Strawberry planting water and fertilizer integrated zoned irrigation method and system

Abstract

The invention provides a water and fertilizer integrated zoned irrigation method and system for strawberry planting, relates to the technical field of strawberry planting and fertilization, and aims to solve the problem that in related technologies, the water and fertilizer requirements of strawberry plants are affected by growth stages, environments and self states, but traditional zoned irrigation lacks of real-time dynamic and accurate determination of irrigation volume based on regions, and the irrigation efficiency is poor. The utilization efficiency of water and fertilizer is low, and the quality and yield of strawberries are influenced. The method comprises the following steps: acquiring environment data and matrix data of an area in a first time period; acquiring a substrate humidity change rate and a substrate conductivity change rate of the region in a second time period according to the substrate data; according to the environment data, the substrate humidity change rate, the substrate conductivity change rate and preset parameters, determining the required intensity of the current irrigation period of the area; according to the current irrigation period demand intensity and a preset reference irrigation volume, the current irrigation volume of the area is obtained; when the area meets the irrigation triggering condition, the nutrient solution with the irrigation volume is applied to the area.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of strawberry planting and fertilization, and in particular, to a method and system for integrated water and fertilizer zoning irrigation in strawberry planting. Background Art

[0002] Strawberries are important cash crops. Soilless cultivation technology has been increasingly widely used in strawberry production due to its advantages such as land saving, efficient utilization of water and fertilizer, and easy management. Soilless cultivated strawberries usually adopt nutrient solution cultivation, and the prepared nutrient solution is transported to the roots of plants through drip irrigation and other methods. In order to improve management efficiency and carry out refined control according to the characteristics of different regions, large-scale soilless cultivation systems are often divided into multiple independent irrigation areas, and each area can be independently controlled for irrigation, that is, integrated water and fertilizer zoning irrigation.

[0003] However, the water and fertilizer requirements of strawberry plants are not constant, but are affected by multiple factors such as growth stage, environmental conditions (such as light, temperature, humidity), and the physiological state of the plants themselves. In a zoning irrigation system, even in different areas within the same greenhouse, the demand for nutrient solution may vary due to local environmental differences, differences in plant growth, etc.

[0004] In related technologies, common zoning irrigation management methods mostly use control based on time or a set general irrigation amount. For example, set to perform irrigation several times at specific time points every day, and apply a preset volume of nutrient solution each time; or trigger according to the threshold of substrate humidity or EC, but still apply a preset fixed volume. Although these methods achieve zonal control, they lack precise perception and response to the real-time and dynamic demands of each area. Simple timing and quantification may lead to insufficient water supply when the demand is high, and waste or nutrient leaching when the demand is low; while the method of fixed threshold triggering plus fixed volume application, although it responds to a single index (such as humidity), does not comprehensively consider the comprehensive impact of environmental changes on demand, and may also lead to a mismatch between the application amount and the actual demand.

[0005] In related technologies, although some studies have tried to use sensor data to construct complex models (such as machine learning models) to predict plant requirements or optimize irrigation decisions, these models are often complex in structure, difficult to train, and their decision-making processes are usually "black boxes" that are difficult for those skilled in the art to interpret, not easy to understand and debug, and also have problems of over-reliance on specific data sets and insufficient generalization ability. At the same time, many solutions focus on the overall optimization at the system level, but lack a refined and deterministic regulation mechanism for each independent irrigation area. Summary of the Invention

[0006] The embodiments of the present application provide a method and system for integrated water and fertilizer partition irrigation in strawberry cultivation, which are used to solve the technical problem in the related art that there is a lack of technology for accurately determining the irrigation volume based on regional real-time dynamics.

[0007] To achieve the above object, the embodiments of the present application adopt the following technical solutions: In a first aspect, the present application provides a method for integrated water and fertilizer partition irrigation in strawberry cultivation, which is applied to a soilless cultivation system for strawberries. The soilless cultivation system has multiple strawberry irrigation areas for applying nutrient solution, including: obtaining environmental data of the strawberry irrigation area and substrate data in the nutrient solution, and determining the demand intensity of the current irrigation cycle for the area according to the environmental data, the substrate data, and preset parameters; obtaining the current irrigation volume of the area according to the demand intensity of the current irrigation cycle and a preset reference irrigation volume; obtaining characteristic factors affecting irrigation demand; constructing a prediction model based on the characteristic factors, where the prediction model is used to predict the number of partitions that need to be irrigated; obtaining the matching degree between the current environmental parameters and past environmental parameters; obtaining a first predicted irrigation demand according to the matching degree and past irrigation-related historical data, and the data of the first predicted irrigation demand includes the number of partitions that need to be irrigated in multiple different time periods; obtaining a preliminary value of the characteristic factor according to the first predicted irrigation demand, the current actual irrigation demand data, and the prediction model; and predicting the number of partitions that need to be irrigated according to the prediction model corresponding to the preliminary value of the characteristic factor.

[0008] In a possible implementation manner of the first aspect, the obtaining of the environmental data of the strawberry irrigation area and the substrate data in the nutrient solution in the first time period includes: obtaining the average temperature of the area in the first time period, and the average light intensity of the area in the first time period; obtaining the average substrate humidity of the area in the first time period, and the average substrate conductivity of the area in the first time period.

[0009] In a possible implementation manner of the first aspect, the method further includes: detecting whether the area meets the irrigation trigger condition, and when the area meets the irrigation trigger condition, applying the current irrigation volume of the nutrient solution to the area.

