Fertilizer reduction ratio intelligent recommendation system for planting traditional chinese medicinal material astragalus membranaceus

Abstract

The present application discloses a chemical fertilizer reduction ratio intelligent recommendation system for Chinese herbal medicine Astragalus planting, relates to the technical field of chemical fertilizer ratio, and is used for solving the problem that the fertilization strategy is disconnected with the soil adsorption characteristics and the root absorption capacity, monitoring the soil water content of the planting area to be measured before pulse fertilization, calculating the water and fertilizer adsorption half-life, detecting the surface roughness at the same time, calculating the chemical fertilizer loss index based on the standardization factor, comparing with the threshold value to determine the loss state. When the loss risk is low, the initial pulse period and the default fertilizer water ratio are set, the Astragalus root electrical impedance data is collected in the fertilization stage to generate the absorption delay coefficient, the pulse period is dynamically adjusted, the Astragalus flavone and saponin content is monitored, the component accumulation rate is analyzed and classified, the reduction ratio is adjusted according to the stage, the chemical fertilizer application amount is dynamically matched with the effective component accumulation, the fertilizer utilization efficiency is improved, the loss risk is reduced, the growth cycle and the microtopography are adapted, and the precise intelligent fertilization is realized.

Description

Technical Field

[0001] This invention relates to the field of fertilizer ratio technology, and more specifically, to an intelligent recommendation system for reducing fertilizer application ratios in the cultivation of the Chinese medicinal herb Astragalus membranaceus. Background Technology

[0002] The physiological state of Astragalus root system has a significant impact on fertilizer absorption efficiency. Among them, root tissue impedance is related to root activity and absorption flux. In the traditional Astragalus planting process, fertilizer application is mainly based on manual experience, and fertilization is usually carried out in a quantitative and periodic manner.

[0003] The existing technology has the following shortcomings:

[0004] Currently, existing technologies fail to quantitatively assess the risk of fertilizer loss based on soil moisture content, water and fertilizer adsorption half-life, and surface roughness. They also lack real-time monitoring of the trend of Astragalus root impedance changes and a pulse-cycle regulation mechanism based on absorption capacity. Consequently, they cannot achieve intelligent recommendation of dynamic fertilizer reduction ratios based on the accumulation rate of effective components. This leads to a disconnect between fertilization strategies and soil adsorption characteristics and root absorption capacity, resulting in low fertilizer utilization efficiency and a high risk of environmental loss. Therefore, an intelligent recommendation system for fertilizer reduction ratios in the cultivation of Astragalus membranaceus is proposed. Summary of the Invention

[0005] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide an intelligent recommendation system for reducing fertilizer application ratios in the cultivation of Astragalus membranaceus (a traditional Chinese medicinal herb). This system utilizes a soil moisture monitoring module to acquire moisture data of the planting area and calculate the water and fertilizer adsorption half-life; a surface roughness detection module to assess micro-topographic characteristics; a risk assessment module to calculate the fertilizer loss index based on the water and fertilizer adsorption half-life and surface roughness and assess fertilization risk; a root resistance control module to collect real-time root tissue impedance data of Astragalus membranaceus and analyze absorption trends to dynamically adjust the pulse fertilization cycle; and a component adjustment module to monitor the flavonoid and saponin content of Astragalus membranaceus and analyze the component accumulation rate to classify accumulation stages, and intelligently adjust the fertilizer and water reduction ratio based on the accumulation stage to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent recommendation system for reducing fertilizer application ratios in the cultivation of Astragalus membranaceus (a traditional Chinese medicinal herb), comprising an adsorption assessment module, a risk determination module, a root resistance regulation module, and a component adjustment module. The functions of each module are as follows:

[0007] Before applying pulse fertilization to Astragalus membranaceus, the adsorption assessment module monitors the planting area to be tested, obtains soil moisture content data of the planting area to be tested, calculates the water and fertilizer adsorption half-life of the planting area to be tested using the soil moisture content data, detects the surface roughness of the planting area to be tested, and transmits the water and fertilizer adsorption half-life and surface roughness to the risk judgment module.

[0008] The risk assessment module receives the water and fertilizer adsorption half-life and surface roughness and assesses the fertilizer loss status of the planting area to be tested. When the fertilizer loss status is low risk, the initial pulse cycle and default fertilizer-water ratio are set. Based on the setting results, the pulse execution stage is entered, and the initial pulse cycle is passed to the root resistance control module and the default fertilizer-water ratio is passed to the component adjustment module.

[0009] The root resistance control module receives the initial pulse period, collects electrical impedance data of Astragalus root tissue during the pulse execution phase, analyzes the trend of electrical impedance change, determines whether to generate an absorption delay coefficient based on the trend of electrical impedance change, and adjusts the initial pulse period accordingly.

