, it is marked as a suspicious image and verified again. After summarizing all qualified images, the image of the farmland area is obtained.

[0047] The benefit of the formula is that by taking the logarithm of the ratio of near-infrared and red light reflectances in the numerator and superimposing the difference index of green and blue light, and using a combination of arc tangent and cosine in the denominator, multi-band information is integrated into a quantitative indicator, thereby more completely reflecting the differences in the response of surface vegetation cover to different visible light and near-infrared bands.

[0048] The steps to obtain the NIR parameters are as follows: NIR represents the reflectivity of the near-infrared band. The pixel brightness needs to be captured by a multispectral camera. The pixel brightness can be converted into reflectivity using the following formula: ,in is the pixel brightness, To unify the brightness of the white board or gray board recorded under the lighting conditions, for example, if the measured brightness of a certain pixel is 230 and the synchronous white board brightness is 250, then the near-infrared reflectivity of the pixel is .

[0049] The steps to obtain the R parameter are as follows: R represents the reflectivity of the red light band. The red reflection intensity of the farmland pixels is observed through the red light channel of the multispectral camera, and then the brightness is compared with a similar control white board or gray board to eliminate the deviation of the red light measurement caused by the change of ambient light. If the ground light is strong, the shooting time is set between 9 am and 4 pm and the sunlight angle is guaranteed to be in a relatively stable range to obtain a more accurate red light reflectivity. If the original red light grayscale value of a pixel is 160, and the white board value is 200 during the same period, the red light reflectivity .

[0050] The steps for obtaining the G parameter are as follows: G represents the reflectivity of the green light band. The measurement method is similar to that of near-infrared and red light. After obtaining the grayscale value through the green light channel, it is compared with the specified calibration plate to obtain the normalized result. For example, if the green light grayscale value of a pixel is 140 and the white plate value is 200, then the green light reflectivity of the pixel is .

[0051] The steps to obtain the B parameter are as follows: B represents the reflectivity of the blue light band, which is obtained by dividing the pixel brightness of the blue light channel by the reference white board brightness. If the blue light grayscale value of a pixel is 90 and the white board value is 200, the blue light reflectivity .

[0052] Calculation process: To demonstrate the application process of this calculation formula, a set of representative input parameter example values ​​are selected for calculation. This set of values ​​is intended to illustrate the complete process and is different from the previous examples used to explain individual calculation steps or parameter acquisition: In the first step, let the example NIR=0.45, R=0.38, G=0.42, B=0.36; The second step is to calculate the logarithm and exponential part of the numerator: ; Then: , ; Therefore, the molecular part: ; The third step is to calculate the denominator: ; Step 4: Divide the numerator by the denominator ,but The results show that in the example case, the vegetation index obtained by calculating the multi-band reflectance extracted from the farmland image using this formula is about 0.136. If the NDVI is greater than 0.6, it usually indicates lush vegetation, and if it is less than 0.2, it often indicates that the vegetation coverage is weak. The corresponding results can also be combined with the NDVI distribution in different regions to form subsequent judgment and processing plans.

[0053] Based on the vegetation index, the index is compared with the local historical vegetation sample data one by one. In order to complete the judgment of the vegetation coverage rate, it is necessary to first divide the shooting picture into plots in the system and provide the corresponding NDVI mean for each partition. By comparing the NDVI mean of these partitions with the pre-established threshold range, for example, the threshold range of relatively high coverage is set to an interval greater than 0.5. If the NDVI mean of a partition is between 0.4 and 0.5, it is classified as a medium coverage rate, and if it is lower than 0.2, it is classified as a low coverage rate. This threshold range is obtained by collecting and counting aerial photography data of similar plots for 12 consecutive months. The upper and lower limits of each interval are determined by experts based on plant density and growth observation results. Then, in each partition, multiple aerial photography records are further combined to form a time series. If the current NDVI increases by more than 5% compared with the average value of the past month and is higher than the historical average, it can be marked as being in a stable increase state. If it is lower than the historical average and the decline exceeds 10%, it will be determined as an area that requires special attention so that subsequent adjustments can be made around water or nutrient management. After all partition statistics are completed, they are arranged in the system by partition number and summarized into growth process data, so as to determine the current vegetation growth trend and finally output the vegetation growth status results.

