Method for collecting ecological environment information of orchard

Abstract

The invention discloses a method for collecting orchard ecological environment information, and the method comprises the steps: collecting soil parameters, space environment parameters, insect pest parameters and fruit parameters of an orchard in real time through deploying a plurality of collection devices, and carrying out the collection to form an orchard ecological basic data set; a backstepping model is constructed to indirectly calculate key ecological parameters including soil nutrients, fruit quality and insect pest diffusion trend, and an optimization result is corrected through experimental verification and the model; comparing a backstepping result with an orchard target value to analyze a deviation source, and establishing a macroscopic cause chain through data matching and a visual tool; according to the deviation analysis and the cause chain result, determining an executable regulation and control measure matched with the regulation and control direction; and monitoring the effect after the implementation of the regulation and control measures, verifying the regulation and control effect by comparing the data before and after the implementation of the regulation and control measures, and applying feedback data to iterative optimization of the backstepping model. The method is scientific and efficient and can be used for improving orchard production benefits and ecological environment quality.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment information collection, and in particular to a method for collecting orchard ecological environment information. Background Art

[0002] With the rapid development of modern agriculture, refined management of orchards has become the key to improving production efficiency and ecological environment quality. However, traditional orchard management methods mainly rely on experience-based decision-making, and there are limitations in the collection and analysis of key ecological information such as soil, water, pests and fruit quality. This management method lacking data support is prone to waste of resources, reduced yields and degradation of the ecological environment.

[0003] At present, some technologies have been applied to orchard management, such as soil sensors for monitoring moisture and pH, drones for aerial photography of orchard vegetation coverage, or pest monitoring based on insect traps. However, the data collected by different monitoring equipment is difficult to integrate, and there is a lack of a unified analysis platform. Parameters that are difficult to measure directly cannot be efficiently calculated and predicted. There is a lack of closed-loop feedback for the regulation of environmental issues, and it is difficult to optimize management measures in a timely manner. Traditional monitoring methods are difficult to fully capture the spatial distribution differences of soil, environment, and pests in orchards, resulting in insufficient targeted management measures.

[0004] In response to the above problems, the present invention provides a method for collecting orchard ecological environment information, which can combine multiple sensing technologies, data analysis models and feedback optimization mechanisms to form a closed-loop management model to achieve precise management and ecological optimization of the orchard. Summary of the invention

[0005] In view of the above problems, the present invention provides a method for collecting orchard ecological environment information to solve the problems that parameters that are difficult to directly measure in the existing technology cannot be efficiently calculated and predicted, there is a lack of closed-loop feedback on the regulation of environmental issues, and it is difficult to optimize management measures in a timely manner.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides a method for collecting orchard ecological environment information, characterized in that it comprises the following steps:

[0008] Step S1, by deploying a variety of collection equipment to collect the soil parameters, spatial environment parameters, pest parameters and fruit parameters of the orchard in real time, and summarize them to form an orchard ecological basic data set;

[0009] Wherein step S1 also includes the following sub-steps:

[0010] S1-1, divide the orchard into several preliminary management units according to the terrain characteristics, planting plan and irrigation zoning of the orchard, and set the collection content and scope, including: soil parameters, spatial environment parameters, pest parameters, and fruit parameters;

[0011] Soil parameters: moisture, pH;

[0012] Space environment parameters: light intensity, temperature and humidity;

[0013] Pest parameters: pest types and density;

[0014] Fruit parameters: fruit size, color;

[0015] Collection scope: The management units initially divided serve as the layout scope of the collection equipment;

[0016] S1-2, data collection equipment includes soil parameter monitoring equipment, pest monitoring equipment, and fruit monitoring equipment;

[0017] Soil parameter monitoring equipment: soil moisture sensor, pH sensor, temperature and humidity sensor, light intensity sensor;

[0018] Pest monitoring equipment: intelligent insect trap;

[0019] Fruit monitoring equipment: drones, high-resolution cameras;

[0020] Equipment layout covers terrain types, vegetation differences, and orchard center and boundary areas; sensor density is set according to orchard area and complexity, such as 1 soil sensor per 100 square meters and 1 light or temperature and humidity sensor per 500 square meters; equipment is arranged in areas with significant environmental changes or prone to problems, including low-lying areas, areas with insufficient light, and areas prone to pests, based on historical data or observation points; equipment is close to main roads or work channels for easy inspection and maintenance;

[0021] S1-3, data transmission, using LoRa wireless technology to upload the collected data to the cloud, the cloud platform automatically classifies and stores the uploaded data to form a basic data set of the current ecological status of the orchard;

[0022] Simply aggregate and summarize the real-time data to generate a preliminary state map of the orchard ecological information, including: soil moisture distribution map, light intensity distribution map, pest density distribution map, fruit distribution and size change trend map;

[0023] S1-4, based on the data collected in S1-3, the orchard is divided into more detailed management units. According to parameters such as soil moisture, pH, and conductivity, units with similar soil characteristics are divided; according to light intensity and temperature and humidity, units with similar environmental conditions are divided; combined with pest type and density data, areas with high incidence of pests are marked separately; according to fruit size, color, and distribution trend, areas with similar yield characteristics are divided together; combined with terrain (highlands, lowlands) and historical management information;

[0024] The clustering algorithm K-Means is used to automatically divide similar areas according to the collected ecological parameters. The clustering results are imported into the GIS platform to visualize the geographic spatial data of the orchard, as shown in formula (1):

[0025]

[0026] Where J is the total sum of squared errors of the clusters, x is the data point belonging to the i-th cluster, and μ i is the center point of the ith cluster, C i is the set of the i-th cluster, K is the number of clusters;

[0027] Each management unit has a clear description of its ecological characteristics, including soil characteristics, pest risk, and fruit distribution. The division results are output through the GIS platform as a management unit distribution map, detailed tables and summaries of the ecological parameters of each unit.

