An adaptive pest control method and system based on crop growth period

Abstract

An adaptive pest control method and system based on the growth period of crops, in the method, a control sensitive coefficient of the crops is obtained; a control rule matrix is established according to the control sensitive coefficient; a target control rule is executed by a self-walking pesticide spraying terminal, and growth information of the crops is obtained every interval preset time length; when the growth rate of the crops is calculated to be lower than a preset threshold according to the growth information, a reference pesticide spraying concentration and a reference pesticide spraying amount are reduced by a preset ratio; the control rule matrix is re-determined according to the reduced reference pesticide spraying concentration and the reference pesticide spraying amount, a corrected control rule matrix is obtained, and the target control rule obtaining step is executed according to the corrected control rule matrix in the next period. The application realizes the cooperative optimization of the control operation and the growth characteristics of the crops by monitoring the growth characteristics of the crops in real time and dynamically adjusting the control rule, and improves the pest control effect.

Description

Technical Field

[0001] This application belongs to the field of pest control, and in particular relates to an adaptive pest control method and system based on the crop growth period. Background Technology

[0002] In crop cultivation, pest control is a crucial step in ensuring healthy crop growth. Traditional pest control methods mainly rely on regular manual spraying of pesticides. This method not only consumes a lot of manpower, but also often results in excessive or improper use of pesticides due to a lack of accurate identification of pest species and numbers. This increases planting costs and may also cause pesticide damage to crops.

[0003] Among related technologies, a 5G-based pest control system and method can be applied. This system includes autonomously walking pesticide spraying terminals, detection terminals, and a control center. The detection terminals patrol a preset area, and when they detect pests through real-time analysis of video stream data, they send their location information to the control center. The control center generates working time periods based on a neural network model and sends this information to the corresponding spraying terminals for pesticide spraying. The system can also allocate pesticides between spraying terminals based on their remaining pesticide levels, improving the intelligence and efficiency of pest control.

[0004] However, crops undergo different growth stages, each with variations in plant morphology, leaf distribution, and stem height. Furthermore, the attraction of crops to various pests differs at different growth stages, meaning that pest activity patterns and distribution locations are often closely related to the crop's growth period. Current pest control technologies primarily respond to the real-time location of pests without fully considering their synergy with the crop's growth stage characteristics, thus reducing the effectiveness of pest control. Summary of the Invention

[0005] This application provides an adaptive pest control method and system based on crop growth stage. By monitoring crop growth stage characteristics in real time and dynamically adjusting control rules, it achieves synergistic optimization between control operations and crop growth characteristics, thereby improving the pest control effect.

[0006] In the first aspect, this application provides an adaptive pest control method based on crop growth stage, which extracts the corresponding baseline spraying concentration and baseline spraying amount from a preset growth stage parameter library according to the current growth stage information of the crop. The control sensitivity coefficient of crops is determined based on the baseline spray concentration and baseline spray amount. The control sensitivity coefficient characterizes the crop's tolerance to pesticides. Establish a time-segmented prevention and control rule matrix based on the prevention and control sensitivity coefficient; When the detection terminal detects pests, it obtains the target control rule corresponding to the control rule matrix for the current time period. The spraying concentration in the target control rule does not exceed the baseline spraying concentration, and the spraying amount does not exceed the baseline spraying amount. The autonomous walking spraying terminal is controlled to operate according to the target prevention and control rules, and to acquire crop growth information at preset intervals. When the crop growth rate calculated based on the growth information is lower than the preset threshold, the baseline spray concentration and baseline spray amount will be reduced according to the preset ratio. The control rule matrix is ​​redefined based on the reduced baseline spray concentration and baseline spray amount, resulting in a revised control rule matrix. In the next time period, the target control rule acquisition step is executed based on the revised control rule matrix.

[0007] By adopting the above technical solution, and establishing a control rule matrix that is dynamically adjusted according to crop growth status, precise pest control is achieved. Appropriate baseline spraying parameters are selected based on the current crop growth stage, and crop resistance is quantified using a control sensitivity coefficient, ensuring that control measures adapt to the crop's physiological characteristics. During control operations, crop growth is continuously monitored, and spraying parameters are adjusted accordingly. When a decrease in crop growth rate is detected, the application intensity is promptly reduced. This adaptive adjustment mechanism effectively prevents damage to crops from excessive pesticide use. By dividing the control rules into multiple time periods and dynamically correcting them, this method ensures control effectiveness while avoiding pesticide residue accumulation and reducing environmental pollution. By monitoring crop growth characteristics in real time and dynamically adjusting control rules, synergistic optimization between control operations and crop growth characteristics is achieved, improving pest control efficacy.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the crop's control sensitivity coefficient is determined based on the baseline spray concentration and the baseline spray amount, specifically including: Obtain historical baseline pesticide concentrations and pesticide application rates for crops; The concentration variation coefficient is obtained by dividing the baseline spray concentration by the historical baseline spray concentration, and the dosage variation coefficient is obtained by dividing the baseline spray amount by the historical baseline spray amount. The product of the concentration variation coefficient and the dosage variation coefficient is used as the rate of change of the control parameter. The control sensitivity coefficient is obtained by multiplying the rate of change of control parameters by the preset baseline control sensitivity coefficient.

[0009] By adopting the above technical solution, the current benchmark spraying parameters are compared with historical data. The dynamic change characteristics of the control parameters are quantified by calculating the change coefficients of concentration and dosage. Combined with the preset basic control sensitivity coefficient, the final control sensitivity coefficient is generated. This allows the control sensitivity coefficient to accurately reflect the actual tolerance of crops to pesticides, reduces the deviation that may be caused by relying solely on experience values, and improves the accuracy and reliability of the control sensitivity coefficient.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, a time-segmented prevention and control rule matrix is ​​established based on the prevention and control sensitivity coefficient, specifically including: A prevention and control cycle is divided into multiple time periods, and the duration of each time period is directly proportional to the prevention and control sensitivity coefficient by a preset fixed coefficient. For each time period, the upper limit of the number of sprayings and the upper limit of the spray concentration are determined according to the prevention and control sensitivity coefficient. The upper limit of the number of sprayings is inversely proportional to the prevention and control sensitivity coefficient, and the upper limit of the spray concentration does not exceed the benchmark spray concentration. Based on the upper limit of the number of sprayings and the baseline spraying amount, calculate the minimum interval between two adjacent sprayings and the duration of a single spraying within each time period, and establish a time-segmented prevention and control rule matrix based on the minimum interval and the duration of a single spraying.