[0054] The steps for obtaining the adjusted watering plan are: based on the vegetation growth results, calculate the adjusted optimized irrigation amount, and the calculation formula is: ; in, To adjust and optimize irrigation volume, The basic irrigation amount, is the vegetation index, To grow, is the evaporation amount, is the rainfall; Based on the adjustment and optimization of irrigation volume, the water pump output is adjusted to generate an adjusted watering plan.

[0055] Specifically, the benefit of the formula lies in that the basic irrigation amount is multiplied by the vegetation index and combined with the growing degree day (GDD) to correct the square root factor, and then the impact of the external environment on irrigation demand is comprehensively evaluated through the combination of evaporation and rainfall with the logarithmic function. The actual growth level of crops, temperature accumulation and water balance are unified in one calculation structure, which can take into account the crop growth conditions in the planting area, the impact of climate on water supply and evaporation loss, so that the final irrigation amount that is closer to the actual conditions can be obtained.

[0056] The steps for obtaining the parameters are as follows: this parameter represents the basic irrigation volume, which is determined by multi-dimensional information such as crop planting scale, crop growth cycle requirements, local historical climate data, etc. The acquisition method includes accumulating the water demand and soil water storage capacity in different time periods during the entire crop growth cycle, and comparing and correcting with the multi-year average value of the same crop under similar planting conditions. For example, in a field with an area of ​​4,000 square meters, evaporation data and rainfall data for each month of the year are collected, and the water consumption patterns of the crops planted are subdivided. The monthly or quarterly irrigation volume required can be accumulated and then summarized into the basic irrigation volume for planting in the current season. After sorting, the single irrigation volume is 15 cubic meters.

[0057] The steps to obtain the NDVI parameter are as follows: This parameter represents the vegetation index. Its acquisition process is related to the steps of calculating NDVI after deploying multi-spectral drone photography and extracting the reflectance of red light, near-infrared light, green light, blue light, etc. It is necessary to obtain the reflectance of each band by comparing the pixel grayscale with the reference light source, and then combine the calculation formula Analyze and obtain the NDVI of pixels or areas.

[0058] The steps to obtain the GDD parameter are as follows: This parameter refers to Growing Degree Days, which is used to quantify the growth potential of crops within a specific temperature range. It is necessary to extract the difference between the daily average temperature and the benchmark temperature from the local historical temperature monitoring data and accumulate it. The daily average temperature is subtracted from the benchmark temperature (such as 10°C or the lower limit of the temperature suitable for crop growth). If the difference is lower than 0, it is recorded as 0. If it is higher than 0, the actual difference is included in the cumulative sum. In this way, the GDD is accumulated for many consecutive days. If a GDD of about 400 degrees Celsius per day is obtained after 45 days of accumulation during the planting season, it means that the crop has fully completed the early growth stage. This GDD is added to the formula in this paragraph. To show the weight of cumulative heat on crop growth, for example: the average daily temperature in an area is 15℃, 16℃, 17℃... The base temperature is set to 10℃. When continuous statistics are performed, the differences are added one by one and then summarized to 400.

[0059] The steps for obtaining the Evap parameter are as follows: This parameter represents the evaporation amount. The measurement method includes setting up a standard evaporation dish at the edge of the farmland and reading the water level drop value every certain hours, and then obtaining the pure evaporation loss after excluding external interference such as rainwater entering. If the water level is monitored to drop from 100 mm to 98 mm within 24 hours, the evaporation amount on that day is recorded as 2 mm. Thereafter, the evaporation data for consecutive days are accumulated day by day to obtain Evap. If the recent total evaporation is 5 mm, it is recorded as Evap=5.

[0060] The steps for obtaining the Precip parameter are as follows: This parameter represents rainfall, which is mainly collected by rain gauges and other instruments in the center or edge of the plot after rainfall. The readings correspond to the accumulated rainfall in different periods, and then compared with the plot area or production cycle in the irrigation management system to form the total rainfall in a certain period. If the total precipitation depth recorded in 3 days in a certain observation is 12 mm, Precip=12 is registered.

