Health cultivation management method and system for agricultural fruit trees

Abstract

The invention relates to the technical field of plant cultivation management, in particular to a healthy cultivation management method and system for agricultural fruit trees, and the method comprises the steps: obtaining monitoring images of the agricultural fruit trees through monitoring equipment at regular intervals, analyzing the growth state of the agricultural fruit trees, and obtaining a state analysis result; acquiring soil detection data according to a preset sampling date, and analyzing the soil detection data to obtain a detection result; performing anomaly analysis on a fusion analysis result obtained by fusing the state analysis result and the detection result, and matching a corresponding anomaly processing decision; and performing cultivation management on the agricultural fruit trees according to the exception handling decision to enable the growth state of the agricultural fruit trees to meet a preset growth standard. By the adoption of the mode, the problems that the state of agricultural fruit trees cannot be adjusted in time due to the fact that abnormal conditions cannot be handled in time in a traditional planting management mode, and then the taste of fruits does not meet the requirements of customers are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of plant cultivation management, and in particular to a healthy cultivation management method and system for agricultural fruit trees. Background Art

[0002] With the improvement of living standards, people pay more and more attention to the quality of life, which is reflected in all aspects of social life. In terms of diet, with the increase in fruit production and variety, people's requirements for fruit taste have become higher accordingly. However, many fruit tree planting bases still maintain the traditional planting management mode, that is, pest control and watering until the fruit is ripe, so that the taste of the fruit does not change. For those fruits that taste bad, few customers will buy them, resulting in a decline in fruit sales and reduced profits. In addition, when agricultural fruit trees were first planted in the agricultural fruit tree planting area, they were planted only according to the traditional planting method, ignoring the equipment for dealing with abnormal situations, etc., resulting in the abnormal handling of agricultural fruit trees. It will cost a lot of manpower, material resources and time, increasing costs. And for those customers who want to engage in agricultural fruit tree planting, it takes a lot of time to familiarize themselves with the knowledge related to agricultural fruit tree planting, and they will experience a lot of unnecessary exploration time, which greatly increases the time cost. In view of this, in order to keep the fruits produced by agricultural fruit trees in good taste and be loved by customers, it is necessary to carry out comprehensive management of the cultivation of agricultural fruit trees.

[0003] Therefore, the present invention provides a method and system for healthy cultivation and management of agricultural fruit trees to solve the above problems. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a healthy cultivation management method and system for agricultural fruit trees, so as to solve the problem that the traditional planting management model cannot handle abnormal situations in time, resulting in the inability to adjust the status of agricultural fruit trees in time, and further resulting in the taste of the fruits not meeting customer needs.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention provides a method for healthy cultivation and management of agricultural fruit trees, comprising:

[0007] Obtain 3D map data, historical weather data, and soil testing data for agricultural fruit tree planting areas;

[0008] Analyze historical weather data and soil test data based on a preset agricultural fruit tree growth condition database to determine a set of agricultural fruit tree varieties that meet the conditions, and obtain target varieties selected from the set of agricultural fruit tree varieties;

[0009] According to the target variety, the healthy cultivation management data of the target variety is obtained; and the three-dimensional map data, the target variety and the healthy cultivation management data are processed and analyzed to generate planning decisions for the target variety;

[0010] After planting the target varieties and deploying the target equipment and testing instruments according to the planning decision, regularly monitoring the growth status of the target varieties through monitoring equipment, and obtaining soil testing data according to the preset sampling date;

[0011] Performing image analysis on the growth state based on preset sample images of target varieties at various stages to obtain state analysis results, and analyzing soil test data based on healthy cultivation management data to obtain test results; performing abnormal analysis on the state analysis results and test results to obtain fusion analysis results;

[0012] If there is abnormal information in the fusion analysis result, the corresponding abnormal handling decision is matched according to the abnormal information in the fusion analysis result; and the agricultural fruit trees are managed according to the abnormal handling decision so that the growth status of the agricultural fruit trees meets the preset growth standards.

