A precise fertilization closed-loop system for tea garden based on multispectral imaging and environmental response

Abstract

The present application is suitable for the field of intelligent agricultural technology, and provides a tea garden precision fertilization closed-loop system based on multispectral imaging and environmental response, comprising: a data acquisition subsystem that acquires multispectral data, environmental sensor data and weather forecast data of the tea garden in real time; a data processing and analysis subsystem that inverts the nutrient content and calculates the influence coefficient of temperature on the fertilizer decomposition rate, the influence coefficient of humidity on the root absorption efficiency and the predicted loss amount; a fertilization decision subsystem that comprehensively considers the nutrient gap, the influence coefficient and the predicted loss amount, and calculates the corrected fertilization amount and the target fertilization time window in combination with the weather forecast, generates a variable fertilization prescription map; a fertilization execution and feedback subsystem that executes zoned variable fertilization and re-measures the multispectral data to evaluate the effect; and a cloud management platform that realizes data storage, visualization and alarm. The present application effectively improves the accuracy of fertilization decision and the fertilizer utilization rate, and realizes the precision and intelligence of tea garden fertilization.

Description

Technical Field

[0001] This invention relates to the field of intelligent agriculture technology, and in particular to a closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response. Background Technology

[0002] In precision agriculture, tea garden fertilization management typically relies on soil sampling and testing, empirical fertilization plans, or vegetation index assessments based on satellite remote sensing imagery. Currently, some solutions utilize drones equipped with multispectral cameras to acquire canopy reflectance spectra, calculate normalized vegetation index (NVI) to retrieve leaf nitrogen content, and then generate fertilization prescription maps based on spatial heterogeneity. Other solutions deploy IoT sensors in the field to collect real-time data on soil temperature and humidity, air temperature and humidity, and rainfall to monitor changes in environmental conditions. The fertilization execution phase often employs variable-rate fertilizer applicators or drones, applying fertilization in designated zones based on the prescription map.

[0003] However, soil temperature and humidity significantly affect microbial activity and root absorption capacity, and rainfall can cause leaching and loss of nutrients already applied to the soil. Existing fertilization decision models determine the amount of fertilizer based only on the current nutrient deficit, lacking a quantitative response to the real-time status and changing trends of environmental factors such as temperature, humidity, and rainfall. This leads to a large discrepancy between the amount of fertilizer applied and the actual amount of nutrients that can be effectively absorbed by crops, resulting in problems such as low fertilizer utilization, insufficient local nutrient supply, and over-fertilization. Summary of the Invention

[0004] This invention provides a closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response, which addresses the problems of low fertilizer utilization, insufficient local nutrient supply, and excessive fertilization.

[0005] This invention provides a closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response, comprising: The system includes a data acquisition subsystem, a data processing and analysis subsystem, a fertilization decision-making subsystem, a fertilization execution feedback subsystem, and a cloud management platform. The data acquisition subsystem is communicatively connected to the data processing and analysis subsystem, and is used to collect multispectral data, environmental sensor data and weather forecast data of the tea garden, and transmit the collected data to the data processing and analysis subsystem. The data processing and analysis subsystem is used to invert the nutrient content of each area of ​​the tea garden based on the multispectral data, calculate the influence coefficient of temperature on fertilizer decomposition rate and the influence coefficient of humidity on root absorption efficiency based on the environmental sensor data, and assess the predicted loss caused by rainfall based on meteorological forecast data. The fertilization decision subsystem is connected to the data processing and analysis subsystem and the fertilization execution feedback subsystem. The fertilization decision subsystem is used to calculate and correct the fertilization amount and target fertilization time window based on the nutrient content, influence coefficient and predicted loss output by the data processing and analysis subsystem, combined with the weather forecast, and generate a variable fertilization prescription map. The fertilization execution feedback subsystem is communicatively connected to the fertilization decision subsystem. The fertilization execution feedback subsystem is used to execute zonal variable fertilization operations according to the variable fertilization prescription map, and to re-measure multispectral data after fertilization to evaluate the fertilization effect. The evaluation results are fed back to the data processing and analysis subsystem, so that the data processing and analysis subsystem updates the calculation parameters of the influence coefficient and the evaluation model of the predicted loss based on the evaluation results. The cloud management platform is communicatively connected to the data acquisition subsystem, data processing and analysis subsystem, fertilization decision-making subsystem, and fertilization execution feedback subsystem, and is used for data storage, visualization display, and intelligent alarm.

[0006] Furthermore, the data processing and analysis subsystem includes: A vegetation index calculation module is used to calculate multiple vegetation indices based on the multispectral data; Nutrient inversion model, used to invert nitrogen, phosphorus and potassium nutrient content in various areas of tea garden based on the vegetation index; An environmental response model is used to calculate the influence coefficients of temperature on fertilizer decomposition rate and humidity on root absorption efficiency based on the environmental sensor data, and to assess the predicted loss due to rainfall based on the weather forecast data.