[0010] In a possible implementation manner of the first aspect, the irrigation trigger condition includes: the substrate humidity of the area is lower than a preset humidity threshold; or the time interval since the last irrigation of the area is greater than a preset minimum irrigation interval time.

[0011] In a possible implementation of the first aspect, the method further includes: within a preset correction period, obtaining an actual irrigation frequency based on the number of times irrigation actually occurs in the area; determining a target irrigation frequency corresponding to the current growth stage of the area; obtaining a deviation between the actual irrigation frequency and the target irrigation frequency; and when the absolute value of the deviation is greater than a preset frequency deviation threshold, adjusting the preset parameters used in calculating the demand intensity of the current irrigation cycle corresponding to the area.

[0012] In a possible implementation of the first aspect, adjusting the preset parameters includes: when the actual irrigation frequency is significantly higher than the target irrigation frequency, reducing the weight coefficient in the preset parameters; or when the actual irrigation frequency is significantly lower than the target irrigation frequency, increasing the weight coefficient in the preset parameters.

[0013] In a possible implementation of the first aspect, the characteristic factors include a first characteristic factor and a second characteristic factor. The first characteristic factor is used to characterize the water absorption and growth ability of strawberry plants in this sub-region, and the second characteristic factor is used to characterize the influence of environmental factors on water evapotranspiration.

[0014] In a possible implementation of the first aspect, the expression of the prediction model is: where is the number of sub-regions that need to be irrigated at time t, is the number of sub-regions with good current water conditions at time t, represents the total number of sub-regions, p is the first characteristic factor, and q is the second characteristic factor.

[0015] In a possible implementation of the first aspect, obtaining the preliminary values of the characteristic factors according to the first predicted irrigation demand, the current actual irrigation demand data, and the prediction model includes: Constructing an intermediate model according to the first predicted irrigation demand; Obtaining a second predicted irrigation demand according to the intermediate model; Obtaining a third predicted irrigation demand according to the current actual irrigation demand data and the second predicted irrigation demand; Obtaining the preliminary values of the characteristic factors according to the data of the trend nodes in the third predicted irrigation demand and the prediction model.

[0016] In a second aspect, the present application provides a strawberry planting water and fertilizer integrated sub-region irrigation system suitable for soilless cultivation, including a plurality of independent irrigation regions, and a control device configured for the method according to any one of the first aspect.

[0017] This application calculates the demand intensity based on the real-time environmental and substrate dynamic changes in the area, and dynamically adjusts the single irrigation volume instead of using a fixed amount, making the irrigation more in line with the actual needs of the plants, avoiding the excess or deficiency that may be caused by fixed-amount irrigation, reducing the waste and leaching of water and fertilizer, and improving the utilization efficiency of the nutrient solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flowchart related to obtaining the irrigation volume in the irrigation method provided for some embodiments of this application; Figure 2 It is a schematic flowchart related to obtaining the deviation between the actual irrigation frequency and the target irrigation frequency in the irrigation method provided for other embodiments of this application; Figure 3 It is a schematic flowchart related to predicting the number of irrigation zones in the irrigation method provided for other embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Next, the technical solutions in the embodiments of this application will be described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments.

[0020] Hereinafter, terms such as "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0021] In addition, in this application, orientation terms such as "upper", "lower", "left", "right", etc. may include but are not limited to being defined relative to the schematic placement of components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and they may change accordingly with the change of the orientation of the components placed in the drawings.

[0022] In this application, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" may be a fixed connection, a detachable connection, or an integral body; it may be directly connected, or indirectly connected through an intermediate medium. In addition, the term "electrical connection" may be a way of realizing electrical connection for signal transmission.

[0023] As used herein, "about", "substantially" or "approximately" includes the stated value and a reference value within an acceptable deviation range of the specific value, characterized in that the acceptable deviation range is determined by a person of ordinary skill in the art considering the measurement being discussed and the errors associated with the measurement of a particular quantity (i.e., the limitations of the measurement method).

[0024] In the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplarily" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, words such as "exemplarily" or "for example" are used.

[0025] The present application provides a method and system for integrated water and fertilizer zoning irrigation for strawberry cultivation, which is applicable to a soilless cultivation system with multiple independent irrigation areas, aiming to dynamically and precisely determine the irrigation amount according to the real-time needs of strawberry plants in each area, improve the utilization efficiency of water and fertilizer, and optimize plant growth.

[0026] The method of the present application can be carried out for each independent irrigation area in the soilless cultivation zoning irrigation system. The soilless cultivation system can be divided into multiple irrigation areas, each area can contain a certain number of strawberry plants, and is equipped with independent irrigation control valves and sensors for monitoring environmental and substrate conditions. The division of the irrigation areas can be set based on planting beds, rows, or according to a specific sensor layout.

[0027] As Figure 1 shown, the method includes: S110. Obtain the environmental data of the strawberry irrigation area and the substrate data in the nutrient solution. The environmental data may include the temperature, light intensity, humidity, CO2 concentration, etc. in the strawberry planting irrigation area.

[0028] Exemplarily, the environmental data mainly refers to the average temperature and average light intensity (e.g., PAR, Photosynthetically Active Radiation). The first time period is a fixed and relatively short time window, such as the past 15 minutes, 30 minutes, or 1 hour. It can be understood that the setting of this time period should ensure that it can capture the rapid changes in the environment, but not affect the calculation stability due to excessive instantaneous fluctuations.

