Intelligent pest control system and method for juglans sigillata forest

Abstract

The invention relates to the field of forestry pest prevention and control, and discloses a juglans sigillata forest intelligent pest prevention and control system and method.The juglans sigillata forest intelligent pest prevention and control system comprises a data acquisition module used for collecting basic information of a juglans sigillata forest, and the basic information comprises meteorological environment parameters, soil physical and chemical properties and in-forest visual image data; the identification and analysis module is used for deeply analyzing data characteristics of the basic information, discriminating types and severity of diseases and pests, predicting potential occurrence risks of the diseases and pests and forming an evaluation result; and the early warning response module is used for presetting a risk level threshold value and rule logic and generating an early warning signal of a corresponding level according to an evaluation result. A data acquisition module integrating multiple types of monitoring units and communication units is constructed, so that the juglans sigillata forest can be sensed in all directions, the limitation of manual inspection is eliminated, and the problems of inaccurate monitoring and difficulty in data backtracking are solved; and the identification analysis module comprises sample management, model training and data analysis and prediction.

Description

Technical Field

[0001] The invention relates to the technical field of forestry harmful organism prevention and control, and in particular to an intelligent pest prevention and control system and method for a walnut forest. Background Art

[0002] Forest pest control is a key area focused on maintaining the health and stability of forest ecosystems. It conducts research and responds to biological hazards such as various harmful insects, pathogens and weeds to reduce the negative impact of pests on forest resources, the ecological environment and human economic activities, and to ensure the sustainable development of forestry and ecological balance.

[0003] In the prevention and control of pests and diseases in walnut (Juglans sigillata Dode) forests, the shortcomings of traditional methods are obvious. In terms of monitoring, it mainly relies on regular manual inspections. Plant protection personnel carry heavy equipment and shuttle through the forest. Due to the limitations of subjective experience, physical energy and energy, it is difficult to conduct high-frequency and full-area inspections, and minor pests and diseases are easy to be neglected. In addition, data records are scattered and retrospective analysis is difficult. During prevention and control, chemical pesticides are mostly applied in an extensive manner, and the dosage and concentration of pesticides are determined based on experience. The severely affected areas lack the right temperature, and the lightly affected areas suffer from serious pesticide damage. It also pollutes the soil and water sources, destroys the ecological chain in the forest, and causes a sharp decline in natural enemy insects, thus affecting the prevention and control of pests and diseases in walnut forests. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent pest and disease control system and method for walnut forests, which solves the problems that traditional pest and disease control monitoring in walnut forests mainly relies on regular manual inspections, and plant protection personnel carry heavy equipment to shuttle through the forest. They are restricted by subjective experience, physical energy and energy, and it is difficult to conduct high-frequency and full-area inspections. Minor pests and diseases are easily overlooked, and data records are scattered and retrospective analysis is difficult. During prevention and control, chemical pesticides are mostly applied in an extensive manner, and the dosage and concentration of the pesticide are determined based on experience. The severely affected areas lack the right temperature, and the lightly affected areas suffer from serious pesticide damage. In addition, the soil and water sources are polluted, the ecological chain in the forest is destroyed, and the natural enemy insects are sharply reduced, thus affecting the pest and disease control in walnut forests.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent pest control system for walnut forests, comprising:

[0006] Data acquisition module: used to collect basic information of the walnut forest, including meteorological environmental parameters, soil physical and chemical properties, and intuitive image data in the forest;

[0007] Identification and analysis module: used to deeply analyze the data characteristics of basic information, identify the types and severity of pests and diseases, and predict the potential risk of pests and diseases to form an assessment result;

[0008] Early warning response module: used to preset risk level thresholds and rule logic, generate corresponding level early warning signals based on assessment results, and initiate emergency response processes;

[0009] Prevention and control execution module: used to implement pest control operations on walnut forests using multiple means;

[0010] Human-computer interaction module: used to provide data visualization display, issue operation instructions, and receive prevention and control feedback information.

[0011] Preferably, the data acquisition module includes a meteorological environment monitoring unit, a soil condition monitoring unit, a forest image monitoring unit, a pest and disease monitoring unit, and a communication unit. The meteorological environment monitoring unit is used to monitor the meteorological environment parameters of the walnut forest in real time and capture dynamic meteorological changes. The meteorological environment parameters include temperature, humidity, light intensity, wind speed, and wind direction. The soil condition monitoring unit is used to monitor the soil physical and chemical properties of the walnut forest in real time. The soil physical and chemical properties include soil texture, soil porosity, soil moisture content, soil pH, and soil fertility indicators. The forest image monitoring unit is used to monitor the intuitive image data in the walnut forest in real time. The intuitive image data in the forest include the overall picture of the walnut plants presented by high-resolution visible light images, the subtle lesions, wormholes, and abnormal color of branches and leaves of the walnut leaves under close-up shots, and the abnormal heat areas caused by pests and diseases highlighted by thermal imaging images. The pest and disease monitoring unit is used to monitor the pest and disease data of the walnut forest, and the pest and disease data include changes in species and quantity. The communication unit is used to provide data transmission and communication channels between modules.

[0012] Preferably, the soil condition monitoring unit includes a soil moisture detection subunit, a soil pH detection subunit, and a soil fertility monitoring subunit. The soil moisture detection subunit is used to monitor the moisture content of the soil in the walnut forest in real time. The soil pH detection subunit is used to monitor the salinity of the soil in the walnut forest in real time. The soil fertility monitoring subunit is used to monitor the fertility index of the soil in the walnut forest in real time. The fertility index includes nitrogen, phosphorus, and potassium content.

[0013] The forest image monitoring unit includes an image acquisition subunit and a satellite remote sensing subunit. The image acquisition subunit is used to regularly collect image data of the walnut forest, and the image data includes fine lesions, wormholes, and abnormal color of branches and leaves of the walnut forest. The satellite remote sensing subunit is used to regularly collect macro information of the walnut forest, and the macro information includes vegetation coverage and abnormal growth areas of the walnut forest.

