A quinoa fertilization recommendation method and system fusing soil detection data and historical planting cases

Abstract

This invention provides a method and system for quinoa fertilization recommendation that integrates soil testing data and historical planting cases, belonging to the field of quinoa cultivation. The method includes: acquiring image data of the current growth stage of quinoa, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data; identifying the developmental stage and development degree index of quinoa ears based on the image data; determining whether fertilization recommendation needs to be initiated based on the comparison result of the development degree index and a preset development threshold; if so, constructing a multi-source data fusion model to obtain the soil fertility decay rate, transpiration compensation coefficient, and expected ear development improvement rate; inputting these into an adaptive recommendation engine to output the recommended fertilization amount, fertilization interval, and fertilization type. This improves the accuracy, environmental adaptability, and fertilizer utilization rate of fertilization recommendations, achieving intelligent and dynamic closed-loop optimization of quinoa fertilization.

Description

Technical Field

[0001] This invention relates to the field of quinoa cultivation technology, specifically to a method and system for recommending quinoa fertilization that integrates soil testing data with historical planting cases. Background Technology

[0002] Quinoa, as a crop with high nutritional value, has a decisive impact on yield and quality through water and fertilizer management during its cultivation. Traditional quinoa fertilization management relies heavily on growers' experience, lacking quantitative analysis of dynamic changes in soil fertility, the influence of meteorological environment, and crop growth response. This leads to delayed fertilization decisions, low fertilizer utilization, and even soil degradation and environmental pollution due to excessive fertilization.

[0003] In recent years, some intelligent agricultural systems have introduced sensor and image recognition technologies to attempt precision fertilization. By acquiring images of quinoa during the heading stage, analyzing the development of the quinoa ears, and combining this with soil conductivity testing, the system can determine the causes of insufficient soil fertility (such as insufficient irrigation, excessively long fertilization intervals, or insufficient fertilization), and then adjust the fertilization interval or fertilization amount accordingly. However, this system has the following shortcomings: First, the system relies solely on the comparison between the current soil electrical conductivity and the preset threshold for judgment, failing to consider the dynamic decline of soil fertility over time and lacking analysis of the coupled effects of historical fertilization records and crop growth stages, resulting in fertilization decisions lagging behind actual changes in soil fertility.

[0004] Second, the system only uses meteorological factors (light intensity, air humidity) to calculate irrigation parameters, without considering them as input factors for fertilization decisions. In reality, light and humidity significantly affect the efficiency of crop absorption and utilization of fertilizers, and ignoring this coupling relationship will reduce the accuracy of fertilization recommendations.

[0005] Third, the system uses fixed thresholds and linear adjustment rules, lacks the ability to utilize historical fertilization response data and adaptive optimization, and is unable to cope with the differentiated needs of different plots, varieties and climate conditions, resulting in insufficient generalization ability of fertilization recommendations.

[0006] Therefore, there is an urgent need for a quinoa fertilization recommendation method that can integrate multi-source data, dynamically assess soil fertility decline, quantify meteorological impacts, and utilize historical response data to achieve adaptive optimization, so as to improve the scientificity and accuracy of fertilization decisions. Summary of the Invention

[0007] The purpose of this invention is to provide a quinoa fertilization recommendation method and system that integrates soil testing data and historical planting cases, thereby improving the accuracy of fertilization recommendations, environmental adaptability, and fertilizer utilization, and realizing intelligent and dynamic closed-loop optimization of quinoa fertilization.

[0008] To achieve the above objectives, embodiments of the present invention provide a method for recommending quinoa fertilization that integrates soil testing data and historical planting cases, including: Acquire image data of quinoa at its current growth stage, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data; The developmental stage and development degree index of quinoa ears were identified based on the image data. Based on the comparison between the developmental index and the preset developmental threshold, it is determined whether fertilization recommendation needs to be initiated. If so, a multi-source data fusion model is constructed to obtain the influencing factors of fertilization. The multi-source data fusion model includes: a soil fertility dynamic assessment unit, used to calculate the soil fertility decay rate based on soil electrical conductivity, historical fertilization records, and the current growth stage; a meteorological influence factor correction unit, used to calculate the transpiration compensation coefficient based on light intensity and air humidity; and a fertilization response prediction unit, used to predict the expected panicle development improvement rate under the current conditions based on historical fertilization response data. The soil fertility decay rate, transpiration compensation coefficient, and expected panicle development improvement rate are input into the adaptive recommendation engine, which outputs the recommended fertilization amount, fertilization interval, and fertilization type.