[0010] The component adjustment module receives the default fertilizer-water ratio, monitors the flavonoid and saponin content of Astragalus membranaceus, analyzes the component accumulation rate of Astragalus membranaceus, classifies the component accumulation stage of Astragalus membranaceus according to the component accumulation rate, and determines whether to adjust the default fertilizer-water ratio based on the component accumulation stage.

[0011] In a preferred embodiment, before applying pulse fertilization to Astragalus membranaceus in the adsorption evaluation module, the planting area to be tested is monitored to obtain soil moisture content data of the planting area to be tested.

[0012] By applying a fixed amount of simulated water and fertilizer solution, a soil moisture content detection device set in the near-surface layer of the planting area to be tested collects time-series data of soil moisture content in real time and records the timestamps of each collection point to obtain soil moisture content data.

[0013] In a preferred embodiment, in the adsorption assessment module, the time required for the soil moisture content in the time series data of soil moisture content to decay to half of the initial value is defined as the water and fertilizer adsorption half-life.

[0014] Multiple points were selected in the planting area to be tested, and the height values ​​were measured using a laser rangefinder. The average difference between the height values ​​of the multiple points was then calculated as the surface roughness.

[0015] The water and fertilizer adsorption half-life and surface roughness are input into the risk assessment module.

[0016] In a preferred embodiment, the water and fertilizer adsorption half-life and surface roughness are standardized in the risk assessment module to obtain the adsorption half-life factor and the surface roughness factor.

[0017] The fertilizer loss index is obtained by adding the adsorption half-life factor and the surface roughness factor.

[0018] When the fertilizer loss index is greater than or equal to the preset state assessment threshold, the fertilizer loss status of the planting area to be tested is determined to be high risk. Then the pulse fertilization process is terminated and a prompt signal to adjust the surface structure is output.

[0019] When the fertilizer loss index is less than the preset state assessment threshold, the fertilizer loss status of the planting area to be tested is determined to be low risk. Then, based on the current assessment results, the initial pulse cycle and default fertilizer-water ratio are set, and the pulse execution phase is entered.

[0020] In a preferred embodiment, in the risk assessment module, when the fertilizer loss status is determined to be low risk, an initial pulse cycle and a default fertilizer-water ratio are set.

[0021] The initial pulse period is used to define the time interval between each micro-pulse during pulse fertilization. The initial pulse period is obtained by multiplying the water and fertilizer adsorption half-life by the preset absorption coefficient.

[0022] The default fertilizer-water ratio is the ratio of fertilizer to water components in a single pulse. The current soil moisture content is multiplied by the absorption coefficient to obtain the default fertilizer-water ratio.

[0023] After setting the initial pulse cycle and default fertilizer-water ratio, the pulse execution phase begins.

[0024] In a preferred embodiment, in the root resistance control module, during the pulse execution phase, the analysis cycle is preset and divided into multiple analysis times. A micro AC excitation signal is applied to the rhizosphere region where the Astragalus root tissue is located through the rhizosphere impedance sensor, and voltage response signal and current response signal are collected simultaneously.

[0025] After analyzing the voltage response signal using a frequency domain analysis algorithm, the voltage fundamental amplitude and voltage fundamental phase at each analysis time are obtained.

[0026] After analyzing the current response signal using a frequency domain analysis algorithm, the fundamental amplitude and phase of the current at each analysis time are obtained.

[0027] The absolute value of the difference between the voltage fundamental phase and the current fundamental phase is taken as the phase offset.

[0028] The ratio of the voltage fundamental amplitude to the current fundamental amplitude is used as the amplitude characteristic.

[0029] In a preferred embodiment, in the root resistance control module, impedance data is calculated using phase offset and amplitude characteristics, and the impedance data at each analysis time are combined into an impedance set according to time sequence.

[0030] In the set of electrical impedances, if the electrical impedance data at the previous analysis time and the next analysis time are both less than the electrical impedance data at the current analysis time, then the electrical impedance data at the current analysis time is marked as the peak electrical impedance.

[0031] Conversely, the impedance data at the current analysis time is not marked;

[0032] After sorting the peak impedances in chronological order, the impedance difference is obtained by subtracting adjacent peak impedances, and the average value of each impedance difference is taken to obtain the impedance change trend.

[0033] In a preferred embodiment, in the root resistance control module, if the trend of resistance change is greater than a preset resistance change threshold, it is determined that an absorption delay coefficient is generated.

[0034] Conversely, it is determined that no absorption delay coefficient is generated;

[0035] When determining the generation absorption delay coefficient, the absorption delay coefficient is calculated using the trend of impedance change, and the initial pulse period is adjusted based on the absorption delay coefficient.

[0036] In a preferred embodiment, after pulse fertilization of Astragalus membranaceus in the component adjustment module, the root tissue sample of Astragalus membranaceus is monitored by a near-infrared spectroscopy detection device to obtain near-infrared spectral data of the root tissue of Astragalus membranaceus.