[0014] Preferably, the identification and analysis module includes a sample library management unit, a deep learning model unit, and a data analysis and prediction unit. The sample library management unit is used to collect, organize, and store sample resources of pests and diseases in the walnut forest, and annotate attributes for classified storage. The sample resources include high-definition images and videos of diseases and pests. The attributes include pest and disease category, lesion site, and development stage. The deep learning model unit is used to build a target detection and recognition model architecture based on a mainstream deep learning framework, pre-train and extract basic features in a general image data set, and then migrate them to the walnut forest pest and disease sample library for special training, so that the model can identify pests and diseases in the walnut forest. The data analysis and prediction unit is used to use a deep learning semantic segmentation model to parse the data obtained from the forest image monitoring unit, construct a pest and disease occurrence probability prediction model, repeatedly check and adjust according to historical pest and disease occurrence data, set corresponding low, medium, and high risk warning thresholds, and output quantitative prediction results to guide prevention and control decisions.

[0015] Preferably, the early warning response module includes an early warning unit and an emergency dispatch unit. The early warning unit is used to determine the risk level of pests and diseases based on the output results of the data analysis and prediction unit and the preset rule logic, and generate blue, yellow and red three-color early warning information accordingly, which is pushed to the management personnel through multiple channels simultaneously. The early warning information is accompanied by a pest and disease diagnosis report, a prevention and control operation manual, and surrounding agricultural material reserves. In the three-color early warning information, blue is a low risk prompt, yellow is a medium risk warning, and red is a high emergency risk alarm. The multiple channels include mobile phone APP, text messages, and computer management systems. The diagnostic report includes the type, symptoms, severity, and development trend prediction. The prevention and control operation manual includes adaptive prevention and control means and processes. The agricultural material reserves include inventory categories, quantities, and distance information. The emergency dispatch unit is used to quickly activate the prevention and control emergency plan after receiving a red high-emergency risk alarm, and push the pest control prescription map to the prevention and control execution module. At the same time, the emergency operation process is recorded in the local database throughout the process, and retrospectively reviewed and optimized. The pest control prescription map includes the definition of the prevention and control area, the application concentration, and the operation path. The emergency operation process includes equipment trajectory, application parameters, and operation start and end time.

[0016] Preferably, the prevention and control execution module includes an intelligent spray prevention and control unit, a biological control unit, and a physical control unit. The intelligent spray prevention and control unit is used to spray medicine on the walnut forest through intelligent spray equipment, and perform variable spraying on different pest and disease areas of the walnut forest according to the instructions of the early warning response module and the prevention and control prescription map. The intelligent spray equipment includes agricultural drones and self-propelled sprayers. The biological control unit is used to release pest natural enemies and microbial strains into the walnut forest according to the pest and disease data collected by the data acquisition module. The release ratio of the pest natural enemies is 10:1-20:1, and they are tracked and monitored after release. The physical control unit is used to perform intelligent laser trapping and microwave control on the walnut forest.

[0017] Preferably, the intelligent spray control unit includes a positioning and navigation subunit, a sensor monitoring subunit, and a spray control subunit. The positioning and navigation subunit is used to accurately locate the real-time position of the intelligent spray equipment in the walnut forest, and plan the operation route and path in combination with the pest control prescription map. The sensor monitoring subunit is used to monitor the spray data in real time, and collect the equipment status parameters at the same time, and feed them back to the spray control subunit. The spray data includes spray flow rate and spray pressure. The equipment status parameters include battery power and motor speed. The spray control subunit is used to adjust the parameters of the intelligent spray equipment according to the feedback data of the sensor monitoring subunit and the instructions of the pest control prescription map, and apply the pesticide in different areas with different pest severity. The parameters include spray angle and flow rate.

[0018] The biological control unit includes a natural enemy breeding and release subunit and a microorganism application subunit. The natural enemy breeding and release subunit is used to use artificial feed and alternative hosts to breed natural enemy insects on a large scale and release them in proportion. After release, monitoring plots are set up to regularly evaluate colonization survival and pest control effects, and replenish releases are made in a timely manner. The microorganism application subunit is used to select stress-resistant and highly active microbial strains from the ecological environment of the walnut forest, improve them through genetic engineering, optimize the fermentation process to make an adapted formulation, and spray them with natural enemy insects at a dilution of 500-1000 times according to the types and development stages of pests and diseases. The adapted formulations include water-dispersible granules and nano-suspension concentrates.

[0019] The physical control unit includes a laser trapping subunit and a microwave control subunit. The laser trapping subunit is used to activate high-power blue light and ultraviolet light to attract pests at dusk through an intelligent laser trapping device. After the device has a built-in insect behavior analysis algorithm to identify the pests, it immediately switches to a pulse laser to accurately kill the pests and records the capture data. The capture data includes the type, quantity, and time. The microwave control subunit is used to target trunk-boring pests that harm branches and trunks. The microwave antenna probe is accurately implanted using drilling equipment, and the microwave radiation time and power level are set according to the condition of the insect tunnel. The temperature of the tree trunk is monitored in real time with a thermal imager to kill the pests. The condition of the insect tunnel includes direction and length.

[0020] Preferably, the human-computer interaction module includes a data visualization unit, a command interaction unit, and an information feedback unit. The data visualization unit is used to visualize the data of each module in the form of intuitive charts, high-definition images, and map annotations. The command interaction unit is used to build a two-way interaction channel between managers and the system. The information feedback unit is used to collect system information in real time and push it to the management end. The system information includes the operating status of front-line prevention and control equipment and the actual prevention and control effects of diseases and pests in the forest.