[0007] Furthermore, the coefficient of influence of temperature on fertilizer decomposition rate is calculated using the following formula: in: This is the coefficient representing the effect of temperature on the fertilizer decomposition rate. The fertilizer decomposition rate constant at the reference temperature. This is a temperature coefficient constant, representing the factor by which the decomposition rate increases for every 10°C increase in temperature. Given the current soil temperature, This is the reference temperature.

[0008] Furthermore, the influence coefficient of humidity on root absorption efficiency is calculated using the following formula: in: This represents the coefficient of influence of temperature on root absorption efficiency. Current soil moisture This represents the lower limit of the temperature at which root absorption efficiency begins to be significantly inhibited. To achieve the optimal humidity level for root absorption efficiency; when hour, ;when hour, .

[0009] Furthermore, the fertilization decision-making subsystem includes: The nutrient requirement assessment module is used to calculate the nutrient gap based on the difference between the nutrient content output by the data processing and analysis subsystem and the preset target nutrient level. An environmental correction module is used to correct the basic fertilization amount corresponding to the nutrient gap based on the influence coefficient and the predicted loss amount, so as to obtain the corrected fertilization amount. The fertilization window optimization module is used to filter and recommend target fertilization time windows based on the meteorological forecast data to avoid preset periods before rainfall and periods outside preset extreme temperature ranges. The variable fertilization prescription map generation module is used to divide the tea garden into grids and generate a variable fertilization prescription map for each grid, which includes the modified fertilization amount and fertilizer ratio.

[0010] Furthermore, the environmental correction module calculates the corrected fertilization amount using the following formula: in: This is the revised fertilizer application rate. This is the basic fertilizer application amount determined based on the nutrient deficit. This is the nutrient loss compensation coefficient. To predict churn.

[0011] Furthermore, the step of recommending a target fertilization time window based on the weather forecast data, avoiding preset periods before rainfall and periods outside preset extreme temperature ranges, includes: Obtain the hourly probability of rainfall, predicted rainfall, and predicted temperature for a future preset time period from the meteorological forecast data; The first candidate time period set is obtained by removing the time period within the first preset duration before rainfall from the future preset time period; The second candidate time period set is obtained by removing time periods from the first candidate time period set where the predicted temperature value is lower than the first temperature threshold or higher than the second temperature threshold. If the second candidate time period set is not empty, the earliest time period with a continuous duration that meets the operational requirements is selected as the target fertilization time window; if the second candidate time period set is empty, an extension instruction is output and the acquisition, elimination and selection operations are re-executed after the next round of weather forecast data is updated.

[0012] Furthermore, the fertilization execution feedback subsystem includes: The variable fertilization execution module is used to perform zonal variable fertilization operations according to the variable fertilization prescription map; The fertilization effect evaluation module is used to evaluate the fertilization effect by retesting multispectral data after fertilization, and to feed the evaluation results back to the data processing and analysis subsystem.

[0013] Furthermore, the variable fertilization execution module performs zonal variable fertilization operations based on the variable fertilization prescription map, including: The variable fertilization prescription map is parsed into operation instructions that can be recognized by agricultural machinery. The operation instructions include the geographic coordinates of each grid, the amount of fertilizer applied, and the fertilizer solution ratio parameters. The current location of the fertilization equipment is obtained in real time through a preset positioning device; Based on the correspondence between the current location and the prescription map grid, the fertilization equipment is controlled to apply fertilizer in the corresponding grid according to the operation instructions, and the actual amount of fertilizer applied is recorded in real time and uploaded to the cloud management platform.

[0014] Furthermore, the fertilization effect evaluation module evaluates the fertilization effect by retesting multispectral data after fertilization, including: After fertilization, within a preset time period, multispectral data of various areas of the tea garden were re-measured using multispectral imaging equipment; The vegetation index after fertilization was calculated based on the re-measured multispectral data, and the vegetation index included the normalized vegetation index. The fertilization response index is compared with a preset response threshold to generate a fertilization effect rating.

[0015] As can be seen from the above technical solutions, the present invention has the following advantages: The system of this invention collects multispectral data, environmental sensor data, and weather forecast data from tea gardens in real time through a data acquisition subsystem. The data processing and analysis subsystem uses this data to invert nutrient content and calculate the influence coefficients of temperature on fertilizer decomposition rate, humidity on root absorption efficiency, and predicted loss due to rainfall. The fertilization decision subsystem integrates nutrient deficit, influence coefficients, and predicted loss, and combines weather forecasts to calculate and correct fertilization amount and target fertilization time window, generating a variable fertilization prescription map. The fertilization execution feedback subsystem executes zonal variable fertilization according to the prescription map and re-measures multispectral data after fertilization to evaluate the effect. The evaluation results are fed back to the data processing and analysis subsystem to update calculation parameters and evaluation models. The cloud management platform realizes data storage, visualization, and alarm. This invention quantifies the dynamic impact of environmental factors such as temperature, humidity, and rainfall into correction coefficients and loss predictions, achieving dynamic environmental response compensation for fertilizer application and avoiding low fertilizer utilization and nutrient supply-demand discrepancies caused by changes in environmental conditions. Simultaneously, through post-fertilization effect retesting and adaptive model parameter updates, the system continuously improves decision-making accuracy. Combining weather forecasts with the selection of target fertilization time windows effectively avoids the impact of rainfall loss and extreme temperatures. Therefore, it effectively improves the accuracy of fertilization decisions and fertilizer utilization, achieving precise and intelligent fertilization in tea gardens. Attached Figure Description