[0028] Step S2, constructing a reverse inference model, using the collected data to indirectly calculate key ecological parameters, including soil nutrients, fruit quality and pest spread trends, and optimizing the results through experimental verification and model correction;

[0029] Wherein step S2 also includes the following sub-steps:

[0030] S2-1, indirect estimation of key parameters that are difficult to measure based on collected data, including soil nutrients, fruit quality, and pest spread trends;

[0031] Soil nutrients: nitrogen, phosphorus, potassium;

[0032] Fruit quality: sugar content, maturity;

[0033] Pest spread trend: distribution throughout the garden;

[0034] S2-2, establish the mathematical logic relationship between ecological collection data and key parameters that are difficult to measure, and construct reverse inference models: soil nutrient model, fruit quality model, and pest spread model;

[0035] The soil nutrient model uses a linear regression formula to establish the linear relationship between soil moisture, electrical conductivity, pH and nitrogen, phosphorus and potassium nutrient concentrations, as shown in formulas (2) to (4):

[0036] N=a1·(H2O)+b1·(pH)+c1 Formula (2)

[0037] P=a2·(H2O)+b2·(pH)+c2 Formula (3)

[0038] K=a3·(H2O)+b3·(pH)+c3 Formula (4)

[0039] Where N, P, K represent the current concentrations of nitrogen, phosphorus, and potassium in the soil, respectively; H2O is the water content of the soil; pH is the pH value of the soil; a1, a2, and a3 are model coefficients related to soil moisture; b1, b2, and b3 are model coefficients related to soil pH; and c1, c2, and c3 are intercept terms of the model.

[0040] The fruit quality model uses light intensity, temperature and humidity, and the rate of change of fruit size to establish a predictive relationship with sugar content and maturity, as shown in formulas (5) to (6):

[0041] T_H=0.7·T+0.3·H Formula (5)

[0042] S=d1·(Lux)+d2·(T_H)+d3·(v)+d4 Formula (6)

[0043] Among them, T_H is the temperature and humidity index, T is the air temperature of the orchard, and H is the air humidity; S is the sugar content of the fruit, Lux is the light intensity, v is the fruit change rate, d1, d2, d3 are the model coefficients related to light, temperature and humidity index, and fruit change rate, respectively, and d4 is the intercept term of the model;

[0044] The pest diffusion model uses insect trap data, wind speed, humidity and temperature to predict the pest diffusion trend, as shown in formula (7):

[0045] D(t+1)=D(t)·(1+rc) Formula (7)

[0046] Where D(t) is the pest density at the current time t, D(t+1) is the pest density at the next time t+1, r is the pest reproduction rate (affected by temperature and humidity), c is the pest capture rate, and 1+rc is the increase or decrease factor of the pest density;

[0047] S2-3, use the collected real-time data as input, substitute it into the constructed reverse inference model, and calculate the predicted values ​​of key parameters; randomly select a small area in the orchard to conduct actual measurement or experimental testing of the reverse inference parameters: soil nutrients: analyze soil nitrogen, phosphorus and potassium concentrations through laboratory testing; fruit quality: manually measure the sugar content and maturity of the fruit; pest density: manually record the actual distribution data of pests;

[0048] The inference results are compared with the small-scale experimental data, the error value is calculated, the model parameters are optimized for the part with larger error, and the model is retrained based on the new data.

[0049] Step S3, compare the inference result with the target value of the orchard, analyze the source of the deviation, establish the macro-cause chain of the deviation and environmental parameters through data matching and visualization tools, and intuitively present the cause of the problem and its spatial distribution;

[0050] Wherein step S3 also includes the following sub-steps:

[0051] S3-1, compare the key parameter values ​​calculated by the inverse model with the target values ​​of the orchard to analyze the deviation. The deviation is the difference between the actual calculated value and the target value, which mainly manifests as: soil nutrient deviation, fruit quality deviation, pest density deviation, and analyze the cause chain of the deviation;

[0052] Causes of low soil nitrogen: high soil moisture → severe nitrogen leaching → reduced nitrogen content;

[0053] Reasons for low sugar content in fruits: insufficient light → low photosynthesis efficiency → insufficient sugar accumulation;

[0054] Reasons for high pest density: suitable temperature and humidity → rapid pest reproduction → local pest spread;

[0055] S3-2, match the analyzed deviations with the environmental parameters of the orchard, establish a macro-cause chain, understand the causes of the deviations, and associate the deviations with the spatial distribution of the orchard environmental parameters through data analysis and visualization tools;

[0056] Use GIS tools to draw a cause chain distribution map of the orchard, show the spatial distribution relationship between deviations and environmental parameters, generate regional problem charts, including light distribution maps, high-incidence areas of pests, and form a visual report of deviations and environmental parameters;

[0057] According to the matching and visualization results, the logical relationship between the deviation and the cause chain is sorted out, and the spatial distribution of the deviation data, the main macro-cause chain of the deviation in each region, and the key driving factors of the deviation are output.