[0021] The intelligent pest control method for walnut forests includes the following steps:

[0022] S1. Data collection: The meteorological environment monitoring unit is used to monitor the meteorological environment parameters of the walnut forest in real time. The soil condition monitoring unit monitors the physical and chemical properties of the soil. The forest image monitoring unit collects intuitive image data in the forest. The pest and disease monitoring unit collects pest and disease data. The data of each unit is summarized by the communication unit to build a basic information database of the walnut forest.

[0023] S2. Identification and analysis: The sample library management unit then organizes and stores high-definition images and video samples of pests and diseases and labels them for classification. The deep learning model unit is pre-trained based on a general data set and then specially trained with local samples to identify pests and diseases. The data analysis and prediction unit combines multi-source data with a semantic segmentation model to build a prediction model, set an early warning threshold, and output the evaluation results.

[0024] S3, early warning response: The early warning unit then triggers blue, yellow, and red warnings based on the assessment, and pushes detailed information through multiple channels. In the event of a red alarm, the emergency dispatch unit activates the plan, pushes the prevention and control prescription map, and records the operation process;

[0025] S4. Prevention and control execution: The intelligent spray control unit then controls the agricultural drone and self-propelled sprayer to spray variable quantities according to the prescription map; the biological control unit releases natural enemy insects and microbial strains according to the pest data and tracks them; the physical control unit uses laser trapping and microwave methods for prevention and control;

[0026] S5. Human-computer interaction: Data is visualized through the data visualization unit, operation instructions are issued through the instruction interaction unit, and prevention and control status is received through the information feedback unit.

[0027] The present invention provides an intelligent pest control system and method for walnut forests, which has the following beneficial effects:

[0028] 1. The present invention realizes information automation and all-round perception of walnut forests by constructing a data acquisition module integrating multiple types of monitoring units and communication units, gets rid of the limitations of manual inspections, and solves the problems of inaccurate monitoring and difficult data backtracking; relies on an identification and analysis module including sample management, model training, and data analysis and prediction to provide scientific evaluation and early warning, and change the blindness of prevention and control based on experience; with the help of a prevention and control execution module that coordinates intelligent spraying, biological, and physical prevention and control, precise pesticide application, ecological checks and balances, and efficient pest control are achieved, extensive chemical pesticide application is abandoned, the ecological chain is repaired, and the prevention and control effect is improved.

[0029] 2. The present invention uses intelligent spraying to apply pesticides according to the variable of the prescription map, accurately controls the amount, and greatly increases the utilization rate of pesticides; biological control releases natural enemy insects in scientific proportions and applies improved microbial strains, using insects to control insects and bacteria to control bacteria, thereby rebuilding the ecological balance between forests; physical control laser trapping and microwave pest control are targeted and accurate, reducing interference with beneficial organisms, curbing the spread of pests and diseases in all directions, improving the overall health level of the walnut forest, and reducing dependence on chemical pesticides. While ensuring the effectiveness of prevention and control, it maintains the ecological balance of the walnut forest and promotes sustainable development.

[0030] 3. The present invention integrates meteorological, soil, imaging and special pest and disease monitoring units through the data acquisition module, and collects multi-source data in an all-round and real-time manner, just like weaving a dense perception network in the forest. With the help of deep learning models and data analysis and prediction units, the value of data is deeply mined, the types and degrees of pests and diseases are accurately identified, and the risks of occurrence are predicted in advance. The early warning response module efficiently generates three-color early warnings and pushes them through multiple channels in real time, so that managers can quickly know and take decisive actions in the early stages of the germination of pests and diseases, seize the initiative in prevention and control, and nip disaster losses in the bud. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a system architecture diagram of the intelligent pest and disease prevention and control system for walnut forests proposed by the present invention;

[0032] Figure 2 This is a data acquisition module architecture diagram of the intelligent pest control system for walnut forests proposed by the present invention;

[0033] Figure 3 This is a structural diagram of the soil condition monitoring unit of the intelligent pest and disease control system for walnut forests proposed by the present invention;

[0034] Figure 4This is a diagram of the forest image monitoring unit architecture of the intelligent pest and disease control system for walnut forests proposed by the present invention;

[0035] Figure 5 This is the architecture diagram of the identification and analysis module of the intelligent pest control system for walnut forests proposed by the present invention;

[0036] Figure 6 This is a diagram of the early warning response module architecture of the intelligent pest control system for walnut forests proposed by the present invention;

[0037] Figure 7 This is a diagram of the prevention and control execution module architecture of the intelligent pest control system for walnut forests proposed by the present invention;

[0038] Figure 8 This is a diagram of the intelligent spray control unit architecture of the intelligent pest control system for walnut forests proposed by the present invention;

[0039] Fig. 9 This is a diagram of the biological control unit architecture of the intelligent pest control system for walnut forests proposed by the present invention;

[0040] Fig.10 This is a physical control unit architecture diagram of the intelligent pest control system for walnut forests proposed by the present invention;

[0041] Fig.11 This is a human-computer interaction module architecture diagram of the intelligent pest control system for walnut forests proposed by the present invention;

[0042] Fig.12 This is a method flow chart of the intelligent pest and disease prevention method for walnut forests proposed by the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only 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 creative work are within the scope of protection of the present invention.

[0044] Example:

[0045] Please refer to the attached Figure 1 The embodiment of the present invention provides an intelligent pest control system for walnut forests, including:

[0046] Data collection module: used to collect basic information of walnut forests, including meteorological environmental parameters, soil physical and chemical properties, and intuitive image data in the forest;

[0047] Identification and analysis module: used to deeply analyze the data characteristics of basic information, identify the types and severity of pests and diseases, and predict the potential risk of pests and diseases to form an assessment result;

[0048] Early warning response module: used to preset risk level thresholds and rule logic, generate corresponding level early warning signals based on assessment results, and initiate emergency response processes;

[0049] Prevention and control execution module: used to implement pest control operations on walnut forests using multiple means;

[0050] Human-computer interaction module: used to provide data visualization display, issue operation instructions, and receive prevention and control feedback information.