[0016] Figure 1 This is an architectural diagram of a closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response, as described in this invention. Detailed Implementation

[0017] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] Example 1 Please see Figure 1This application's system includes a data acquisition subsystem, a data processing and analysis subsystem, a fertilization decision-making subsystem, a fertilization execution feedback subsystem, and a cloud management platform. The data acquisition subsystem is communicatively connected to the data processing and analysis subsystem, used to collect multispectral data, environmental sensor data, and weather forecast data from the tea garden, and transmit the collected data to the data processing and analysis subsystem. The data processing and analysis subsystem is used to invert the nutrient content of each area of ​​the tea garden based on the multispectral data, calculate the influence coefficient of temperature on fertilizer decomposition rate and the influence coefficient of humidity on root absorption efficiency based on the environmental sensor data, and assess the predicted loss due to rainfall based on the weather forecast data. The fertilization decision-making subsystem is connected to both the data processing and analysis subsystem and the fertilization execution feedback subsystem. The fertilization decision-making subsystem is used for root... Based on the nutrient content, influence coefficient, and predicted loss output by the data processing and analysis subsystem, the fertilizer application rate and target fertilization time window are calculated and corrected in conjunction with weather forecasts, and a variable fertilization prescription map is generated. The fertilization execution feedback subsystem communicates with the fertilization decision-making subsystem. The fertilization execution feedback subsystem is used to execute zonal variable fertilization operations according to the variable fertilization prescription map, and to re-measure multispectral data after fertilization to evaluate the fertilization effect. The evaluation results are fed back to the data processing and analysis subsystem, which then updates the calculation parameters of the influence coefficient and the evaluation model of the predicted loss based on the evaluation results. The cloud management platform communicates with the data acquisition subsystem, data processing and analysis subsystem, fertilization decision-making subsystem, and fertilization execution feedback subsystem for data storage, visualization, and intelligent alarms.

[0019] The principle of the present invention system will be explained in detail below using a scenario of a large-scale tea plantation in a hilly area: A data acquisition subsystem was pre-deployed within the tea plantation. This subsystem includes a drone equipped with a multispectral camera capable of capturing spectral images across multiple bands, including blue, green, red, red-edge, and near-infrared light. The drone conducts aerial photography once a week on a clear, windless morning to obtain multispectral images covering the entire tea plantation. Simultaneously, fixed multispectral monitoring nodes are installed at key locations at different altitudes and slopes within the tea plantation, automatically collecting multispectral data daily. An IoT sensor network is deployed within the tea plantation at a specific grid spacing, including air temperature and humidity sensors collecting data hourly, rainfall sensors collecting real-time rainfall data, soil moisture sensors buried at different depths collecting data hourly, and soil temperature sensors collecting data hourly. All sensors are wirelessly connected to a gateway, from which the data is then uploaded to a cloud server. Furthermore, the data acquisition subsystem connects to an open application programming interface (API) from the meteorological bureau, obtaining forecasts of rainfall probability, rainfall amount, temperature, and wind speed for the next several days every three hours.

[0020] The data acquisition subsystem transmits the aforementioned multispectral data, environmental sensor data, and weather forecast data to the data processing and analysis subsystem in real time. The data processing and analysis subsystem first performs radiometric calibration and geometric correction on the multispectral images, extracting the reflectance of each band for each pixel in the tea garden. Then, it calculates multiple vegetation indices that reflect vegetation growth and chlorophyll content. The data processing and analysis subsystem is equipped with a pre-trained nutrient inversion model, which takes the calculated vegetation indices as input and outputs the nitrogen, phosphorus, and potassium content of each area of ​​the tea garden. Simultaneously, based on the current temperature collected by the soil temperature sensor, the data processing and analysis subsystem calculates the influence coefficient of temperature on fertilizer decomposition rate. This influence coefficient quantifies the degree to which temperature changes affect the rate of fertilizer decomposition in the soil. Based on the current humidity collected by the soil moisture sensor, it calculates the influence coefficient of humidity on root absorption efficiency. This influence coefficient quantifies the degree to which soil moisture levels affect the tea tree roots' ability to absorb fertilizer. Based on the future cumulative rainfall data in the meteorological forecast, assess the predicted loss caused by the rainfall. This predicted loss is used to quantify the amount of nutrients that the upcoming rainfall may cause to be leached from the current soil.