[0061] Calculation process: In order to demonstrate the application process of this calculation formula, a set of representative input parameter example values ​​are selected for calculation. This set of values ​​is intended to illustrate the complete process, which is different from the previous examples used to explain individual calculation steps or parameter acquisition: Step 1, the example introduces the values ​​of each parameter: =15, NDVI=0.5, GDD=400, Evap=5, Precip=12; The second step is to calculate ; Step 3: Calculate the molecular part ; Step 4: Calculate the logarithmic denominator ,therefore ; Step 5: Multiply the results of the previous two steps ,thus cubic meter; The results show that under the current environmental conditions, after correction by vegetation index, accumulated heat and evaporation rainfall, the irrigation volume per irrigation in this area is about 0.1295 cubic meters. If this value increases significantly, it means that the crops face a higher water demand. If the value tends to be lower, it means that the environment and natural rainfall have a strong compensatory effect on the water demand.

[0062] Based on the adjusted and optimized irrigation volume obtained above, the corresponding values ​​are recorded in the irrigation management system and allocated to different irrigation sites according to the location of the field. First, a water pump that can provide a flow rate close to the irrigation demand is selected from the registered water pump model information, and the motor power and flow sensor readings of the water pump are compared before activation to exclude abnormal conditions such as sudden failures or excessive reading deviations. If the motor current is detected to be in the range of 0A to 5A and the water pump outlet diameter is not less than 40 mm, it is confirmed that the pump can perform subsequent water supply tasks, and then the start and stop time periods are set to ensure that the irrigation process can be carried out sequentially in the local plot. If multiple plots need to be irrigated continuously on the same day, the order is arranged in the time period planning to avoid overlapping time periods. Cause water pressure fluctuations. According to the meteorological monitoring results and soil moisture sensor feedback in the system, the operation time of the section with higher evaporation rate is appropriately extended, and the working time of the corresponding water pump is adjusted from, for example, 1 hour to 1 hour and 15 minutes. The changes are summarized and recorded. If the moisture collection data of any plot within the specified time period shows that the moisture content is still lower than the preset 80% threshold, it will be marked as a plot that needs repeated irrigation. This 80% is the lower limit of the ideal moisture content of the crop obtained based on many years of local planting experience, and is determined after screening out the relationship between yield per hectare and moisture content in many years of control experiments. Finally, based on the irrigation completion information of each plot and the comparison results of soil moisture collection, an adjusted watering plan is generated.

[0063] The steps for obtaining the watering strategy are as follows: based on the adjusted watering plan, monitor the sunshine intensity and duration, collect real-time light data, and combine with historical light records to remove and normalize abnormal light data to obtain a light data set; Based on the light data set, the optimized watering frequency is calculated using the formula: ; in, For the optimized watering frequency, is the sunlight intensity, is the duration of sunshine, is the average daily temperature, is the soil moisture content, is the wind speed; Based on the optimized watering frequency, the water pump start and stop cycle is set to generate a watering strategy.

[0064] Specifically, based on the adjusted watering plan, light intensity sensors are first deployed at multiple locations in the field and the reading sampling frequency is set in the software. If the instantaneous light value of any sensor deviates from the data of other sensors by more than 50%, the record is marked as abnormal and discarded. At the same time, the light data of the same period from the previous day to the previous week are compared. If it is found that it exceeds the specified range, the historical light records collected previously are compared to further confirm whether the data is caused by obstruction or failure. The light intensity is compared with the range of 0W / m² to 1000W / m² with reference to the long-term records of the local meteorological station. If the output of the sampling point falls outside the range, it is judged as an invalid value and discarded. Then, the sunshine duration is matched in the verified data. The specific method is to check whether the light intensity recorded by the sensor is continuously maintained above 10W / m² for more than 10 minutes. If this condition is met, the accumulated light intensity for that period is Add it to the sunshine duration D. If the interruption time exceeds 15 minutes, the next sunshine duration is recorded again. After all valid light values ​​are collected, they are normalized according to the benchmark lighting standard. The normalization process uses a fixed reference value of 1000W / m² as the maximum light benchmark. Each reading is compared with this benchmark value to obtain the relative light intensity I. If a certain period is recorded as 700W / m², I=0.7. Then all periods are integrated into the light data set to form the light intensity and sunshine duration information saved in time series for each monitoring point. If cloudy or shadowy conditions occur in individual periods, only the cumulative amount of D is retained for a duration of more than 10W / m², and records below 10W / m² are eliminated. After the entire processing process is completed, it is confirmed that the light data set has completed the verification and excluded abnormal values, and finally real-time light data that can support subsequent calculations is obtained.