[0051] Specifically, by collecting basic information on the walnut forest, including meteorological and environmental parameters, soil physical and chemical properties, and intuitive image data within the forest, we can comprehensively and meticulously grasp the real-time status and environmental conditions of the walnut forest, provide rich and accurate first-hand data for the subsequent precise analysis and prevention of pests and diseases, and build a comprehensive information database on the walnut forest, laying the data foundation for the entire prevention and control system.

[0052] By deeply analyzing the data characteristics of basic information, identifying the types and severity of pests and diseases, and predicting the potential risks of pests and diseases, an assessment result is formed, thereby converting massive amounts of raw data into information with targeted and decision-making value. The specific situation of pests and diseases can be accurately located, and clear directional guidance can be provided for subsequent early warning and prevention work, so that prevention and control measures can be targeted and avoid blind prevention and control.

[0053] By presetting risk level thresholds and rule logic, corresponding level warning signals are generated based on the assessment results, and the emergency response process is initiated, so that alarms can be issued in time at different stages of the development of pests and diseases, allowing managers to know the severity of the situation at the first time, and quickly allocate resources and take corresponding measures to ensure effective intervention before pests and diseases cause serious harm, thereby minimizing losses.

[0054] By using a variety of means to control pests and diseases in the walnut forest, the most appropriate and effective prevention and control measures can be taken for pests and diseases of different types and severity, achieving all-round and multi-level pest and disease control. This can not only improve the prevention and control effects, but also reduce the negative impact on the environment, ensure the healthy growth of the walnut forest, and take into account the ecological balance.

[0055] Through data visualization, issuing of operation instructions, and receiving of prevention and control feedback information, a convenient and efficient communication bridge is built between managers and the prevention and control system, enabling managers to clearly understand the progress and effects of prevention and control work, make timely adjustments and decisions, and ensure the smooth and efficient operation of the entire prevention and control process.

[0056] Through the coordinated operation of the above modules, intelligent, precise and efficient prevention and control of pests and diseases in walnut forests is achieved. From data collection to analysis and evaluation, to early warning response and specific prevention and control implementation, as well as human-computer interaction throughout the process, a complete and closed-loop prevention and control system is formed. It also solves the problems of traditional walnut forest pest and disease control monitoring, which mainly rely on regular manual inspections. Plant protection personnel carry heavy equipment and shuttle through the forest. They are restricted by subjective experience and physical energy, and it is difficult to conduct high-frequency and full-area inspections. Minor pests and diseases are easy to be neglected, and data records are scattered and retrospective analysis is difficult. During prevention and control, chemical pesticides are mostly applied in an extensive manner, and the dosage and concentration of the pesticide are determined by experience. The severely affected areas lack the fire and the lightly affected areas suffer from serious pesticide damage. It also pollutes the soil and water sources, destroys the ecological chain in the forest, and causes a sharp decline in natural enemy insects, thus affecting the prevention and control of pests and diseases in walnut forests.

[0057] Please refer to the attached Figure 2 The data acquisition module includes a meteorological environment monitoring unit, a soil condition monitoring unit, a forest image monitoring unit, a pest and disease monitoring unit, and a communication unit. The meteorological environment monitoring unit is used to monitor the meteorological environment parameters of the walnut forest in real time and capture dynamic meteorological changes. The meteorological environment parameters include temperature, humidity, light intensity, wind speed, and wind direction. The soil condition monitoring unit is used to monitor the soil physical and chemical properties of the walnut forest in real time. The soil physical and chemical properties include soil texture, soil porosity, soil moisture content, soil pH, and soil fertility indicators. The forest image monitoring unit is used to monitor the intuitive image data in the walnut forest in real time. The intuitive image data in the forest include the overall picture of the walnut plant presented by high-resolution visible light images, the subtle lesions, wormholes, and abnormal color of branches and leaves of the walnut leaf under close-up shots, and the abnormal heat areas caused by pests and diseases highlighted by thermal imaging images. The pest and disease monitoring unit is used to monitor the pest and disease data of the walnut forest. The pest and disease data include changes in species and quantity. The communication unit is used to provide data transmission and communication channels between modules.

[0058] Specifically, by real-time monitoring of the meteorological environmental parameters of the walnut forest and capturing dynamic meteorological changes, detailed data can be accumulated for the correlation analysis between the occurrence of pests and diseases and meteorological conditions, and it can be accurately judged that high temperature and high humidity are conducive to the growth of fungi and strong winds help the spread of pests and diseases, thereby achieving quantitative control over the impact of meteorological factors on the growth and spread of pests and diseases.

[0059] By real-time monitoring of the soil physical and chemical properties of the walnut forest, we can gain a clear insight into the health of the soil microenvironment, evaluate its impact on the root system and overall disease and insect resistance of the walnut trees, and achieve accurate early warning of disease and insect susceptibility based on soil conditions.

[0060] By real-time monitoring of intuitive image data within the walnut forest, a visual health record from micro branches and leaves to macro forest stands is constructed, the occurrence points and severity of pests and diseases are accurately located, and intuitive, comprehensive and dynamic tracking of pests and diseases in the forest is achieved. This solves the problems of narrow manual inspection range, difficulty in discovering hidden pests and diseases in the forest, and difficulty in assessing the overall disaster situation from a macro perspective.

[0061] By monitoring the pest and disease data of walnut forests, we can grasp the occurrence and development of pests and diseases in real time from multiple dimensions, provide accurate basis for subsequent prevention and control timing decisions, and realize full monitoring of pests and diseases from latency to outbreak, solving the dilemma that traditional monitoring can only detect obvious pests and diseases, but is slow in detecting hidden pests and diseases in the early stages.