[0011] By adopting the above technical solution, the control cycle is divided into multiple time periods, and a time-segmented control rule matrix is ​​established, thus achieving temporal optimization of control measures. The duration of each time period is adaptively adjusted based on the control sensitivity coefficient, and the upper limits for the number of sprays and concentration for each time period are determined accordingly, establishing a dynamic matching mechanism between control intensity and crop tolerance. By calculating the minimum spraying interval and the duration of a single spray, the damage to crops caused by excessively frequent spraying is avoided while ensuring control effectiveness. This time-segment-based refined management approach allows control measures to fully utilize pesticide efficacy while avoiding excessive pesticide use, reducing adverse effects on crops while ensuring control effectiveness. Since the control parameters for each time period are dynamically calculated based on crop tolerance, the scientific nature and targeted nature of pest control are improved.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of obtaining the target prevention and control rules according to the modified prevention and control rule matrix is ​​performed in the next time period, the method further includes: The photosynthetic index and stem strength of the crop during its current growth period are obtained, and the photosynthetic index is divided into multiple photosynthetic levels according to a preset range, and the stem strength is divided into multiple intensity levels according to a preset intensity range. A coupling strength assessment matrix is ​​constructed based on multiple photosynthetic levels and multiple intensity levels, and the current growth period is divided into multiple coupling prevention and control intervals based on the coupling strength assessment matrix; The ratio of the photosynthetic index to the preset baseline photosynthetic index in each coupled prevention and control interval is used as the photosynthetic efficiency coefficient, and the ratio of the stem strength to the preset baseline stem strength is used as the damage resistance coefficient. The upper limit of control energy is calculated based on the photosynthetic efficiency coefficient, and the crop's damage resistance index is calculated based on the damage resistance coefficient. The difference in photosynthetic index and the difference in stem strength between adjacent coupled prevention and control zones are obtained, and the ratio of the difference in photosynthetic index and the difference in stem strength to the preset benchmark difference is used as the transition weight coefficient. The upper limit of prevention and control energy and the crop damage resistance index are weighted according to the transition weight coefficient to generate the interval conversion coefficient. The interval conversion coefficient is then multiplied by the target prevention and control rule to obtain the modified target prevention and control rule.

[0013] By employing the aforementioned technical solution, and through constructing a coupling strength evaluation matrix and introducing an interval transformation mechanism, the method achieves refined adjustment of pest control rules. The method assesses crop status from two dimensions: photosynthesis and stem strength. It quantifies crop physiological characteristics by calculating photosynthetic efficiency coefficients and damage resistance coefficients, and determines the upper limit of control energy and damage resistance index based on these parameters. By analyzing the parameter differences between adjacent coupled control intervals and calculating transition weight coefficients, the method establishes a smooth transition regulation mechanism between intervals, avoiding abrupt changes in control rules during transitions between different intervals. This multi-dimensional physiological index-based regulation approach allows control rules to more comprehensively adapt to the crop's growth state, improving the accuracy and safety of pest control. By introducing interval transformation coefficients to modify control rules, the method maintains both control effectiveness and healthy crop growth.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, obtaining the photosynthetic index and stem strength of the crop during its current growth stage specifically includes: The control and detection terminal collects fluorescence images of crops and cross-sectional images of stems during their current growth stage; Extract the chlorophyll fluorescence intensity value from the fluorescence image, and perform segmented statistical analysis of the chlorophyll fluorescence intensity value according to a preset wavelength range to obtain the leaf fluorescence parameters; The change in carbon dioxide concentration around crop leaves is measured by the gas sensor of the detection terminal, and the change in carbon dioxide concentration per unit time is converted into net photosynthetic rate. The ratio of leaf fluorescence parameters to net photosynthetic rate is used as the photosynthetic index. Image segmentation is performed on the cross-sectional image to obtain the vascular bundle region; The number of vascular bundles per unit area in the vascular bundle region is calculated as the vascular bundle density. Calculate the cross-sectional area of ​​the stem based on the number of pixels in the cross-sectional image of the stem. The product of vascular bundle density and stem cross-sectional area is used as stem strength.

[0015] By employing the above technical solutions, and by acquiring fluorescence images and stem cross-sectional images of crops, combined with data measured by gas sensors, the photosynthetic index and stem strength of crops can be accurately obtained. Extracting chlorophyll fluorescence intensity values ​​from the fluorescence images and performing segmented statistical analysis provides a comprehensive reflection of the photosynthetic capacity of the leaves. Calculating the net photosynthetic rate by measuring changes in carbon dioxide concentration, and using its ratio to the leaf fluorescence parameters as the photosynthetic index, comprehensively characterizes the photosynthetic efficiency of the crop. Segmenting the stem cross-sectional images to obtain the vascular bundle region, calculating the vascular bundle density, and multiplying it by the stem cross-sectional area yields stem strength, which objectively reflects the mechanical support capacity of the crop stem, improving the accuracy and scientific rigor of pest control.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, a coupling strength evaluation matrix is ​​constructed based on multiple photosynthetic levels and multiple intensity levels, specifically including: A two-dimensional matrix is ​​constructed using multiple photosynthetic levels and multiple intensity levels, where rows represent photosynthetic levels and columns represent intensity levels. For each element in the two-dimensional matrix, the product of the corresponding photosynthetic level value and the intensity level value is used as the coupling contribution. Cluster analysis of the two-dimensional matrix based on the magnitude of the coupling contribution yields multiple coupling prevention intervals; Calculate the center value of each coupling prevention interval, and number and sort each coupling prevention interval based on the center value to obtain the coupling strength evaluation matrix.

[0017] By employing the above technical solution, a refined classification of crop growth states is achieved by constructing a two-dimensional matrix from multiple photosynthetic levels and intensity levels, and then calculating the coupling contribution of matrix elements for cluster analysis. The coupled control intervals obtained through cluster analysis reflect the comprehensive performance of crops under different growth states. Calculating the interval center values ​​and numbering and sorting them forms a coupling strength evaluation matrix, making the evaluation results more objective and systematic. This evaluation method fully considers the complexity of crop growth, transforming qualitative indicators into quantitative indicators, improving the accuracy and operability of the evaluation, enabling control measures to better adapt to the actual growth state of crops, and enhancing the targeting and effectiveness of pest control.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the upper limit of control energy is calculated based on the photosynthetic efficiency coefficient, and the crop's damage resistance index is calculated based on the damage resistance coefficient, specifically including: The product of the photosynthetic efficiency coefficient and the preset baseline control energy is used as the initial control energy upper limit; When the photosynthetic efficiency coefficient is greater than the first preset threshold, the initial control energy limit is increased by the first preset ratio; when the photosynthetic efficiency coefficient is less than the second preset threshold, the initial control energy limit is decreased by the second preset ratio, thus obtaining the control energy limit. The product of the damage resistance coefficient and the preset benchmark damage resistance value is used as the initial damage resistance index; When the damage resistance coefficient is greater than the third preset threshold, the initial damage resistance index is increased by the third preset ratio. When the damage resistance coefficient is less than the fourth preset threshold, the initial damage resistance index is decreased by the fourth preset ratio to obtain the crop's damage resistance index.