[0013] Preferably, the three-dimensional map data, target varieties and healthy cultivation management data are processed and analyzed to generate planning decisions for target varieties, including:

[0014] Obtain the planting requirement range of a single tree of the target variety from the healthy cultivation management data of the target variety;

[0015] Determine the planting quantity and preset planting location of the target variety according to the planting demand range and the three-dimensional map data to obtain an initial planting plan map;

[0016] Determine multiple initial planting planning strategies including planning biases according to the initial planting planning map, healthy cultivation management data and adjusted preset cultivation auxiliary equipment information;

[0017] Obtain and determine the final planning decision for the target variety from each initial planting planning strategy based on the planning preference selected by the customer.

[0018] Preferably, the method comprises determining a plurality of initial planting planning strategies including planning biases based on the initial planting planning map, the healthy cultivation management data and the adjusted preset cultivation auxiliary equipment information, including: determining the target equipment and the detection instrument from the preset cultivation auxiliary equipment information according to the preset standards and the preset planning biases in the healthy cultivation management data; adjusting the initial planting planning map according to the preset planning biases, the target equipment and the detection instrument to obtain a plurality of secondary planting planning maps; and obtaining the corresponding planting planning strategy according to each secondary planting planning map.

[0019] Preferably, the image analysis of the growth status based on the preset sample images of each stage of the target variety is performed to obtain a status analysis result, including: obtaining the cultivation date of the current target variety, determining the growth stage of the target variety based on the cultivation date and obtaining the preset sample images of the corresponding stage; performing image segmentation on the monitoring image, and performing image analysis and screening statistics on the segmented block images to obtain a feature image containing statistical information; performing image analysis and matching on the feature image based on the preset sample images, and taking the preset sample image corresponding to the first-ranked matching degree among each matching degree as the target image; determining the current status analysis result of the target variety according to the correspondence between the target image and the status information in the preset growth status database.

[0020] Preferably, the preset growth status database includes at least a mapping relationship between preset sample images and the current status, abnormality type, and abnormality level of the target variety.

[0021] Preferably, the analyzing the soil test data according to the healthy cultivation management data to obtain the test results includes: determining the soil test data standard of the current stage in the healthy cultivation management data corresponding to the current target variety according to the cultivation date, and comparing and analyzing the soil test data according to the soil test data standard to obtain the test results including abnormal levels corresponding to different degrees of deviation.

[0022] Preferably, the abnormal analysis of the state analysis results and the detection results to obtain a fusion analysis result includes: judging whether there is a correlation between the abnormal data in the state analysis results and the abnormal data in the detection results based on a preset abnormal causal library, and if so, obtaining the corresponding correlation analysis result in the preset abnormal causal library; screening and deduplication analysis of the correlation analysis results, the state analysis results and the detection results to obtain a fusion analysis result containing multiple abnormal levels.

[0023] Preferably, the matching of the corresponding exception handling decision according to the exception information in the fusion analysis result includes: when the exception level in the fusion analysis result exceeds the corresponding preset exception level, obtaining the corresponding exception analysis result in the fusion analysis result, matching the sub-exception handling decision corresponding to the exception analysis result according to the preset exception decision library; integrating the sub-exception handling decisions to obtain the exception handling decision.

[0024] Preferably, a method for healthy cultivation and management of agricultural fruit trees also includes: acquiring weather data in real time, judging the impact level of weather data on the growth status of the current target variety based on the healthy cultivation management data, and if the impact level exceeds a preset level, generating and sending corresponding warning information and response measures.

[0025] In a second aspect, the present invention provides a healthy cultivation management system for agricultural fruit trees, comprising:

[0026] Data acquisition module, which obtains three-dimensional map data, historical weather data and soil detection data of agricultural fruit tree planting areas;

[0027] A variety determination module analyzes historical weather data and soil test data based on a preset agricultural fruit tree growth condition database to determine a set of agricultural fruit tree varieties that meet the conditions and obtain a target variety selected from the set of agricultural fruit tree varieties;

[0028] The planting planning module obtains the healthy cultivation management data of the target variety according to the target variety; and processes and analyzes the three-dimensional map data, the target variety and the healthy cultivation management data to generate planning decisions for the target variety;

[0029] A data acquisition module, after planting the target varieties and deploying the target equipment and testing instruments according to the planning decision, regularly monitors the growth status of the target varieties through monitoring equipment, and obtains soil testing data according to the preset sampling date;

[0030] A data analysis module performs image analysis on the growth status based on preset sample images of target varieties at various stages to obtain status analysis results, and analyzes soil test data based on healthy cultivation management data to obtain test results; performs abnormal analysis on status analysis results and test results to obtain fusion analysis results;

[0031] The exception handling module matches the corresponding exception handling decision according to the exception information in the fusion analysis result if there is exception information in the fusion analysis result; and manages the agricultural fruit trees according to the exception handling decision so that the growth status of the agricultural fruit trees meets the preset growth standards.