[0037] Near-infrared spectral data were matched with preset flavonoid quantitative analysis spectral models and preset saponin quantitative spectral models to obtain the flavonoid and saponin contents of Astragalus root tissue.

[0038] After standardizing the flavonoid content and saponin content respectively, the flavonoid content coefficient and saponin content coefficient were obtained.

[0039] The product of the flavonoid content coefficient and the saponin content coefficient is used as the component accumulation rate of Astragalus membranaceus.

[0040] In a preferred embodiment, in the component adjustment module, if the component accumulation rate is greater than a preset component accumulation threshold, then the component accumulation stage of Astragalus membranaceus is a high-efficiency accumulation stage.

[0041] Conversely, the accumulation stage of Astragalus membranaceus components is an inefficient accumulation stage;

[0042] When the component accumulation stage of Astragalus membranaceus is the inefficient accumulation stage, the difference between the preset component accumulation threshold and the component accumulation rate is standardized and multiplied by the preset water and fertilizer adjustment weight to obtain the water and fertilizer correction coefficient.

[0043] The default fertilizer-water ratio is adjusted based on the water-fertilizer correction coefficient.

[0044] The technical effects and advantages of this invention are as follows:

[0045] This invention monitors the planting area before pulse fertilization to obtain soil moisture content and calculate the water and fertilizer adsorption half-life. Simultaneously, it detects surface roughness and calculates a fertilizer loss index based on the water and fertilizer adsorption half-life and the standardized factor of surface roughness. This index is then compared with a state assessment threshold to determine the fertilizer loss status. When the loss risk is low, an initial pulse cycle and a default fertilizer-water ratio are set. During the pulse execution phase, electrical impedance data of Astragalus root tissue is collected, and the changing trend is analyzed to generate an absorption delay coefficient. The pulse cycle is dynamically adjusted, and the flavonoid and saponin content of Astragalus is monitored. The component accumulation rate is analyzed and the component accumulation stage is classified. The reduction ratio is adjusted according to the stage to achieve dynamic matching between fertilizer application and the accumulation of effective components in the crop, improving fertilizer utilization efficiency, reducing loss risk, adapting to the Astragalus growth cycle and micro-topographical differences, and achieving precise and intelligent fertilization management. Attached Figure Description

[0046] Figure 1 This is a module framework diagram of the intelligent recommendation system for reducing fertilizer application ratios in the cultivation of Astragalus membranaceus, a medicinal herb, according to the present invention.

[0047] Figure 2 This is a flowchart illustrating the implementation of the intelligent recommendation system for reducing fertilizer application ratios in the cultivation of Astragalus membranaceus, a medicinal herb, according to the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] This invention monitors the planting area before pulse fertilization to obtain soil moisture content and calculate the water and fertilizer adsorption half-life. Simultaneously, it detects surface roughness and calculates a fertilizer loss index based on the water and fertilizer adsorption half-life and the standardized factor of surface roughness. This index is then compared with a state assessment threshold to determine the fertilizer loss status. When the loss risk is low, an initial pulse cycle and a default fertilizer-water ratio are set. During the pulse execution phase, electrical impedance data of Astragalus root tissue is collected, and the changing trend is analyzed to generate an absorption delay coefficient. The pulse cycle is dynamically adjusted, and the content of Astragalus flavonoids and saponins is monitored. The component accumulation rate is analyzed and the component accumulation stage is classified. The reduction ratio is adjusted according to the stage to achieve dynamic matching between fertilizer application and the accumulation of effective components in the crop, thereby improving fertilizer utilization efficiency and reducing the risk of loss.

[0050] Example 1, such as Figures 1 to 2 As shown, the intelligent recommendation system for reducing fertilizer application ratios in the cultivation of Astragalus membranaceus (a traditional Chinese medicinal herb) includes an adsorption assessment module, a risk determination module, a root resistance control module, and a component adjustment module. The functions of each module are as follows:

[0051] Before applying pulse fertilization to Astragalus membranaceus, the adsorption assessment module monitors the planting area to be tested, obtains soil moisture content data of the planting area to be tested, calculates the water and fertilizer adsorption half-life of the planting area to be tested using the soil moisture content data, detects the surface roughness of the planting area to be tested, and transmits the water and fertilizer adsorption half-life and surface roughness to the risk judgment module.

[0052] The risk assessment module receives the water and fertilizer adsorption half-life and surface roughness and assesses the fertilizer loss status of the planting area to be tested. When the fertilizer loss status is low risk, the initial pulse cycle and default fertilizer-water ratio are set. Based on the setting results, the pulse execution stage is entered, and the initial pulse cycle is passed to the root resistance control module and the default fertilizer-water ratio is passed to the component adjustment module.

[0053] The root resistance control module receives the initial pulse period, collects electrical impedance data of Astragalus root tissue during the pulse execution phase, analyzes the trend of electrical impedance change, determines whether to generate an absorption delay coefficient based on the trend of electrical impedance change, and adjusts the initial pulse period accordingly.