[0019] By adopting the above technical solution and setting multiple thresholds and adjusting ratios, precise control of control energy is achieved. When crop growth is good, the upper limit of control energy is appropriately increased to fully utilize the crop's resistance; when crop growth is weak, the upper limit of control energy is decreased to avoid causing additional damage. Similarly, the dynamic adjustment of the damage resistance index ensures that control measures match the crop's tolerance. This multi-threshold-based adaptive adjustment method allows control measures to be effective without causing excessive damage to the crop, improving the safety and reliability of pest control.

[0020] Secondly, embodiments of this application provide an adaptive pest control system based on crop growth period. The adaptive pest control system based on crop growth period includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation of the first aspect.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application provides an adaptive pest control method based on crop growth stages. By establishing a control rule matrix and dynamically adjusting it according to crop growth status, precise pest control is achieved. Appropriate baseline spraying parameters are selected based on the current crop growth stage, and crop resistance is quantified using a control sensitivity coefficient, ensuring that control measures adapt to the crop's physiological characteristics. During control operations, crop growth is continuously monitored, and spraying parameters are adjusted accordingly. When a decrease in crop growth rate is detected, the application intensity is promptly reduced. This adaptive adjustment mechanism effectively prevents damage to crops from excessive pesticide use. By dividing the control rules into multiple time periods and dynamically correcting them, this method ensures control effectiveness while avoiding pesticide residue accumulation and reducing environmental pollution. By real-time monitoring of crop growth characteristics and dynamic adjustment of control rules, synergistic optimization between control operations and crop growth characteristics is achieved, improving pest control efficacy.

[0024] 2. This application provides an adaptive pest control method based on crop growth stages. By constructing a coupling strength evaluation matrix and introducing an interval transformation mechanism, it achieves refined adjustment of control rules. The method assesses crop status from two dimensions: photosynthesis and stem strength. It quantifies crop physiological characteristics by calculating photosynthetic efficiency coefficients and damage resistance coefficients, and determines the upper limit of control energy and damage resistance index based on these parameters. By analyzing the parameter differences between adjacent coupled control intervals and calculating transition weight coefficients, the method establishes a smooth transition adjustment mechanism between intervals, avoiding abrupt changes in control rules during transitions between different intervals. This multi-dimensional physiological index-based regulation method allows control rules to more comprehensively adapt to the crop's growth state, improving the accuracy and safety of pest control. By introducing interval transformation coefficients to modify control rules, it ensures control effectiveness while maintaining healthy crop growth. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating an adaptive pest control method based on crop growth stage in an embodiment of this application.

[0026] Figure 2 This is a flowchart illustrating a comprehensive evaluation method based on photosynthesis and mechanical strength, as described in an embodiment of this application.

[0027] Figure 3 This is a schematic diagram of the physical device structure of an adaptive pest control system based on the crop growth period provided in an embodiment of this application. Detailed Implementation

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0030] The following example is used in conjunction with Figure 1 The following describes an adaptive pest control method based on crop growth stage in an embodiment of this application: Please see Figure 1 This is a flowchart illustrating an adaptive pest control method based on crop growth stage in an embodiment of this application.

[0031] S101. Extract the corresponding baseline spraying concentration and baseline spraying amount from the preset growth period parameter library based on the current growth period information of the crop. In this step, the system first needs to obtain the crop's current growth stage information. This information can be obtained in various ways, such as manual input by agricultural experts, real-time monitoring of crop growth status via sensors, or analysis of historical crop growth data. After obtaining the current growth stage information, the system needs to extract the baseline spraying concentration and dosage corresponding to the current growth stage from a pre-set growth stage parameter database. This database can store recommended pesticide concentrations and dosages for different crop growth stages, provided by agricultural experts or relevant research institutions.

[0032] Specifically, the system can divide the entire crop growth cycle into several discrete growth stages and set corresponding baseline spray concentration and dosage parameters for each growth stage. Once the system obtains the current growth stage information of the crop, it can match it with preset growth stages in a parameter library to extract the baseline spray concentration and dosage values ​​corresponding to the current growth stage. These extracted parameters can be used as input for subsequent steps to determine the final spraying plan.

[0033] S102. Determine the crop's sensitivity coefficient for pest control based on the baseline spray concentration and baseline spray amount; The system determines the crop's control sensitivity coefficient based on the baseline spray concentration and baseline spray amount. The control sensitivity coefficient characterizes the crop's tolerance to the pesticide. Specifically, it obtains the historical baseline spray concentration and historical baseline spray amount of the crop; divides the baseline spray concentration by the historical baseline spray concentration to obtain the concentration change coefficient, divides the baseline spray amount by the historical baseline spray amount to obtain the dosage change coefficient, and multiplies the concentration change coefficient and the dosage change coefficient as the control parameter change rate; multiplies the control parameter change rate by the preset basic control sensitivity coefficient to obtain the control sensitivity coefficient.

[0034] The purpose of this step is to determine a control sensitivity coefficient that reflects the crop's sensitivity to the pesticide, based on the baseline spray concentration and application rate extracted in the previous step. This coefficient can be used in subsequent steps to generate more refined and dynamic pesticide application plans.

[0035] To calculate the control sensitivity coefficient, the system first needs to obtain historical data on pesticide applications during the crop's previous growing seasons, mainly including historical baseline spray concentrations and application rates. By dividing the current baseline spray concentration and application rate by their respective historical values, the concentration variation coefficient and application rate variation coefficient can be obtained. These two coefficients reflect whether the required pesticide concentration and application rate for the crop have increased or decreased compared to before. Multiplying the concentration variation coefficient and application rate variation coefficient yields a comprehensive control parameter change rate. This rate of change is then multiplied by a preset baseline control sensitivity coefficient to obtain the crop's control sensitivity coefficient.