[0021] The data processing and analysis subsystem outputs the calculated spatial distribution map of nutrient content, temperature influence coefficient, humidity influence coefficient, and predicted loss to the fertilization decision-making subsystem. The fertilization decision-making subsystem first determines the current phenological stage of the tea plant, such as budding, growth, harvesting, or dormancy, and accordingly determines the target nutrient level for each growth stage. The nutrient requirement assessment module calculates the difference between the current nutrient content and the target nutrient level for each region, obtaining the nutrient deficit. The environmental correction module adjusts the base fertilization amount calculated based on the nutrient deficit according to the temperature influence coefficient, humidity influence coefficient, and predicted loss, obtaining a corrected fertilization amount that compensates for slowed decomposition due to low or high temperatures, hindered absorption due to drought or excessive moisture, and expected losses due to rainfall. The fertilization window optimization module acquires weather forecast data, filters hourly data for future periods, first eliminating all periods within a certain timeframe before rainfall begins, and then eliminating periods where the predicted temperature exceeds the suitable range, resulting in a candidate period set. If there are consecutive windows in the candidate time period that meet the operational requirements, the earliest one is selected as the target fertilization time window; if no window meets the requirements, an extension instruction is output and the system waits for the next round of weather forecast data updates before recalculating. The variable fertilization prescription map generation module divides the entire tea garden into grids of a certain size, generating a record for each grid, including the grid's geographic coordinates, corrected fertilization amount, and nitrogen, phosphorus, and potassium fertilizer ratio, and finally exports a prescription map file that can be recognized by agricultural machinery.

[0022] The fertilization decision-making subsystem sends the generated prescription map and target fertilization time window to the fertilization execution feedback subsystem. Within the target fertilization time window, the variable fertilization execution module in the fertilization execution feedback subsystem dispatches one or more drones or ground-based fertilizer applicators, equipped with high-precision positioning modules. After loading the prescription map file, the equipment automatically travels or flies along a preset path, acquiring its current position in real time during travel or flight. It then queries the corresponding grid's fertilizer application amount and ratio from the prescription map, controlling the fertilization execution mechanism to precisely apply fertilizer to each grid. The equipment records the actual fertilizer application amount for each grid in real time and uploads it to the cloud management platform via wireless network.

[0023] Several days after fertilization, the fertilization effect evaluation module in the fertilization execution feedback subsystem re-invokes the drone or fixed multispectral monitoring node to re-measure the tea garden's multispectral data along the same flight path or location as before fertilization. The re-measured data is transmitted to the data processing and analysis subsystem, which recalculates the vegetation index after fertilization. The fertilization effect evaluation module obtains the vegetation index of the same area before fertilization and calculates the fertilization response index, which reflects the degree of improvement in vegetation growth after fertilization. Based on the value of the fertilization response index, a fertilization effect rating is generated, such as excellent, satisfactory, or insufficient. The evaluation results, along with the re-measured multispectral data, are fed back to the data processing and analysis subsystem. Based on the received evaluation results, the data processing and analysis subsystem initiates a model adaptive update process. Specifically, if the fertilization effect is excellent, the current environmental response model parameters remain unchanged; if the effect is insufficient, the temperature influence coefficient calculation parameters, humidity influence coefficient calculation parameters, and relevant parameters in the rainfall loss assessment model are fine-tuned through optimization algorithms to make the predicted loss amount closer to the actual observed value. The updated model parameters are saved and used for decision-making in subsequent rounds.

[0024] The cloud-based management platform maintains communication connectivity with all subsystems. The platform displays a tea garden map on a 3D geographic information system interface, overlaying heatmaps to show the nutrient content distribution, corrected fertilization amount distribution, and fertilization response index distribution for each area. The platform stores all collected multispectral raw data, sensor time-series data, historical weather forecast data, each decision prescription map, execution trajectory, and feedback evaluation results, supporting cross-year queries and tea quality traceability. The platform also features intelligent alarm rules: a sensor fault alarm is triggered when a sensor fails to upload data for an extended period; a data anomaly alarm is triggered when the retrieved nutrient content exceeds the normal range; and a fertilization operation deviation alarm is triggered when the actual fertilization amount deviates from the prescription map's set value by more than a preset threshold. Tea garden managers can log in to the cloud-based management platform via a mobile application or computer web interface to view system status, management zone information, and alarm information in real time. They can also manually adjust the target fertilization time window or correct the fertilization amount limit through the control interface.

[0025] Through the above process, the tea garden precision fertilization closed-loop system based on multispectral imaging and environmental response of the present invention has achieved complete operation in the tea plantation, from data acquisition, nutrient inversion, environmental response correction, fertilization decision-making, variable execution to effect evaluation and model adaptation.