[0029] Exemplarily, data can be periodically read through environmental sensors (such as temperature sensors, light sensors) deployed in or representing the area, and the average value within this time period can be calculated.

[0030] Exemplarily, assume that the first time period is set to the past 30 minutes. The system reads the regional environmental sensor data, including temperature and light intensity, once per minute within the past 30 minutes. Then, it calculates the average value of these 30 temperature readings as the actual temperature and the average value of these 30 light intensity readings as the actual light intensity.

[0031] The substrate data may include substrate humidity, substrate electrical conductivity (EC), substrate pH, etc.

[0032] Exemplarily, the substrate data mainly refers to substrate humidity and substrate EC. The acquisition method can be to periodically read data through sensors (such as substrate humidity sensors and substrate EC sensors) deployed in the regional substrate within the same first time period as when acquiring environmental data, and calculate the average value within this time period.

[0033] Exemplarily, assume that the first time period is set to the past 30 minutes. The system reads the regional substrate sensor data, including substrate humidity and substrate EC, once per minute within the past 30 minutes. Then, it calculates the average value of these 30 humidity readings as the actual substrate humidity and the average value of these 30 EC readings as the actual substrate EC.

[0034] Based on the substrate data, obtain the substrate humidity change rate and substrate electrical conductivity change rate in the second time period immediately following the first time period for the region. The substrate humidity change rate and substrate electrical conductivity change rate can be used to quantify the change trend of substrate humidity in the recent period and reflect the plant's water absorption and the evaporation rate of substrate moisture.

[0035] The second time period is immediately adjacent to the first time period and is usually slightly longer or equal to the first time period to obtain a more stable change rate. For example, if the first time period is the past 30 minutes, the second time period can be the past 1 hour or 2 hours. The substrate humidity change rate can be obtained by calculating the ratio of the difference between the substrate humidity values at the start and end of the second time period to the time interval.

[0036] Exemplarily, assume that the first time period is the past 30 minutes, the second time period is the past 1 hour, and the substrate humidity value is recorded at the whole hour of each hour. If at the current calculation moment (e.g., 10:00), the past 30 minutes is from 9:30 to 10:00, then the second time period (the past 1 hour) is from 9:00 to 10:00. The system obtains the substrate humidity value at 9:00 as H_start and the substrate humidity value at 10:00 as H_end. The actual substrate humidity change rate = (H_end - H_start) / 1 hour. If the humidity drops, the change rate is negative, indicating the drying speed; if the humidity rises (e.g., just irrigated), the change rate is positive. In the present invention, for the convenience of expressing the "descent rate", the negative change rate can be inverted, or its meaning can be adjusted by symbols in the subsequent formula.

[0037] The change rate of substrate conductivity can be used to quantify the change trend of substrate EC in the recent period of time, reflecting the relative absorption rate of nutrients and water by plants and the evaporation concentration effect. The calculation method of the change rate of substrate conductivity is similar to that of the substrate humidity change rate. For example, by obtaining the substrate EC values at the start and end of the second time period, the ratio of the difference to the time interval can be calculated.

[0038] Exemplarily, the substrate EC value at 9:00 is obtained as EC_start, and the substrate EC value at 10:00 is obtained as EC_end. The actual substrate conductivity change rate = (EC_end - EC_start) / 1 hour. The EC change rate is usually positive, indicating an increase in salt concentration (water absorption is faster than nutrient absorption, or evaporation), or negative (nutrient absorption is faster than water absorption, or leaching).

[0039] S120. Determine the demand intensity of the current irrigation cycle for the area according to the environmental data, the substrate data, and the preset parameters.

[0040] Exemplarily, the calculation method of the demand intensity is as follows: where, represents a preset weight coefficient, represents the target humidity descent rate, represents the actual substrate humidity change rate, represents the actual substrate conductivity change rate, represents the target conductivity change rate, represents the actual average light intensity in the first time period, represents the light threshold, represents the actual average temperature in the first time period, represents the temperature threshold.

[0041] In the above formula, multiple factors reflecting the dynamic changes of plant physiological activities and substrate environment are combined to evaluate the demand intensity. The higher the demand intensity, the stronger the current demand of plants in this area for water and fertilizer.

[0042] The actual substrate humidity change rate is negative (decreasing), and the larger its absolute value, the faster the decrease (the faster the drying). The target humidity decrease rate can be a preset small negative constant, representing the desired drying rate. If the actual change rate is more negative than the target (for example, the target is -0.01% / min and the actual is -0.02% / min), then the target humidity decrease rate - the actual substrate humidity change rate = -0.01 - (-0.02) = 0.01, getting a positive value, and the faster the actual decrease, the larger this item, the greater the contribution, thus reflecting the high demand intensity corresponding to rapid water loss.

[0043] The target conductivity change rate is a preset constant, which can be zero or a small positive value. If the actual EC change rate is higher than the target (for example, the target is 0.01 mS / cm / hr and the actual is 0.05 mS / cm / hr), then the actual substrate conductivity change rate - the target conductivity change rate = 0.05 - 0.01 = 0.04, getting a positive value. A higher EC rising speed may reflect that the plant absorbs water faster than salts (high transpiration demand) or a strong evaporation concentration effect, so it is positively correlated with the demand intensity. The greater the contribution of this item, the higher the demand intensity.