[0036] The preset baseline sensitivity coefficient can be set to different values ​​based on factors such as crop type, variety, and growth stage. For example, some crop varieties with strong resistance can be assigned a relatively low baseline sensitivity coefficient, while some seedlings can be assigned a relatively high baseline sensitivity coefficient. When determining the final control sensitivity coefficient, the system can further incorporate the effects of concentration and dosage changes on these preset baseline values.

[0037] S103. Establish a time-segmented prevention and control rule matrix based on the prevention and control sensitivity coefficient; The system establishes a time-segmented prevention and control rule matrix based on the prevention and control sensitivity coefficient. Specifically, it includes: dividing a prevention and control cycle into multiple time periods, with the duration of each time period being directly proportional to the prevention and control sensitivity coefficient by a preset fixed coefficient; for each time period, determining the upper limit of the number of sprays and the upper limit of the spray concentration based on the prevention and control sensitivity coefficient, with the upper limit of the number of sprays being inversely proportional to the prevention and control sensitivity coefficient, and the upper limit of the spray concentration not exceeding the baseline spray concentration; and calculating the minimum interval between two adjacent sprays and the duration of a single spray within each time period based on the upper limit of the number of sprays and the baseline spray amount, and establishing a time-segmented prevention and control rule matrix based on the minimum interval and the duration of a single spray.

[0038] In this step, the system needs to establish a time-segmented control rule matrix based on the control sensitivity coefficient calculated in the previous step. This matrix contains specific rules and parameters for pesticide application in different time periods. The purpose of this step is to generate a more refined and dynamic application plan to adapt to the dynamic changes in the crop's control needs throughout its entire growth cycle.

[0039] First, the system divides a complete pest control cycle into several time periods, the length of which can be determined based on the pest control sensitivity coefficient. Specifically, the length of the time period is directly proportional to the pest control sensitivity coefficient; the higher the sensitivity coefficient, the shorter the time period, and vice versa. This allows for more frequent application during the crop's sensitive periods and a relatively reduced number of applications during non-sensitive periods. When dividing the time periods, the system can refer to empirical data on agricultural pest control to select an appropriate time period length and a proportionality coefficient based on the pest control sensitivity coefficient.

[0040] For each defined time period, the system needs to set corresponding application rules based on the crop's sensitivity within that period. This mainly includes the maximum number of applications and the maximum pesticide concentration per application. The maximum number of applications can be inversely proportional to the crop's sensitivity coefficient; the higher the sensitivity coefficient, the lower the maximum number of applications. This is to avoid negative impacts on crops from overly frequent applications. The maximum pesticide concentration is typically set to not exceed the baseline spray concentration for that time period to prevent pesticide residues and other problems caused by excessively high concentrations.

[0041] After determining the number of applications and the maximum concentration for each time period, the system also needs to rationally arrange the interval and duration of each application and write them into the control rule matrix. Specifically, the system first calculates the minimum interval between two adjacent applications based on the maximum number of applications and the total length of the time period. Then, based on the amount of pesticide required for a single application and a given baseline spraying amount, it estimates the duration of a single application. Using the minimum interval and duration of a single application within each time period as parameters, a time-segmented application rule matrix is ​​generated.

[0042] S104. When the detection terminal detects pests, obtain the target control rule corresponding to the control rule matrix for the current time period. When the system detection terminal detects pests, it obtains the target control rule corresponding to the control rule matrix for the current time period. The spraying concentration in the target control rule does not exceed the baseline spraying concentration, and the spraying amount does not exceed the baseline spraying amount.

[0043] This step primarily describes how to use the previously generated control rule matrix to guide specific pesticide application operations during actual pest control. When the system's detection terminal detects pests in the field, the system needs to respond promptly and, based on the current time period, find the corresponding pesticide application rule in the rule matrix as the guiding principle for this control action.

[0044] Specifically, the system's detection terminal can employ various methods to monitor the presence of pests, including visual monitoring, image recognition, and vibration sensing. When the number or density of pests exceeds a preset threshold, the detection terminal sends an alarm to the system and reports the specific time of the pest detection. Upon receiving this alarm, the system first determines which time period in the control rule matrix the current time falls into, then extracts the corresponding application rule from the matrix and uses it as the target control rule for this control action.

[0045] The targeted pest control rules mainly include two key parameters: the maximum concentration of pesticide that can be used in this application and the maximum dosage. The system should ensure that these two parameters do not exceed the previously set baseline spray concentration and dosage limits. Furthermore, if an application has already been performed within the same time period, this application must also meet the minimum interval requirement between the previous application and the current application. Only when the requirements for concentration, dosage, and application interval are simultaneously met can the system ultimately determine the specific application plan.

[0046] S105. Control the autonomous walking spraying terminal to operate according to the target prevention and control rules, and obtain crop growth information once at a preset time interval. In this step, the system performs autonomous spraying based on the target prevention rules obtained in the previous step, and at the same time, it collects crop growth information regularly to provide feedback and decision-making basis for subsequent steps.

[0047] The autonomous spraying terminal controlled by the system can be an intelligent agricultural robot. It can autonomously plan its movement path and spray pesticides on designated areas of crops according to system commands. The spraying terminal's pesticide tank is pre-loaded with pesticide solution of the required concentration according to the target control rules. The system only needs to control the spraying terminal's movement and spraying time to evenly distribute the required amount of pesticide solution onto the crops. Through autonomous movement and targeted spraying, precise and unmanned management of farmland can be achieved, improving control efficiency.

[0048] Throughout the entire control cycle, the system periodically acquires crop growth information at preset time intervals (such as daily or half-day). This information includes crop height, leaf area, and biomass, indicators of which are automatically extracted and calculated using image processing algorithms from photos of the crop taken by a high-definition camera mounted on the spraying terminal. Crop growth information objectively reflects the effectiveness of pesticide control and the healthy growth status of the crop. The system then summarizes and analyzes the collected crop growth data to provide data support for optimizing control strategies.

[0049] S106. When the crop growth rate calculated based on the growth information is lower than the preset threshold, the baseline spray concentration and the baseline spray amount are reduced according to the preset ratio. In this step, the system first calculates the crop growth rate based on the acquired crop growth information. Then, it compares the calculated growth rate with a preset threshold. When the growth rate is lower than the preset threshold, the system determines that the crop's growth is poor and requires reducing the pesticide concentration and dosage to avoid further damage. The system can set multiple preset thresholds. Based on the comparison results between the growth rate and different thresholds, it reduces the baseline pesticide concentration and dosage by different preset ratios. These preset ratios can be set according to actual conditions and are not limited to fixed values.