[0054] The component adjustment module receives the default fertilizer-water ratio, monitors the flavonoid and saponin content of Astragalus membranaceus, analyzes the component accumulation rate of Astragalus membranaceus, classifies the component accumulation stage of Astragalus membranaceus according to the component accumulation rate, and determines whether to adjust the default fertilizer-water ratio based on the component accumulation stage.

[0055] The specific implementation is as follows:

[0056] In the adsorption assessment module, before applying pulse fertilization to Astragalus membranaceus, the planting area to be tested is monitored to obtain soil moisture content data of the planting area.

[0057] By applying a fixed amount of simulated water and fertilizer solution, a soil moisture content detection device placed near the surface of the planting area to be tested collects time series data of soil moisture content in real time and records the timestamps of each collection point to obtain soil moisture content data. Soil moisture content is used to characterize the water content of the soil under the current state and reflects the degree of pore water saturation. Changes in soil moisture content data indicate the diffusion rate and residence time of fertilizer and water in the soil. The greater the change, the faster the diffusion rate of fertilizer and water in the soil.

[0058] Subsequently, after acquiring time-series data of soil moisture content, the water and fertilizer adsorption half-life was calculated by analyzing the decay process of soil moisture content over time. The time required for soil moisture content to decay to half of its initial value was defined as the water and fertilizer adsorption half-life. The physical meaning of the water and fertilizer adsorption half-life is the time interval during which fertilizer and water decay from the highest saturation state to the intermediate saturation state in soil pores. The water and fertilizer adsorption half-life reflects the soil's ability to retain water and fertilizer pulses under pulsed fertilization conditions. A longer water and fertilizer adsorption half-life indicates a longer retention time of water and fertilizer in soil pores, making it more suitable for multiple small-dose applications of fertilizer using a pulsed method. A shorter water and fertilizer adsorption half-life indicates that water and fertilizer are prone to rapid leakage, which is not conducive to the segmented absorption mechanism of pulsed fertilization.

[0059] The surface roughness of the planting area under test is detected by measuring the height change of the surface of the planting area. It is used to characterize the undulation and micro-scale morphological features of the surface. Specifically, multiple points are selected in the planting area to be tested, and the height values ​​are measured by a laser rangefinder. The average difference of the height values ​​of multiple points is calculated as the surface roughness. The surface roughness reflects the diffusion and interception capabilities of water and fertilizer before the formation of surface runoff. The larger the value, the wider the lateral diffusion range of water and fertilizer on the surface, and the lower the probability of direct runoff, which is conducive to keeping the pulsed fertilizer and water retained near the root zone. The smaller the surface roughness, the easier it is for water and fertilizer to form runoff along the smooth surface, resulting in a decrease in fertilization effect.

[0060] It should be noted that the soil moisture content detection device is used to acquire real-time soil moisture content data. Its working principle is based on the characteristic that the soil dielectric constant varies with the moisture content. It uses the time-domain reflectometry method to emit electromagnetic wave signals into the soil and detect the propagation speed of the signal in the soil medium, thereby calculating the volumetric soil moisture content. The laser rangefinder is used to acquire the height information of the target point on the ground. Its working principle is based on the optical pulse time-of-flight ranging method. The laser rangefinder emits a single or multiple laser beams into the ground. After the laser encounters the target, it is reflected back to the receiving end. By measuring the time difference of the laser's round-trip propagation and calculating the distance from the rangefinder to the target point based on the speed of light in the air, the vertical height value of the ground measurement point can be obtained by combining this distance with the calibration reference height of the laser rangefinder's location.

[0061] By acquiring soil moisture content data, calculating the water and fertilizer adsorption half-life, and detecting surface roughness, a comprehensive characterization of the soil's water and fertilizer retention capacity and water and fertilizer flow distribution conditions before pulse fertilization can be formed, providing basic data support for the estimation of subsequent pulse fertilization parameters.

[0062] The adsorption assessment module inputs the water and fertilizer adsorption half-life and surface roughness into the risk assessment module.

[0063] In the risk assessment module, the water and fertilizer adsorption half-life and surface roughness are standardized to obtain the adsorption half-life factor and surface roughness factor, so as to eliminate the influence of different physical dimensions and numerical ranges on the comprehensive evaluation results.

[0064] The fertilizer loss index is obtained by adding the adsorption half-life factor and the surface roughness factor, which reflects the combined strength of the soil's ability to retain fertilizer and water and the surface's ability to intercept fertilizer and water.

[0065] It should be noted that standardization refers to the process of mapping raw data of different physical quantities or different dimensions to a uniform dimension, uniform numerical range or uniform statistical distribution through a specific mathematical transformation. Standardization methods include, but are not limited to, standard linear transformation based on interval scaling, Z-Score standardization based on statistics or normalization method based on nonlinear mapping function. The application methods of standardization will not be elaborated here.