[0062] By providing data transmission and communication channels between modules, the coordinated operation of the entire data collection system is guaranteed, allowing the back-end analysis module to obtain the latest data in real time and achieve seamless integration of multi-source data; through the collaborative operation of each unit, the all-round, all-weather, multi-level basic information of the walnut forest can be accurately collected and quickly summarized and integrated, and first-hand data from meteorology, soil, forest phase to pests and diseases can be updated in real time, laying a solid foundation for subsequent identification and analysis, early warning and prevention, thus solving the problem that the pest and disease control data of traditional walnut forests are scattered, one-sided, and lagging, making it difficult to support the formulation of accurate prevention and control decisions.

[0063] Please refer to the attached Figure 3 , Attachment Figure 4 The soil condition monitoring unit includes a soil moisture detection subunit, a soil pH detection subunit, and a soil fertility monitoring subunit. The soil moisture detection subunit is used to monitor the moisture content of the soil in the walnut forest in real time. The soil pH detection subunit is used to monitor the salinity of the soil in the walnut forest in real time. The soil fertility monitoring subunit is used to monitor the fertility indicators of the soil in the walnut forest in real time. The fertility indicators include nitrogen, phosphorus and potassium content. The forest image monitoring unit includes an image acquisition subunit and a satellite remote sensing subunit. The image acquisition subunit is used to regularly collect image data of the walnut forest. The image data includes subtle lesions, wormholes, and abnormal color of branches and leaves of the walnut forest. The satellite remote sensing subunit is used to regularly collect macro information of the walnut forest. The macro information includes vegetation coverage and abnormal growth areas of the walnut forest.

[0064] Please refer to the attached Figure 5The recognition and analysis module includes a sample library management unit, a deep learning model unit, and a data analysis and prediction unit. The sample library management unit is used to collect, organize, and store sample resources of pests and diseases in the walnut forest, and annotate attributes for classified storage. The sample resources include high-definition images and videos of diseases and pests. The attributes include pest and disease categories, lesion sites, and development stages. The deep learning model unit is used to build a target detection and recognition model architecture based on the mainstream deep learning framework, pre-train in the general image data set to extract basic features, and then migrate to the sample library of pests and diseases in the walnut forest for special training, so that the model can identify pests and diseases in the walnut forest. The data analysis and prediction unit is used to use the deep learning semantic segmentation model to parse the data obtained from the forest image monitoring unit, build a probability prediction model for pest and disease occurrence, repeatedly check and adjust according to historical pest and disease occurrence data, and set corresponding low, medium, and high risk warning thresholds, and output quantitative prediction results to guide prevention and control decisions.

[0065] Specifically, through the coordinated operation of the sample library management unit, the deep learning model unit, and the data analysis and prediction unit, a complete link is formed from sample collection and organization, model training and identification to data analysis and prediction, thereby achieving a comprehensive, accurate and forward-looking analysis and evaluation of pests and diseases in walnut forests, and providing key decision-making support for subsequent early warning response and prevention and control implementation.

[0066] Please refer to the attached Figure 6 The early warning response module includes an early warning unit and an emergency dispatch unit. The early warning unit is used to determine the risk level of pests and diseases based on the output results of the data analysis and prediction unit and the preset rule logic, and generate blue, yellow and red three-color early warning information accordingly, which is pushed to the management personnel through multiple channels simultaneously. The early warning information is accompanied by a pest and disease diagnosis report, a prevention and control operation manual, and surrounding agricultural material reserves. In the three-color early warning information, blue is a low-risk prompt, yellow is a medium-risk warning, and red is a high-emergency risk alarm. Multiple channels include mobile phone APP, SMS, and computer management system. The pest and disease diagnosis report includes types, symptoms, severity, and development trend predictions. The prevention and control operation manual includes adaptive prevention and control methods and processes. Agricultural material reserves include inventory categories, quantities, and distance information. The emergency dispatch unit is used to quickly activate the prevention and control emergency plan after receiving a red high-emergency risk alarm, push the pest and disease control prescription map to the prevention and control execution module, and record the emergency operation process to the local database throughout the process, and backtrack and optimize. The pest and disease control prescription map includes the definition of the prevention and control area, the concentration of the pesticide, and the operation path. The emergency operation process includes equipment trajectory, pesticide application parameters, and the start and end time of the operation.

[0067] Specifically, based on the results of the probability of occurrence and development trend of pests and diseases output by the data analysis and prediction unit, combined with the pre-set rule logic, the risk level of pests and diseases is carefully judged with the help of rule engines such as Droo ls, and then blue, yellow and red warning information is generated according to the judgment results. At the same time, the diagnostic report containing the type, symptoms, severity and development trend of pests and diseases, the prevention and control operation manual suitable for prevention and control means and processes, and the surrounding agricultural supplies reserves with inventory categories, quantities and distance information are attached to the warning information, and then pushed to the management personnel through multiple channels such as mobile phone APP, text messages, and computer management systems. It ensures that the management personnel can obtain the relevant situation of pests and diseases in the walnut forest and the information needed for response in a timely, comprehensive and accurate manner, and can know the warning content at the first time no matter where they are or what device they are using.

[0068] Upon receiving a red alert indicating a high emergency risk, the prevention and control emergency plan is immediately activated, and a pest control prescription map with detailed markings of key information such as the defined prevention and control area, pesticide concentration, and operation path is pushed to the prevention and control execution module according to the preset process. At the same time, relevant technical means are used to record the emergency operation process such as equipment trajectory, pesticide application parameters, and operation start and end time, and these records are stored in the local database for subsequent retrospective review and optimization. In this way, when a pest and disease crisis occurs, resources can be deployed quickly and orderly, targeted prevention and control operations can be carried out, and the entire emergency response process can be fully recorded.

[0069] Through close cooperation between the early warning unit and the emergency dispatch unit, the early warning unit first issues an accurate early warning in a timely manner, and in high-risk situations the emergency dispatch unit quickly follows up and carries out emergency disposal, thus forming a complete link from early warning to emergency disposal, ensuring a quick response and effective response when pest and disease risks appear in the walnut forest, achieving comprehensive monitoring and efficient response to pest and disease risks in the walnut forest, and ensuring the timeliness and effectiveness of prevention and control work.