[0065] The benefit of the formula is that it integrates the effects of light intensity and duration on crop transpiration in the numerator, as well as the correction of changes in water demand by soil moisture content and average daily temperature, and then combines wind speed and soil parameters in the denominator and additional terms to dynamically balance the watering frequency, thereby more accurately reflecting the cyclical demand of crops for water replenishment.

[0066] The steps to obtain the parameters are as follows: this parameter is the sunshine intensity. Light sensors need to be placed in the field and sampled at fixed time intervals. If the maximum value of 900W / m² is obtained on a certain observation day, it can be regarded as I=900W / m², which is normalized by dividing it by 1000 to get 0.9.

[0067] The steps for obtaining the parameter are as follows: This parameter represents the duration of sunshine. When obtaining it, it can be based on the cumulative period of time during which the light sensor maintains a light intensity of more than 10W / m² in the same day and night cycle. Continuously track and record the time and take the average or combined value based on multi-point measurements. If the record shows that the continuous light reaches 8 hours, record D=8.

[0068] The steps for obtaining the parameter are as follows: This parameter represents the average daily temperature. By recording the temperature values ​​of multiple temperature sensors for 24 consecutive hours and taking the average value, if the total value X is added up after 24 temperature records are measured in one day, , and then compare it with the long-term data of the meteorological station to determine whether there are abnormal measuring points. If there are no abnormalities, confirm the average daily temperature value based on the multi-point average.

[0069] The steps for obtaining the parameter are as follows: this parameter represents the soil moisture content. It can be tested on-site at different depths through a soil moisture sensor, and then aggregated into an average value after sampling at intervals. If the cultivated land integral blocks are obvious, take the average of each block and then get the overall average. If the moisture content recorded in a test section is 0.35, then M=0.35.

[0070] The steps for obtaining the parameters are as follows: This parameter is wind speed. The average or continuous wind speed is obtained by setting an anemometer to continuously measure near the crop height. If the average wind speed within 24 hours is 2m / s, then S=2 in the formula.

[0071] Calculation process: In order to demonstrate the application process of this calculation formula, a set of representative input parameter example values ​​are selected for calculation. This set of values ​​is intended to illustrate the complete process, which is different from the examples used to explain individual calculation steps or parameter acquisition. The first step is to introduce example values. Let I=0.7, D=6, =25, M=0.35, S=2; The second step is to calculate the first half of the numerator: ; ; so: ; The third step is to calculate: , ; Step 4: Calculate the second half: , ; Then: ; Therefore the denominator: ; So the second half: ; Step 5: Add the two results together: 1.8101+1.112=2.9221. .

[0072] The results show that in this example, the optimized watering frequency is approximately 2.92 times, which can be rounded or subdivided into time periods based on actual user needs. If the value is greater than 5, it means a higher irrigation frequency is required, and if it is less than 1, it means that the current water demand frequency is low.

[0073] Based on the optimized watering frequency, check the water pump operation plan and pipe network allocation plan registered in advance. You need to fill in the value of 2.92 times in the management software and compare it with the executable operation period every day. If it is found that the number of irrigation window segments that can be allocated throughout the day is 3 or 4, select three of them to start the water pump, and fine-tune each operation time in combination with the soil moisture sensor reading obtained earlier. If the actual monitoring current is in the range of 0A to 5A and the displacement of the water pump can meet the flow requirements of 50 minutes to 1 hour each time, you can query the new data of the sensor after each irrigation period. If monitoring shows that the soil moisture content is higher than the lower limit of 80% and does not exceed the upper limit of 90%, the same frequency of startup will be maintained for the next period. If the sensing value is still less than 80%, the startup time of the next period will be moved forward to make up for the soil moisture gap. The 80% and 90% ranges are determined by the local agricultural experimental station based on the optimal moisture content of crops in many years of control observations and fixed after large-scale promotion and verification. No additional frequency correction is required for the readings within the range, and further subdivision and adjustment are made for situations exceeding or falling below this range. In this way, the start and stop cycle of the water pump is set and the final watering strategy is recorded in the system.