[0058] Step S4, determining the control direction according to the deviation analysis and cause chain results, and matching executable control measures;

[0059] Wherein step S4 also includes the following sub-steps:

[0060] S4-1, based on the results of deviation analysis and macro-cause mapping, formulate clear control directions to narrow the gap between key parameters and target values. According to the degree of deviation of key parameters, including low soil nitrogen content, low fruit sugar content, and high pest density, determine the issues that need to be addressed first. Combined with the results of macro-cause analysis, clarify the core driving factors that affect the deviation and ensure that the control direction is in line with the overall management objectives of the orchard;

[0061] S4-2, according to the control direction determined in S4-1, match specific executable measures to optimize ecological parameters and meet the orchard target value requirements. Typical control measures are designed as follows:

[0062] Soil nutrient regulation: Increase soil nitrogen content, reduce irrigation frequency or switch to drip irrigation technology to reduce water loss; regularly apply slow-release nitrogen fertilizer locally in low-content areas;

[0063] Fruit quality control: increase the sugar content of the fruit, prune the tree crown, and increase the light-collecting area of ​​the fruit; spray fertilizers containing potassium and magnesium on the leaves to promote sugar accumulation;

[0064] Pest control: Reduce pest density, install insect nets or spray biological pesticides in areas with high pest incidence; introduce natural enemies for biological control; adjust the humidity level in the orchard to reduce the conditions for pest reproduction.

[0065] Step S5, monitor the effect of the control measures after implementation, verify the effectiveness of the control measures by comparing the data before and after, and use the feedback data for iterative optimization of the reverse model, ultimately achieving closed-loop management and automated control.

[0066] Wherein, in step S5, the following sub-steps are also included:

[0067] S5-1, monitor the effect of regulation. After implementing the regulation measures, continuously collect soil, fruit and pest data, dynamic changes in water and nutrients, the trend of increasing fruit sugar content, and the reduction of pest density;

[0068] S5-2, compare the core data before and after the adjustment, including soil nitrogen content, fruit sugar content, and pest density, with the changes in target values ​​to evaluate the effectiveness of the control measures;

[0069] S5-3, input the feedback data after regulation into the inverse model, correct the model parameters, introduce historical data and adjustment data to enrich the training set, gradually introduce more complex nonlinear models to optimize the inverse logic, improve the closed-loop control, and realize automated decision-making and regulation.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] The present invention realizes the comprehensive collection of soil, environment, pest and fruit parameters by deploying a variety of collection equipment, including soil sensors, drones, and intelligent insect traps, and integrates the data into the cloud using wireless transmission technology to form a unified basic data set of ecological information, thus solving the problem of data isolation in the prior art.

[0072] The present invention constructs a back-calculation system based on linear regression and other mathematical models, which can use the collected data to accurately calculate key parameters that are difficult to measure directly, such as soil nutrients, fruit sugar content, and pest spread trends, thereby improving the integrity of ecological information and the scientific nature of the analysis, and making up for the limitations of traditional methods.

[0073] The present invention can identify ecological deviations through comparative analysis of inverse results and orchard target values, and establish cause chains and spatial distribution maps in combination with environmental parameters, providing a scientific basis for precise regulation. Traditional technologies have limited analytical capabilities for the causes of deviations.

[0074] The present invention forms a closed-loop automated decision-making system by real-time monitoring of the implementation effects of control measures, and uses feedback data for model parameter correction and optimization, thereby greatly improving the dynamic response capability and continuous optimization capability of orchard management.

[0075] The present invention uses a GIS platform to visualize the collection and reverse deduction results into a management unit distribution map within the orchard, captures the spatial differences in soil, environment and pests, provides intuitive support for zoning management and precise policy implementation, and makes up for the shortcomings of existing technologies in insufficient consideration of spatial heterogeneity. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It is understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0077] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but is only for selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0079] Please refer to Figure 1 The method for collecting orchard ecological environment information provided by the embodiment of the present invention comprises the following steps:

[0080] Step S1, by deploying a variety of collection equipment to collect the soil parameters, spatial environment parameters, pest parameters and fruit parameters of the orchard in real time, and summarize them to form an orchard ecological basic data set;

[0081] Wherein step S1 also includes the following sub-steps:

[0082] S1-1, divide the orchard into several preliminary management units according to the terrain characteristics, planting plan and irrigation zoning of the orchard, and set the collection content and scope, including: soil parameters, spatial environment parameters, pest parameters, and fruit parameters;

[0083] Soil parameters: moisture, pH;

[0084] Space environment parameters: light intensity, temperature and humidity;

[0085] Pest parameters: pest types and density;

[0086] Fruit parameters: fruit size, color;

[0087] Collection scope: The management units initially divided serve as the layout scope of the collection equipment;

[0088] S1-2, data collection equipment includes soil parameter monitoring equipment, pest monitoring equipment, and fruit monitoring equipment;

[0089] Soil parameter monitoring equipment: soil moisture sensor, pH sensor, temperature and humidity sensor, light intensity sensor;

[0090] Pest monitoring equipment: intelligent insect trap;

[0091] Fruit monitoring equipment: drones, high-resolution cameras;

[0092] Equipment layout covers terrain types, vegetation differences, and orchard center and boundary areas; sensor density is set according to orchard area and complexity, such as 1 soil sensor per 100 square meters and 1 light or temperature and humidity sensor per 500 square meters; equipment is arranged in areas with significant environmental changes or prone to problems, including low-lying areas, areas with insufficient light, and areas prone to pests, based on historical data or observation points; equipment is close to main roads or work channels for easy inspection and maintenance;

[0093] S1-3, data transmission, using LoRa wireless technology to upload the collected data to the cloud, the cloud platform automatically classifies and stores the uploaded data to form a basic data set of the current ecological status of the orchard;

[0094] Simply aggregate and summarize the real-time data to generate a preliminary state map of the orchard ecological information, including: soil moisture distribution map, light intensity distribution map, pest density distribution map, fruit distribution and size change trend map;

[0095] S1-4, based on the data collected in S1-3, the orchard is divided into more detailed management units. According to parameters such as soil moisture, pH, and conductivity, units with similar soil characteristics are divided; according to light intensity and temperature and humidity, units with similar environmental conditions are divided; combined with pest type and density data, areas with high incidence of pests are marked separately; according to fruit size, color, and distribution trend, areas with similar yield characteristics are divided together; combined with terrain (highlands, lowlands) and historical management information;