[0026] Example 2 In this embodiment, the data processing and analysis subsystem includes a vegetation index calculation module, a nutrient inversion model, and an environmental response model; wherein: The vegetation index calculation module is used to calculate multiple vegetation indices based on multispectral data. Nutrient inversion model, used to invert nitrogen, phosphorus and potassium nutrient content in different areas of tea garden based on vegetation index; An environmental response model is used to calculate the influence coefficients of temperature on fertilizer decomposition rate and humidity on root absorption efficiency based on environmental sensor data, and to assess the predicted loss due to rainfall based on weather forecast data.

[0027] 1. The coefficient of influence of temperature on fertilizer decomposition rate is calculated using the following formula: in: This is the coefficient representing the effect of temperature on the fertilizer decomposition rate. The fertilizer decomposition rate constant at the reference temperature. This is a temperature coefficient constant, representing the factor by which the decomposition rate increases for every 10°C increase in temperature. Given the current soil temperature, This is the reference temperature.

[0028] 2. The coefficient of influence of humidity on root absorption efficiency is calculated using the following formula: in: This represents the coefficient of influence of temperature on root absorption efficiency. Current soil moisture This represents the lower limit of the temperature at which root absorption efficiency begins to be significantly inhibited. To achieve the optimal humidity level for root absorption efficiency; when hour, ;when hour, .

[0029] Specifically, the vegetation index calculation module combines the reflectance of each pixel in the multispectral image across five bands to generate the Normalized Difference Vegetation Index (NDVEI), Red-edge Normalized Difference Vegetation Index (RBD), Chlorophyll Absorption Ratio Index (CYARI), and Soil-Adjusted Vegetation Index (SDI). These indices reflect the chlorophyll density, nitrogen accumulation, and background soil interference of the tea canopy from different perspectives. The nutrient inversion model employs a random forest algorithm, using the calculated vegetation indices as input features. The model is trained using pre-collected measured data of leaf nitrogen, phosphorus, and potassium content along with the corresponding spectral indices. After training, the model can perform local inference at edge computing nodes, outputting the nitrogen, phosphorus, and potassium content per square meter of the tea garden grid. The nitrogen content inversion accuracy reaches a coefficient of determination greater than 0.85. Based on the future cumulative rainfall forecast and combined with the currently inverted nutrient content, the potential future nutrient loss is estimated using a pre-fitted power function relationship.

[0030] Example 3 In this embodiment, the fertilization decision-making subsystem includes a nutrient requirement assessment module, an environmental correction module, a fertilization window optimization module, and a variable fertilization prescription map generation module; wherein: The nutrient requirement assessment module is used to calculate the nutrient gap based on the difference between the nutrient content output by the data processing and analysis subsystem and the preset target nutrient level. The environmental correction module is used to correct the basic fertilization amount corresponding to the nutrient gap based on the impact coefficient and the predicted loss amount, so as to obtain the corrected fertilization amount. The fertilization window optimization module is used to filter and recommend target fertilization time windows based on weather forecast data to avoid preset periods before rainfall and periods outside preset extreme temperature ranges. 1. Obtain hourly rainfall probability, rainfall forecast, and temperature forecast values ​​for a future preset time period from meteorological forecast data; 2. Remove the time periods within the first preset duration before rainfall from the future preset time periods to obtain the first candidate time period set; 3. Remove the time periods with predicted temperatures below the first temperature threshold or above the second temperature threshold from the first candidate time period set to obtain the second candidate time period set; 4. If the second candidate time period set is not empty, select the earliest time period whose continuous duration meets the operation requirements as the target fertilization time window; if the second candidate time period set is empty, output the postponement instruction and wait for the next round of weather forecast data to be updated before re-executing the acquisition, elimination and selection operations.

[0031] The variable fertilization prescription map generation module is used to divide the tea garden into grids and generate a variable fertilization prescription map for each grid, which includes the corrected fertilization amount and fertilizer ratio.

[0032] The environmental remediation module uses the following formula to calculate the corrected fertilization amount: in: This is the revised fertilizer application rate. This is the basic fertilizer application amount determined based on the nutrient deficit. This is the nutrient loss compensation coefficient. To predict churn.

[0033] Specifically, the preset target nutrient levels are determined based on the physiological needs of tea trees at different growth stages. For example, during the spring tea budding stage, tea trees require higher nitrogen levels to promote new shoot growth, with a target nitrogen content set at 3.5 grams per kilogram of leaves; during the growing season, nitrogen demand is relatively stable, with a target nitrogen content of 3.0 grams per kilogram of leaves; around the harvesting period, nitrogen levels are appropriately reduced to improve tea quality, with a target nitrogen content of 2.8 grams per kilogram of leaves; and during dormancy, the target nitrogen content is reduced to 2.0 grams per kilogram of leaves. The target levels for phosphorus and potassium are also set according to the nutrient requirements of each growth stage, with a target range of 0.3 to 0.5 grams per kilogram of leaves for phosphorus and 2.0 to 3.0 grams per kilogram of leaves for potassium. After obtaining the current nutrient content of each region from the data processing and analysis subsystem, the nutrient requirement assessment module subtracts the current content from the target nutrient level to obtain the nutrient deficit for each grid. If the difference is positive, it indicates that the nutrient needs to be supplemented; if the difference is negative or zero, it indicates that the nutrients are sufficient, and the basic fertilization amount is set to zero. The basic fertilizer application amount is calculated based on the nutrient deficit and the yield target per unit area. For example, if there is a nitrogen deficit of 1 gram per square meter, approximately 2.2 grams of urea per square meter should be applied as top dressing.