[0032] The beneficial effects of the present invention are:

[0033] 1. The present invention adopts the method of regularly acquiring monitoring images of agricultural fruit trees through monitoring equipment to analyze the growth status of agricultural fruit trees and obtain status analysis results; and acquiring soil test data according to a preset sampling date, analyzing the soil test data and obtaining test results; and then performing an abnormal analysis on the fusion analysis results obtained by fusing the status analysis results and the test results, matching the corresponding abnormal processing decisions, and finally cultivating and managing the agricultural fruit trees according to the abnormal processing decisions, so that the growth status of the agricultural fruit trees meets the preset growth standards. By adopting the above method, the present invention solves the problem that the traditional planting management model cannot handle abnormal situations in a timely manner, resulting in the inability to adjust the status of agricultural fruit trees in a timely manner, and then resulting in the taste of the fruits not meeting customer needs.

[0034] 2. The present invention uses fusion analysis based on three-dimensional map data, historical weather data and soil detection data of the planting area, which can select target varieties for customers and provide planning decisions for planting layout. The growth of agricultural fruit trees can be analyzed based on monitoring images and soil detection data, and when abnormal conditions are found, abnormal handling decisions are generated in a timely manner. For customers who are new to agricultural fruit tree planting or want to engage in agricultural fruit tree planting, the present invention can carry out comprehensive planning such as planting layout, equipment installation guidance, regular soil testing, and adjustment of growth management strategies based on the planting area selected by the customer, saving customers a lot of query time, providing customers with full-process services, and greatly providing convenience for customers.

[0035] 3. The present invention also uses real-time acquisition of weather data, and judges the impact level of weather data on the target variety in the current state based on the healthy cultivation management data of the target variety, and timely evaluates and makes corresponding emergency measures to avoid excessive weather damage to agricultural fruit trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of a healthy cultivation management method for agricultural fruit trees according to the present invention;

[0037] Figure 2 The present invention is a schematic module diagram of a healthy cultivation management system for agricultural fruit trees. DETAILED DESCRIPTION

[0038] The following will refer to the attached Figure 1 To Attachment Figure 2 The embodiments of the present invention are described in detail. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0039] A healthy cultivation and management method for agricultural fruit trees, as shown in the following Figure 1 As shown, the following steps are included:

[0040] Step S11: Acquire three-dimensional map data, historical weather data and soil detection data of the agricultural fruit tree planting area.

[0041] Among them, the method of obtaining three-dimensional map data includes but is not limited to using one or more drones to record videos and then generating three-dimensional map data based on the drone video data; or manually drawing map data; or obtaining three-dimensional map data of agricultural fruit tree planting areas from relevant three-dimensional map software, etc. The three-dimensional map data is the terrain information and spatial information of the planting area.

[0042] Step S12: Analyze historical weather data and soil testing data based on a preset agricultural fruit tree growth condition database to determine a set of agricultural fruit tree varieties that meet the conditions, and obtain a target variety selected from the set of agricultural fruit tree varieties.

[0043] Among them, the information recorded in the preset agricultural fruit tree growth condition database includes various weather data, various soil test data, various types of agricultural fruit tree data, and the corresponding relationship between various weather data, various soil test data and various types of agricultural fruit tree data. Soil test data at least includes soil pH value, organic matter content, content of three elements of nitrogen, phosphorus and potassium, content of trace elements, heavy metal content, pesticide residues, and microbial indicators. When analyzing historical weather data, the main analysis is on temperature and rainfall.

[0044] Step S13: according to the target variety, obtaining the healthy cultivation management data of the target variety; and processing and analyzing the three-dimensional map data, the target variety and the healthy cultivation management data to generate a planning decision for the target variety.

[0045] Among them, in the preset agricultural fruit tree growth condition database, the healthy cultivation management data of each stage and various growth stages corresponding to the target varieties that meet the healthy cultivation management standards are recorded.

