Method and equipment for monitoring growth condition of waxberry tree

Abstract

The invention is suitable for the technical field of agricultural monitoring, and particularly relates to a method and equipment for monitoring the growth condition of a waxberry tree, and the method comprises the steps: obtaining the growth data of a current waxberry tree under the condition that a monitoring state is detected to be an open state; wherein the growth data is used for indicating the planting time and the growth time of the current waxberry tree and the current growth condition of the waxberry tree; detecting the waxberry tree based on the growth data of the waxberry tree to obtain detection data; wherein the detection data is used for indicating physiological indexes and chemical indexes of the waxberry tree; analyzing the waxberry tree based on the growth data of the waxberry tree to obtain analysis data; wherein the analysis data is used for reflecting the thermal imaging condition and the insect influence condition of the waxberry tree; and obtaining monitoring data of the waxberry tree according to the detection data and the analysis data. According to the method for monitoring the growth condition of the waxberry tree, factors threatening the growth of the waxberry tree can be found in the early stage, and the problem of processing hysteresis is reduced.

Description

Technical Field

[0001] The present application belongs to the field of agricultural monitoring technology, and in particular, relates to a method and equipment for monitoring the growth of bayberry trees. Background Art

[0002] Bayberry tree growth monitoring uses sensors, IoT devices, image recognition, big data analysis and other technologies to continuously monitor and intelligently analyze the bayberry tree's growth environment (such as soil, climate, water, etc.), physiological state (such as leaf health, fruit development, diseases and pests, etc.), and growth trends, thereby providing growers with a basis for scientific management decisions.

[0003] The bayberry tree growth monitoring method in related technologies relies on a single indicator (such as soil moisture or leaf color) to evaluate growth, which is difficult to fully reflect the health status of the bayberry tree. Traditional visual observation or regular sampling is difficult to detect stress (such as root diseases and epidermal drought) at an early stage, which leads to delayed treatment. Summary of the invention

[0004] The embodiments of the present application provide a method and device for monitoring the growth of bayberry trees, which can solve the problem of delayed treatment due to the single method for monitoring the growth of bayberry trees and difficulty in detecting stress at an early stage.

[0005] In a first aspect, an embodiment of the present application provides a method for monitoring the growth of a bayberry tree, comprising: When it is detected that the monitoring state is turned on, the growth data of the current bayberry tree is obtained; wherein the growth data is used to indicate the planting time, growth time and current growth status of the current bayberry tree; Detecting the bayberry tree based on the growth data of the bayberry tree to obtain detection data; wherein the detection data is used to indicate the physiological indicators and chemical indicators of the bayberry tree; Analyzing the bayberry tree based on the growth data of the bayberry tree to obtain analysis data; wherein the analysis data is used to reflect the thermal imaging situation and insect impact situation of the bayberry tree; The monitoring data of the bayberry tree is obtained according to the detection data and the analysis data; wherein the monitoring data is used to reflect the growth condition of the bayberry tree obtained under the monitoring device.

[0006] The bayberry tree growth monitoring method provided in the present application obtains the current growth data of the bayberry tree when it is detected that the monitoring state is on, so that the planting time, growth time and current growth condition of the bayberry tree can be clearly understood when the monitoring state is on; the bayberry tree is detected based on the growth data of the bayberry tree to obtain detection data, and the bayberry tree is analyzed based on the growth data of the bayberry tree to obtain analysis data, and then the monitoring data of the bayberry tree is obtained based on the detection data and the analysis data. The growth condition of the bayberry tree is monitored by multiple evaluation indicators such as physiological indicators and chemical indicators in the detection data and thermal imaging conditions and insect impact conditions of the bayberry tree in the analysis data, which helps to comprehensively evaluate the health status of the bayberry tree, solve the situation that threatens the healthy growth of the bayberry tree as much as possible at an early stage, reduce the problem of processing lag, and improve or optimize the monitoring method for the growth of the bayberry tree.