[0044] Light intensity is the main driving factor for photosynthesis and transpiration. When the actual light intensity exceeds a certain threshold (for example, a value below the light compensation point or light saturation point of strawberries), it usually means that light is no longer a limiting factor, and the plant demand increases with the increase of light. This item only makes a positive contribution when the light is above the threshold, and the stronger the light, the greater the contribution.

[0045] Temperature affects the biochemical reaction rate and transpiration rate. When the actual temperature exceeds a certain suitable temperature threshold, the plant demand may increase. This item only makes a positive contribution when the temperature is above the threshold, and the higher the temperature, the greater the contribution.

[0046] w1, w2, w3, w4 are weight coefficients used to balance the relative contributions of different factors to the demand intensity. It can be understood that these weights are preset non - negative constants.

[0047] The target humidity decrease rate, the target conductivity change rate, the light threshold, and the temperature threshold are all preset constant parameters, and their specific values should be set and stored according to different growth stages of strawberries, for example, obtained by referring to a preset parameter lookup table.

[0048] Exemplarily, assume that for the current growth stage, the preset parameters are as follows: w1 = 1000 (because the humidity change rate value is small), w2 = 500 (because the EC change rate value is small), w3 = 0.1, w4 = 0.2; the target humidity decrease rate = -0.01% / min (i.e., the minute change rate); the target conductivity change rate = 0.005 mS / cm / hr (i.e., the hourly change rate, assuming the humidity / EC change rate calculation time period is 1 hour); the light threshold = 200 PAR; the temperature threshold = 22 °C.

[0049] At a certain moment, the obtained environmental data are: the average light in the past 30 minutes = 350 PAR, the average temperature in the past 30 minutes = 25 °C. The substrate humidity has decreased from 65% to 63% in the past 1 hour, and the substrate EC has increased from 1.2 mS / cm to 1.28 mS / cm.

[0050] Then the actual substrate humidity change rate = (63% - 65%) / 1 hour = -2% / hour = -2 / 60% / min = -0.0333% / min. The actual substrate conductivity change rate = (1.28 - 1.2) / 1 hour = 0.08 mS / cm / hr. The actual light intensity = 350 PAR, and the actual temperature = 25 °C. Then the demand intensity is: 1000 * (-0.01 - (-0.0333)) + 500 * (0.08 - 0.005) + 0.1 * max(0, 350 - 200) + 0.2 * max(0, 25 - 22) = 1000 * (0.0233) + 500 * (0.075) + 0.1 * 150 + 0.2 * 3 = 23.3 + 37.5 + 15 + 0.6 = 76.4. That is, the demand intensity of this area is 76.4.

[0051] S130. Calculate the irrigation volume of this area according to the demand intensity of this irrigation cycle and the preset benchmark irrigation volume.

[0052] Exemplarily, the calculation method of the irrigation volume of this area is: Among them, Vg represents the irrigation volume of this area, represents the preset benchmark irrigation volume, represents the preset maximum volume coefficient (greater than 1), k represents the preset coefficient, and S represents the demand intensity score calculated in the previous step. The benchmark irrigation volume, the maximum volume coefficient, and the coefficient are preset parameters.

[0053] In the above formula, the benchmark irrigation volume is the single irrigation recommended volume related to the current growth stage and under normal conditions in this stage. The coefficient k is a preset non - negative constant used to control the influence degree of the demand intensity on the volume adjustment. By multiplying , the higher the demand intensity, the larger the calculated irrigation volume for this time. Set a maximum volume limit for single irrigation through the min function to prevent excessive irrigation volume caused by too high values and ensure the system operates within a reasonable range. The maximum volume coefficient is a preset constant greater than 1. Understandably, the reference irrigation volume, the maximum volume coefficient, and the coefficient should also be set and stored according to the current growth stage of the strawberries.

[0054] Exemplarily, assume that for the current growth stage, the preset parameters are as follows: reference irrigation volume = 200 ml / plant (or calculate the total reference volume according to the total number of plants in the area); coefficient = 0.005; maximum volume coefficient = 1.5. Continuing with the demand intensity of 76.4 calculated in the previous step. Then the irrigation volume for this time is: min(200 * 1.5, 200 * (1 + 0.005 * 76.4)) = min(300, 200 * (1 + 0.382)) = min(300, 200 * 1.382) = min(300, 276.4) = 276.4 ml / plant.

[0055] If there are 100 strawberry plants in this area, the calculated irrigation volume for this time is 276.4 * 100 = 27640 ml (i.e., 27.64 liters).

[0056] S140. Detect whether the area meets the irrigation trigger condition; and when the area meets the irrigation trigger condition, apply the nutrient solution of the irrigation volume for this time to the area. The irrigation trigger condition can be set in various ways. For example, it can be triggered based on the threshold of substrate humidity or based on the time interval.

[0057] Exemplarily, continuously monitor the substrate humidity of this area. When the humidity drops below the preset humidity threshold (for example, 45% or 50% related to the growth stage), trigger irrigation.

[0058] Exemplarily, when the time since the last successful irrigation in this area has exceeded the preset minimum irrigation interval time (for example, 1 hour or 2 hours), trigger irrigation. At the same time, comprehensive determination can also be made according to other conditions, such as within the preset irrigation period, or when the light / temperature is higher than a specific value.