[0046] Step S14: After the target variety is planted and the target equipment and detection instruments are deployed according to the planning decision, the growth status of the target variety is monitored regularly through monitoring equipment, and soil detection data is obtained according to the preset sampling date.

[0047] Step S15: performing image analysis on the growth status based on preset sample images of preset target varieties at each stage to obtain status analysis results, and analyzing soil detection data according to healthy cultivation management data to obtain detection results; performing abnormal analysis on the status analysis results and detection results to obtain fusion analysis results.

[0048] Among them, when there is no abnormal data in the fusion analysis result, the fusion analysis result is normal and the subsequent steps are not executed.

[0049] Step S16: If there is abnormal information in the fusion analysis result, a corresponding abnormal processing decision is matched according to the abnormal information in the fusion analysis result.

[0050] Step S17: managing the agricultural fruit trees according to the abnormal processing decision so that the growth status of the agricultural fruit trees meets the preset growth standards.

[0051] Through the above-mentioned implementation mode, for customers who are new to agricultural fruit tree planting or want to engage in agricultural fruit tree planting, the present invention can carry out comprehensive planning such as planting layout, equipment installation guidance, regular soil testing, and adjustment of growth management strategies according to the planting area selected by the customer, saving customers a lot of query time, providing customers with full-process services, and greatly providing convenience for customers.

[0052] Furthermore, the present invention adopts the method of regularly acquiring monitoring images of agricultural fruit trees through monitoring equipment to analyze the growth status of agricultural fruit trees and obtain status analysis results; and acquiring soil test data according to a preset sampling date, analyzing the soil test data and obtaining test results; and then performing abnormal analysis on the fusion analysis results obtained by fusing the status analysis results and the test results, matching the corresponding abnormal processing decisions, and finally cultivating and managing the agricultural fruit trees according to the abnormal processing decisions, so that the growth status of the agricultural fruit trees meets the preset growth standards. By adopting the above-mentioned method, the present invention solves the problem that the traditional planting management mode cannot handle abnormal situations in time, resulting in the inability to adjust the status of agricultural fruit trees in time, and then resulting in the taste of the fruits not meeting customer needs.

[0053] In one embodiment of the present invention, the processing and analysis of the three-dimensional map data, the target variety and the healthy cultivation management data in step S13 to generate a planning decision for the target variety mainly includes the following steps:

[0054] Step S21: Obtain the planting requirement range of a single tree of the target variety from the healthy cultivation management data of the target variety, wherein the planting requirement range is the required area of ​​the target variety during its maturity period.

[0055] Step S22: Determine the planting quantity and preset planting positions of the target variety according to the planting demand range and the three-dimensional map data to obtain an initial planting plan.

[0056] Due to the different terrains in the planting area, before determining the planting quantity and preset planting location, the specific value will be determined from the planting requirement range of a single tree of the target variety based on the slope of the terrain.

[0057] Step S23: Determine a plurality of initial planting planning strategies including planning biases according to the initial planting planning diagram, the healthy cultivation management data and the adjusted preset cultivation auxiliary equipment information.

[0058] Among them, the preset cultivation auxiliary equipment includes various soil testing instruments, transplanting equipment, planting equipment, pruning equipment, etc. According to customer needs, customers can add or reduce modifiable auxiliary equipment from the preset cultivation auxiliary equipment to make the initial planting planning strategy meet customer preferences.

[0059] Step S24: Obtain and determine the final planning decision of the target variety from each initial planting planning strategy based on the planning preference selected by the customer.

[0060] Among them, the planning tends to be based on the customer's agricultural fruit tree planting preferences, including but not limited to cost savings, easy and convenient management, and good fruit taste.

[0061] Through the configuration of this embodiment, the present invention can perform fusion analysis based on the three-dimensional map data, historical weather data and soil detection data of the planting area, select target varieties for customers, and provide planning decisions for planting layout, which greatly saves customers' time for planting planning. In addition, the present invention can carry out planting planning for mountainous areas with a certain slope, making the planting of fruit trees in mountainous areas more reasonable.