[0007] In a second aspect, an embodiment of the present application provides a system for monitoring the growth of a bayberry tree, comprising: An acquisition unit, used for acquiring the current growth data of the bayberry tree when the monitoring state is detected to be on; wherein the growth data is used to indicate the current planting time, growth time and growth condition of the bayberry tree; A detection unit, used to detect the bayberry tree based on the growth data of the bayberry tree to obtain detection data; wherein the detection data is used to indicate the physiological indexes and chemical indexes of the bayberry tree; An analysis unit, configured to analyze the bayberry tree based on the growth data of the bayberry tree to obtain analysis data; wherein the analysis data reflects the thermal imaging and pollination conditions of the bayberry tree; A result unit is used to obtain monitoring data of the bayberry tree according to the detection data and the analysis data; wherein the monitoring data is used to reflect the growth condition of the bayberry tree obtained under monitoring conditions.

[0008] In a third aspect, an embodiment of the present application provides a device for monitoring the growth of bayberry trees, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method described in any one of the first aspects above is implemented.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a bayberry tree growth monitoring device, the bayberry tree growth monitoring device executes the bayberry tree growth monitoring method described in any one of the first aspects above.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 It is a flow chart of a method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Figure 2 It is a schematic diagram of the implementation flow of steps S101 to S104 in the method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Figure 3 It is a schematic diagram of the implementation process of step S200 in the method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Figure 4 It is a schematic diagram of the implementation flow of step S210 in the method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Figure 5 It is a schematic diagram of the implementation process of step S220 in the method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Figure 6 It is a schematic diagram of the implementation process of step S300 in the method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Figure 7 It is a schematic diagram of the implementation process of step S310 in the method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Figure 8 It is a schematic diagram of the implementation process of step S320 in the method for monitoring the growth of bayberry trees provided in one embodiment of the present application; Fig. 9 It is a structural schematic diagram of a bayberry tree growth monitoring system provided in an embodiment of the present application; Fig.10 It is a structural schematic diagram of the bayberry tree growth monitoring device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] The bayberry tree growth monitoring method in related technologies relies on a single indicator (such as soil moisture or leaf color) to evaluate growth, which is difficult to fully reflect the health status of the bayberry tree. Traditional visual observation or regular sampling is difficult to detect stress (such as root diseases and epidermal drought) at an early stage, which leads to delayed treatment.

[0020] To solve the above problems, the embodiment of the present application provides a method and device for monitoring the growth of a bayberry tree. In the method, when it is detected that the monitoring state is turned on, the growth data of the current bayberry tree is obtained, and the planting time, growth time and current growth condition of the bayberry tree can be clearly understood when the monitoring state is turned on; the bayberry tree is detected based on the growth data of the bayberry tree to obtain the detection data, and the bayberry tree is analyzed based on the growth data of the bayberry tree to obtain the analysis data, and then the monitoring data of the bayberry tree is obtained based on the detection data and the analysis data. The growth of the bayberry tree is monitored through multiple evaluation indicators such as physiological indicators and chemical indicators in the detection data and thermal imaging conditions and insect impact conditions of the bayberry tree in the analysis data, which helps to comprehensively evaluate the health status of the bayberry tree, solve the situation that threatens the healthy growth of the bayberry tree as much as possible in the early stage, reduce the problem of processing lag, and improve or optimize the monitoring method for the growth of the bayberry tree.

[0021] The bayberry tree growth monitoring method provided in the embodiment of the present application can be applied to a bayberry tree growth monitoring device. In this case, the bayberry tree growth monitoring device is the executor of the bayberry tree growth monitoring method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the bayberry tree growth monitoring device.

[0022] For example, the bayberry tree growth monitoring device can be a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a smart large screen, a computer, etc.

[0023] In order to better understand the method for monitoring the growth of bayberry trees provided in the embodiment of the present application, the specific implementation process of the method for monitoring the growth of bayberry trees provided in the embodiment of the present application is exemplarily introduced below.

[0024] Figure 1 A schematic flow chart of a method for monitoring the growth of a bayberry tree provided in an embodiment of the present application is shown. The method for monitoring the growth of a bayberry tree comprises: S100, when it is detected that the monitoring state is turned on, the growth data of the current bayberry tree is obtained; wherein the growth data is used to indicate the planting time, growth time and current growth status of the current bayberry tree.