[0074] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent watering control method for alfalfa seed production, characterized in that: The following steps are involved: The soil moisture sensor collects the current soil moisture data, analyzes the soil moisture status, and generates a soil moisture result; the required amount of water is evaluated according to the soil moisture result, the water pump output is adjusted, and an adjusted watering plan is obtained; Monitor the current temperature and expected rainfall conditions to generate temperature and rainfall results; use the temperature and rainfall results to optimize the adjusted watering plan to obtain an optimized watering plan; Using a camera mounted on a drone to regularly capture farmland images, calculate vegetation indexes, and generate vegetation growth status results; adjusting an optimized watering plan based on the vegetation growth status results to obtain an adjusted watering plan; Based on the adjusted watering plan, the sunshine intensity and duration are monitored, the light data are collected, and the watering frequency is adjusted in combination with the light data to obtain a watering strategy.

2. The intelligent watering control method for alfalfa seed production according to claim 1, characterized in that: The steps of obtaining the soil moisture result are: collecting the current soil moisture data, recording the soil moisture values ​​of different monitoring points, arranging the data in time series, formatting and storing the data, and eliminating invalid data to obtain a soil moisture data set; Based on the soil moisture data set, the soil moisture change trend is determined, the moisture change rate is calculated, and the soil moisture result is obtained.

3. The intelligent watering control method for alfalfa seed production according to claim 1, characterized in that: The steps of obtaining the adjusted watering plan are: judging the current soil moisture deficit according to the soil moisture result, analyzing the soil type, infiltration capacity and surface runoff, and obtaining target water replenishment data; Based on the target water replenishment data, the water pump output is calculated using the following formula: ； in, is the pump output, is the irrigated area, is the evaporation rate, is the soil water deficit, is the soil permeability coefficient, is the rainfall influencing factor, For the estimated irrigation time, is the surface runoff ratio; Based on the water pump output, the water pump start and stop strategy and the water pump power are adjusted to generate an adjusted watering plan.

4. The intelligent watering control method for alfalfa seed production according to claim 1, characterized in that: The steps for obtaining the temperature and rainfall results are: collecting temperature data from different monitoring points, arranging them in chronological order, establishing a data storage structure, performing abnormal data elimination, and obtaining temperature data; Based on the temperature data, obtain the precipitation monitoring information in the weather forecast, determine the precipitation probability, precipitation level and precipitation duration, and obtain the temperature and rainfall results.

5. The intelligent watering control method for alfalfa seed production according to claim 1, characterized in that: The step of obtaining the optimized watering plan is: based on the temperature and rainfall results, calculating the adjusted irrigation amount, the calculation formula is: ； in, is the adjusted irrigation amount, is the initial irrigation plan amount, is the difference between the current temperature and the reference temperature, is the constant affecting the irrigation demand due to temperature change, is the rainfall, is the humidity index; Based on the adjusted irrigation amount, the pump output settings and irrigation schedule are configured to generate an optimized watering plan.

6. The intelligent watering control method for alfalfa seed production according to claim 1, characterized in that: The steps of obtaining the vegetation growth status result are: deploying a drone equipped with a camera to fly over the farmland and collect images of the farmland area; Based on the image of the farmland area, the vegetation index is calculated using the following formula: ； in, represents the vegetation index, is the reflectivity in the near-infrared band, is the reflectivity in the red light band, is the reflectivity of the green light band, is the reflectivity of the blue light band; Based on the vegetation index, the vegetation coverage rate and growth health status are analyzed, the vegetation index is compared with historical data, the growth trend of the vegetation is determined, and the vegetation growth status results are obtained.

7. The intelligent watering control method for alfalfa seed production according to claim 1, characterized in that: The step of obtaining the adjusted watering plan is: based on the vegetation growth condition result, calculating the adjusted optimized irrigation amount, the calculation formula is: ； in, To adjust and optimize irrigation volume, The basic irrigation amount, is the vegetation index, To grow and live, is the evaporation amount, is the rainfall; Based on the adjustment and optimization of irrigation amount, the water pump output is adjusted to generate an adjusted watering plan.

8. The intelligent watering control method for alfalfa seed production according to claim 1, characterized in that: The steps of obtaining the watering strategy are: based on the adjusted watering plan, monitoring the sunshine intensity and duration, collecting real-time light data, and combining with historical light records, removing and normalizing abnormal light data to obtain a light data set; Based on the light data set, the optimized watering frequency is calculated using the formula: ； in, For the optimized watering frequency, is the sunlight intensity, is the duration of sunshine, is the average daily temperature, is the soil moisture content, is the wind speed; Based on the optimized watering frequency, the water pump start and stop cycle is set to generate a watering strategy.

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