[0059] When the area meets the irrigation trigger condition, apply the nutrient solution of the irrigation volume for this time to the area. The system, according to the detection result of the previous step, if the area meets the irrigation trigger condition, then activates the irrigation control valve corresponding to this area and controls the amount of nutrient solution applied to be the irrigation volume for this time calculated above.

[0060] Exemplarily, assume that the calculated irrigation volume for a certain area this time is 27.64 liters. When it is detected that the substrate humidity in this area drops to 48% (the preset threshold is 50%), the irrigation trigger condition is met. The system controls the solenoid valve to open and starts to drip-feed nutrient solution to this area. At the same time, it monitors the flow rate or controls the valve opening time according to the preset flow rate / time relationship until the applied volume reaches 27.64 liters, and then closes the solenoid valve.

[0061] In this way, it is ensured that when irrigation is required, an accurate volume adjusted dynamically according to the real-time situation is applied, rather than a fixed volume.

[0062] It should be noted that the preset parameters in this application can be determined according to the current growth stage of the strawberries. For example, it can be achieved by establishing a look-up table of corresponding parameter values for different growth stages. For example, for different stages such as the seedling stage, vegetative growth stage, flowering and fruit-setting stage, fruiting stage, etc., different weight coefficients, target change rates, thresholds, reference volumes, etc. are preset. The system can actively identify or be input with the current growth stage information.

[0063] In some embodiments, as Figure 2 shown, the method further includes: S210. During a preset correction period, monitor and record the actual number of irrigations that occur in this area to obtain the actual irrigation frequency. The preset correction period can be a relatively long time, such as one week, one month, or a complete growth cycle. The system continuously records how many times irrigation has actually been carried out in this area because the irrigation trigger condition is met during the correction period. The actual irrigation frequency can be calculated as the total number of irrigations divided by the length of the correction period.

[0064] Exemplarily, the correction period is set to one week (7 days). The system records that a total of 21 irrigations have occurred in Area A during this week. Then the actual irrigation frequency of Area A is 21 times / week.

[0065] S220. Determine the target irrigation frequency corresponding to the current growth stage of this area.

[0066] The target irrigation frequency is a desired irrigation frequency range or value set for the current growth stage based on experience, historical data, or the professional knowledge of those skilled in the art. This can also be stored in a look-up table indexed by growth stage.

[0067] Exemplarily, for the current growth stage, the preset target irrigation frequency is 18 - 20 times / week, such as 19 times / week.

[0068] S230. Obtain the deviation between the actual irrigation frequency and the target irrigation frequency. For example, if the actual irrigation frequency is 21 times / week and the target irrigation frequency is 19 times / week, then the deviation = 21 - 19 = 2 times / week.

[0069] When the absolute value of the deviation is greater than a preset frequency deviation threshold, adjust the preset parameters used in the steps of determining the demand intensity of the current irrigation cycle of the area and / or calculating the current irrigation volume of the area corresponding to the current growth stage of the area.

[0070] The preset frequency deviation threshold can be a constant, such as ±2 times / week or ±10%. If the deviation between the actual frequency and the target frequency exceeds this threshold, it is considered that some of the preset parameters used in the current growth stage may need to be adjusted. Understandably, the adjustment is deterministic. According to the direction (higher or lower than the target) and magnitude of the deviation, a certain amount or proportion is added to or subtracted from a specific parameter.

[0071] Exemplarily, the frequency deviation threshold is set to ±2 times / week. In the above example, the deviation is +2 times / week. If the threshold is set to ±1 time / week, then |2|>1, triggering an adjustment. If the threshold is set to ±3 times / week, then |2|<3, not triggering an adjustment. Taking the threshold of ±1 time / week and triggering an adjustment as an example, the adjustment includes: S231. When the actual irrigation frequency is significantly higher than the target irrigation frequency, reduce the weight coefficient and / or the coefficient in the preset parameters; If the actual irrigation frequency is too high, it may mean that the calculated demand intensity or the final volume is too large, resulting in the substrate humidity reaching the threshold faster or the irrigation amount exceeding the actual need, thus triggering irrigation more frequently. To reduce the irrigation frequency, the weight w1 that is positively correlated with the humidity decrease rate can be reduced (so that the contribution of the rapid humidity decrease becomes smaller), or the coefficient k that will be converted into volume can be reduced (so that the corresponding volume for the same becomes smaller). The adjustment amount can be a fixed proportion (such as reducing by 5%) or a fixed value.

[0072] Exemplarily, the actual frequency of 21 times / week > the target frequency of 19 times / week + the threshold of 1 time / week (i.e., 20 times / week), triggering an adjustment. Reduce the weight w1 used in the current growth stage by 5%, or reduce the coefficient by 5%. For example, the original w1 = 1000, and after adjustment, w1 = 1000*(1 - 0.05) = 950. The original coefficient = 0.005, and after adjustment, the coefficient = 0.005*(1 - 0.05) = 0.00475. The adjusted parameter values are used for the calculations of the area in the subsequent same growth stage.

[0073] S232. When the actual irrigation frequency is significantly lower than the target irrigation frequency, increase the weight coefficient and / or the coefficient in the preset parameters.

[0074] If the actual irrigation frequency is too low, it may mean that the calculated demand intensity or the final volume is small, resulting in the substrate moisture not reaching the threshold or the irrigation amount being insufficient for a long time. To increase the irrigation frequency, the weight w1 or the coefficient can be increased. The adjustment amount can also be a fixed ratio or value.