[0025] It can be understood that the monitoring state can include an open state and a closed state; the growth time is the time that the bayberry tree has grown from the planting time to the current time when the growth data is obtained. For example, the planting time is March of XX, and the current time when the growth data of the current bayberry tree is obtained is August of XX, so the current bayberry tree has grown for five months. The way to obtain the growth data can be obtained manually from the record sheet, and can also be obtained from the recording system. The record sheet can be the planting time of the bayberry tree recorded on the record sheet when the bayberry tree is planted, and then the growth time is calculated by the planting time and the current growth of the bayberry tree is checked. The recording system can be a storage system written using the Internet or program code, and the recording system can store the basic information of the bayberry tree. The current growth status of the bayberry tree includes the current stage of the bayberry tree (flowering stage or fruiting stage, etc.), the current health status, appearance, etc., but is not limited to this.

[0026] In one possible implementation, see Figure 2 , S100, when it is detected that the monitoring state is turned on, before obtaining the growth data of the current bayberry tree, including: S101, obtaining an ideal bayberry tree model that meets various planting indicators, and extracting indicators from the ideal bayberry tree model to obtain ideal data; wherein the ideal data is used to indicate various indicator values ​​of the ideal bayberry tree model.

[0027] Exemplarily, the ideal bayberry tree model that meets various planting indicators can be obtained from the historical records of model establishment; the index extraction of the ideal bayberry tree model can be manually obtained through banquet equipment, or can be automatically extracted using a computer program. The various indicators can include the degree of root development, root integrity, crown fullness, main root to lateral root ratio (main root length ≤ 20cm, lateral root fibrous root ratio > 60%), root secretion detection (root mucus pH value 4.5-5.2), leaf area index (crown width / plant height ratio > 0.6 (for example, the height of the bayberry tree to be planted is 1m, and the crown width must be ≥ 60cm)).

[0028] S102, extracting indicators of all the bayberry trees to be planted to obtain indicator data of each bayberry tree; wherein the indicator data is used to indicate various indicator values ​​of the bayberry trees to be planted.

[0029] It can be understood that the indicators of all the bayberry trees to be planted are extracted to obtain the degree of root development, root integrity, crown fullness, main root to lateral root ratio (main root length ≤ 20cm, lateral root fibrous root ratio > 60%), root secretions (root mucus pH 4.5-5.2), leaf area index (crown width / plant height ratio > 0.6 (for example, if the height of the bayberry tree to be planted is 1m, the crown width must be ≥ 60cm)).

[0030] S103, comparing various indicator values ​​indicated by the ideal data with various indicator values ​​indicated by the indicator data to obtain comparison results of various indicator values.

[0031] It can be understood that the root development, root integrity, crown fullness, taproot to lateral root ratio, root secretions and leaf area index of the various indicators indicated by the ideal data are compared with the values ​​of the root development, root integrity, crown fullness, taproot to lateral root ratio, root secretions and leaf area index of the various indicators indicated by the indicator data of each bayberry tree, so as to obtain the comparison values ​​of the ideal data and the indicator data. For example, the root development of the indicators indicated by the ideal data is 5, the root integrity is 7, the crown fullness is 7, the taproot to lateral root ratio is 8, the pH value of the root secretions is 5 and the leaf area index is 0.8. The indicator values ​​indicated by the indicator data of the bayberry tree to be planted are root development of 7, root integrity of 7, crown fullness of 8, taproot to lateral root ratio of 7, the pH value of the root secretions is 5 and the leaf area index is 0.7. The various prepared values ​​indicated by the indicator data are subtracted from the various indicator values ​​indicated by the ideal data, and the calculated indicator difference values ​​are as follows: the difference in the root development is 2, the difference in the root integrity is 0, and the pH value of the root secretions is 0. The difference in crown fullness is 1, the difference in the ratio of taproot to lateral roots is -1, the difference in the pH value of root secretions is 0 and the difference in leaf area index is -0.1; then the difference in root development is 2, the difference in root integrity is 0, the difference in crown fullness is 1, the difference in the ratio of taproot to lateral roots is -1, the difference in the pH value of root secretions is 0 and the difference in leaf area index is -0.1, which are all comparison results of the values ​​of various indicators. Among them, indicators with negative values ​​do not meet and do not meet the planting standards (indicators that do not meet the standards can be determined whether they can be ignored based on their impact on the bayberry tree).

[0032] S104, weighting the comparison results of various index values ​​to obtain satisfying data; wherein the satisfying data is used to indicate the bayberry trees to be planted that meet the planting requirements among all the bayberry trees to be planted.