[0009] Optionally, the step of identifying the developmental stage and developmental degree index of quinoa ears based on the image data includes: The image data is segmented, and the regions containing quinoa ears are extracted from the segmented images to generate corresponding binary masks and bounding boxes; Extract the color, texture, and morphological features of each region containing quinoa ears, and map these features to a pre-constructed developmental stage identifier table to output the developmental stage of each quinoa ear. The developmental stages include early heading stage, grain filling stage, waxy ripening stage, and full ripening stage. The developmental degree index of quinoa ears is determined by the average of the ratio of the mask area of ​​the quinoa ear to the reference area of ​​the corresponding developmental stage, the ratio of the vertical axis length of the quinoa ear to the reference length of the corresponding developmental stage, and the ratio of the proportion of green pixels in the region containing the quinoa ear to the reference green proportion of the corresponding developmental stage. The mask area of ​​the quinoa ear is obtained by counting the number of pixels in the binary mask, the vertical axis length of the quinoa ear is obtained based on the height of the bounding box, and the proportion of green pixels is obtained by superimposing the image data with the binary mask and counting the color of the pixels in the mask area.

[0010] Optionally, the developmental level index can be calculated using the following formula:

[0011] In the formula, The area of ​​the quinoa ear covered by the film. This is a reference area for the corresponding developmental stage. is the length of the longitudinal axis of the wheat ear. This is a reference length for the corresponding developmental stage. This represents the percentage of green pixels in the region containing quinoa ears. The percentage of green is a reference for the corresponding developmental stage.

[0012] Optionally, the rate of soil fertility decline can be calculated based on soil electrical conductivity, historical fertilization records, and the current growth stage, including: Collect soil electrical conductivity data from quinoa roots; and obtain the growth stage coefficient corresponding to the current growth stage and the preset basic consumption coefficient. The actual conductivity decay rate is calculated based on the difference in soil conductivity at the roots of quinoa over two consecutive days and the number of days between sampling; at the same time, the theoretical decay rate is calculated based on the product of the basic consumption coefficient and the conductivity of the previous day. Within a preset time window, the ratio of the amount of fertilizer applied for each application to the reference amount of fertilizer is multiplied by an exponential decay factor with the number of days since the application as the variable. The calculation results of all fertilization events are then summed to obtain the fertilization aftereffect function value. Divide the actual decay rate by the theoretical decay rate to obtain the first ratio; take the reciprocal of the product of the preset weighting coefficient and the fertilization aftereffect function value as the second ratio; multiply the first ratio, the second ratio, and the growth stage coefficient to obtain the soil fertility decay rate index.

[0013] Optionally, the evapotranspiration compensation coefficient can be calculated using the following formula:

[0014] In the formula, D is the current light intensity, D0 is the standard light intensity, S is the current air humidity, and S0 is the standard air humidity.

[0015] Optionally, the soil fertility decay rate, transpiration compensation coefficient, and expected panicle development improvement rate are input into the adaptive recommendation engine, which outputs recommended fertilization amount, fertilization interval, and fertilization type, including: Based on the current growth stage of quinoa, a set of candidate schemes is extracted from the pre-set fertilization scheme library; For each candidate scheme in the group, the soil fertility decay rate, transpiration compensation coefficient, and expected panicle development improvement rate are multiplied by their respective preset weighting coefficients to obtain fertility decay contribution score, transpiration contribution score, and improvement rate contribution score. The fertility attenuation contribution score, the transpiration contribution score, and the improvement rate contribution score of each candidate scheme in the group are added together to obtain the preliminary score of each candidate scheme; Based on the degree of deviation between the fertilizer application amount in each candidate scheme and the standard fertilizer application amount, a penalty term is applied to the preliminary score to obtain the final comprehensive score of each candidate scheme. The fertilizer application rate, fertilization interval, and fertilizer type from the candidate scheme with the highest final comprehensive score will be output as the recommendation result.

[0016] Optionally, the fertilization type includes a recommended combination of nitrogen fertilizer, phosphorus fertilizer, potassium fertilizer and micronutrient fertilizer, and the recommendation engine outputs the proportion and application amount of each type of fertilizer.

[0017] Secondly, the present invention also provides a quinoa fertilization recommendation system that integrates soil testing data and historical planting cases, comprising: The data acquisition module is used to acquire image data of quinoa at its current growth stage, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data. An image recognition module is used to identify the developmental stage and development degree index of quinoa ears based on the image data; The model building module is used to determine whether fertilization recommendation needs to be activated based on the comparison result between the development degree index and the preset development threshold. If so, a multi-source data fusion model is constructed to obtain the fertilization influencing factors. The multi-source data fusion model includes: a soil fertility dynamic assessment unit, used to calculate the soil fertility decay rate based on soil electrical conductivity, historical fertilization records and the current growth stage; a meteorological influence factor correction unit, used to calculate the transpiration compensation coefficient based on light intensity and air humidity; and a fertilization response prediction unit, used to predict the expected panicle development improvement rate under the current conditions based on historical fertilization response data. The fertilization recommendation module is used to input the soil fertility decay rate, transpiration compensation coefficient and expected panicle development improvement rate into the adaptive recommendation engine, and output the recommended fertilization amount, fertilization interval and fertilization type.