[0066] Compare the fertilizer loss index with the preset state assessment threshold:

[0067] When the fertilizer loss index is greater than or equal to the preset state assessment threshold, it indicates that the soil adsorption capacity is insufficient or the surface interception effect is weak, and the possibility of fertilizer outflow is high. The fertilizer loss status of the planting area to be tested is determined to be high risk. Then the pulse fertilization process is terminated and a prompt signal to adjust the surface structure is output.

[0068] When the fertilizer loss index is less than the preset state assessment threshold, it indicates that the soil can absorb fertilizer and water well and the surface has a strong blocking ability, and the risk of fertilizer outflow is low. The fertilizer loss status of the planting area to be tested is judged to be low risk. Then, based on the current assessment results, the initial pulse cycle and default fertilizer-water ratio are set and the pulse execution phase is entered.

[0069] It should be noted that the state assessment threshold is used to distinguish whether the fertilizer loss status of the planting area under test is within an acceptable range. The state assessment threshold is determined by combining historical monitoring data, field test results, and regional soil type characteristics. Specifically, based on the water and fertilizer adsorption half-life and surface roughness sample data of different plots under known loss risk levels, the corresponding standardized factor distribution interval is calculated. Then, plots with fertilizer and water migration or nutrient loss are selected as risk reference groups. The mean and variance of the fertilizer loss index of the risk reference group are statistically analyzed, and the mean plus the variance is used as the state assessment threshold. After determining that the fertilizer loss status of the planting area under test is high-risk, the control unit sends a prompt signal to the user interface, prompting the user to adjust the surface structure to reduce the risk of fertilizer loss. Specific adjustment methods include, but are not limited to: constructing micro-topographical undulations on the surface of the planting area by machinery, such as setting up furrows, digging shallow pits, or laying organic mulch, to increase surface roughness, thereby enhancing the surface's ability to intercept and retain water and fertilizer, improving soil structure, and increasing water and fertilizer adsorption capacity, creating suitable surface and soil conditions for subsequent pulse fertilization.

[0070] When the fertilizer loss status is determined to be low risk, the initial pulse cycle and default fertilizer-water ratio are set according to the soil retention capacity and surface structure stability of the current area.

[0071] The initial pulse period is used to define the time interval between each micro-pulse during pulse fertilization. The initial pulse period is obtained by multiplying the water and fertilizer adsorption half-life by the preset absorption coefficient. The absorption coefficient ranges from 0 to 1 to ensure that the cumulative fertilizer effect of pulse fertilization can be fully absorbed by the soil without causing surface accumulation.

[0072] The default fertilizer-water ratio is the ratio of fertilizer to water components in a single pulse. The current soil moisture content is multiplied by the absorption coefficient to form the default fertilizer-water ratio, which is used to form a stable fertilizer and water input structure in the initial stage of fertilization.

[0073] After setting the initial pulse cycle and default fertilizer-water ratio, the determined parameters are used as the basic configuration for pulse fertilization scheduling. Based on the configuration results, the pulse execution phase is initiated to ensure that the subsequent micro-pulse fertilization process is carried out under conditions of low risk of loss.

[0074] In the root resistance control module, during the pulse execution phase, the preset analysis cycle is divided into multiple analysis times. At each analysis time, a small amount of AC excitation signal is applied to the rhizosphere region where the Astragalus root tissue is located through the rhizosphere impedance sensor, and the voltage response signal and current response signal generated by the root tissue under the excitation are collected simultaneously.

[0075] Voltage response signal and current response signal are AC waveform data that change with time, reflecting the conductivity and polarization characteristics of Astragalus root tissue and surrounding soil under AC excitation.

[0076] After analyzing the voltage response signal using a frequency domain analysis algorithm, the fundamental voltage amplitude and phase at each analysis time are obtained. Similarly, after analyzing the current response signal using a frequency domain analysis algorithm, the fundamental current amplitude and phase at each analysis time are obtained.

[0077] The absolute value of the difference between the voltage fundamental phase and the current fundamental phase is taken as the phase offset.

[0078] The ratio of the voltage fundamental amplitude to the current fundamental amplitude is used as the amplitude characteristic. The larger the amplitude characteristic, the more limited the ion conduction channels between the Astragalus root tissue and the soil. The smaller the amplitude characteristic, the higher the overall electrical conductivity of the rhizosphere region, which means that the electrical conduction pathways of the Astragalus root tissue are more unobstructed and the conduction of water and fertilizer in the rhizosphere is more active.

[0079] The phase shift coefficient and amplitude coefficient are obtained by standardizing the phase shift and amplitude characteristics respectively.

[0080] Calculate electrical impedance data using phase shift coefficient and amplitude coefficient: ,in, and To preset the adjustment weight, This is the phase offset coefficient. The amplitude coefficient, This is electrical impedance data.