[0033] It can be understood that, referring to the index difference obtained in the example part in step S103, that is, the difference in root development is 2, the difference in root integrity is 0, the difference in crown fullness is 1, the difference in the ratio of main root to lateral root is -1, the difference in the pH value of root secretions is 0, and the difference in leaf area index is -0.1, and the negative index is obtained from all the indicators. The negative indexes are the difference in the ratio of main root to lateral root is -1, and the difference in leaf area index is -0.1. The weights are assigned to the ratio of main root to lateral root and the leaf area index, and the difference can be calculated based on the difference values. The size of is used to allocate weights for the ratio of main roots to lateral roots and the leaf area index. For example, the difference in the ratio of main roots to lateral roots has 10 levels, such as the difference is -1 to -10. At present, the difference in the ratio of main roots to lateral roots is -1, so it is the first level and the difference is small, so the weight is 10%. The difference in leaf area index is -0.1, and similarly it is also 10% (10 levels, from -0.1 to -1, or from -0.1 to -2, etc., with a step size of 0.2, and a total of 10 levels). There can also be more levels, and the more levels, the finer the weight allocation.

[0034] With such arrangement, by selecting the bayberry trees to be planted as described above, suitable bayberry trees to be planted can be selected according to the required monitoring conditions and monitoring methods, and the growth of the bayberry trees can be monitored more comprehensively, preventing the bayberry trees from dying accidentally during the monitoring process due to failure to meet the planting conditions, thereby wasting time and resources.

[0035] S200, detecting the bayberry tree based on the growth data of the bayberry tree to obtain detection data; wherein the detection data is used to indicate physiological indicators and chemical indicators of the bayberry tree.

[0036] It can be understood that by monitoring the planting time, growth time and current growth conditions of the bayberry tree, the physiological signals of the bayberry tree and the secretions secreted by the bayberry tree can be monitored. The physiological indicators of the bayberry tree include the water status and nutrient flow of the bayberry tree, etc., and the chemical indicators of the bayberry tree include the secretions secreted by the bayberry tree.

[0037] In one possible implementation, see Figure 3 S200: Detect the bayberry tree based on the growth data of the bayberry tree to obtain detection data, including: S210, monitoring physiological signals of the bayberry tree based on the growth data of the bayberry tree to obtain monitoring data; wherein the monitoring data is used to reflect the epidermis condition of the trunk of the bayberry tree.

[0038] Exemplarily, the current growth condition of the bayberry tree indicated by the growth data of the bayberry tree is monitored to obtain the epidermal condition of the trunk of the bayberry tree; the epidermal condition includes whether there are cracks, whether there are traces of insect bites, etc., and then the epidermal condition of the bayberry tree is determined.

[0039] In one possible implementation, see Figure 4 S210: Monitoring physiological signals of the bayberry tree based on the growth data of the bayberry tree to obtain monitoring data, including: S211, acquiring electrode array data; wherein the electrode array data is used to indicate a flexible biological electrode array deployed on the epidermis of a tree trunk.

[0040] As you can understand, the flexible bioelectrode array is a monitoring tool that is specially designed to fit on the trunk epidermis of the bayberry tree. The electrode array is made of flexible material and can be in close contact with the surface of the trunk, so that it can accurately capture and transmit weak physiological signals from the trunk. These signals contain key information about the health of the bayberry tree, such as water status and nutrient flow. The flexible bioelectrode array is not only highly sensitive, but also has good durability and biocompatibility.

[0041] Exemplarily, the electrode array data can be obtained by monitoring a flexible bio-electrode array deployed on the epidermis of the tree trunk; the flexible bio-electrode deployed on the epidermis of the tree trunk can be connected to the monitoring device. The flexible bio-electrode array is circumferentially arranged on the epidermis of the tree trunk.

[0042] S212, collecting spontaneous or induced electrical signals generated by ion flow in the body of the bayberry tree according to the state of the flexible biological electrode array indicated by the electrode array data.

[0043] It can be understood that according to the state of the flexible biological electrode array indicated by the electrode array data, non-invasive monitoring technology can be used for the bayberry tree, using the close contact between the flexible biological electrode array and the trunk epidermis to capture the electrical signals in the bayberry tree. The captured electrical signals can reflect the physiological processes of the bayberry tree, such as water balance and nutrient transfer. By continuously monitoring the changes in these signals, possible physiological abnormalities of the bayberry tree can be discovered in a timely manner.