[0018] Optionally, the image recognition module is specifically used for: The image data is segmented, and the regions containing quinoa ears are extracted from the segmented images to generate corresponding binary masks and bounding boxes; Extract the color, texture, and morphological features of each region containing quinoa ears, and map these features to a pre-constructed developmental stage identifier table to output the developmental stage of each quinoa ear. The developmental stages include early heading stage, grain filling stage, waxy ripening stage, and full ripening stage. The developmental degree index of quinoa ears is determined by the average of the ratio of the mask area of ​​the quinoa ear to the reference area of ​​the corresponding developmental stage, the ratio of the vertical axis length of the quinoa ear to the reference length of the corresponding developmental stage, and the ratio of the proportion of green pixels in the region containing the quinoa ear to the reference green proportion of the corresponding developmental stage. The mask area of ​​the quinoa ear is obtained by counting the number of pixels in the binary mask, the vertical axis length of the quinoa ear is obtained based on the height of the bounding box, and the proportion of green pixels is obtained by superimposing the image data with the binary mask and counting the color of the pixels in the mask area.

[0019] Optionally, the dynamic soil fertility assessment unit is specifically used for: Collect soil electrical conductivity data from quinoa roots; and obtain the growth stage coefficient corresponding to the current growth stage and the preset basic consumption coefficient. The actual conductivity decay rate is calculated based on the difference in soil conductivity at the roots of quinoa over two consecutive days and the number of days between sampling; at the same time, the theoretical decay rate is calculated based on the product of the basic consumption coefficient and the conductivity of the previous day. Within a preset time window, the ratio of the amount of fertilizer applied for each application to the reference amount of fertilizer is multiplied by an exponential decay factor with the number of days since the application as the variable. The calculation results of all fertilization events are then summed to obtain the fertilization aftereffect function value. Divide the actual decay rate by the theoretical decay rate to obtain the first ratio; take the reciprocal of the product of the preset weighting coefficient and the fertilization aftereffect function value as the second ratio; multiply the first ratio, the second ratio, and the growth stage coefficient to obtain the soil fertility decay rate index.

[0020] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for recommending quinoa fertilization by integrating soil testing data and historical planting cases.

[0021] Fourthly, the present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for recommending quinoa fertilization by integrating soil testing data with historical planting cases.

[0022] By integrating image data, soil electrical conductivity, meteorological parameters, and historical fertilization response data, a multi-source model was constructed using the above technical solution. An adaptive recommendation engine was then used to output the optimal fertilization plan, dynamically track the rate of soil fertility decay, quantify the impact of light and humidity on nutrient absorption, and continuously optimize decisions based on historical response data. This improved the accuracy of fertilization recommendations, environmental adaptability, and fertilizer utilization, achieving intelligent and dynamic closed-loop optimization of quinoa fertilization.

[0023] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a quinoa fertilization recommendation method that integrates soil testing data and historical planting cases, provided by an embodiment of the present invention; Figure 2 This is a flowchart of an embodiment of the present invention for identifying the developmental stage and developmental degree index of quinoa ears based on image data; Figure 3 This is a flowchart of an adaptive recommendation engine processing method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a quinoa fertilization recommendation system that integrates soil testing data and historical planting cases, provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] Various embodiments of this disclosure will be described more fully in the following detailed description. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0026] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions or operations and do not limit the addition of one or more functions or operations. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, or combinations of the foregoing, or the possibility of adding one or more features, numbers, steps, operations, or combinations of the foregoing.

[0027] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] See Figure 1 The diagram shows a flowchart of a quinoa fertilization recommendation method that integrates soil testing data and historical planting cases in a specific embodiment, including the following execution steps: Step 100: Obtain image data of the current growth stage of quinoa, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data.

[0030] Specifically, using multispectral drones or fixed high-definition cameras, field images were captured daily from 10:00 AM to 11:00 AM (during periods of stable light) during the quinoa heading to full maturity stage. The images were taken from a height of 5–10 meters above the canopy, with a resolution of at least 1920 × 1080 pixels, covering at least 10 representative quadrats (each quadrat measuring 2m × 2m). Three images were continuously collected from each quadrat for subsequent optimization or fusion. Histogram equalization or reflectance correction based on a standard white board was used to eliminate uneven light distribution. Contrast stretching was applied to the green channel to highlight the differences between quinoa ears and leaves. A soil conductivity sensor was embedded in the center of each quadrat, recording data twice daily, once in the morning and once in the afternoon, and the average value was taken as the daily conductivity value. Soil temperature at the sensor location was also recorded for subsequent temperature compensation. A temperature and humidity sensor was installed in the center of the field, recording air humidity every 30 minutes, and the average of all records was taken as the daily average air humidity.