[0075] Exemplarily, assume that the actual frequency in another area is 16 times / week < the target frequency of 19 times / week - the threshold of 1 time / week (i.e., 18 times / week), triggering an adjustment. Increase the weight w1 used in the current growth stage by 5%, or increase the coefficient by 5%. For example, the original w1 = 1000, and after adjustment, w1 = 1000*(1 + 0.05) = 1050. The original coefficient = 0.005, and after adjustment, the coefficient = 0.005*(1 + 0.05) = 0.00525.

[0076] In this way, as the running time increases, the system can fine-tune the parameters at the end of each correction cycle, making the irrigation strategy for each area more in line with the actual needs and environmental changes, and achieving better self-adaptability.

[0077] In an embodiment of the present application, in order to better manage the total amount of water and fertilizer, the number of partitions that need to be irrigated within a certain period of time can also be predicted. As Figure 3 shown, the method may further include: S310. Obtain characteristic factors affecting irrigation demand.

[0078] The water demand of strawberry plants is closely related to their own physiological state, growth stage, and variety characteristics. These factors together determine the ability of the plants to absorb and utilize water. For example, strawberry plants in the rapid vegetative growth stage or the fruit swelling stage have a high transpiration rate and a significantly increased water demand. The health status of the plants, the degree of root development, the leaf area size, and variety differences will all affect their water absorption ability. In a soilless cultivation system, the water holding and air permeability of the substrate will also indirectly affect the efficiency of root water absorption. Therefore, the water absorption and growth ability of strawberry plants in this partition are used as the first characteristic factor. The larger the value of the first characteristic factor, the stronger the water demand potential of the plants.

[0079] Irrigation demand is also closely linked to environmental factors, and multiple environmental factors together constitute the external conditions for the plant to carry out transpiration and evaporation on the substrate surface. Environmental factors such as air temperature, humidity, light intensity, wind speed, and substrate temperature all affect the evapotranspiration rate of water in different ways. For example, high temperature, low humidity, strong light, and high wind speed will significantly increase the transpiration water loss of the plant and the evaporation on the substrate surface, thus quickly consuming the water in the substrate and increasing the irrigation demand. Substrate humidity directly reflects the current water status available for the plant to absorb, and its change rate is affected by both plant absorption and environmental evapotranspiration. Therefore, the key parameters of this application also include a second characteristic factor, which is used to characterize the impact of environmental factors on water evapotranspiration. The larger the value of the second characteristic factor, the stronger the role of environmental factors in promoting water evapotranspiration.

[0080] In this way, a prediction model composed of the water absorption and growth ability of strawberry plants in this partition as the first characteristic factor and the impact of environmental factors on water evapotranspiration as the second characteristic factor can combine multiple influencing factors and better predict the number of partitions that need irrigation.

[0081] S320. Construct a prediction model based on the characteristic factors, and the prediction model is used to predict the number of partitions that need irrigation. Exemplarily, the expression of the prediction model is: Wherein, is the number of partitions that need irrigation at time t, is the number of partitions with good current water status at time t, represents the total number of partitions, p is the first characteristic factor, and q is the second characteristic factor.

[0082] This model is a dynamic system model, which describes how the change or state of predicting the number of partitions that need irrigation at a given time point t is affected by the plant's own water demand potential (p) and the potential of evapotranspiration water loss caused by the environment (q). represents the area that needs to replenish water, represents the area with sufficient current water. By introducing the concepts of the area that already needs water and the area that does not need water in this model form, it can reflect the impact of the current state of the system on the total water demand to a certain extent.

[0083] The prediction model in this application predicts the number of water - demand areas through characteristic factors and in combination with the current state of the system. Among them, the key parameters p and q can be estimated and calibrated through historical data and actual monitoring data, ensuring the feasibility of the model in practical applications.

[0084] S330. Obtain the matching degree between the current environmental parameters and the past environmental parameters. Exemplarily, the environmental parameters include temperature, humidity, light intensity, wind speed, and substrate humidity, etc. These parameters can be obtained by the Internet of Things sensors deployed in the soilless cultivation area.

[0085] Exemplarily, the S330 includes: S331. Obtain the similarity between the temperature, humidity, light intensity, wind speed, substrate humidity, etc. in the current environment and the corresponding temperature, humidity, light intensity, wind speed, substrate humidity, etc. in the past environment.

[0086] Obtain the real-time or recent environmental data in the current environment, and the past environmental data stored in the system historical database. This process can be obtained by the acquisition module (including various sensors and data acquisition units) established in the monitoring area. The sensors continuously monitor the environmental parameters and upload the monitored data to the storage module / unit with storage function, etc.

[0087] Based on the current environmental data and the past environmental data, the similarity between the two can be obtained. It can be understood that this similarity refers to the similarity of the environmental parameter sequence within a period of time (such as the past 24 hours, the past week), or rather, the proximity of the environmental conditions. The evaluation of similarity or proximity can be selected by those skilled in the art with appropriate analysis methods, such as by calculating the Pearson correlation coefficient, Euclidean distance, dynamic time warping (DTW), etc. of the multivariate time series to quantify the degree of similarity between them. It should be noted that this is not a limitation to this application, and those skilled in the art can select appropriate analysis methods based on requirements.

[0088] S332. Obtain the matching degree between the current environmental parameters and the past environmental parameters according to the similarity.