[0070] Please refer to the attached Figure 7 The prevention and control execution module includes an intelligent spray prevention unit, a biological control unit, and a physical control unit. The intelligent spray prevention unit is used to spray medicine on the walnut forest through intelligent spray equipment, and to perform variable spraying on different pest and disease areas in the walnut forest according to the instructions of the early warning response module and the prevention and control prescription map. The intelligent spray equipment includes agricultural drones and self-propelled sprayers. The biological control unit is used to release pest natural enemies and microbial strains into the walnut forest according to the pest and disease data collected by the data acquisition module. The ratio of pest natural enemies released is 10:1-20:1, and they are tracked and monitored after release. The physical control unit is used for intelligent laser trapping and microwave control of the walnut forest.

[0071] Specifically, variable spraying is carried out on different pest and disease areas in the walnut forest according to the instructions of the early warning response module and the prevention and control prescription map, so as to ensure that pesticides are applied to the target areas accurately and efficiently, avoid excessive or insufficient dosage, and reduce pesticide waste and environmental pollution.

[0072] Based on the detailed data on the types and quantity of pests and diseases monitored and summarized by the data collection module, natural enemy insects such as ladybugs and trichogrammatids are released into the walnut forest at a scientific ratio of 10:1-20:1 between pests and natural enemies, and microbial strains such as Bacillus subtilis that have been screened and improved and made into water-dispersible granules or nanosuspensions are applied. After release, a fixed monitoring plot is set up to regularly evaluate the colonization and survival of natural enemy insects and the pest control effect, and replenish the release in a timely manner according to the actual situation, so as to utilize the natural balance principle of the ecosystem, use insects to control insects, use bacteria to inhibit bacteria, rebuild the advantages of the beneficial biological population in the forest, reduce the base number of pests and diseases, reduce the dependence on chemical pesticides, and achieve eco-friendly pest and disease control, while ensuring the effectiveness of prevention and control, maintaining the ecological balance of the walnut forest and promoting sustainable development.

[0073] By using intelligent laser trapping and microwave control in walnut forests, we can specifically kill phototactic pests and trunk-boring pests, reduce the degree of damage caused by pests and diseases, and achieve a green, efficient physical pest control method with little interference to beneficial organisms, which serves as a supplement to chemical and biological control and enriches the means of prevention and control.

[0074] By integrating the advantages of three control units: intelligent spray, biological and physical, and flexibly selecting and coordinating according to the characteristics of different pests and diseases and early warning information, we can build a comprehensive and multi-level prevention and control system to curb the spread of pests and diseases in all directions, improve the overall health level of the walnut forest, and achieve the goal of comprehensive, efficient and ecological pest and disease control, ensuring the stable output and ecological stability of the walnut forest, thus solving a series of problems in traditional walnut forests, such as the single means of pest and disease control, limited effects, and insufficient ecological considerations.

[0075] Please refer to the attached Figure 8 -Attached Fig.10 The intelligent spray control unit includes a positioning and navigation subunit, a sensor monitoring subunit, and a spray control subunit. The positioning and navigation subunit is used to accurately locate the real-time position of the intelligent spray equipment in the walnut forest, and plan the operation route and path in combination with the pest control prescription map. The sensor monitoring subunit is used to monitor the spray data in real time, collect equipment status parameters at the same time, and feed them back to the spray control subunit. The spray data includes spray flow rate and spray pressure. The equipment status parameters include battery power and motor speed. The spray control subunit is used to adjust the parameters of the intelligent spray equipment according to the feedback data of the sensor monitoring subunit and the instructions of the pest control prescription map, and apply pesticides in areas with different pest severity levels. The parameters include spray angle and flow rate.

[0076] The biological control unit includes a natural enemy breeding and release subunit and a microbial application subunit. The natural enemy breeding and release subunit is used to use artificial feed and alternative hosts to breed natural enemy insects on a large scale and release them in proportion. After release, monitoring plots are set up to regularly evaluate colonization survival and pest control effects, and replenish releases are made in a timely manner. The microbial application subunit is used to select stress-resistant and highly active microbial strains from the ecological environment of the walnut forest, improve them through genetic engineering, optimize the fermentation process to make an adapted formulation, and spray them with natural enemy insects at a dilution of 500-1000 times according to the types and development stages of pests and diseases. The adapted formulations include water-dispersible granules and nano-suspension concentrates.

[0077] The physical control unit includes a laser trapping sub-unit and a microwave control sub-unit. The laser trapping sub-unit is used to activate high-power blue light and ultraviolet light to attract pests at dusk through an intelligent laser trapping device. After the device uses a built-in insect behavior analysis algorithm to identify the pests, it immediately switches to a pulse laser to accurately kill the pests and records the capture data. The capture data includes type, quantity, and time. The microwave control sub-unit is used to target trunk-boring pests that damage branches and trunks. The microwave antenna probe is accurately implanted using drilling equipment, and the microwave radiation time and power level are set according to the condition of the insect tunnel. The thermal imager is used to monitor the trunk temperature in real time to kill pests. The condition of the insect tunnel includes direction and length.

[0078] Please refer to the attached Fig.11 The human-computer interaction module includes a data visualization unit, a command interaction unit, and an information feedback unit. The data visualization unit is used to visualize the data of each module in the form of intuitive charts, high-definition images, and map annotations. The command interaction unit is used to build a two-way interaction channel between managers and the system. The information feedback unit is used to collect system information in real time and push it to the management end. The system information includes the operating status of the front-line prevention and control equipment and the actual prevention and control effects of diseases and pests in the forest.