[0031] Step 101: Identify the developmental stage and development degree index of quinoa ears based on the image data.

[0032] For details, please refer to Figure 2 As shown, when executing step 101, the following steps can be specifically performed: S1010: Segment the image data and extract the regions containing quinoa ears from the segmented image to generate corresponding binary masks and bounding boxes.

[0033] S1011: Extract the color features, texture features, and morphological features of each region containing quinoa ears, and map the color features, texture features, and morphological features to a pre-constructed developmental stage identifier table, and output the developmental stage of each quinoa ear.

[0034] The developmental stages include the early heading stage, the grain-filling stage, the waxy ripening stage, and the full ripening stage.

[0035] For example, the developmental stages include: Stage 1: early heading stage (heading not fully emerged, light green in color); Stage 2: grain filling stage (heading full, greenish-yellow in color); Stage 3: waxy ripening stage (heading turns yellow, grains harden); Stage 4: full ripening stage (heading golden yellow or brown, grains fully mature).

[0036] Specifically, color features include: mean H component and mean saturation in HSV space; texture features include: local binary pattern (LBP) histogram; morphological features include: ear length / width ratio and ratio of convex hull area to mask area.

[0037] S1012: The development degree index of quinoa ears is determined based on the average of the ratio of the mask area of ​​the quinoa ear to the reference area of ​​the corresponding development stage, the ratio of the longitudinal length of the quinoa ear to the reference length of the corresponding development stage, and the ratio of the proportion of green pixels in the region containing the quinoa ear to the reference green proportion of the corresponding development stage.

[0038] The mask area of ​​the quinoa ear is obtained by counting the number of pixels in the binary mask, the vertical axis length of the quinoa ear is obtained based on the height of the bounding box, and the proportion of green pixels is obtained by superimposing the image data with the binary mask and counting the color of the pixels within the mask area.

[0039] More specifically, the developmental level index can be calculated using the following formula:

[0040] In the formula, The area of ​​the quinoa ear covered by the film. This is a reference area for the corresponding developmental stage. is the length of the longitudinal axis of the wheat ear. This is a reference length for the corresponding developmental stage. This represents the percentage of green pixels in the region containing quinoa ears. The percentage of green is a reference for the corresponding developmental stage.

[0041] Step 102: Based on the comparison results between the developmental level index and the preset developmental threshold, determine whether fertilization recommendation needs to be initiated. If so, construct a multi-source data fusion model to obtain the influencing factors of fertilization.

[0042] The multi-source data fusion model includes: a soil fertility dynamic assessment unit, used to calculate the soil fertility decay rate based on soil electrical conductivity, historical fertilization records, and the current growth stage; a meteorological influence factor correction unit, used to calculate the transpiration compensation coefficient based on light intensity and air humidity; and a fertilization response prediction unit, used to predict the expected panicle development improvement rate under current conditions based on historical fertilization response data.

[0043] Specifically, the soil fertility decline rate is calculated based on soil electrical conductivity, historical fertilization records, and the current growth stage, including the following sub-steps: S1: Collect the soil electrical conductivity of quinoa roots; and obtain the growth stage coefficient corresponding to the current growth stage and the preset basic consumption coefficient.

[0044] S2: Calculate the actual conductivity decay rate based on the difference in soil conductivity at the root of quinoa over two consecutive days and the number of days between sampling; at the same time, calculate the theoretical decay rate based on the product of the basic consumption coefficient and the conductivity of the previous day.

[0045] S3: Within a preset time window, the ratio of the amount of fertilizer applied for each application to the reference amount of fertilizer is multiplied by an exponential decay factor with the number of days since the application as the variable. The calculation results of all fertilization events are then summed to obtain the fertilization aftereffect function value.

[0046] S4: Divide the actual decay rate by the theoretical decay rate to obtain the first ratio; take the reciprocal of the product of the preset weighting coefficient and the fertilization aftereffect function value as the second ratio; multiply the first ratio, the second ratio and the growth stage coefficient to obtain the soil fertility decay rate index.

[0047] More specifically, the soil fertility decline rate index can be calculated using the following formula: ; ; ; ; In the formula, It is an index of the rate of soil fertility decline. This represents the actual conductivity decay rate. The preset weighting coefficients, This is a fertilization aftereffect function. This represents the growth stage coefficient corresponding to the current growth stage. Let t be the soil electrical conductivity of the root zone collected at time t. The soil electrical conductivity in the root zone was collected at time t-1. Basic consumption coefficient, This is the theoretical decay rate. This is the amount of fertilizer applied for the i-th time. For reference fertilizer application rate, Let be the number of days since the i-th fertilization. This represents the time constant for the corresponding fertilizer type.