[0081] It should be explained that the preset adjustment weights are set based on factors such as the fluctuation range of soil moisture content, the physiological water absorption behavior of Astragalus root tissue, and the level of electrical noise in the actual monitoring environment.

[0082] The higher the electrical impedance data, the slower the response of Astragalus root tissue to pulsed fertilizer and water; the lower the electrical impedance data, the better the conductivity of Astragalus root tissue and the faster the response to fertilizer and water pulses.

[0083] The impedance data at each analysis time are combined into an impedance set in chronological order. In the impedance set, if the impedance data at the previous analysis time and the next analysis time are both less than the impedance data at the current analysis time, then the impedance data at the current analysis time is marked as the peak impedance; otherwise, the impedance data at the current analysis time is not marked.

[0084] After sorting the peak impedances in chronological order, the impedance difference is obtained by subtracting adjacent peak impedances, and the average value of each impedance difference is taken to obtain the impedance change trend.

[0085] When the electrical impedance trend is greater than 0, it indicates that the absorption rate of fertilizer and water applied by pulse in Astragalus root tissue is relatively lagging; when the electrical impedance trend is less than 0, it indicates that the absorption of fertilizer and water pulse in Astragalus root tissue gradually becomes synchronized.

[0086] The trend of impedance change is compared with a preset impedance change threshold to determine whether an absorption delay coefficient is generated.

[0087] If the change trend of electrical impedance is greater than the preset electrical impedance change threshold, then it is determined that an absorption delay coefficient is generated.

[0088] Conversely, it is determined that no absorption delay coefficient is generated;

[0089] When the trend of resistance change is greater than the preset resistance change threshold, it is determined that the Astragalus root tissue has an absorption lag effect under the current pulse fertilization. The equivalent impedance of the Astragalus root tissue shows a continuous increasing trend over time, and an absorption delay coefficient is generated at this time.

[0090] When determining the generation absorption delay coefficient, the absorption delay coefficient is calculated using the trend of electrical impedance change: ,in, The absorption delay coefficient, The trend of electrical impedance change. To preset the threshold for impedance change, This is the upper limit value for the preset trend of impedance change;

[0091] The initial pulse period is adjusted based on the absorption delay coefficient: ,in, The preset adjustment coefficient, This is the initial pulse period. The absorption delay coefficient, The adjusted initial pulse period;

[0092] The initial pulse period is updated based on the adjusted initial pulse period.

[0093] It should be explained that the rhizosphere impedance sensor is an electrical measurement device deployed near the roots of Astragalus membranaceus, used to sense electrical changes without damaging the root structure; the frequency domain analysis algorithm is used to convert the acquired voltage and current response signals from time domain data to frequency domain data, and extract the fundamental amplitude and fundamental phase; the preset impedance change threshold can be set according to the statistical distribution of historical monitoring samples and the normal absorption response range of Astragalus membranaceus roots to fertilizer and water pulses; the upper limit of the preset impedance change trend can be set according to the maximum impedance growth rate obtained from long-term monitoring; the preset adjustment coefficient can be set according to the influence of soil texture on the water and fertilizer infiltration rate and the metabolic characteristics of Astragalus membranaceus varieties at different growth stages.

[0094] In the component adjustment module, after pulse fertilization of Astragalus membranaceus, the root tissue sample of Astragalus membranaceus is monitored by a near-infrared spectroscopy detection device to obtain the near-infrared spectral data of Astragalus membranaceus root tissue. The near-infrared spectral data is matched with the preset flavonoid quantitative analysis spectral model and the preset saponin quantitative spectral model to obtain the flavonoid content and saponin content of Astragalus membranaceus root tissue.

[0095] The higher the flavonoid and saponin content, the higher the accumulation of medicinal active substances in the root tissue of Astragalus membranaceus; the lower the flavonoid and saponin content, the less the accumulation of medicinal active substances in the root tissue.

[0096] After standardizing the flavonoid content and saponin content respectively, the flavonoid content coefficient and saponin content coefficient were obtained.

[0097] The product of the flavonoid content coefficient and the saponin content coefficient is taken as the component accumulation rate of Astragalus membranaceus.

[0098] The component accumulation rate of Astragalus membranaceus was compared with a preset component accumulation threshold to classify the component accumulation stages:

[0099] If the component accumulation rate is greater than the preset component accumulation threshold, then the component accumulation stage of Astragalus membranaceus is the high-efficiency accumulation stage.

[0100] Conversely, the accumulation stage of Astragalus membranaceus components is an inefficient accumulation stage;

[0101] When the accumulation rate of components exceeds the preset accumulation threshold, it indicates that the effective components of Astragalus membranaceus are on a continuous upward trend, the accumulation of medicinal components is strong, and the fertilizer supply can meet the current metabolic needs; conversely, it indicates that the accumulation rate of medicinal components of Astragalus membranaceus tends to slow down or decrease, and the plant's efficiency in absorbing and utilizing fertilizer and water is reduced.