[0062] In one embodiment of the present invention, the initial planting planning map, healthy cultivation management data and adjusted preset cultivation auxiliary equipment information are used to determine multiple initial planting planning strategies including planning biases, including: determining target equipment and detection instruments from preset cultivation auxiliary equipment information according to preset standards and preset planning biases in the healthy cultivation management data; adjusting the initial planting planning map according to the preset planning biases, target equipment and detection instruments to obtain multiple secondary planting planning maps; and obtaining corresponding planting planning strategies according to each secondary planting planning map.

[0063] For example, the preset planning tends to save the number of cultivation auxiliary equipment to reduce costs. The present invention will select the preset cultivation auxiliary equipment and quantity according to the minimum standard of healthy cultivation management data, and mark the location of the preset cultivation auxiliary equipment on the initial planting planning map to obtain the corresponding secondary planting planning map; then, according to the layout of the secondary planting planning map, generate a planting plan with a corresponding planting sequence, and finally form a corresponding planting planning strategy according to the planting plan and the secondary planting planning map.

[0064] For example, the preset planning tends to be reasonable in layout for cultivation, so as to reduce manpower, facilitate the management of fruit trees, and save time and effort. The present invention will select preset cultivation auxiliary equipment according to the highest standard of healthy cultivation management data, and mark the position of the preset cultivation auxiliary equipment in the initial planting planning map to obtain the corresponding secondary planting planning map; then, according to the layout of the secondary planting planning map, a planting plan of the corresponding planting sequence is generated, and finally, a corresponding planting planning strategy is formed according to the planting plan and the secondary planting planning map. In this way, the planting time during the planting period can be reduced, and the fruit tree planting and the arrangement of the corresponding detection sensors and management equipment can be completed as soon as possible.

[0065] Through the configuration of this embodiment, the present invention can take into account the planting preferences of customers, provide customers with reasonable planting planning strategies, meet customer needs, and thereby enhance customers' trust in and experience of the present invention.

[0066] In one embodiment of the present invention, step S15 performs image analysis on the growth state based on preset sample images of each stage of the target variety to obtain a state analysis result, including: obtaining the cultivation date of the current target variety, determining the growth stage of the target variety based on the cultivation date and obtaining the preset sample images of the corresponding stage; performing image segmentation on the monitoring image, and performing image analysis and screening statistics on the segmented block images to obtain a feature image containing statistical information; performing image analysis and matching on the feature image based on the preset sample images, and taking the preset sample image corresponding to the first-ranked match among the matching degrees as the target image; determining the current state analysis result of the target variety according to the correspondence between the target image and the state information in the preset growth state database.

[0067] Among them, the sample images of the preset varieties collect images of the known target varieties in various growth states and different situations, including but not limited to images of leaves, book covers, and branches in various states. Through the above method, the present invention can determine the intuitive state of the target variety by comparing the sample images and the monitoring images, and obtain the state analysis results of the target variety. The statistical information includes at least the number of images with the same characteristics and the name of the characteristic classification; for example, leaf color, leaves with holes, etc.

[0068] In this embodiment, the present invention periodically acquires monitoring images of agricultural fruit trees through monitoring equipment to analyze the growth status of agricultural fruit trees and obtain status analysis results; and acquires soil test data according to a preset sampling date, analyzes the soil test data, and obtains test results; and then performs abnormal analysis on the fusion analysis results obtained by fusing the status analysis results and the test results, matches the corresponding abnormal processing decisions, and finally cultivates and manages the agricultural fruit trees according to the abnormal processing decisions, so that the growth status of the agricultural fruit trees meets the preset growth standards. By adopting the above method, the present invention solves the problem that the traditional planting management model cannot handle abnormal situations in a timely manner, resulting in the inability to adjust the status of agricultural fruit trees in a timely manner, and then the taste of the fruit produced does not meet customer needs.

[0069] Furthermore, in one embodiment of the present invention, the preset growth status database includes at least a mapping relationship between preset sample images and the current state, abnormality type, and abnormality level of the target variety. In the process of continuous practice and use, the preset growth status database will be updated, and images with typical features will be added to the preset growth status database in a timely manner.

[0070] In one embodiment of the present invention, the step S15 of analyzing the soil detection data according to the healthy cultivation management data to obtain the detection results includes: determining the soil detection data standard of the current stage in the healthy cultivation management data corresponding to the current target variety according to the cultivation date, and comparing and analyzing the soil detection data according to the soil detection data standard to obtain the detection results including abnormal levels corresponding to different degrees of deviation.