[0048] like If the actual decay rate is faster than the theoretical rate, it indicates that the soil has poor fertilizer retention capacity or that the crop absorbs fertilizer quickly, requiring a shorter fertilization interval or an increased fertilization amount.

[0049] like If the decay is slow, it may be due to excessive fertilization or strong soil adsorption. Fertilization should be reduced appropriately.

[0050] Specifically, the evapotranspiration compensation coefficient is calculated using the following formula:

[0051] In the formula, D is the current light intensity, D0 is the standard light intensity, S is the current air humidity, and S0 is the standard air humidity.

[0052] Step 103: Input the soil fertility decay rate, transpiration compensation coefficient and expected panicle development improvement rate into the adaptive recommendation engine, and output the recommended fertilization amount, fertilization interval and fertilization type.

[0053] For details, please refer to Figure 3 As shown, when executing step 103, the following steps can be specifically performed: S1030: Based on the current growth stage of quinoa, extract a set of candidate schemes from the preset fertilization scheme library.

[0054] S1031: For each candidate scheme in the group, the soil fertility decay rate, transpiration compensation coefficient, and expected panicle development improvement rate are multiplied by their respective preset weight coefficients to obtain fertility decay contribution score, transpiration contribution score, and improvement rate contribution score.

[0055] It should be noted that the greater the rate of soil fertility decline, the higher the contribution score to fertility decline; the greater the transpiration compensation coefficient, the drier the current environment, and the higher the contribution score to transpiration; the higher the expected improvement rate of panicle development, the higher the contribution score to improvement rate.

[0056] In one specific embodiment, before calculating the score of each candidate scheme, the fertility attenuation weight coefficient, transpiration weight coefficient, and improvement rate weight coefficient are dynamically updated based on the current growth stage and the actual change in panicle development after the three most recent fertilizations. Specifically: if the actual improvement in panicle development is higher than expected, the improvement rate weight coefficient is increased and the fertility attenuation weight coefficient is decreased; if the actual improvement in panicle development is lower than expected and the soil electrical conductivity decreases too rapidly, the fertility attenuation weight coefficient is increased.

[0057] S1032: Add the fertility attenuation contribution score, the transpiration contribution score, and the improvement rate contribution score of each candidate scheme in the group to obtain a preliminary score for each candidate scheme.

[0058] Specifically, the preliminary score is calculated according to the following formula:

[0059] In the formula, i represents the i-th candidate solution. It is an index of the rate of soil fertility decline. This is the evapotranspiration compensation coefficient. To improve the expected rate of ear development, These are the fertility attenuation weighting coefficient, transpiration weighting coefficient, and growth rate weighting coefficient, respectively.

[0060] S1033: Based on the degree of deviation between the fertilization amount in each candidate scheme and the standard fertilization amount, a penalty term is applied to the preliminary score to obtain the final comprehensive score of each candidate scheme.

[0061] Specifically, the formula for the penalty term is as follows:

[0062] The final comprehensive score is calculated using the following formula:

[0063] In the formula, This represents the amount of fertilizer applied in the candidate schemes. β represents the standard fertilization rate, and β is the penalty coefficient.

[0064] S1034: The fertilizer application rate, fertilization interval, and fertilizer type from the candidate scheme with the highest final comprehensive score will be output as the recommendation result.

[0065] In this embodiment, by fusing image data, soil electrical conductivity, meteorological parameters, and historical fertilization response data, a multi-source model is constructed that includes dynamic assessment of soil fertility, meteorological correction, and prediction of fertilization response. An adaptive recommendation engine is used to output the optimal fertilization plan. Compared with existing technologies that rely solely on static electrical conductivity thresholds and fixed adjustment rules, this model can dynamically track the rate of soil fertility decay, quantify the impact of light and humidity on nutrient absorption, and continuously optimize decisions based on historical response data. This improves the accuracy of fertilization recommendations, environmental adaptability, and fertilizer utilization, and realizes intelligent and dynamic closed-loop optimization of quinoa fertilization.

[0066] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0067] like Figure 4 As shown, the following are embodiments of the quinoa fertilization recommendation system that integrates soil testing data and historical planting cases provided in this disclosure. This system belongs to the same inventive concept as the quinoa fertilization recommendation method that integrates soil testing data and historical planting cases in the above embodiments. For details not described in detail in the embodiments of the quinoa fertilization recommendation system that integrates soil testing data and historical planting cases, please refer to the embodiments of the quinoa fertilization recommendation method that integrates soil testing data and historical planting cases described above.