[0050] Specifically, the system can acquire crop growth information, such as crop height, number of leaves, and leaf color, through image recognition and sensors. This information is then compared with previously acquired crop growth data to calculate the rate of change of various crop parameters over a certain period. These rates of change are then weighted and averaged to obtain the crop's growth rate. The system pre-sets multiple thresholds for the growth rate, such as 80%, 60%, and 40% of the normal growth rate. When the calculated crop growth rate falls below a certain threshold, the system selects a corresponding proportion from preset values, such as halving or reducing to one-third, and simultaneously reduces the baseline pesticide concentration and dosage according to this proportion to generate new, reduced baseline pesticide concentration and dosage.

[0051] S107. Based on the reduced baseline spray concentration and baseline spray amount, the control rule matrix is ​​re-determined to obtain the revised control rule matrix, and the target control rule acquisition step is executed in the next time period based on the revised control rule matrix.

[0052] In this step, the system, based on the reduced baseline spray concentration and application rate from step S106, redetermines the control rule matrix according to the method in step S103, generating a revised control rule matrix. Because the baseline spray concentration and application rate have changed, the original control rule matrix is ​​no longer applicable. It is necessary to recalculate all parameters based on the new baseline values ​​to generate a revised control rule matrix adapted to the current crop growth status, guiding subsequent pest control efforts. At the next time period, the system will execute the target control rule acquisition step in step S104 based on the revised control rule matrix, selecting the target control rule applicable to the current time period.

[0053] Specifically, the system can recalculate the control sensitivity coefficient based on the reduced baseline spray concentration and spray amount in step S106, and then re-divide the time periods according to this coefficient. It then determines the upper limit of spraying frequency and concentration for each time period, calculates parameters such as the minimum interval between two adjacent sprays and the duration of a single spray, and generates a revised control rule matrix. Upon entering the next time period, the system determines the current time period based on the current time and extracts the corresponding control rule from the revised control rule matrix as the new target control rule. The system then controls the autonomous spraying terminal to perform pest control operations according to the new target control rule.

[0054] In the above embodiments, precise pest control is achieved by establishing a control rule matrix and dynamically adjusting it according to crop growth status. Appropriate baseline spraying parameters are selected based on the current crop growth stage, and crop resistance is quantified using a control sensitivity coefficient, ensuring that control measures adapt to the crop's physiological characteristics. During control operations, crop growth is continuously monitored, and spraying parameters are adjusted accordingly. When a decrease in crop growth rate is detected, the application intensity is promptly reduced. This adaptive adjustment mechanism effectively prevents damage to crops from excessive pesticide use. By dividing the control rules into multiple time periods and dynamically correcting them, this method ensures control effectiveness while avoiding pesticide residue accumulation and reducing environmental pollution. By monitoring crop growth characteristics in real time and dynamically adjusting control rules, synergistic optimization between control operations and crop growth characteristics is achieved, improving pest control effectiveness.

[0055] In the above embodiments, by establishing a control rule matrix based on growth stage characteristics and dynamically adjusting it, synergistic optimization of pest control and crop growth was achieved. However, crops exhibit complex physiological changes during growth, and relying solely on the growth rate as a single indicator may be insufficient to comprehensively reflect the actual growth status of the crop. To more accurately assess the crop's growth status and optimize control measures, this application also provides a comprehensive assessment method based on photosynthesis and mechanical strength. This method establishes a coupled strength assessment system by measuring the crop's photosynthetic index and stem strength, thereby enabling more detailed adjustments to control rules. The following section combines... Figure 2 The following describes a comprehensive evaluation method based on photosynthesis and mechanical strength in an embodiment of this application: Please see Figure 2 This is a flowchart illustrating a comprehensive evaluation method based on photosynthesis and mechanical strength in an embodiment of this application.

[0056] S201. Obtain the photosynthetic index and stem strength of the crop during the current growth period, and divide the photosynthetic index into multiple photosynthetic levels according to a preset range, and divide the stem strength into multiple intensity levels according to a preset intensity range. The system first acquires the photosynthetic index and stem strength of the crop during its current growth stage. Specifically, this includes: controlling the detection terminal to collect fluorescence images and cross-sectional images of the stem during the current growth stage; extracting chlorophyll fluorescence intensity values ​​from the fluorescence images and segmenting these values ​​according to a preset wavelength range to obtain leaf fluorescence parameters; measuring the change in carbon dioxide concentration around the crop leaves using the gas sensor on the detection terminal and converting the change in carbon dioxide concentration per unit time into net photosynthetic rate; using the ratio of leaf fluorescence parameters to net photosynthetic rate as the photosynthetic index; segmenting the cross-sectional image to obtain the vascular bundle region; calculating the number of vascular bundles per unit area in the vascular bundle region as the vascular bundle density; calculating the stem cross-sectional area based on the number of pixels in the stem cross-sectional image; and using the product of the vascular bundle density and the stem cross-sectional area as the stem strength. The system then divides the photosynthetic index into multiple photosynthetic levels according to a preset range and the stem strength into multiple intensity levels according to a preset intensity range.

[0057] In this step, the system acquires the photosynthetic index and stem strength of the crop at its current growth stage using multiple sensors and image processing technologies. These two indicators reflect the crop's photosynthetic efficiency and damage resistance, respectively. The system can also use other physiological indicators to assess the crop's growth status, such as leaf water content and chlorophyll content, not limited to the photosynthetic index and stem strength. After acquiring the photosynthetic index and stem strength, the system divides them into multiple levels according to preset intervals for subsequent evaluation matrix construction. The preset intervals can be adjusted based on factors such as crop type and growth stage, and are not limited to fixed numerical ranges.

[0058] Specifically, the system can control the detection terminal to acquire fluorescence images of crop leaves and cross-sectional images of stems. Image processing algorithms extract characteristic parameters such as chlorophyll fluorescence intensity and vascular bundle density, and combine this with carbon dioxide concentration changes measured by a gas sensor to calculate the photosynthetic index and stem strength. Fluorescence images can be acquired using hyperspectral imaging technology to improve the accuracy of photosynthetic index calculation; cross-sectional images of stems can be acquired using a micro-tissue slicer and a high-powered microscope to improve the accuracy of stem strength calculation. When classifying photosynthetic levels and strength, the system can automatically grade photosynthetic index and stem strength using clustering algorithms, or it can preset level ranges based on expert knowledge to classify photosynthetic index and stem strength into discrete levels of high, medium, and low.