[0034] The future preset time period for weather forecast data in the fertilization window optimization module refers to the next 72 hours. This preset duration is based on the fact that most water-soluble fertilizers have a critical absorption period of 48 to 72 hours after application. Avoiding heavy rainfall during this period can significantly reduce loss. In addition, the accuracy of weather forecasts within 72 hours is relatively high. The first preset time period before rainfall is set to 24 hours. This is because if rainfall occurs within 24 hours after fertilization, the fertilizer has not yet been absorbed by the soil or tea trees and is easily lost with runoff. Therefore, the module removes all time periods within 24 hours before the start of rainfall from the future 72 hours. For example, if rainfall is predicted to start at the 30th hour, the time period between the 6th and 30th hours is removed, and the remaining time periods constitute the first candidate time period set. The preset extreme temperature range is determined based on the physiological characteristics of tea tree root absorption and fertilizer decomposition. When the temperature is below 10 degrees Celsius, root activity and microbial decomposition enzyme activity are significantly reduced; when the temperature is above 30 degrees Celsius, ammonia nitrogen in the fertilizer is easily volatilized and excessive transpiration by the tea trees hinders absorption. The first temperature threshold is set to 10 degrees Celsius, and the second temperature threshold is set to 30 degrees Celsius. All time periods with predicted temperatures below 10 degrees Celsius or above 30 degrees Celsius are removed from the first candidate time period set to obtain the second candidate time period set. Each time period in the second candidate time period set represents a workable period with no rainfall risk and suitable temperature. If this set is not empty, meaning there is at least one consecutive time period, the earliest time period whose continuous duration meets the time required for fertilization is selected as the target fertilization time window. The time required for fertilization depends on the tea garden area and equipment efficiency. For example, if a 500-acre tea garden requires 4 hours using a drone, the earliest window with a continuous duration greater than or equal to 4 hours is selected. If the second candidate time period set is empty, meaning there is no time period meeting the conditions within the next 72 hours, a postponement command is output, the system pauses fertilization decisions, and waits for the next round of weather forecast data updates before re-executing the above acquisition, removal, and selection operations until a suitable window is found.

[0035] In the variable fertilization prescription map generation module, the grid size is determined based on the tea garden terrain and the accuracy of the operating equipment. A 5m x 5m grid is used for flat tea gardens, while the grid is reduced to 2m x 2m in areas with steeper slopes. Each grid generates a prescription record, including the geographic coordinates of the grid center, the corrected fertilizer application rate, and the nitrogen, phosphorus, and potassium fertilizer ratio. The fertilizer ratio is dynamically adjusted according to the tea plant's growth stage and the availability of nutrients in the soil. The prescription map is finally exported in a general agricultural format, supporting import into the control systems of variable fertilization drones or ground-based fertilizer applicators.

[0036] Example 4 In this embodiment, the fertilization execution feedback subsystem includes a variable fertilization execution module and a fertilization effect evaluation module; wherein: The variable fertilization execution module is used to perform zonal variable fertilization operations based on the variable fertilization prescription map; it includes the following steps: 1. The variable fertilizer prescription map is parsed into operation instructions that can be recognized by agricultural machinery. The operation instructions include the geographic coordinates of each grid, the amount of fertilizer and the fertilizer solution ratio parameters; 2. The current location of the fertilization equipment is obtained in real time through a preset positioning device; 3. Based on the correspondence between the current location and the grid of the prescription map, control the fertilization equipment to apply fertilizer in the corresponding grid according to the operation instructions, and record the actual amount of fertilizer applied in real time and upload it to the cloud management platform.

[0037] The pre-set positioning device uses a Global Navigation Satellite System (GNSS) receiver, which can calculate the latitude and longitude coordinates of the fertilization equipment in real time, with a planar positioning accuracy of ±2 centimeters. Specifically, it receives a variable fertilization prescription map file from the fertilization decision subsystem. This file uses a common geographic data format and includes the geographic boundary coordinates of all grids in the tea garden, the recommended fertilization amount for each grid, and the ratio of nitrogen, phosphorus, and potassium fertilizers. The module calls the prescription map parsing program to convert the geometric information of each grid into flight paths and waypoints that can be recognized by the UAV or ground fertilization machine, as well as the corresponding feeding instructions.