[0068] A quinoa fertilization recommendation system that integrates soil testing data and historical planting cases includes: The data acquisition module is used to acquire image data of quinoa at its current growth stage, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data. An image recognition module is used to identify the developmental stage and development degree index of quinoa ears based on the image data; The model building module is used to determine whether fertilization recommendation needs to be activated based on the comparison result between the development degree index and the preset development threshold. If so, a multi-source data fusion model is constructed to obtain the fertilization influencing factors. The multi-source data fusion model includes: a soil fertility dynamic assessment unit, used to calculate the soil fertility decay rate based on soil electrical conductivity, historical fertilization records and the current growth stage; a meteorological influence factor correction unit, used to calculate the transpiration compensation coefficient based on light intensity and air humidity; and a fertilization response prediction unit, used to predict the expected panicle development improvement rate under the current conditions based on historical fertilization response data. The fertilization recommendation module is used to input the soil fertility decay rate, transpiration compensation coefficient and expected panicle development improvement rate into the adaptive recommendation engine, and output the recommended fertilization amount, fertilization interval and fertilization type.

[0069] Optionally, the image recognition module is specifically used for: The image data is segmented, and the regions containing quinoa ears are extracted from the segmented images to generate corresponding binary masks and bounding boxes; Extract the color, texture, and morphological features of each region containing quinoa ears, and map these features to a pre-constructed developmental stage identifier table to output the developmental stage of each quinoa ear. The developmental stages include early heading stage, grain filling stage, waxy ripening stage, and full ripening stage. The developmental degree index of quinoa ears is determined by the average of the ratio of the mask area of ​​the quinoa ear to the reference area of ​​the corresponding developmental stage, the ratio of the vertical axis length of the quinoa ear to the reference length of the corresponding developmental stage, and the ratio of the proportion of green pixels in the region containing the quinoa ear to the reference green proportion of the corresponding developmental stage. The mask area of ​​the quinoa ear is obtained by counting the number of pixels in the binary mask, the vertical axis length of the quinoa ear is obtained based on the height of the bounding box, and the proportion of green pixels is obtained by superimposing the image data with the binary mask and counting the color of the pixels in the mask area.

[0070] Optionally, the dynamic soil fertility assessment unit is specifically used for: Collect soil electrical conductivity data from quinoa roots; and obtain the growth stage coefficient corresponding to the current growth stage and the preset basic consumption coefficient. The actual conductivity decay rate is calculated based on the difference in soil conductivity at the roots of quinoa over two consecutive days and the number of days between sampling; at the same time, the theoretical decay rate is calculated based on the product of the basic consumption coefficient and the conductivity of the previous day. Within a preset time window, the ratio of the amount of fertilizer applied for each application to the reference amount of fertilizer is multiplied by an exponential decay factor with the number of days since the application as the variable. The calculation results of all fertilization events are then summed to obtain the fertilization aftereffect function value. Divide the actual decay rate by the theoretical decay rate to obtain the first ratio; take the reciprocal of the product of the preset weighting coefficient and the fertilization aftereffect function value as the second ratio; multiply the first ratio, the second ratio, and the growth stage coefficient to obtain the soil fertility decay rate index.

[0071] Figure 5 This is a schematic diagram of the hardware structure of an electronic device that implements various embodiments of the present invention.

[0072] The quinoa fertilization recommendation method integrating soil testing data and historical planting cases provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0073] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0074] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0075] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0076] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0077] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0078] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0079] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0080] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0081] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0082] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0083] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0084] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0085] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0086] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0087] The storage medium provided in this application stores a program product capable of implementing a quinoa fertilization recommendation method that integrates soil testing data with historical planting cases.

[0088] The quinoa fertilization recommendation method integrating soil testing data and historical planting cases includes: acquiring image data of the current growth stage of quinoa, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data; identifying the developmental stage and development degree index of quinoa ears based on the image data; determining whether fertilization recommendation needs to be initiated based on the comparison result of the development degree index and a preset development threshold; if so, constructing a multi-source data fusion model to obtain fertilization influencing factors; wherein, the multi-source data fusion model includes: a soil fertility dynamic assessment unit, used to calculate the soil fertility decay rate based on soil electrical conductivity, historical fertilization records, and the current growth stage; a meteorological influence factor correction unit, used to calculate the transpiration compensation coefficient based on light intensity and air humidity; a fertilization response prediction unit, used to predict the expected ear development improvement rate under the current conditions based on historical fertilization response data; inputting the soil fertility decay rate, transpiration compensation coefficient, and expected ear development improvement rate into an adaptive recommendation engine, and outputting the recommended fertilization amount, fertilization interval duration, and fertilization type.