[0096] The clustering algorithm K-Means is used to automatically divide similar areas according to the collected ecological parameters. The clustering results are imported into the GIS platform to visualize the geographic spatial data of the orchard, as shown in formula (1):

[0097]

[0098] Where J is the total sum of squared errors of the clusters, x is the data point belonging to the i-th cluster, and μ i is the center point of the ith cluster, C i is the set of the i-th cluster, K is the number of clusters;

[0099] Each management unit has a clear description of its ecological characteristics, including soil characteristics, pest risk, and fruit distribution. The division results are output through the GIS platform as a management unit distribution map, detailed tables and summaries of the ecological parameters of each unit.

[0100] It should be noted that the "management unit" can be understood as a partition unit within the orchard. Each unit can be deployed with sensors, data collection and regulation, and the boundaries can be divided according to terrain, tree spacing or artificial areas. The area is determined according to the total size of the orchard. Usually, the area of ​​each unit ranges from several hundred square meters to several thousand square meters, ensuring that the monitoring data is representative and easy to manage. The soil, water, light and vegetation conditions of each unit are relatively consistent, which is convenient for modeling and analysis.

[0101] Soil moisture sensors are used to monitor soil moisture, conductivity and temperature in real time; pH sensors are used to monitor the acidity and alkalinity of the soil; temperature and humidity sensors are used to monitor the temperature and humidity in the air; light intensity sensors are used to measure the light distribution under the canopy of fruit trees; intelligent insect traps, equipped with UV insect traps and cameras, are used to monitor the types and density of pests; drones are used to take vegetation coverage maps and fruit distribution maps; high-resolution cameras assist in obtaining fruit size and color data.

[0102] GIS is a technical tool that combines geographic spatial data and attribute data. It can collect, store, analyze, visualize and manage space-related data, visualize ecological data such as soil parameters, light intensity, pest density and fruit distribution to the specific geographical location of the orchard, help identify the ecological differences in different areas of the orchard, and combine the calculation results of the clustering algorithm to generate a distribution map of management units in geographic space, and spatially display the ecological characteristics of the management units, such as annotating the distribution maps of soil moisture and pest density parameters.

[0103] Step S2, constructing a reverse inference model, using the collected data to indirectly calculate key ecological parameters, including soil nutrients, fruit quality and pest spread trends, and optimizing the results through experimental verification and model correction;

[0104] Wherein step S2 also includes the following sub-steps:

[0105] S2-1, indirect estimation of key parameters that are difficult to measure based on collected data, including soil nutrients, fruit quality, and pest spread trends;

[0106] Soil nutrients: nitrogen, phosphorus, potassium;

[0107] Fruit quality: sugar content, maturity;

[0108] Pest spread trend: distribution throughout the garden;

[0109] S2-2, establish the mathematical logic relationship between ecological collection data and key parameters that are difficult to measure, and construct reverse inference models: soil nutrient model, fruit quality model, and pest spread model;

[0110] The soil nutrient model uses a linear regression formula to establish the linear relationship between soil moisture, electrical conductivity, pH and nitrogen, phosphorus and potassium nutrient concentrations, as shown in formulas (2) to (4):

[0111] N=a1·(H2O)+b1·(pH)+c1 Formula (2)

[0112] P=a2·(H2O)+b2·(pH)+c2 Formula (3)

[0113] K=a3·(H2O)+b3·(pH)+c3 Formula (4)

[0114] Where N, P, K represent the current concentrations of nitrogen, phosphorus, and potassium in the soil, respectively; H2O is the water content of the soil; pH is the pH value of the soil; a1, a2, and a3 are model coefficients related to soil moisture; b1, b2, and b3 are model coefficients related to soil pH; and c1, c2, and c3 are intercept terms of the model.

[0115] The fruit quality model uses light intensity, temperature and humidity, and the rate of change of fruit size to establish a predictive relationship with sugar content and maturity, as shown in formulas (5) to (6):

[0116] T_H=0.7·T+0.3·H Formula (5)

[0117] S=d1·(Lux)+d2·(T_H)+d3·(v)+d4 Formula (6)

[0118] Among them, T_H is the temperature and humidity index, T is the air temperature of the orchard, and H is the air humidity; S is the sugar content of the fruit, Lux is the light intensity, v is the fruit change rate, d1, d2, d3 are the model coefficients related to light, temperature and humidity index, and fruit change rate, respectively, and d4 is the intercept term of the model;

[0119] The pest diffusion model uses insect trap data, wind speed, humidity and temperature to predict the pest diffusion trend, as shown in formula (7):

[0120] D(t+1)=D(t)·(1+rc) Formula (7)

[0121] Where D(t) is the pest density at the current time t, D(t+1) is the pest density at the next time t+1, r is the pest reproduction rate (affected by temperature and humidity), c is the pest capture rate, and 1+rc is the increase or decrease factor of the pest density;

[0122] S2-3, use the collected real-time data as input, substitute it into the constructed reverse inference model, and calculate the predicted values ​​of key parameters; randomly select a small area in the orchard to conduct actual measurement or experimental testing of the reverse inference parameters: soil nutrients: analyze soil nitrogen, phosphorus and potassium concentrations through laboratory testing; fruit quality: manually measure the sugar content and maturity of the fruit; pest density: manually record the actual distribution data of pests;

[0123] The inference results are compared with the small-scale experimental data, the error value is calculated, the model parameters are optimized for the part with larger error, and the model is retrained based on the new data.