[0102] When the accumulation stage of Astragalus membranaceus components is in the low-efficiency accumulation stage, the default fertilizer-water ratio is adjusted to further reduce the default fertilizer-water ratio, so as to avoid excessive fertilization or fertilizer and water retention when the accumulation of components slows down, and improve the efficiency of fertilizer and water utilization.

[0103] The difference between the preset component accumulation threshold and the component accumulation rate is standardized and then multiplied by the preset water and fertilizer adjustment weight to obtain the water and fertilizer correction coefficient.

[0104] The default fertilizer-to-water ratio is adjusted based on the water-fertilizer correction coefficient: ,in, The default fertilizer-to-water ratio is used. This is the water and fertilizer correction factor. This is the adjusted default fertilizer-water ratio.

[0105] It should be noted that the near-infrared spectroscopy detection device is a spectral acquisition device deployed in the sampling area of ​​Astragalus root tissue. It acquires near-infrared spectral data by emitting a near-infrared light source onto the sample surface and collecting its reflectance spectrum. The preset quantitative spectral analysis model for flavonoids and the preset quantitative spectral model for saponins are constructed based on the calibration data of Astragalus samples at different growth stages. This allows the models to map the collected near-infrared spectral data to the corresponding flavonoid and saponin contents, thereby achieving quantitative prediction of medicinal components. The preset component accumulation threshold can be set according to the component accumulation patterns of historical Astragalus samples at different growth stages and the statistical distribution range of component accumulation rates. The preset water and fertilizer regulation weights can be set according to soil infiltration rate, the sensitivity of Astragalus to fluctuations in fertilizer and water, and the physiological response curves under different fertilizer concentrations.

[0106] This module determines whether Astragalus membranaceus is in a high-efficiency or low-efficiency accumulation stage based on the component accumulation rate, and dynamically adjusts the default fertilizer ratio when component accumulation slows down, so that the fertilization intensity matches the actual accumulation needs of medicinal components. This can effectively avoid fertilizer and water retention and nutrient waste that occur in the low-efficiency accumulation stage, and improve the response efficiency and fertilizer utilization rate of pulse fertilization.

[0107] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0108] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0109] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0110] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the above specification.

[0112] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent recommendation system for reducing fertilizer application ratios in the cultivation of Astragalus membranaceus (a traditional Chinese medicinal herb), characterized by: It includes an adsorption assessment module, a risk assessment module, a root resistance control module, and a composition adjustment module. The functions of each module are as follows: Before applying pulse fertilization to Astragalus membranaceus, the adsorption assessment module monitors the planting area to be tested, obtains soil moisture content data of the planting area to be tested, calculates the water and fertilizer adsorption half-life of the planting area to be tested using the soil moisture content data, detects the surface roughness of the planting area to be tested, and transmits the water and fertilizer adsorption half-life and surface roughness to the risk judgment module. The risk assessment module receives the water and fertilizer adsorption half-life and surface roughness and assesses the fertilizer loss status of the planting area to be tested. When the fertilizer loss status is low risk, the initial pulse cycle and default fertilizer-water ratio are set. Based on the setting results, the pulse execution stage is entered, and the initial pulse cycle is passed to the root resistance control module and the default fertilizer-water ratio is passed to the component adjustment module. The root resistance control module receives the initial pulse period, collects electrical impedance data of Astragalus root tissue during the pulse execution phase, analyzes the trend of electrical impedance change, determines whether to generate an absorption delay coefficient based on the trend of electrical impedance change, and adjusts the initial pulse period accordingly. The component adjustment module receives the default fertilizer-water ratio, monitors the flavonoid and saponin content of Astragalus membranaceus, analyzes the component accumulation rate of Astragalus membranaceus, classifies the component accumulation stage of Astragalus membranaceus according to the component accumulation rate, and determines whether to adjust the default fertilizer-water ratio based on the component accumulation stage. In the root resistance control module, during the pulse execution phase, the analysis cycle is preset and divided into multiple analysis times. A small amount of AC excitation signal is applied to the rhizosphere region where the Astragalus root tissue is located through the rhizosphere impedance sensor, and the voltage response signal and current response signal are collected simultaneously. After analyzing the voltage response signal using a frequency domain analysis algorithm, the voltage fundamental amplitude and voltage fundamental phase at each analysis time are obtained. After analyzing the current response signal using a frequency domain analysis algorithm, the fundamental amplitude and phase of the current at each analysis time are obtained. The absolute value of the difference between the voltage fundamental phase and the current fundamental phase is taken as the phase offset. The ratio of the voltage fundamental amplitude to the current fundamental amplitude is used as the amplitude characteristic; In the root resistance control module, the impedance data is calculated using the phase offset and amplitude characteristics, and the impedance data at each analysis time are combined into an impedance set according to the time sequence. In the set of electrical impedances, if the electrical impedance data at the previous analysis time and the next analysis time are both less than the electrical impedance data at the current analysis time, then the electrical impedance data at the current analysis time is marked as the peak electrical impedance. Conversely, the impedance data at the current analysis time is not marked; After sorting the peak impedances in chronological order, the impedance difference is obtained by subtracting adjacent peak impedances, and the average value of each impedance difference is taken to obtain the impedance change trend. In the root resistance control module, if the trend of resistance change is greater than the preset resistance change threshold, it is determined that an absorption delay coefficient is generated. Conversely, it is determined that no absorption delay coefficient is generated; When determining the generation absorption delay coefficient, the absorption delay coefficient is calculated using the trend of impedance change, and the initial pulse period is adjusted based on the absorption delay coefficient; In the component adjustment module, after pulse fertilization of Astragalus membranaceus, the root tissue samples of Astragalus membranaceus are monitored by a near-infrared spectroscopy detection device to obtain near-infrared spectral data of Astragalus membranaceus root tissue; Near-infrared spectral data were matched with preset flavonoid quantitative analysis spectral models and preset saponin quantitative spectral models to obtain the flavonoid and saponin contents of Astragalus root tissue. After standardizing the flavonoid content and saponin content respectively, the flavonoid content coefficient and saponin content coefficient were obtained. The product of the flavonoid content coefficient and the saponin content coefficient is taken as the component accumulation rate of Astragalus membranaceus. In the component adjustment module, if the component accumulation rate is greater than the preset component accumulation threshold, the component accumulation stage of Astragalus membranaceus is the high-efficiency accumulation stage. Conversely, the accumulation stage of Astragalus membranaceus components is an inefficient accumulation stage; When the component accumulation stage of Astragalus membranaceus is the inefficient accumulation stage, the difference between the preset component accumulation threshold and the component accumulation rate is standardized and multiplied by the preset water and fertilizer adjustment weight to obtain the water and fertilizer correction coefficient. The default fertilizer-water ratio is adjusted based on the water-fertilizer correction coefficient.