[0089] In some possible implementations, the subject matter of this disclosure, namely, "Method and System for Recommending Quinoa Fertilization by Integrating Soil Testing Data and Historical Planting Cases," can be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0090] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may 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 (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0091] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for recommending quinoa fertilization by integrating soil testing data and historical planting cases, characterized in that, include: Acquire image data of quinoa at its current growth stage, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data; The developmental stage and development degree index of quinoa ears were identified based on the image data. Based on the comparison between the developmental index and the preset developmental threshold, it is determined whether fertilization recommendation needs to be initiated. If so, a multi-source data fusion model is constructed to obtain the influencing factors of fertilization. The multi-source data fusion model includes: a soil fertility dynamic assessment unit, used to calculate the soil fertility decay rate based on soil electrical conductivity, historical fertilization records, and the current growth stage; a meteorological influence factor correction unit, used to calculate the transpiration compensation coefficient based on light intensity and air humidity; and a fertilization response prediction unit, used to predict the expected panicle development improvement rate under the current conditions based on historical fertilization response data. The soil fertility decay rate, transpiration compensation coefficient, and expected panicle development improvement rate are input into the adaptive recommendation engine, which outputs the recommended fertilization amount, fertilization interval, and fertilization type.

2. The quinoa fertilization recommendation method based on the integration of soil testing data and historical planting cases as described in claim 1, characterized in that, The identification of the developmental stage and developmental degree index of quinoa ears based on the image data includes: The image data is segmented, and the regions containing quinoa ears are extracted from the segmented images to generate corresponding binary masks and bounding boxes; Extract the color, texture, and morphological features of each region containing quinoa ears, and map these features to a pre-constructed developmental stage identifier table to output the developmental stage of each quinoa ear. The developmental stages include early heading stage, grain filling stage, waxy ripening stage, and full ripening stage. The developmental degree index of quinoa ears is determined by the average of the ratio of the mask area of ​​the quinoa ear to the reference area of ​​the corresponding developmental stage, the ratio of the vertical axis length of the quinoa ear to the reference length of the corresponding developmental stage, and the ratio of the proportion of green pixels in the region containing the quinoa ear to the reference green proportion of the corresponding developmental stage. The mask area of ​​the quinoa ear is obtained by counting the number of pixels in the binary mask, the vertical axis length of the quinoa ear is obtained based on the height of the bounding box, and the proportion of green pixels is obtained by superimposing the image data with the binary mask and counting the color of the pixels in the mask area.

3. The quinoa fertilization recommendation method based on the integration of soil testing data and historical planting cases as described in claim 2, characterized in that, The developmental level index is calculated using the following formula: In the formula, The area of ​​the quinoa ear covered by the film. This is a reference area for the corresponding developmental stage. is the length of the longitudinal axis of the wheat ear. This is a reference length for the corresponding developmental stage. This represents the percentage of green pixels in the region containing quinoa ears. The percentage of green is a reference for the corresponding developmental stage.

4. The quinoa fertilization recommendation method based on the integration of soil testing data and historical planting cases as described in claim 1, characterized in that, The rate of soil fertility decline is calculated based on soil electrical conductivity, historical fertilization records, and the current growth stage, including: Collect soil electrical conductivity data from quinoa roots; and obtain the growth stage coefficient corresponding to the current growth stage and the preset basic consumption coefficient. The actual conductivity decay rate is calculated based on the difference in soil conductivity at the roots of quinoa over two consecutive days and the number of days between sampling; at the same time, the theoretical decay rate is calculated based on the product of the basic consumption coefficient and the conductivity of the previous day. Within a preset time window, the ratio of the amount of fertilizer applied for each application to the reference amount of fertilizer is multiplied by an exponential decay factor with the number of days since the application as the variable. The calculation results of all fertilization events are then summed to obtain the fertilization aftereffect function value. Divide the actual decay rate by the theoretical decay rate to obtain the first ratio; take the reciprocal of the product of the preset weighting coefficient and the fertilization aftereffect function value as the second ratio; multiply the first ratio, the second ratio, and the growth stage coefficient to obtain the soil fertility decay rate index.

5. The quinoa fertilization recommendation method based on the integration of soil testing data and historical planting cases as described in claim 1, characterized in that, The evapotranspiration compensation coefficient is calculated using the following formula: In the formula, D is the current light intensity, D0 is the standard light intensity, S is the current air humidity, and S0 is the standard air humidity.