[0059] S202. Construct a coupling strength assessment matrix based on multiple photosynthetic levels and multiple intensity levels, and divide the current growth period into multiple coupling prevention and control intervals based on the coupling strength assessment matrix; The system constructs a coupling strength assessment matrix based on multiple photosynthetic levels and intensity levels. Specifically, this involves: establishing a two-dimensional matrix using multiple photosynthetic and intensity levels, where rows represent photosynthetic levels and columns represent intensity levels; for each element in the two-dimensional matrix, the product of the corresponding photosynthetic level value and intensity level value is used as the coupling contribution; clustering analysis is performed on the two-dimensional matrix based on the magnitude of the coupling contribution to obtain multiple coupling control intervals; the interval center value of each coupling control interval is calculated, and the intervals are numbered and sorted based on the interval center values ​​to obtain the coupling strength assessment matrix. The current growth stage is then divided into multiple coupling control intervals based on the coupling strength assessment matrix.

[0060] In this step, the system constructs a two-dimensional coupling strength evaluation matrix based on the photosynthetic level and intensity level obtained in step S201. The rows of the matrix represent photosynthetic levels, and the columns represent intensity levels. The value of each matrix element represents the coupling contribution of the crop under the corresponding combination of photosynthetic and intensity levels. The coupling contribution can be represented by the product of the photosynthetic level value and the intensity level value, or other calculation methods such as weighted average or nonlinear combination can be used to reflect the synergistic effect of photosynthesis and damage resistance on crop growth. Based on the constructed evaluation matrix, the system divides the current growth period into several coupling control intervals. Crops within each interval have similar coupling contributions and can be subject to similar control strategies. The method for dividing the control intervals can be adjusted according to actual needs and is not limited to methods based on cluster analysis.

[0061] Specifically, the system can use a two-dimensional matrix composed of all combinations of photosynthetic level and intensity level as the initial matrix, calculate the coupling contribution of each matrix element, and form a coupling contribution matrix. Then, the system performs cluster analysis on the coupling contribution matrix, grouping matrix elements with similar coupling contributions into the same class, resulting in several coupling control intervals. To facilitate subsequent adjustment of control rules, the system can calculate the interval center value for each coupling control interval, i.e., the average coupling contribution of all matrix elements within that interval, and number and sort each coupling control interval according to the interval center value, forming a coupling intensity assessment matrix. The system determines the coupling control interval to which the current growth stage's photosynthetic index and stem intensity belong based on their positions in the coupling intensity assessment matrix.

[0062] S203. The ratio of the photosynthetic index to the preset baseline photosynthetic index in each coupled prevention and control interval is used as the photosynthetic efficiency coefficient, and the ratio of the stem strength to the preset baseline stem strength is used as the damage resistance coefficient. In this step, within each coupled prevention and control interval, the system calculates the ratio of the crop's photosynthetic index to a preset baseline photosynthetic index, using this ratio as the photosynthetic efficiency coefficient to characterize the relative level of the crop's photosynthetic efficiency within that interval. Simultaneously, it calculates the ratio of the crop's stem strength to a preset baseline stem strength, using this ratio as the damage resistance coefficient to characterize the crop's relative ability to resist pesticide and mechanical damage within that interval. The preset baseline photosynthetic index and preset baseline stem strength can be set according to factors such as crop type, growth stage, and cultivation conditions, representing the average level of photosynthetic efficiency and damage resistance of the crop under ideal growth conditions. The system can use other indicators as numerators or denominators, such as photosynthetic rate and stem stiffness, to calculate the photosynthetic efficiency coefficient and damage resistance coefficient, and is not limited to photosynthetic index and stem strength.

[0063] Specifically, within each coupled prevention and control interval, the system can acquire the photosynthetic index and stem strength of all crop samples within that interval, and calculate their average value as the representative value of the photosynthetic index and stem strength for that interval. Then, the system divides the representative value by a preset baseline photosynthetic index and a preset baseline stem strength, respectively, to obtain the photosynthetic efficiency coefficient and damage resistance coefficient. The preset baseline values ​​can be obtained through extensive experiments and statistical analysis, or set based on plant physiology knowledge and expert experience. The photosynthetic efficiency coefficient and damage resistance coefficient can be expressed as a percentage or a decimal. When the coefficient is greater than 1, it indicates that the crop's photosynthetic efficiency or damage resistance is above average; when the coefficient is less than 1, it indicates that the corresponding ability of the crop is below average.

[0064] S204. Calculate the upper limit of control energy based on the photosynthetic efficiency coefficient, and calculate the crop's damage resistance index based on the damage resistance coefficient; The system calculates the upper limit of control energy based on the photosynthetic efficiency coefficient and the crop's damage resistance index based on the damage resistance coefficient. Specifically, this includes: multiplying the photosynthetic efficiency coefficient by a preset baseline control energy as the initial upper limit of control energy; increasing the initial upper limit of control energy by a first preset ratio when the photosynthetic efficiency coefficient is greater than a first preset threshold, and decreasing the initial upper limit of control energy by a second preset ratio when the photosynthetic efficiency coefficient is less than a second preset threshold, to obtain the upper limit of control energy; multiplying the damage resistance coefficient by a preset baseline damage resistance value as the initial damage resistance index; increasing the initial damage resistance index by a third preset ratio when the damage resistance coefficient is greater than a third preset threshold, and decreasing the initial damage resistance index by a fourth preset ratio when the damage resistance coefficient is less than a fourth preset threshold, to obtain the crop's damage resistance index.

[0065] In this step, the system uses the photosynthetic efficiency coefficient and damage resistance coefficient calculated in step S203 to calculate the upper limit of control energy and the crop damage resistance index, respectively. The upper limit of control energy represents the maximum amount of pesticide or the intensity of physical and chemical control measures that can be applied without affecting crop photosynthesis; the crop damage resistance index represents the degree of pesticide or mechanical damage that the crop can withstand during control. The system introduces preset baseline control energy and preset baseline damage resistance values, and calculates them in conjunction with the photosynthetic efficiency coefficient and damage resistance coefficient to obtain dynamically adjusted upper limits of control energy and damage resistance indices. The preset baseline values ​​can be obtained through extensive experiments and statistical analysis, representing the average control capacity and crop damage resistance under conventional control conditions.