[0071] In this embodiment, the present invention can compare and analyze the regularly acquired soil detection data according to the soil detection data standard of the corresponding growth stage to obtain the detection result.

[0072] In one embodiment of the present invention, step S15 performs an abnormal analysis on the state analysis results and the detection results to obtain a fusion analysis result, including: judging whether there is a correlation between the abnormal data in the state analysis results and the abnormal data in the detection results based on a preset abnormal causal library, and if so, obtaining the corresponding correlation analysis result in the preset abnormal causal library; and screening and deduplicating the correlation analysis results, the state analysis results, and the detection results to obtain a fusion analysis result containing multiple abnormal levels.

[0073] Preferably, the association relationship is mainly the relationship of abnormal growth state of the target variety caused by the target variety being in the environment of abnormal data in the soil detection data for a long time. The preset abnormal causal library is the abnormal detection data in the soil detection data, and the corresponding relationship between the type of the corresponding preset target sample image and the abnormal cause.

[0074] In this embodiment, the present invention performs correlation analysis on the basis of the state analysis results obtained by image analysis and the detection results obtained by soil detection data analysis to determine whether there is a causal relationship between the soil detection data and the state analysis results, so as to exclude pest and disease factors, thereby making the obtained fusion analysis results concise and accurate.

[0075] In one embodiment of the present invention, matching the corresponding exception handling decision according to the exception information in the fusion analysis result in step S16 includes: when the exception level in the fusion analysis result exceeds the corresponding preset exception level, obtaining the corresponding exception analysis result in the fusion analysis result, matching the sub-exception handling decision corresponding to the exception analysis result according to the preset exception decision library; integrating the sub-exception handling decisions to obtain the exception handling decision.

[0076] Through the setting method of this embodiment, the present invention matches the corresponding exception handling decision according to the exception level and exception data in the fusion analysis result, and sends the exception handling decision to the customer, so that the customer can adjust the cultivation method of agricultural fruit trees in time, so that the growth state of agricultural fruit trees can be restored to a good preset growth state, so that the taste of the fruit finally produced by the agricultural fruit trees meets the needs of consumers.

[0077] In one embodiment of the present invention, a method for healthy cultivation and management of agricultural fruit trees further comprises:

[0078] Acquire weather data in real time, and judge the impact level of weather data on the growth status of the current target variety based on healthy cultivation management data. If the impact level exceeds the preset level, generate and send corresponding warning information and response measures.

[0079] Through the setting method of this embodiment, the present invention obtains weather data in real time, determines the impact level of weather data on the target variety in the current state based on the healthy cultivation management data of the target variety, and makes timely assessments and corresponding emergency measures, thereby preventing agricultural fruit trees from suffering excessive weather damage.

[0080] In one embodiment of the present invention, as shown in the attached Figure 2 As shown, the present invention also provides a healthy cultivation management system for agricultural fruit trees. The system mainly includes a data acquisition module, a variety determination module, a planting planning module, a data collection module, a data analysis module, and an exception handling module. The specific contents are as follows:

[0081] The data acquisition module obtains three-dimensional map data, historical weather data and soil detection data of agricultural fruit tree planting areas.

[0082] The variety determination module analyzes historical weather data and soil test data based on a preset agricultural fruit tree growth condition database to determine a set of agricultural fruit tree varieties that meet the conditions and obtain target varieties selected from the agricultural fruit tree variety set.

[0083] The planting planning module obtains the healthy cultivation management data of the target variety according to the target variety; and processes and analyzes the three-dimensional map data, the target variety and the healthy cultivation management data to generate planning decisions for the target variety.

[0084] The data acquisition module, after planting the target varieties and deploying the target equipment and testing instruments according to the planning decision, regularly monitors the growth status of the target varieties through monitoring equipment, and obtains soil testing data according to the preset sampling date.

[0085] The data analysis module performs image analysis on the growth status based on preset sample images of the target variety at each stage to obtain the status analysis result, and analyzes the soil detection data according to the healthy cultivation management data to obtain the detection result; and performs abnormal analysis on the status analysis result and the detection result to obtain the fusion analysis result.