6. The quinoa fertilization recommendation method based on the integration of soil testing data and historical planting cases as described in claim 1, characterized in that, The soil fertility decline rate, transpiration compensation coefficient, and expected panicle development improvement rate are input into the adaptive recommendation engine, which outputs recommended fertilization amount, fertilization interval, and fertilization type, including: Based on the current growth stage of quinoa, a set of candidate schemes is extracted from the pre-set fertilization scheme library; For each candidate scheme in the group, the soil fertility decay rate, transpiration compensation coefficient, and expected panicle development improvement rate are multiplied by their respective preset weighting coefficients to obtain fertility decay contribution score, transpiration contribution score, and improvement rate contribution score. The fertility attenuation contribution score, the transpiration contribution score, and the improvement rate contribution score of each candidate scheme in the group are added together to obtain the preliminary score of each candidate scheme; Based on the degree of deviation between the fertilizer application amount in each candidate scheme and the standard fertilizer application amount, a penalty term is applied to the preliminary score to obtain the final comprehensive score of each candidate scheme. The fertilizer application rate, fertilization interval, and fertilizer type from the candidate scheme with the highest final comprehensive score will be output as the recommendation result.

7. The quinoa fertilization recommendation method based on the integration of soil testing data and historical planting cases as described in claim 1, characterized in that, The fertilization type includes a combination recommendation of nitrogen fertilizer, phosphorus fertilizer, potassium fertilizer and micronutrient fertilizer, and the recommendation engine outputs the proportion and application amount of each type of fertilizer.

8. A quinoa fertilization recommendation system integrating soil testing data and historical planting cases, characterized in that, include: The data acquisition module is used to acquire image data of quinoa at its current growth stage, soil electrical conductivity, air humidity, light intensity, and historical fertilization response data. An image recognition module is used to identify the developmental stage and development degree index of quinoa ears based on the image data; The model building module is used to determine whether fertilization recommendation needs to be activated based on the comparison result between the development degree index and the preset development threshold. If so, a multi-source data fusion model is constructed to obtain the fertilization influencing factors. The multi-source data fusion model includes: a soil fertility dynamic assessment unit, used to calculate the soil fertility decay rate based on soil electrical conductivity, historical fertilization records and the current growth stage; a meteorological influence factor correction unit, used to calculate the transpiration compensation coefficient based on light intensity and air humidity; and a fertilization response prediction unit, used to predict the expected panicle development improvement rate under the current conditions based on historical fertilization response data. The fertilization recommendation module is used to input the soil fertility decay rate, transpiration compensation coefficient and expected panicle development improvement rate into the adaptive recommendation engine, and output the recommended fertilization amount, fertilization interval and fertilization type.

9. The quinoa fertilization recommendation system integrating soil testing data and historical planting cases according to claim 8, characterized in that, The image recognition module is specifically used for: The image data is segmented, and the regions containing quinoa ears are extracted from the segmented images to generate corresponding binary masks and bounding boxes; Extract the color, texture, and morphological features of each region containing quinoa ears, and map these features to a pre-constructed developmental stage identifier table to output the developmental stage of each quinoa ear. The developmental stages include early heading stage, grain filling stage, waxy ripening stage, and full ripening stage. The developmental degree index of quinoa ears is determined by the average of the ratio of the mask area of ​​the quinoa ear to the reference area of ​​the corresponding developmental stage, the ratio of the vertical axis length of the quinoa ear to the reference length of the corresponding developmental stage, and the ratio of the proportion of green pixels in the region containing the quinoa ear to the reference green proportion of the corresponding developmental stage. The mask area of ​​the quinoa ear is obtained by counting the number of pixels in the binary mask, the vertical axis length of the quinoa ear is obtained based on the height of the bounding box, and the proportion of green pixels is obtained by superimposing the image data with the binary mask and counting the color of the pixels in the mask area.

10. The quinoa fertilization recommendation system integrating soil testing data and historical planting cases according to claim 8, characterized in that, The dynamic soil fertility assessment unit is specifically used for: Collect soil electrical conductivity data from quinoa roots; and obtain the growth stage coefficient corresponding to the current growth stage and the preset basic consumption coefficient. The actual conductivity decay rate was calculated based on the difference in soil conductivity at the roots of quinoa over two consecutive days and the number of days between sampling. Simultaneously, the theoretical attenuation rate is calculated based on the product of the basic consumption coefficient and the electrical conductivity of the previous day; Within a preset time window, the ratio of the amount of fertilizer applied for each application to the reference amount of fertilizer is multiplied by an exponential decay factor with the number of days since the application as the variable. The calculation results of all fertilization events are then summed to obtain the fertilization aftereffect function value. Divide the actual decay rate by the theoretical decay rate to obtain the first ratio; The reciprocal of the product of the preset weighting coefficient and the fertilization aftereffect function value is used as the second ratio; the first ratio, the second ratio, and the growth stage coefficient are multiplied to obtain the soil fertility decay rate index.

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