[0089] According to the similarity of the above multiple environmental parameters, the comprehensive matching degree between the current environmental parameters and the past environmental parameters can be obtained. For example, the matching degree between the current environmental parameters and the past environmental parameters can be obtained by weighted summation according to the influence degree of different environmental parameters on the water demand of strawberries; or the average value of the similarities of each environmental parameter can be used as the comprehensive matching degree; or a machine learning algorithm can be used to fuse multiple similarities to obtain the matching degree. It can be understood that there are various ways to calculate the matching degree, and those skilled in the art can select according to requirements. The higher the matching degree, the closer the current environmental conditions are to the environmental conditions in a certain past period.

[0090] S340. Obtain the first predicted irrigation demand according to the matching degree and the past irrigation-related historical data. The first predicted irrigation demand data includes the number of zones to be irrigated in multiple different time periods.

[0091] Exemplarily, find the past time period in the historical database that has the highest degree of match with the current environmental parameters, and use the actual number of zones that need to be irrigated recorded during this past time period as the first predicted irrigation demand. For example, if the current environment highly matches the environment of a certain week in the same period last year, then use the irrigation demand data that actually occurred during that week last year as the first basis for the current prediction.

[0092] S350. Obtain a preliminary value of the characteristic factor based on the first predicted irrigation demand, the current actual irrigation demand data, and the prediction model.

[0093] Exemplarily, S350 includes: S351. Construct an intermediate model based on the first predicted irrigation demand.

[0094] Exemplarily, the intermediate model can be a trend model (such as a regression model) that fits the first predicted irrigation demand, or a prediction model that can capture this historical pattern. This model aims to generate a preliminary prediction of the future irrigation demand trend based on the found most matching historical pattern. For example, the intermediate model can be composed of an integration of multiple models (such as time series models, regression models, or even simplified neural network models), and the weights of each model can be determined by its fitting degree or prediction accuracy for the first predicted data. It should be noted that this is not a limitation to this application, and those skilled in the art can construct or select a model for predicting future data based on historical data according to requirements.

[0095] S352. Obtain a second predicted irrigation demand based on the intermediate model.

[0096] Use the trained intermediate model to make a preliminary prediction of the number of zones that need to be irrigated in a future period of time (such as the next few days or a week) to obtain the second predicted irrigation demand.

[0097] S353. Obtain a third predicted irrigation demand based on the current actual irrigation demand data and the second predicted irrigation demand. S353 includes: Obtain the average deviation between the first data in the second predicted irrigation demand and the current actual irrigation demand data, where the first data is the data in the second predicted irrigation demand corresponding to the current actual irrigation demand data.

[0098] Exemplarily, assume that the current actual irrigation demand data includes the statistics of the zones that need to be irrigated in a recent period of time (such as the past 24 hours). Take out the predicted values of the time period in the second predicted irrigation demand corresponding to this actual data, calculate the difference between the predicted value and the actual value (such as the difference in the number of zones that need to be irrigated) to obtain the average deviation for this period of time.

[0099] The second predicted irrigation demand is corrected according to the average deviation to obtain the third irrigation demand prediction data.

[0100] Exemplarily, the predicted value at each time point (such as every hour or every day) in the second irrigation demand prediction data is added with the calculated average deviation to globally correct the second predicted irrigation demand and obtain the third predicted irrigation demand. In this way, the preliminary prediction trend can be made closer to the recent actual situation.

[0101] Based on the data of the trend nodes in the third predicted irrigation demand and the prediction model, the preliminary values of the characteristic factors are obtained.

[0102] Exemplarily, in the third predicted irrigation demand sequence, key trend nodes are identified. The trend nodes include the time points when the water demand starts to increase significantly, the time points when the water demand reaches the peak, the time points when the water demand starts to decrease significantly, and the time points with the lowest water demand or the turning points of the dormant period. The number of irrigation-required sub-areas predicted at these trend nodes in the third predicted irrigation demand is obtained. The data of these trend nodes are substituted into the prediction model, and combined with the and values at the corresponding time point t, the characteristic factors p and q in the model are solved inversely or optimized and fitted to obtain the estimated values of p and q that can best represent the current plant growth state and environmental impact under this data trend, that is, the preliminary values of the characteristic factors. For example, the values of p and q can be iteratively optimized by minimizing the prediction error of the model at these trend nodes.

[0103] S360. Predict the number of sub-areas that need to be irrigated according to the prediction model corresponding to the preliminary values of the characteristic factors.

[0104] Exemplarily, the preliminary values of the obtained characteristic factors are substituted into the prediction model to predict the number of sub-areas that need to be irrigated in the future. For example, using this model based on the currently estimated p and q, predict how many sub-areas will reach the state of needing irrigation within the next 24 hours or longer.

[0105] Based on the predicted number of sub-areas that need to be irrigated, the amount of water and fertilizer to be prepared within the corresponding time can be estimated. For example, assuming that the irrigation volume required for each sub-area that needs to be irrigated is the set maximum irrigation volume, the amount of water and fertilizer to be prepared within the corresponding time is the product of the number of sub-areas that need to be irrigated and the maximum irrigation volume.

[0106] In this way, the nutrient solution required for irrigation can be prepared as needed according to the predicted number of sub-areas, avoiding excessive storage of the nutrient solution, and the replenishment time of the nutrient solution can be reasonably planned.