[0124] It should be noted that the key parameters are difficult to collect directly because: the concentrations of soil nutrients nitrogen, phosphorus, and potassium require laboratory testing, which is time-consuming and costly; fruit quality, such as sugar content measurement, requires destructive testing and cannot be performed in real time; the spread trend of insect pests is affected by multiple environmental factors and requires dynamic prediction and calculation.

[0125] Soil moisture, electrical conductivity and pH are key factors affecting nitrogen, phosphorus and potassium content. Excessive moisture may lead to nitrogen leaching and reduced effectiveness of phosphorus and potassium, and pH affects the dissolution and activity of nutrients. Therefore, based on these correlations, a linear regression model can describe the mathematical relationship between moisture, electrical conductivity, pH and nutrient concentration.

[0126] The sugar content and maturity of fruits are jointly affected by light intensity, temperature and humidity, and fruit growth rate. Light intensity directly determines the efficiency of photosynthesis, thereby affecting sugar accumulation.

[0127] Temperature and humidity determine the suitability of the fruit growth environment and affect the ripening speed. The rate of change of fruit size reflects the dynamic state of fruit development and can be used as an indirect measurement indicator.

[0128] Pest density is affected by many factors, such as environmental temperature and humidity, wind speed, and the effect of insect traps. High temperature and high humidity environments are usually conducive to pest reproduction, while the capture rate of insect traps reduces their spread trend. Time series models can dynamically describe the changing patterns of pest density.

[0129] Step S3, compare the inference result with the target value of the orchard, analyze the source of the deviation, establish the macro-cause chain of the deviation and environmental parameters through data matching and visualization tools, and intuitively present the cause of the problem and its spatial distribution;

[0130] Wherein step S3 also includes the following sub-steps:

[0131] S3-1, compare the key parameter values ​​calculated by the inverse model with the target values ​​of the orchard to analyze the deviation. The deviation is the difference between the actual calculated value and the target value, which mainly manifests as: soil nutrient deviation, fruit quality deviation, pest density deviation, and analyze the cause chain of the deviation;

[0132] Causes of low soil nitrogen: high soil moisture → severe nitrogen leaching → reduced nitrogen content;

[0133] Reasons for low sugar content in fruits: insufficient light → low photosynthesis efficiency → insufficient sugar accumulation;

[0134] Reasons for high pest density: suitable temperature and humidity → rapid pest reproduction → local pest spread;

[0135] S3-2, match the analyzed deviations with the environmental parameters of the orchard, establish a macro-cause chain, understand the causes of the deviations, and associate the deviations with the spatial distribution of the orchard environmental parameters through data analysis and visualization tools;

[0136] Use GIS tools to draw a cause chain distribution map of the orchard, show the spatial distribution relationship between deviations and environmental parameters, generate regional problem charts, including light distribution maps, high-incidence areas of pests, and form a visual report of deviations and environmental parameters;

[0137] According to the matching and visualization results, the logical relationship between the deviation and the cause chain is sorted out, and the spatial distribution of the deviation data, the main macro-cause chain of the deviation in each region, and the key driving factors of the deviation are output.

[0138] It should be noted that the complete chain of causes of deviation includes the following:

[0139] The chain of causes of soil nutrient deviation:

[0140] Possible causes of low soil nitrogen content:

[0141] 1. Excessive soil moisture → nitrogen leaching intensifies → nitrogen content decreases; 2. Low or high pH value → inhibits microbial activity → nitrogen mineralization and conversion efficiency decreases; 3. Uneven or insufficient fertilization → unbalanced local soil nitrogen supply; 4. Low soil organic matter content → lack of stable nitrogen source → reduced nitrogen content.

[0142] Possible reasons for low soil phosphorus content:

[0143] 1. High pH value (alkaline soil) → phosphorus combines with calcium to form insoluble calcium phosphate → phosphorus utilization rate decreases; 2. Insufficient application of organic fertilizer → lack of available phosphorus source; 3. Excessive precipitation or excessive irrigation → serious surface loss of phosphorus.

[0144] Possible reasons for low soil potassium content: 1. High-intensity absorption by crops → insufficient potassium reserves; 2. Rainwater erosion and leaching → loss of soil potassium; 3. Unreasonable application of potassium fertilizers → potassium fails to effectively penetrate into the crop root zone.

[0145] The chain of causes of fruit quality deviation:

[0146] Possible reasons for low sugar content: 1. Insufficient light → low photosynthesis efficiency → reduced sugar accumulation; 2. Too low temperature and humidity → fruit ripening process slows down → sugar synthesis is limited; 3. Insufficient potassium content in the soil → fruit sugar transport is blocked.

[0147] Possible reasons for low maturity:

[0148] 1. Unsuitable temperature and humidity → deterioration of the fruit growth environment → delayed maturity; 2. Too high fruit density → insufficient nutrient distribution in the tree → prolonged ripening time of a single fruit; 3. Weakened root absorption capacity → insufficient nutrient supply.

[0149] The chain of reasons for high pest density:

[0150] 1. Suitable temperature and humidity → Pest reproduction speeds up; 2. Improper orchard management → Prevention and control measures are not timely or inadequate; 3. Insufficient number of natural enemies → Natural control is weakened; 4. Complex vegetation at the orchard boundary → Provides shelter and transmission paths for pests.