[0044] S213, performing characteristic analysis on the spontaneous or induced electric signals generated by the ion flow in the collected bayberry trees to obtain signal characteristic data; wherein the signal characteristic data is used to indicate frequency, amplitude or waveform.

[0045] Exemplarily, the analysis of the spontaneous or induced electric signals generated by the in vivo ion flow of the collected bayberry tree can be performed by spectrum analysis, amplitude statistics or waveform recognition on the collected electric signals to extract the frequency, amplitude or waveform reflecting the physiological state of the bayberry tree. For example, through spectrum analysis, the main frequency components of the electric signal can be determined, and the frequency components are associated with the water status or nutrient flow rate of the bayberry tree. Amplitude statistics can reveal the intensity changes of the electric signal, which are related to the physiological activity level of the bayberry tree. Waveform recognition can identify electric signals with specific shapes or patterns, which are associated with specific physiological events or states of the bayberry tree.

[0046] S214, obtaining stress response data according to the frequency, amplitude or waveform indicated by the signal characteristic data and the planting time and growth time of the bayberry tree indicated by the growth data; wherein the stress response data is used to indicate that the trunk epidermis of the bayberry tree is in the early stage of drought and / or pests and diseases.

[0047] Exemplarily, by comparing the planting time and growth time of the bayberry tree indicated by the growth data with the frequency, amplitude or waveform indicated by the signal characteristic data, the stress response data indicates that the tree trunk epidermis is in drought and / or the early stages of disease and insect pests; for example, the frequency indicated by the signal characteristic data reflects that the bayberry tree should be in the germination stage under ideal conditions, but it has not sprouted yet, so the frequency, amplitude or waveform indicated by the signal characteristic data can reflect that the tree trunk epidermis may not have reached the germination stage due to water loss caused by drought or the early stages of disease and insect pests.

[0048] S215, obtaining monitoring data based on the stress response data.

[0049] Exemplarily, the bark of the trunk of the bayberry tree in the early stages of drought and / or pests and diseases is used as monitoring data to determine the monitoring data.

[0050] Such a setting can provide early warning and help growers take targeted prevention and intervention measures in advance, such as timely irrigation and pest and disease control, to reduce losses and improve the health level and fruit yield of bayberry trees. Scientific and reasonable planting strategies can also be formulated based on these data, such as adjusting the irrigation frequency, fertilizer type and time according to the actual physiological needs of bayberry trees, to achieve precision agricultural management and improve planting efficiency and economic benefits.

[0051] S220, analyzing the secretions of the bayberry tree to obtain secretion data; wherein the secretion data is used to reflect the influence of the secretions secreted by the root system of the bayberry tree on the bayberry tree.

[0052] Exemplarily, the analysis of the secretions of the bayberry tree can be to collect the secretions secreted by the root system of the bayberry tree, and the component information in the secretions can be obtained, and the component information can reflect the root health of the bayberry tree, the nutrient absorption capacity, and possible soil pollution or stress factors. For example, the excessive content of certain specific components in the secretions may mean that the bayberry tree is being attacked by certain pests and diseases, or that the content of certain elements in the soil exceeds the standard. By comparing the secretion components of the bayberry tree under normal conditions, abnormal conditions can be discovered in time, providing a basis for subsequent management and treatment.

[0053] In one possible implementation, see Figure 5 , S220, analyzing the secretions of the bayberry tree to obtain secretion data, including: S221, acquiring microprobe data; wherein the microprobe data is used to reflect the situation detected by the micromass spectrometry probe buried under the root system of the bayberry tree.

[0054] It can be understood that the micro-mass spectrometer probe is a high-precision analytical tool that can be buried under the root system of the bayberry tree (for example, 30cm, 20cm or 10cm from the ground, etc., which is not specifically limited here. The burial method can be multiple, and it can be set in sequence around the circumference of the bayberry tree, or it can be set evenly, which can better collect the situation of the bayberry tree and improve accuracy), and monitor the plant root secretions of the bayberry tree in real time. Through the micro-mass spectrometer probe, the chemical component information in the root secretions can be directly obtained without extracting the secretions for laboratory analysis, thereby greatly improving the efficiency and accuracy of the analysis. The working principle of the micro-mass spectrometer probe is to use mass spectrometry technology to ionize the secretions contacted by the probe, and separate and detect the ions through an electric field or a magnetic field, so as to obtain the mass and relative abundance information of various chemical components in the secretions, so as to understand the root health status, nutrient absorption capacity and soil environmental conditions of the bayberry tree. The depth of the micro-mass spectrometer probe is 30cm to 50cm.