[0079] Please refer to the attached Fig.12 The intelligent pest control method for walnut forests includes the following steps:

[0080] S1. Data collection: The meteorological environment monitoring unit is used to monitor the meteorological environment parameters of the walnut forest in real time. The soil condition monitoring unit monitors the physical and chemical properties of the soil. The forest image monitoring unit collects intuitive image data in the forest. The pest and disease monitoring unit collects pest and disease data. The data of each unit is summarized by the communication unit to build a basic information database of the walnut forest.

[0081] S2. Identification and analysis: The sample library management unit then organizes and stores high-definition images and video samples of pests and diseases and labels them for classification. The deep learning model unit is pre-trained based on a general data set and then specially trained with local samples to identify pests and diseases. The data analysis and prediction unit combines multi-source data with a semantic segmentation model to build a prediction model, set an early warning threshold, and output the evaluation results.

[0082] S3, early warning response: The early warning unit then triggers blue, yellow, and red warnings based on the assessment, and pushes detailed information through multiple channels. In the event of a red alarm, the emergency dispatch unit activates the plan, pushes the prevention and control prescription map, and records the operation process;

[0083] S4. Prevention and control execution: The intelligent spray control unit then controls the agricultural drone and self-propelled sprayer to spray variable quantities according to the prescription map; the biological control unit releases natural enemy insects and microbial strains according to the pest data and tracks them; the physical control unit uses laser trapping and microwave methods for prevention and control;

[0084] S5. Human-computer interaction: Data is visualized through the data visualization unit, operation instructions are issued through the instruction interaction unit, and prevention and control status is received through the information feedback unit.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent pest control system for walnut forests, characterized in that: include: Data acquisition module: used to collect basic information of the walnut forest, including meteorological environmental parameters, soil physical and chemical properties, and intuitive image data in the forest; Identification and analysis module: used to deeply analyze the data characteristics of basic information, identify the types and severity of pests and diseases, and predict the potential risk of pests and diseases to form an assessment result; Early warning response module: used to preset risk level thresholds and rule logic, generate corresponding level early warning signals based on assessment results, and initiate emergency response processes; Prevention and control execution module: used to implement pest control operations on walnut forests using multiple means; Human-computer interaction module: used to provide data visualization display, issue operation instructions, and receive prevention and control feedback information.

2. The intelligent pest control system for walnut forests according to claim 1 is characterized in that: The data acquisition module includes a meteorological environment monitoring unit, a soil condition monitoring unit, a forest image monitoring unit, a pest and disease monitoring unit, and a communication unit. The meteorological environment monitoring unit is used to monitor the meteorological environment parameters of the walnut forest in real time and capture dynamic meteorological changes. The meteorological environment parameters include temperature, humidity, light intensity, wind speed, and wind direction. The soil condition monitoring unit is used to monitor the soil physical and chemical properties of the walnut forest in real time. The soil physical and chemical properties include soil texture, soil porosity, soil moisture content, soil pH, and soil fertility indicators. The forest image monitoring unit is used to monitor the intuitive image data in the walnut forest in real time. The intuitive image data in the forest includes the overall picture of the walnut plant presented by high-resolution visible light images, the subtle lesions, wormholes, and abnormal color of branches and leaves of the walnut leaves under close-up shots, and the abnormal heat areas caused by pests and diseases highlighted by thermal imaging images. The pest and disease monitoring unit is used to monitor the pest and disease data of the walnut forest. The pest and disease data includes changes in species and quantity. The communication unit is used to provide data transmission and communication channels between modules.

3. The intelligent pest control system for walnut forest according to claim 2 is characterized in that: The soil condition monitoring unit includes a soil moisture detection subunit, a soil pH detection subunit, and a soil fertility monitoring subunit. The soil moisture detection subunit is used to monitor the moisture content of the soil in the walnut forest in real time. The soil pH detection subunit is used to monitor the salinity of the soil in the walnut forest in real time. The soil fertility monitoring subunit is used to monitor the fertility index of the soil in the walnut forest in real time. The fertility index includes nitrogen, phosphorus, and potassium content. The forest image monitoring unit includes an image acquisition subunit and a satellite remote sensing subunit. The image acquisition subunit is used to regularly collect image data of the walnut forest, and the image data includes fine lesions, wormholes, and abnormal color of branches and leaves of the walnut forest. The satellite remote sensing subunit is used to regularly collect macro information of the walnut forest, and the macro information includes vegetation coverage and abnormal growth areas of the walnut forest.

4. The intelligent pest control system for walnut forests according to claim 1 is characterized in that: The identification and analysis module includes a sample library management unit, a deep learning model unit, and a data analysis and prediction unit. The sample library management unit is used to collect, organize, and store sample resources of pests and diseases in the walnut forest, and annotate attributes for classified storage. The sample resources include high-definition images and videos of diseases and pests. The attributes include pest and disease categories, lesion sites, and development stages. The deep learning model unit is used to build a target detection and recognition model architecture based on a mainstream deep learning framework, extract basic features from a general image data set through pre-training, and then migrate them to the walnut forest pest and disease sample library for special training, so that the model can identify pests and diseases in the walnut forest. The data analysis and prediction unit is used to use a deep learning semantic segmentation model to parse the data obtained from the forest image monitoring unit, construct a pest and disease occurrence probability prediction model, repeatedly check and adjust based on historical pest and disease occurrence data, set corresponding low, medium, and high risk warning thresholds, and output quantitative prediction results to guide prevention and control decisions.