[0066] Specifically, the system can multiply the photosynthetic efficiency coefficient by a preset baseline control energy to obtain an initial control energy limit. When the photosynthetic efficiency coefficient is greater than a first preset threshold, such as 0.8, it indicates that the crop's photosynthetic efficiency is high, and the control intensity can be appropriately increased. The system multiplies the initial control energy limit by a first preset proportional coefficient, such as 1.2, to obtain the final control energy limit. When the photosynthetic efficiency coefficient is less than a second preset threshold, such as 0.6, it indicates that the crop's photosynthetic efficiency is low, and the control intensity needs to be reduced. The system multiplies the initial control energy limit by a second preset proportional coefficient, such as 0.8, to obtain the final control energy limit. Similarly, the system can multiply the damage resistance coefficient by a preset baseline damage resistance value to obtain an initial damage resistance index, and multiply it by different preset proportional coefficients according to the relationship between the damage resistance coefficient and the third and fourth preset thresholds to obtain the final crop damage resistance index.

[0067] S205. Obtain the difference in photosynthetic index and the difference in stem strength between adjacent coupled prevention and control intervals, and use the ratio of the difference in photosynthetic index and the difference in stem strength to the preset benchmark difference as the transition weight coefficient. In this step, the system acquires the differences in photosynthetic index and stem strength between the current coupled control zone and adjacent coupled control zones, and compares these differences with preset baseline differences in photosynthetic index and stem strength, respectively, to calculate a transition weight coefficient. The transition weight coefficient reflects the degree of physiological characteristic difference between the current control zone and adjacent control zones; the greater the difference, the larger the transition weight coefficient, indicating a greater need for adjustments to control measures during zone transition. The preset baseline difference can be set based on extensive experimental data and expert experience, representing the common differences in photosynthetic index and stem strength between coupled control zones. The system can also use other methods to calculate the transition weight coefficient, such as the ratio of the difference in the coupling contribution of adjacent zones to the difference in the preset baseline contribution.

[0068] Specifically, the system can calculate the mean photosynthetic index and mean stem strength of the current coupled control interval, obtain the corresponding mean values ​​of adjacent coupled control intervals, and calculate their differences. Then, the system divides the photosynthetic index difference by a preset baseline photosynthetic index difference and the stem strength difference by a preset baseline stem strength difference, obtaining two ratios. The system takes the larger of the two ratios as the transition weight coefficient to reflect the maximum adjustment range of control measures required during interval transitions. When the transition weight coefficient is less than 1, it indicates that the difference between the current interval and adjacent intervals is small, and the adjustment range of control measures can be small; when the transition weight coefficient is greater than 1, it indicates that the difference between the current interval and adjacent intervals is large, and the adjustment range of control measures needs to be larger.

[0069] S206. The upper limit of prevention and control energy and the crop damage resistance index are weighted according to the transition weight coefficient to generate the interval conversion coefficient. The interval conversion coefficient is then multiplied by the target prevention and control rule to obtain the modified target prevention and control rule.

[0070] In this step, the system uses the transition weight coefficient calculated in step S205 to weight the upper limit of control energy and the crop damage resistance index obtained in step S204, generating an interval conversion coefficient. This interval conversion coefficient is then multiplied by the target control rule to obtain the revised target control rule. The transition weight coefficient reflects the degree of change in photosynthetic index and stem strength between adjacent coupled control intervals. The system can dynamically adjust the upper limit of control energy and the crop damage resistance index based on the magnitude of the transition weight coefficient, making them better adaptable to changes in crop growth status, thereby revising the target control rule. This step can select different weighting calculation methods, such as arithmetic mean or geometric mean, according to actual needs, or it can introduce other influencing factors as weights to comprehensively adjust the upper limit of control energy and the crop damage resistance index.

[0071] Specifically, the system can multiply the transition weight coefficient by the upper limit of control energy and the crop damage resistance index respectively to obtain the adjusted upper limit of control energy and the crop damage resistance index, and then add the two to obtain the interval conversion coefficient. Alternatively, the system can first standardize the upper limit of control energy and the crop damage resistance index to make their numerical ranges consistent, and then multiply them by the transition weight coefficient and add them to obtain the interval conversion coefficient. The interval conversion coefficient reflects the transition characteristics between adjacent coupled control intervals. The larger the value, the greater the difference between adjacent intervals, and the greater the adjustment of control measures. The system can preset multiple threshold values ​​for interval conversion coefficients. Based on the relationship between the interval conversion coefficient and the threshold, the system can modify various parameters in the target control rule, such as spray concentration and number of sprays, to different degrees. When the interval conversion coefficient is large, the modification range can be appropriately increased; when the interval conversion coefficient is small, the modification range can be appropriately decreased, thus obtaining the modified target control rule.

[0072] In the above embodiments, by constructing a coupling strength evaluation matrix and introducing an interval transformation mechanism, the control rules are refined and adjusted. The method assesses crop status from two dimensions: photosynthesis and stem strength. It quantifies the crop's physiological characteristics by calculating the photosynthetic efficiency coefficient and damage resistance coefficient, and determines the upper limit of control energy and the damage resistance index based on these parameters. By analyzing the parameter differences between adjacent coupled control intervals and calculating the transition weight coefficient, the method establishes a smooth transition adjustment mechanism between intervals, avoiding abrupt changes in control rules during transitions between different intervals. This multi-dimensional physiological index-based regulation method allows control rules to more comprehensively adapt to the crop's growth state, improving the accuracy and safety of pest control. By introducing an interval transformation coefficient to modify the control rules, the method maintains the healthy growth of the crop while ensuring control effectiveness.

[0073] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of an adaptive pest control system based on the crop growth period provided in an embodiment of this application.

[0074] It should be noted that, Figure 3 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0075] like Figure 3As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0076] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0077] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0078] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0080] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0081] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0082] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0083] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0084] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. An adaptive pest control method based on crop growth stage, characterized in that, include: Based on the current growth stage information of the crop, the corresponding baseline spraying concentration and baseline spraying amount are extracted from the preset growth stage parameter library; The control sensitivity coefficient of the crop is determined based on the baseline spray concentration and baseline spray amount, and the control sensitivity coefficient characterizes the crop's tolerance to the pesticide. Establish a time-segmented prevention and control rule matrix based on the aforementioned prevention and control sensitivity coefficient; When the detection terminal detects pests, it obtains the target control rule corresponding to the control rule matrix for the current time period. The spraying concentration in the target control rule does not exceed the baseline spraying concentration, and the spraying amount does not exceed the baseline spraying amount. The autonomous walking spraying terminal is controlled to operate according to the target prevention and control rules, and the crop growth information is acquired once at preset intervals; When the growth rate of the crop calculated based on the growth information is lower than a preset threshold, the baseline spray concentration and the baseline spray amount are reduced according to a preset ratio. The control rule matrix is ​​re-determined based on the reduced baseline spray concentration and baseline spray amount to obtain the modified control rule matrix, and the target control rule acquisition step is executed according to the modified control rule matrix in the next time period.