[0151] Step S4, determining the control direction according to the deviation analysis and cause chain results, and matching executable control measures;

[0152] Wherein step S4 also includes the following sub-steps:

[0153] S4-1, based on the results of deviation analysis and macro-cause mapping, formulate clear control directions to narrow the gap between key parameters and target values. According to the degree of deviation of key parameters, including low soil nitrogen content, low fruit sugar content, and high pest density, determine the issues that need to be addressed first. Combined with the results of macro-cause analysis, clarify the core driving factors that affect the deviation and ensure that the control direction is in line with the overall management objectives of the orchard;

[0154] S4-2, according to the control direction determined in S4-1, match specific executable measures to optimize ecological parameters and meet the orchard target value requirements. Typical control measures are designed as follows:

[0155] Soil nutrient regulation: Increase soil nitrogen content, reduce irrigation frequency or switch to drip irrigation technology to reduce water loss; regularly apply slow-release nitrogen fertilizer locally in low-content areas;

[0156] Fruit quality control: increase the sugar content of the fruit, prune the tree crown, and increase the light-collecting area of ​​the fruit; spray fertilizers containing potassium and magnesium on the leaves to promote sugar accumulation;

[0157] Pest control: Reduce pest density, install insect nets or spray biological pesticides in areas with high pest incidence; introduce natural enemies for biological control; adjust the humidity level in the orchard to reduce the conditions for pest reproduction.

[0158] It should be noted that the following are supplements and refinements to soil nutrients, fruit quality and pest control measures:

[0159] Soil nutrient regulation:

[0160] For low nitrogen content: 1. Apply slow-release nitrogen fertilizer: Regularly apply slow-release fertilizer in areas with low nitrogen content to reduce nitrogen loss; 2. Optimize irrigation methods: Reduce excessive irrigation, use drip irrigation or micro-sprinkler irrigation to avoid nitrogen leaching with water; 3. Plant green manure crops: Plant green manure (such as alfalfa) in vacant areas of the orchard to increase soil nitrogen levels through nitrogen fixation.

[0161] For low phosphorus content: 1. Use organic fertilizer: Apply phosphorus-containing organic fertilizer (such as compost, farmyard manure) to increase phosphorus content; 2. Adjust soil pH: Apply sulfur powder or ammonium sulfate in alkaline areas to adjust soil pH to neutral and increase the effectiveness of phosphorus; 3. Prevent phosphorus loss: Reduce irrigation loss on slopes and reduce surface water flow by covering the ground with mulch.

[0162] For low potassium content: 1. Apply potassium sulfate fertilizer: Supplement soluble potassium sources in areas with low potassium content; 2. Recycle biomass: Use pruned branches and fallen leaves from fruit trees, return them to the soil after composting, and supplement potassium; 3. Plant deep-rooted plants: Use deep-rooted plants to absorb potassium in the deep soil and increase the potassium content in the surface layer.

[0163] Fruit quality control:

[0164] For low sugar content: 1. Optimize crown pruning: Increase the light-collecting area of ​​the fruit through pruning and improve lighting conditions; 2. Apply foliar fertilizer: Spray foliar fertilizer containing potassium, magnesium, boron and other elements to promote sugar accumulation; 3. Regulate fruit load: Thin the fruit reasonably to reduce the burden of single fruit and increase the distribution of sugar in a single fruit.

[0165] For low fruit maturity: 1. Control temperature and humidity: By adjusting the frequency and time of irrigation, avoid the adverse effects of low temperature and high humidity on maturity; 2. Promote nutrient supply: Combine soil nutrient regulation to enhance root absorption efficiency; 3. Artificial ripening: Spray low-concentration ethylene preparations before picking to promote fruit ripening.

[0166] To address uneven fruit color: 1. Adjust the orientation of the fruit: manually rotate the fruit or adjust the supporting structure to ensure that the fruit is fully exposed to light; 2. Use reflective film: lay reflective film at the base of the fruit tree to increase the light level of the fruit under the crown; 3. Regulate light quality: install fill lights (a combination of red and blue light) to enhance fruit color.

[0167] Pest Control:

[0168] In response to high pest density: 1. Release natural enemies: Release ladybugs or trichogrammatids in high-incidence areas for biological control; 2. Spray biological pesticides: Use microbial pesticides (such as Bacillus thuringiensis preparations) to safely control pests; 3. Install insect attractants: Deploy more ultraviolet insect attractants in management units to centrally control pests.

[0169] To prevent the spread of pests: 1. Clearing of border vegetation: remove weeds and host plants at the border of the orchard to cut off the path for pest spread; 2. Humidity control: reduce the humidity in the orchard to reduce the environment suitable for pest reproduction; 3. Isolation and protection: set up physical isolation nets in areas where pests are prone to invade to prevent the spread of pests.

[0170] For specific pest types:

[0171] Aphids: 1. Spray a solution of vegetable oil soap to prevent them from attaching and breathing; 2. Plant aphid-repellent plants (such as garlic) as natural protection.

[0172] Leaf roller: 1. Set up sex traps to disrupt mating; 2. Use low-toxic biological pesticides for targeted control.

[0173] Step S5, monitor the effect of the control measures after implementation, verify the effectiveness of the control measures by comparing the data before and after, and use the feedback data for iterative optimization of the reverse model, ultimately achieving closed-loop management and automated control.

[0174] Wherein, in step S5, the following sub-steps are also included:

[0175] S5-1, monitor the effect of regulation. After implementing the regulation measures, continuously collect soil, fruit and pest data, dynamic changes in water and nutrients, the trend of increasing fruit sugar content, and the reduction of pest density;

[0176] S5-2, compare the core data before and after the adjustment, including soil nitrogen content, fruit sugar content, and pest density, with the changes in target values ​​to evaluate the effectiveness of the control measures;

[0177] S5-3, input the feedback data after regulation into the inverse model, correct the model parameters, introduce historical data and adjustment data to enrich the training set, gradually introduce more complex nonlinear models to optimize the inverse logic, improve the closed-loop control, and realize automated decision-making and regulation.