5. The intelligent pest control system for walnut forests according to claim 1 is characterized in that: The early warning response module includes an early warning unit and an emergency dispatch unit. The early warning unit is used to determine the risk level of pests and diseases based on the output results of the data analysis and prediction unit and the preset rule logic, and generate blue, yellow and red three-color early warning information accordingly, which is pushed to the management personnel through multiple channels simultaneously. The early warning information is accompanied by a pest and disease diagnosis report, a prevention and control operation manual, and surrounding agricultural material reserves. In the three-color early warning information, blue is a low risk prompt, yellow is a medium risk warning, and red is a high emergency risk alarm. The multiple channels include mobile phone APP, text messages, and computer management systems. The report includes the type, symptoms, severity, and development trend prediction. The prevention and control operation manual includes adaptive prevention and control means and processes. The agricultural material reserves include inventory categories, quantities, and distance information. The emergency dispatch unit is used to quickly activate the prevention and control emergency plan after receiving a red high-emergency risk alarm, and push the pest control prescription map to the prevention and control execution module. At the same time, the emergency operation process is recorded in the local database throughout the process, and retrospectively reviewed and optimized. The pest control prescription map includes the definition of prevention and control areas, pesticide concentrations, and operation paths. The emergency operation process includes equipment trajectories, pesticide application parameters, and operation start and end times.

6. The intelligent pest control system for walnut forests according to claim 1 is characterized in that: The prevention and control execution module includes an intelligent spray prevention unit, a biological control unit, and a physical control unit. The intelligent spray prevention unit is used to spray medicine on the walnut forest through intelligent spray equipment, and perform variable spraying on different pest and disease areas in the walnut forest according to the instructions of the early warning response module and the prevention and control prescription map. The intelligent spray equipment includes agricultural drones and self-propelled sprayers. The biological control unit is used to release pest natural enemies and microbial strains into the walnut forest according to the pest and disease data collected by the data acquisition module. The ratio of pest natural enemies released is 10:1-20:1, and they are tracked and monitored after release. The physical control unit is used to perform intelligent laser trapping and microwave control on the walnut forest.

7. The intelligent pest control system for walnut forests according to claim 6 is characterized in that: The intelligent spray control unit includes a positioning and navigation subunit, a sensor monitoring subunit, and a spray control subunit. The positioning and navigation subunit is used to accurately locate the real-time position of the intelligent spray equipment in the walnut forest, and plan the operation route and path in combination with the pest control prescription map. The sensor monitoring subunit is used to monitor the spray data in real time, and collect the equipment status parameters at the same time, and feed them back to the spray control subunit. The spray data includes spray flow rate and spray pressure. The equipment status parameters include battery power and motor speed. The spray control subunit is used to adjust the parameters of the intelligent spray equipment according to the feedback data of the sensor monitoring subunit and the instructions of the pest control prescription map, and apply the pesticide in different areas with different pest severity. The parameters include spray angle and flow rate. The biological control unit includes a natural enemy breeding and release subunit and a microorganism application subunit. The natural enemy breeding and release subunit is used to use artificial feed and alternative hosts to breed natural enemy insects on a large scale and release them in proportion. After release, monitoring plots are set up to regularly evaluate colonization survival and pest control effects, and replenish releases are made in a timely manner. The microorganism application subunit is used to select stress-resistant and highly active microbial strains from the ecological environment of the walnut forest, improve them through genetic engineering, optimize the fermentation process to make an adapted formulation, and spray them with natural enemy insects at a dilution of 500-1000 times according to the types and development stages of pests and diseases. The adapted formulations include water-dispersible granules and nano-suspension concentrates. The physical control unit includes a laser trapping subunit and a microwave control subunit. The laser trapping subunit is used to activate high-power blue light and ultraviolet light to attract pests at dusk through an intelligent laser trapping device. After the device has a built-in insect behavior analysis algorithm to identify the pests, it immediately switches to a pulse laser to accurately kill the pests and records the capture data. The capture data includes the type, quantity, and time. The microwave control subunit is used to target trunk-boring pests that harm branches and trunks. The microwave antenna probe is accurately implanted using drilling equipment, and the microwave radiation time and power level are set according to the condition of the insect tunnel. The temperature of the tree trunk is monitored in real time with a thermal imager to kill the pests. The condition of the insect tunnel includes direction and length.

8. The intelligent pest control system for walnut forests according to claim 1 is characterized in that: The human-computer interaction module includes a data visualization unit, a command interaction unit, and an information feedback unit. The data visualization unit is used to visualize the data of each module in the form of intuitive charts, high-definition images, and map annotations. The command interaction unit is used to build a two-way interaction channel between managers and the system. The information feedback unit is used to collect system information in real time and push it to the management end. The system information includes the operating status of front-line prevention and control equipment and the actual prevention and control effects of pests and diseases in the forest.

9. An intelligent pest control method for walnut forests, characterized in that: The following steps are involved: S1. Data collection: The meteorological environment monitoring unit is used to monitor the meteorological environment parameters of the walnut forest in real time. The soil condition monitoring unit monitors the physical and chemical properties of the soil. The forest image monitoring unit collects intuitive image data in the forest. The pest and disease monitoring unit collects pest and disease data. The data of each unit is summarized by the communication unit to build a basic information database of the walnut forest. S2. Identification and analysis: The sample library management unit then organizes and stores high-definition images and video samples of pests and diseases and labels them for classification. The deep learning model unit is pre-trained based on a general data set and then specially trained with local samples to identify pests and diseases. The data analysis and prediction unit combines multi-source data with a semantic segmentation model to build a prediction model, set an early warning threshold, and output the evaluation results. S3, early warning response: The early warning unit then triggers blue, yellow, and red warnings based on the assessment, and pushes detailed information through multiple channels. In the event of a red alarm, the emergency dispatch unit activates the plan, pushes the prevention and control prescription map, and records the operation process; S4. Prevention and control execution: The intelligent spray control unit then controls the agricultural drone and self-propelled sprayer to spray variable quantities according to the prescription map; the biological control unit releases natural enemy insects and microbial strains according to the pest data and tracks them; the physical control unit uses laser trapping and microwave methods for prevention and control; S5. Human-computer interaction: Data is visualized through the data visualization unit, operation instructions are issued through the instruction interaction unit, and prevention and control status is received through the information feedback unit.

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