[0038] The specific operation instructions include: the three-dimensional coordinates of each waypoint, the total amount of fertilizer to be applied upon arrival at that waypoint, the concentration ratio of each component in the fertilizer solution, and the spraying or spreading flow parameters. Once the fertilization equipment is started, the positioning device continuously outputs the current position at a frequency of 10 Hz. The module performs spatial overlay analysis between the current position and the prescription map grid to determine which grid it is currently in, and then retrieves the fertilizer application amount and ratio for that grid from the operation instructions. The control algorithm adjusts the opening of the discharge valve or the rotation speed of the centrifugal disc in real time according to the equipment's travel speed to ensure that the actual application amount per unit area is consistent with the prescription map requirements. The equipment's built-in flow meter or weight sensor monitors the amount of fertilizer applied in real time. After each grid operation is completed, the actual fertilizer application amount, operation time, and geographical coordinates for that grid are packaged and uploaded to the cloud management platform via a mobile communication network for subsequent traceability and analysis.

[0039] The fertilization effect evaluation module is used to re-measure multispectral data after fertilization to evaluate the fertilization effect and feed the evaluation results back to the data processing and analysis subsystem; it includes the following steps: 1. Within a preset time after fertilization, re-measure the multispectral data of each area of ​​the tea garden using a multispectral imaging device; 2. Calculate the vegetation index after fertilization based on the re-measured multispectral data. The vegetation index includes the normalized difference vegetation index. 3. Compare the fertilization response index with the preset response threshold to generate a fertilization effect rating.

[0040] The preset time for fertilization is determined based on the fertilizer absorption and leaf physiological response cycle, and is between the third and seventh day after fertilization. Retesting too early means the fertilizer has not been fully absorbed, and the leaf spectral response is not obvious; retesting too late may be affected by subsequent agricultural activities or weather changes. In this embodiment, retesting is fixed on the fifth day after fertilization. The same multispectral imaging equipment as before fertilization is used for retesting. This could be the same drone equipped with the same model of multispectral camera, or a fixed multispectral monitoring node at the same location, re-acquiring multispectral images of the entire tea garden along the same flight path or shooting angle. The fertilization effect evaluation module performs the same radiometric calibration, geometric correction, and vegetation index calculation on the retested images as before fertilization to obtain the normalized vegetation index (NVI) after fertilization. Simultaneously, the NVI of the same grid before fertilization is retrieved from the historical database.

[0041] The fertilization response index is calculated using the following formula: the fertilization response index equals the normalized vegetation index after fertilization minus the normalized vegetation index before fertilization, then divided by the normalized vegetation index before fertilization, and finally multiplied by 100%. A positive index indicates improved vegetation growth; the higher the index, the more significant the improvement. Preset response thresholds are obtained through field trials in tea gardens: a fertilization response index greater than 15% indicates excellent fertilization effect; an index between 5% and 15% indicates satisfactory effect; and an index less than 5% indicates insufficient effect. The module compares the calculated fertilization response index with the above thresholds, generates a corresponding rating, and packages the rating result along with the retested normalized vegetation index and fertilization response index, feeding them back to the data processing and analysis subsystem. The data processing and analysis subsystem determines whether to adjust the parameters of the environmental response model based on this rating, thereby achieving closed-loop adaptive optimization.

[0042] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0043] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response, characterized in that, include: The system includes a data acquisition subsystem, a data processing and analysis subsystem, a fertilization decision-making subsystem, a fertilization execution feedback subsystem, and a cloud management platform. The data acquisition subsystem is communicatively connected to the data processing and analysis subsystem, and is used to collect multispectral data, environmental sensor data and weather forecast data of the tea garden, and transmit the collected data to the data processing and analysis subsystem. The data processing and analysis subsystem is used to invert the nutrient content of each area of ​​the tea garden based on the multispectral data, calculate the influence coefficient of temperature on fertilizer decomposition rate and the influence coefficient of humidity on root absorption efficiency based on the environmental sensor data, and assess the predicted loss caused by rainfall based on meteorological forecast data. The fertilization decision subsystem is connected to the data processing and analysis subsystem and the fertilization execution feedback subsystem. The fertilization decision subsystem is used to calculate and correct the fertilization amount and target fertilization time window based on the nutrient content, influence coefficient and predicted loss output by the data processing and analysis subsystem, combined with the weather forecast, and generate a variable fertilization prescription map. The fertilization execution feedback subsystem is communicatively connected to the fertilization decision subsystem. The fertilization execution feedback subsystem is used to execute zonal variable fertilization operations according to the variable fertilization prescription map, and to re-measure multispectral data after fertilization to evaluate the fertilization effect. The evaluation results are fed back to the data processing and analysis subsystem, so that the data processing and analysis subsystem updates the calculation parameters of the influence coefficient and the evaluation model of the predicted loss based on the evaluation results. The cloud management platform is communicatively connected to the data acquisition subsystem, data processing and analysis subsystem, fertilization decision-making subsystem, and fertilization execution feedback subsystem, and is used for data storage, visualization display, and intelligent alarm.

2. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 1, characterized in that, The data processing and analysis subsystem includes: A vegetation index calculation module is used to calculate multiple vegetation indices based on the multispectral data; Nutrient inversion model, used to invert nitrogen, phosphorus and potassium nutrient content in various areas of tea garden based on the vegetation index; An environmental response model is used to calculate the influence coefficients of temperature on fertilizer decomposition rate and humidity on root absorption efficiency based on the environmental sensor data, and to assess the predicted loss due to rainfall based on the weather forecast data.

3. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 2, characterized in that, The coefficient of influence of temperature on fertilizer decomposition rate is calculated using the following formula: in: This is the coefficient representing the effect of temperature on the fertilizer decomposition rate. The fertilizer decomposition rate constant at the reference temperature. This is a temperature coefficient constant, representing the factor by which the decomposition rate increases for every 10°C increase in temperature. Given the current soil temperature, This is the reference temperature.

4. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 2, characterized in that, The coefficient of influence of humidity on root absorption efficiency is calculated using the following formula: in: This represents the coefficient of influence of temperature on root absorption efficiency. Current soil moisture This represents the lower limit of the temperature at which root absorption efficiency begins to be significantly inhibited. To achieve the optimal humidity level for root absorption efficiency; when hour, ;when hour, .

5. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 1, characterized in that, The fertilization decision-making subsystem includes: The nutrient requirement assessment module is used to calculate the nutrient gap based on the difference between the nutrient content output by the data processing and analysis subsystem and the preset target nutrient level. An environmental correction module is used to correct the basic fertilization amount corresponding to the nutrient gap based on the influence coefficient and the predicted loss amount, so as to obtain the corrected fertilization amount. The fertilization window optimization module is used to filter and recommend target fertilization time windows based on the meteorological forecast data to avoid preset periods before rainfall and periods outside preset extreme temperature ranges. The variable fertilization prescription map generation module is used to divide the tea garden into grids and generate a variable fertilization prescription map for each grid, which includes the modified fertilization amount and fertilizer ratio.

6. The tea garden precision fertilization closed-loop system based on multispectral imaging and environmental response according to any one of claims 1 to 5, characterized in that, The environmental remediation module calculates the corrected fertilization amount using the following formula: in: This is the revised fertilizer application rate. This is the basic fertilizer application amount determined based on the nutrient deficit. This is the nutrient loss compensation coefficient. To predict churn.

7. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 5, characterized in that, The method of filtering based on the meteorological forecast data to avoid preset periods before rainfall and periods outside preset extreme temperature ranges, and recommending target fertilization time windows, includes: Obtain the hourly probability of rainfall, predicted rainfall, and predicted temperature for a future preset time period from the meteorological forecast data; The first candidate time period set is obtained by removing the time period within the first preset duration before rainfall from the future preset time period; The second candidate time period set is obtained by removing time periods from the first candidate time period set where the predicted temperature value is lower than the first temperature threshold or higher than the second temperature threshold. If the second candidate time period set is not empty, the earliest time period with a continuous duration that meets the operational requirements is selected as the target fertilization time window; if the second candidate time period set is empty, an extension instruction is output and the acquisition, elimination and selection operations are re-executed after the next round of weather forecast data is updated.

8. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 1, characterized in that, The fertilization execution feedback subsystem includes: The variable fertilization execution module is used to perform zonal variable fertilization operations according to the variable fertilization prescription map; The fertilization effect evaluation module is used to evaluate the fertilization effect by retesting multispectral data after fertilization, and to feed the evaluation results back to the data processing and analysis subsystem.

9. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 8, characterized in that, The variable fertilization execution module performs zonal variable fertilization operations according to the variable fertilization prescription map, including: The variable fertilization prescription map is parsed into operation instructions that can be recognized by agricultural machinery. The operation instructions include the geographic coordinates of each grid, the amount of fertilizer applied, and the fertilizer solution ratio parameters. The current location of the fertilization equipment is obtained in real time through a preset positioning device; Based on the correspondence between the current location and the prescription map grid, the fertilization equipment is controlled to apply fertilizer in the corresponding grid according to the operation instructions, and the actual amount of fertilizer applied is recorded in real time and uploaded to the cloud management platform.

10. The closed-loop system for precision fertilization in tea gardens based on multispectral imaging and environmental response as described in claim 8, characterized in that, The fertilization effect evaluation module evaluates the fertilization effect by retesting multispectral data after fertilization, including: After fertilization, within a preset time period, multispectral data of various areas of the tea garden were re-measured using multispectral imaging equipment; The vegetation index after fertilization was calculated based on the re-measured multispectral data, and the vegetation index included the normalized vegetation index. The fertilization response index is compared with a preset response threshold to generate a fertilization effect rating.

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