[0086] The exception handling module matches the corresponding exception handling decision according to the exception information in the fusion analysis result if there is exception information in the fusion analysis result; and manages the agricultural fruit trees according to the exception handling decision so that the growth status of the agricultural fruit trees meets the preset growth standards.

[0087] Through the mutual cooperation between the modules in the system, the present invention can regularly obtain monitoring images of agricultural fruit trees through monitoring equipment to analyze the growth status of agricultural fruit trees and obtain status analysis results; and obtain soil test data according to the preset sampling date, analyze the soil test data, and obtain test results; and then perform abnormal analysis on the fusion analysis results obtained by fusing the status analysis results and the test results, match the corresponding abnormal processing decisions, and finally cultivate and manage the agricultural fruit trees according to the abnormal processing decisions, so that the growth status of the agricultural fruit trees meets the preset growth standards. By adopting the above method, the present invention solves the problem that the traditional planting management model cannot handle abnormal situations in time, resulting in the inability to adjust the status of agricultural fruit trees in time, and then resulting in the taste of the fruit not meeting customer needs.

[0088] Furthermore, the present invention uses fusion analysis based on three-dimensional map data, historical weather data and soil detection data of the planting area, which can select target varieties for customers and provide planning decisions for planting layout. The growth of agricultural fruit trees can be analyzed based on monitoring images and soil detection data, and when abnormal conditions are found, abnormal handling decisions are generated in a timely manner. For customers who are new to agricultural fruit tree planting or want to engage in agricultural fruit tree planting, the present invention can carry out comprehensive planning such as planting layout, equipment installation guidance, regular soil testing, and adjustment of growth management strategies based on the planting area selected by the customer, saving customers a lot of query time, providing customers with full-process services, and greatly providing convenience for customers. In addition, according to the growth status standards of agricultural fruit trees at different periods, the regularly collected comprehensive data is analyzed to adjust the cultivation strategy in a timely manner, so that the cultivation of agricultural fruit trees returns to the path of the preset cultivation plan.

[0089] In one embodiment of the present invention, individual abnormal agricultural fruit trees, such as those with abnormal growth, abnormal flowering, abnormal fruit setting, abnormal taste, etc., are used as experimental specimens for focused analysis, or the fruit trees are removed for replanting or transplanting.

[0090] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] It should be noted that in the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings, which are only for the convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0092] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0093] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0094] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0096] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0097] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0098] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for healthy cultivation and management of agricultural fruit trees, characterized in that: include: Obtain 3D map data, historical weather data, and soil testing data for agricultural fruit tree planting areas; Analyze historical weather data and soil test data based on a preset agricultural fruit tree growth condition database to determine a set of agricultural fruit tree varieties that meet the conditions, and obtain target varieties selected from the set of agricultural fruit tree varieties; According to the target varieties, obtain the healthy cultivation management data of the target varieties; and processing and analyzing three-dimensional map data, target varieties, and healthy cultivation management data to generate planning decisions for target varieties; After planting the target varieties and deploying the target equipment and testing instruments according to the planning decision, regularly monitoring the growth status of the target varieties through monitoring equipment, and obtaining soil testing data according to the preset sampling date; Performing image analysis on the growth state based on preset sample images of the target variety at each stage to obtain state analysis results, and analyzing soil test data based on healthy cultivation management data to obtain test results; Perform abnormal analysis on the status analysis results and the detection results to obtain fusion analysis results; If there is abnormal information in the fusion analysis result, the corresponding abnormal processing decision is matched according to the abnormal information in the fusion analysis result; And manage agricultural fruit trees based on abnormal processing decisions so that the growth status of agricultural fruit trees meets the preset growth standards.

2. The management method according to claim 1, characterized in that: The processing and analysis of the three-dimensional map data, target varieties and healthy cultivation management data to generate planning decisions for the target varieties includes: Obtain the planting requirement range of a single tree of the target variety from the healthy cultivation management data of the target variety; Determine the planting quantity and preset planting location of the target variety according to the planting demand range and the three-dimensional map data to obtain an initial planting plan map; Determine multiple initial planting planning strategies including planning biases according to the initial planting planning map, healthy cultivation management data and adjusted preset cultivation auxiliary equipment information; Obtain and determine the final planning decision for the target variety from each initial planting planning strategy based on the planning preference selected by the customer.