[0107] The embodiment of the present application also provides a water and fertilizer integrated zoning irrigation system for strawberry cultivation. The system includes multiple independent irrigation areas, and each area is equipped with necessary sensors (such as environmental sensors, substrate sensors, etc.) and actuators (such as irrigation control valves). The system is also configured with a control device for executing the above method. The control device can be a dedicated hardware controller, a programmable logic controller (PLC), an industrial computer, or any device with computing and control capabilities. The control device is connected to the sensors to obtain data, connected to the control valves to execute irrigation, and runs software or firmware to implement the method of the present invention. The control device should also include a storage module for storing preset parameters (including growth stage parameter tables, trigger thresholds, correction periods, correction thresholds, adjustment ratios, etc.), sensor data records, irrigation logs, and calculation results.

[0108] The embodiments of the present invention are not limited to the specific details described above. For example, the lengths of the first time period and the second time period can be adjusted according to specific requirements and the sensor sampling frequency. The calculation formula for the demand intensity can be a variant of the above formula or a different deterministic combination method, as long as it can effectively reflect the demand intensity and maintain determinacy. The irrigation trigger condition can also be other conditions based on sensor data or time. The correction mechanism can adjust other preset parameters or adopt different adjustment rules. All these variations and equivalent replacements should be considered as falling within the protection scope of the present invention.

[0109] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0110] In several embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0111] The unit described as a separating component may or may not be physically separated. The component shown as a unit may be a physical unit or multiple physical units, that is, it may be located in one place or distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware.

[0113] The above content is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for integrated water and fertilizer partition irrigation in strawberry cultivation, which is applied to a soilless cultivation system for strawberries. The soilless cultivation system has multiple strawberry irrigation areas for applying nutrient solution, and is characterized in that, Including: Obtain the environmental data of the strawberry irrigation area and the substrate data in the nutrient solution, and determine the demand intensity of the current irrigation cycle for the area according to the environmental data, the substrate data, and preset parameters; Obtain the current irrigation volume for the area according to the demand intensity of the current irrigation cycle and a preset reference irrigation volume; Obtain characteristic factors affecting irrigation demand; Construct a prediction model based on the characteristic factors, where the prediction model is used to predict the number of sub-areas that need irrigation; Obtain the matching degree between the current environmental parameters and the past environmental parameters; Obtain a first predicted irrigation demand according to the matching degree and past irrigation-related historical data, and the data of the first predicted irrigation demand includes the number of sub-areas that need irrigation in multiple different time periods; Obtain a preliminary value of the characteristic factor according to the first predicted irrigation demand, the current actual irrigation demand data, and the prediction model; Predict the number of sub-areas that need irrigation according to the prediction model corresponding to the preliminary value of the characteristic factor.

2. The method according to claim 1, wherein The obtaining of the environmental data of the strawberry irrigation area and the substrate data in the nutrient solution within the first time period includes: Obtain the average temperature of the area within the first time period and the average light intensity of the area within the first time period; Obtain the average substrate humidity of the area within the first time period and the average substrate conductivity of the area within the first time period.

3. The method according to claim 2, wherein The method further includes: detecting whether the area meets the irrigation trigger condition, and when the area meets the irrigation trigger condition, applying the nutrient solution with the current irrigation volume to the area.

4. The method according to claim 3, wherein The irrigation trigger condition includes: The substrate humidity of the area is lower than a preset humidity threshold; or The time interval since the last irrigation of the area is greater than a preset minimum irrigation interval time.

5. The method according to claim 4, wherein The method further includes: Within a preset correction period, obtain the actual irrigation frequency based on the number of times of actual irrigation in the area; Determine the target irrigation frequency corresponding to the current growth stage of the area; Obtain the deviation between the actual irrigation frequency and the target irrigation frequency; and When the absolute value of the deviation is greater than a preset frequency deviation threshold, adjust the preset parameters used in calculating the demand intensity of the current irrigation cycle corresponding to the area.

6. The method according to claim 5, characterized in that, The adjustment of the preset parameters includes: When the actual irrigation frequency is significantly higher than the target irrigation frequency, reduce the weight coefficient in the preset parameters; or When the actual irrigation frequency is significantly lower than the target irrigation frequency, increase the weight coefficient in the preset parameters.

7. The method according to claim 1, wherein The characteristic factor includes a first characteristic factor and a second characteristic factor. The first characteristic factor is used to characterize the water absorption and growth ability of strawberry plants in the sub-area, and the second characteristic factor is used to characterize the influence of environmental factors on water evapotranspiration.

8. The method according to claim 7, characterized in that, The expression of the prediction model is: Among them, is the number of zones that need to be irrigated at time t, is the number of zones with good current moisture conditions at time t, represents the total number of zones, p is the first characteristic factor, and q is the second characteristic factor.

9. The method according to claim 8, characterized in that, Obtaining the preliminary value of the characteristic factor according to the first predicted irrigation demand, the current actual irrigation demand data, and the prediction model includes: Construct an intermediate model according to the first predicted irrigation demand; Obtain a second predicted irrigation demand according to the intermediate model; Obtain a third predicted irrigation demand based on the current actual irrigation demand data and the second predicted irrigation demand; Obtain a preliminary value of the characteristic factor based on the data of the trend nodes in the third predicted irrigation demand and the prediction model.

10. A water and fertilizer integrated zoning irrigation system for strawberry cultivation, applicable to soilless cultivation, characterized in that, It includes a plurality of independent irrigation areas and a control device configured to execute the method according to any one of claims 1-9.

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