2. The method according to claim 1, characterized in that, The determination of the crop's sensitivity coefficient for pest control based on the baseline spray concentration and baseline spray amount specifically includes: Obtain the historical baseline spray concentration and historical baseline spray amount for the crop; The concentration change coefficient is obtained by dividing the baseline spray concentration by the historical baseline spray concentration, and the dosage change coefficient is obtained by dividing the baseline spray amount by the historical baseline spray amount. The product of the concentration change coefficient and the dosage change coefficient is used as the control parameter change rate. The prevention and control sensitivity coefficient is obtained by multiplying the rate of change of the prevention and control parameters by the preset basic prevention and control sensitivity coefficient.

3. The method according to claim 1, characterized in that, The step of establishing a time-segmented prevention and control rule matrix based on the prevention and control sensitivity coefficient specifically includes: A prevention and control cycle is divided into multiple time periods, and the duration of each time period is proportional to the prevention and control sensitivity coefficient, wherein the proportionality is a preset fixed coefficient. For each time period, the upper limit of the number of sprayings and the upper limit of the spray concentration are determined according to the prevention and control sensitivity coefficient. The upper limit of the number of sprayings is inversely proportional to the prevention and control sensitivity coefficient, and the upper limit of the spray concentration does not exceed the benchmark spray concentration. Based on the upper limit of the number of sprayings and the baseline spraying amount, calculate the minimum interval between two adjacent sprayings and the duration of a single spraying within each time period, and establish a time-segmented prevention and control rule matrix based on the minimum interval and the duration of a single spraying.

4. The method according to claim 1, characterized in that, After the step of obtaining the target prevention and control rules according to the modified prevention and control rule matrix in the next time period, the method further includes: The photosynthetic index and stem strength of the crop during its current growth period are obtained, and the photosynthetic index is divided into multiple photosynthetic levels according to a preset range, and the stem strength is divided into multiple intensity levels according to a preset intensity range. A coupling strength assessment matrix is ​​constructed based on the multiple photosynthetic levels and the multiple intensity levels, and the current growth period is divided into multiple coupling prevention and control intervals based on the coupling strength assessment matrix; The ratio of the photosynthetic index to the preset benchmark photosynthetic index in each of the coupled prevention and control intervals is used as the photosynthetic efficiency coefficient, and the ratio of the stem strength to the preset benchmark stem strength is used as the damage resistance coefficient. The upper limit of the control energy is calculated based on the photosynthetic efficiency coefficient, and the damage resistance index of the crop is calculated based on the damage resistance coefficient. The difference in photosynthetic index and the difference in stem strength between adjacent coupled prevention and control intervals are obtained, and the ratio of the difference in photosynthetic index and the difference in stem strength to the preset benchmark difference is used as the transition weight coefficient. The upper limit of prevention and control energy and the crop damage resistance index are weighted according to the transition weight coefficient to generate an interval conversion coefficient. The interval conversion coefficient is then multiplied by the target prevention and control rule to obtain the modified target prevention and control rule.

5. The method according to claim 4, characterized in that, The acquisition of the photosynthetic index and stem strength of the crop during its current growth stage specifically includes: The detection terminal is controlled to acquire fluorescence images and cross-sectional images of the stems of the crop during its current growth stage; Extract the chlorophyll fluorescence intensity value from the fluorescence image, and perform segmented statistical analysis on the chlorophyll fluorescence intensity value according to a preset wavelength range to obtain the leaf fluorescence parameters; The gas sensor of the detection terminal measures the change in carbon dioxide concentration around the crop leaves, and converts the change in carbon dioxide concentration per unit time into net photosynthetic rate. The ratio of the leaf fluorescence parameter to the net photosynthetic rate is used as the photosynthetic index; The cross-sectional image is segmented to obtain the vascular bundle region; The number of vascular bundles per unit area in the vascular bundle region is calculated as the vascular bundle density. Calculate the cross-sectional area of ​​the stem based on the number of pixels in the cross-sectional image of the stem. The product of the vascular bundle density and the stem cross-sectional area is taken as the stem strength.

6. The method according to claim 4, characterized in that, The construction of the coupling strength evaluation matrix based on the multiple photosynthetic levels and the multiple intensity levels specifically includes: A two-dimensional matrix is ​​established using the multiple photosynthetic levels and the multiple intensity levels, where the rows of the two-dimensional matrix represent photosynthetic levels and the columns represent intensity levels; For each element in the two-dimensional matrix, the product of the corresponding photosynthetic level value and intensity level value is used as the coupling contribution. Cluster analysis is performed on the two-dimensional matrix based on the magnitude of the coupling contribution to obtain multiple coupling prevention intervals; Calculate the interval center value of each of the coupling prevention and control intervals, and number and sort each of the coupling prevention and control intervals based on the interval center value to obtain the coupling strength evaluation matrix.

7. The method according to claim 4, characterized in that, The calculation of the upper limit of control energy based on the photosynthetic efficiency coefficient and the calculation of the crop's damage resistance index based on the damage resistance coefficient specifically include: The product of the photosynthetic efficiency coefficient and the preset baseline prevention and control energy is used as the initial upper limit of prevention and control energy; When the photosynthetic efficiency coefficient is greater than the first preset threshold, the initial control energy limit is increased by the first preset ratio; when the photosynthetic efficiency coefficient is less than the second preset threshold, the initial control energy limit is decreased by the second preset ratio to obtain the control energy limit. The product of the damage resistance coefficient and the preset benchmark damage resistance value is used as the initial damage resistance index; When the damage resistance coefficient is greater than the third preset threshold, the initial damage resistance index is increased by the third preset ratio; when the damage resistance coefficient is less than the fourth preset threshold, the initial damage resistance index is decreased by the fourth preset ratio, thus obtaining the damage resistance index of the crop.

8. An adaptive pest control system based on crop growth stage, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-7.

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