3. The management method according to claim 2, characterized in that: The method of determining multiple initial planting planning strategies including planning preferences based on the initial planting planning map, healthy cultivation management data and adjusted preset cultivation auxiliary equipment information includes: determining target equipment and detection instruments from the preset cultivation auxiliary equipment information based on preset standards and preset planning preferences in the healthy cultivation management data; adjusting the initial planting planning map based on the preset planning preferences, target equipment and detection instruments to obtain multiple secondary planting planning maps; and obtaining corresponding planting planning strategies based on each secondary planting planning map.

4. The management method according to claim 1, characterized in that: The method of performing image analysis on the growth state based on preset sample images of each stage of the target variety to obtain a state analysis result includes: obtaining the cultivation date of the current target variety, determining the growth stage of the target variety based on the cultivation date and obtaining the preset sample images of the corresponding stage; performing image segmentation on the monitoring image, and performing image analysis and screening statistics on the segmented block images to obtain a feature image containing statistical information; performing image analysis and matching on the feature image based on the preset sample images, and taking the preset sample image corresponding to the first-ranked matching degree among each matching degree as the target image; and determining the current state analysis result of the target variety according to the correspondence between the target image and the state information in the preset growth state database.

5. The management method according to claim 4, characterized in that: The preset growth status database at least includes a mapping relationship between preset sample images and the current status, abnormality type, and abnormality level of the target variety.

6. The management method according to claim 4, characterized in that: The soil test data is analyzed according to the healthy cultivation management data to obtain the test results, including: The soil test data standard of the current stage in the healthy cultivation management data corresponding to the current target variety is determined according to the cultivation date, and the soil test data is compared and analyzed according to the soil test data standard to obtain the test results containing abnormal levels corresponding to different degrees of deviation.

7. The management method according to claim 6, characterized in that: The abnormal analysis of the state analysis results and the detection results to obtain the fusion analysis results includes: Based on the preset abnormal causal library, determine whether there is a correlation between the abnormal data in the state analysis results and the abnormal data in the detection results. If so, obtain the corresponding correlation analysis results in the preset abnormal causal library; filter and deduplicate the correlation analysis results, state analysis results, and detection results to obtain a fusion analysis result containing multiple abnormal levels.

8. The management method according to claim 7, characterized in that: The matching of the corresponding exception handling decision according to the exception information in the fusion analysis result includes: When the abnormality level in the fusion analysis result exceeds the corresponding preset abnormality level, the corresponding abnormality analysis result in the fusion analysis result is obtained, and the sub-abnormality processing decision corresponding to the abnormality analysis result is matched according to the preset abnormality decision library; the sub-abnormality processing decisions are integrated to obtain the abnormality processing decision.

9. The management method according to claim 8, characterized in that: Also includes: Acquire weather data in real time, and judge the impact level of weather data on the growth status of the current target variety based on healthy cultivation management data. If the impact level exceeds the preset level, generate and send corresponding warning information and response measures.

10. A healthy cultivation management system for agricultural fruit trees, characterized in that: include: Data acquisition module, which obtains three-dimensional map data, historical weather data and soil detection data of agricultural fruit tree planting areas; A variety determination module analyzes historical weather data and soil test data based on a preset agricultural fruit tree growth condition database to determine a set of agricultural fruit tree varieties that meet the conditions and obtain a target variety selected from the set of agricultural fruit tree varieties; The planting planning module obtains the healthy cultivation management data of the target varieties according to the target varieties; And process and analyze the 3D map data, target varieties and healthy cultivation management data to generate planning decisions for target varieties The data acquisition module, after planting the target varieties and deploying the target equipment and testing instruments according to the planning decision, regularly monitors the growth status of the target varieties through monitoring equipment, and obtains soil testing data according to the preset sampling date A data analysis module performs image analysis on the growth status based on preset sample images of target varieties at various stages to obtain status analysis results, and analyzes soil test data based on healthy cultivation management data to obtain test results; Perform abnormal analysis on the status analysis results and the detection results to obtain fusion analysis results; The exception handling module, if there is exception information in the fusion analysis result, matches the corresponding exception handling decision according to the exception information in the fusion analysis result; And manage agricultural fruit trees based on abnormal processing decisions so that the growth status of agricultural fruit trees meets the preset growth standards.

Images