[0178] It should be noted that the reason for introducing complex nonlinear models to optimize the inverse logic is that the currently used linear or simple regression models, although they have certain explanatory power and computational efficiency, may have the following deficiencies when dealing with complex orchard ecosystems:

[0179] The complex nonlinear relationship of ecological parameters. There may be nonlinear relationships between the ecological data of the orchard. For example, the effect of excessive or insufficient soil moisture on nitrogen absorption may be parabolic rather than linear. The combined effect of light, temperature and humidity on the sugar content of the fruit has a threshold effect, which may not be accurately described by a single linear model.

[0180] Data dimensions interact with features, and there may be complex interactions between multiple parameters. It is difficult for a single variable to independently explain the target parameter, and simple models are difficult to capture the interactions between these high-dimensional features. In addition, with the increase in data scale and the improvement of real-time requirements, simple models may not be able to effectively process massive data or adapt to dynamic changes.

[0181] The implementation method of introducing complex nonlinear models: expanding the data set, combining the collected data with the inverse results to generate a new training data set that contains more dimensions and feature interactions; data standardization, standardizing or normalizing all parameters, eliminating dimensional differences, and avoiding excessive influence of certain features on the model; feature engineering, improving the generalization ability of the model through feature selection or generating new features.

[0182] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention has various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for collecting orchard ecological environment information, characterized in that: The following steps are involved: Step S1, by deploying a variety of collection equipment to collect the soil parameters, spatial environment parameters, pest parameters and fruit parameters of the orchard in real time, and summarize them to form an orchard ecological basic data set; Step S2, constructing a reverse inference model, using the collected data to indirectly calculate key ecological parameters, including soil nutrients, fruit quality and pest spread trends, and optimizing the results through experimental verification and model correction; Step S3, compare the inference result with the target value of the orchard, analyze the source of the deviation, establish the macro-cause chain of the deviation and environmental parameters through data matching and visualization tools, and intuitively present the cause of the problem and its spatial distribution; Step S4, determining the control direction according to the deviation analysis and cause chain results, and matching executable control measures; Step S5, monitor the effect of the control measures after implementation, verify the effectiveness of the control measures by comparing the data before and after, and use the feedback data for iterative optimization of the reverse model, ultimately achieving closed-loop management and automated control.

2. The method for collecting orchard ecological environment information according to claim 1, characterized in that: Wherein step S1 also includes the following sub-steps: S1-1, divide the orchard into several preliminary management units according to the terrain characteristics, planting plan and irrigation zoning of the orchard, and set the collection content and scope, including: soil parameters, spatial environment parameters, pest parameters, and fruit parameters; Soil parameters: moisture, pH; Space environment parameters: light intensity, temperature and humidity; Pest parameters: pest types and density; Fruit parameters: fruit size, color; Collection scope: The management units initially divided serve as the layout scope of the collection equipment; S1-2, data collection equipment includes soil parameter monitoring equipment, pest monitoring equipment, and fruit monitoring equipment; Soil parameter monitoring equipment: soil moisture sensor, pH sensor, temperature and humidity sensor, light intensity sensor; Pest monitoring equipment: intelligent insect trap; Fruit monitoring equipment: drones, high-resolution cameras; Equipment layout covers terrain types, vegetation differences, and orchard center and boundary areas; sensor density is set according to orchard area and complexity, such as 1 soil sensor per 100 square meters and 1 light or temperature and humidity sensor per 500 square meters; equipment is arranged in areas with significant environmental changes or prone to problems, including low-lying areas, areas with insufficient light, and areas prone to pests, based on historical data or observation points; equipment is close to main roads or work channels for easy inspection and maintenance; S1-3, data transmission, using LoRa wireless technology to upload the collected data to the cloud, the cloud platform automatically classifies and stores the uploaded data to form a basic data set of the current ecological status of the orchard; Simply aggregate and summarize the real-time data to generate a preliminary state map of the orchard ecological information, including: soil moisture distribution map, light intensity distribution map, pest density distribution map, fruit distribution and size change trend map; S1-4, based on the data collected in S1-3, the orchard is divided into more detailed management units. According to parameters such as soil moisture, pH, and conductivity, units with similar soil characteristics are divided; according to light intensity and temperature and humidity, units with similar environmental conditions are divided; combined with pest type and density data, areas with high incidence of pests are marked separately; according to fruit size, color, and distribution trend, areas with similar yield characteristics are divided together; combined with terrain (highlands, lowlands) and historical management information; The clustering algorithm K-Means is used to automatically divide similar areas according to the collected ecological parameters. The clustering results are imported into the GIS platform to visualize the geographic spatial data of the orchard, as shown in formula (1): Where J is the total sum of squared errors of the clusters, x is the data point belonging to the i-th cluster, and μ i is the center point of the ith cluster, C i is the set of the i-th cluster, K is the number of clusters; Each management unit has a clear description of its ecological characteristics, including soil characteristics, pest risk, and fruit distribution. The division results are output through the GIS platform as a management unit distribution map, detailed tables and summaries of the ecological parameters of each unit.