2. The intelligent fertilizer reduction ratio recommendation system for Astragalus membranaceus cultivation according to claim 1, characterized in that: In the adsorption assessment module, before applying pulse fertilization to Astragalus membranaceus, the planting area to be tested is monitored to obtain soil moisture content data of the planting area to be tested. By applying a fixed amount of simulated water and fertilizer solution, a soil moisture content detection device set in the near-surface layer of the planting area to be tested collects time-series data of soil moisture content in real time and records the timestamps of each collection point to obtain soil moisture content data.

3. The intelligent fertilizer reduction ratio recommendation system for Astragalus membranaceus cultivation according to claim 2, characterized in that: In the adsorption assessment module, the time required for the soil moisture content to decay to half of its initial value in the time series data of soil moisture content is defined as the water and fertilizer adsorption half-life. Multiple points were selected in the planting area to be tested, and the height values ​​were measured using a laser rangefinder. The average difference between the height values ​​of the multiple points was then calculated as the surface roughness. The water and fertilizer adsorption half-life and surface roughness are input into the risk assessment module.

4. The intelligent fertilizer reduction ratio recommendation system for Astragalus membranaceus cultivation according to claim 3, characterized in that: In the risk assessment module, the water and fertilizer adsorption half-life and surface roughness are standardized to obtain the adsorption half-life factor and surface roughness factor. The fertilizer loss index is obtained by adding the adsorption half-life factor and the surface roughness factor. When the fertilizer loss index is greater than or equal to the preset state assessment threshold, the fertilizer loss status of the planting area to be tested is determined to be high risk. Then the pulse fertilization process is terminated and a prompt signal to adjust the surface structure is output. When the fertilizer loss index is less than the preset state assessment threshold, the fertilizer loss status of the planting area to be tested is determined to be low risk. Then, based on the current assessment results, the initial pulse cycle and default fertilizer-water ratio are set, and the pulse execution phase is entered.

5. The intelligent recommendation system for reducing fertilizer application ratio in the cultivation of Astragalus membranaceus (a traditional Chinese medicinal herb) according to claim 4, characterized in that: In the risk assessment module, when the fertilizer loss status is determined to be low risk, the initial pulse cycle and default fertilizer-water ratio are set. The initial pulse period is used to define the time interval between each micro-pulse during pulse fertilization. The initial pulse period is obtained by multiplying the water and fertilizer adsorption half-life by the preset absorption coefficient. The default fertilizer-water ratio is the ratio of fertilizer to water components in a single pulse. The current soil moisture content is multiplied by the absorption coefficient to obtain the default fertilizer-water ratio. After setting the initial pulse cycle and default fertilizer-water ratio, the pulse execution phase begins.

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