3. The method for collecting orchard ecological environment information according to claim 1, characterized in that: Wherein step S2 also includes the following sub-steps: S2-1, indirect estimation of key parameters that are difficult to measure based on collected data, including soil nutrients, fruit quality, and pest spread trends; Soil nutrients: nitrogen, phosphorus, potassium; Fruit quality: sugar content, maturity; Pest spread trend: distribution throughout the garden; S2-2, establish the mathematical logic relationship between ecological collection data and key parameters that are difficult to measure, and construct reverse inference models: soil nutrient model, fruit quality model, and pest spread model; The soil nutrient model uses a linear regression formula to establish the linear relationship between soil moisture, electrical conductivity, pH and nitrogen, phosphorus and potassium nutrient concentrations, as shown in formulas (2) to (4): N=a1·(H2O)+b1·(pH)+c1 Formula (2) P=a2·(H2O)+b2·(pH)+c2 Formula (3) K=a3·(H2O)+b3·(pH)+c3 Formula (4) Where N, P, K represent the current concentrations of nitrogen, phosphorus, and potassium in the soil, respectively; H2O is the water content of the soil; pH is the pH value of the soil; a1, a2, and a3 are model coefficients related to soil moisture; b1, b2, and b3 are model coefficients related to soil pH; and c1, c2, and c3 are intercept terms of the model. The fruit quality model uses light intensity, temperature and humidity, and the rate of change of fruit size to establish a predictive relationship with sugar content and maturity, as shown in formulas (5) to (6): T_H=0.7·T+0.3·H Formula (5) S=d1·(Lux)+d2·(T_H)+d3·(v)+d4 Formula (6) Among them, T_H is the temperature and humidity index, T is the air temperature of the orchard, and H is the air humidity; S is the sugar content of the fruit, Lux is the light intensity, v is the fruit change rate, d1, d2, d3 are the model coefficients related to light, temperature and humidity index, and fruit change rate, respectively, and d4 is the intercept term of the model; The pest diffusion model uses insect trap data, wind speed, humidity and temperature to predict the pest diffusion trend, as shown in formula (7): D(t+1)=D(t)·(1+rc) Formula (7) Where D(t) is the pest density at the current time t, D(t+1) is the pest density at the next time t+1, r is the pest reproduction rate (affected by temperature and humidity), c is the pest capture rate, and 1+rc is the increase or decrease factor of the pest density; S2-3, use the collected real-time data as input, substitute it into the constructed reverse inference model, and calculate the predicted values ​​of key parameters; randomly select a small area in the orchard to conduct actual measurement or experimental testing of the reverse inference parameters: soil nutrients: analyze soil nitrogen, phosphorus and potassium concentrations through laboratory testing; fruit quality: manually measure the sugar content and maturity of the fruit; pest density: manually record the actual distribution data of pests; The inference results are compared with the small-scale experimental data, the error value is calculated, the model parameters are optimized for the part with larger error, and the model is retrained based on the new data.

4. The method for collecting orchard ecological environment information according to claim 1, characterized in that: Wherein step S3 also includes the following sub-steps: S3-1, compare the key parameter values ​​calculated by the inverse model with the target values ​​of the orchard to analyze the deviation. The deviation is the difference between the actual calculated value and the target value, which mainly manifests as: soil nutrient deviation, fruit quality deviation, pest density deviation, and analyze the cause chain of the deviation; Causes of low soil nitrogen: high soil moisture → severe nitrogen leaching → reduced nitrogen content; Reasons for low sugar content in fruits: insufficient light → low photosynthesis efficiency → insufficient sugar accumulation; Reasons for high pest density: suitable temperature and humidity → rapid pest reproduction → local pest spread; S3-2, match the analyzed deviations with the environmental parameters of the orchard, establish a macro-cause chain, understand the causes of the deviations, and associate the deviations with the spatial distribution of the orchard environmental parameters through data analysis and visualization tools; Use GIS tools to draw a cause chain distribution map of the orchard, show the spatial distribution relationship between deviations and environmental parameters, generate regional problem charts, including light distribution maps, high-incidence areas of pests, and form a visual report of deviations and environmental parameters; According to the matching and visualization results, the logical relationship between the deviation and the cause chain is sorted out, and the spatial distribution of the deviation data, the main macro-cause chain of the deviation in each region, and the key driving factors of the deviation are output.

5. The method for collecting orchard ecological environment information according to claim 1, characterized in that: Wherein step S4 also includes the following sub-steps: S4-1, based on the results of deviation analysis and macro-cause mapping, formulate clear control directions to narrow the gap between key parameters and target values. According to the degree of deviation of key parameters, including low soil nitrogen content, low fruit sugar content, and high pest density, determine the issues that need to be addressed first. Combined with the results of macro-cause analysis, clarify the core driving factors that affect the deviation and ensure that the control direction is in line with the overall management objectives of the orchard; S4-2, according to the control direction determined in S4-1, match specific executable measures to optimize ecological parameters and meet the orchard target value requirements. Typical control measures are designed as follows: Soil nutrient regulation: Increase soil nitrogen content, reduce irrigation frequency or switch to drip irrigation technology to reduce water loss; regularly apply slow-release nitrogen fertilizer locally in low-content areas; Fruit quality control: increase the sugar content of the fruit, prune the tree crown, and increase the light-collecting area of ​​the fruit; spray fertilizers containing potassium and magnesium on the leaves to promote sugar accumulation; Pest control: Reduce pest density, install insect nets or spray biological pesticides in areas with high pest incidence; introduce natural enemies for biological control; adjust the humidity level in the orchard to reduce the conditions for pest reproduction.

6. The method for collecting orchard ecological environment information according to claim 1, characterized in that: Wherein, in step S5, the following sub-steps are also included: S5-1, monitor the effect of regulation. After implementing the regulation measures, continuously collect soil, fruit and pest data, dynamic changes in water and nutrients, the trend of increasing fruit sugar content, and the reduction of pest density; S5-2, compare the core data before and after the adjustment, including soil nitrogen content, fruit sugar content, and pest density, with the changes in target values ​​to evaluate the effectiveness of the control measures; S5-3, input the feedback data after regulation into the inverse model, correct the model parameters, introduce historical data and adjustment data to enrich the training set, gradually introduce more complex nonlinear models to optimize the inverse logic, improve the closed-loop control, and realize automated decision-making and regulation.

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