[0055] S222, identifying secretions of the root system of the bayberry tree based on the micro-probe data to obtain first identification data; wherein the first identification data is used to indicate secretions secreted by the root system of the bayberry tree.

[0056] Exemplarily, a miniature mass spectrometry probe buried under the roots of the bayberry tree is used to identify the root secretions of the bayberry tree, and the identified secretions are analyzed. The miniature mass spectrometry probe then compares the analyzed chemical composition information with the known root secretion components, thereby quickly determining the specific components and concentrations in the secretions, and determining the components and content concentrations as the first identification data.

[0057] S223, re-identify the secretions of the root system of the bayberry tree based on the micro-probe data and the preset identification cycle to obtain second identification data; wherein the second identification data is used to indicate the secretions secreted by the root system of the bayberry tree obtained under the preset identification cycle.

[0058] It can be understood that the preset identification period (the preset identification period can be 15 days, 10 days, 7 days, 5 days, 3 days or 1 day) can be started from obtaining the first identification data and re-identified within the preset identification period.

[0059] Exemplarily, the identification method may be to use a miniature mass spectrometry probe buried under the roots of the bayberry tree to identify the root secretions of the bayberry tree, analyze the identified secretions, and the miniature mass spectrometry probe then compares the analyzed chemical composition information with the known root secretion components to determine the components and concentrations in the secretions, and determine the components and content concentrations as the second identification data.

[0060] S224, performing a secretion concentration analysis on the first identification data and the second identification data to obtain change data; wherein the change data is used to reflect the change in the concentration of the secretion after the concentration analysis.

[0061] Exemplarily, the concentration indicated by the first identification data is compared with the concentration indicated by the second identification data. The concentration comparison is to subtract the concentration indicated by the first identification data from the concentration indicated by the second identification data to obtain the concentration difference. If the concentration difference is 0, it proves that the concentration has not changed. If the concentration difference is positive or negative, it proves that the concentration has not changed. If the concentration difference is positive, it proves that the concentration indicated by the second identification data is greater than the concentration indicated by the first identification data. If the concentration difference is negative, it proves that the concentration indicated by the second identification data is less than the concentration indicated by the first identification data.

[0062] S225, obtaining secretion data according to the change data.

[0063] Exemplarily, a concentration difference value of 0, a positive value, or a negative value is determined as secretion data.

[0064] This setting can effectively and accurately analyze and deeply understand the root system status in real time, deeply understand the root health status, nutrient absorption capacity and soil environmental conditions of the bayberry tree, help to discover potential problems, accurately identify secretions, and monitor the changing trend of the root secretions of the bayberry tree over time in real time; in addition, by setting a preset identification cycle, the root secretions are identified multiple times to obtain the first identification data and the second identification data, and the concentration analysis is performed to obtain the change data, which can dynamically monitor the changes in the concentration of the root secretions. This helps to discover the changing patterns of the root secretions of the bayberry tree at different growth stages or under different environmental conditions.

[0065] S230, obtaining detection data according to the monitoring data and the secretion data.

[0066] Exemplarily, the physiological signals and chemical indicators indicated by the monitoring data, and the secretions of the bayberry tree indicated by the secretion data are determined as the detection data.

[0067] Such a setting helps to fully grasp the growth status of bayberry trees, thereby achieving early warning. By using a micro-mass spectrometry probe to obtain micro-probe data, the root secretions of bayberry trees can be directly monitored in real time without the need for cumbersome laboratory analysis processes, greatly improving the analysis efficiency and accuracy. It can quickly obtain chemical composition information in root secretions, help understand the root health status, nutrient absorption capacity and soil environmental conditions, and also help to judge the growth status of bayberry trees and their response to the environment, monitor the dynamic changes of secretions, and improve the monitoring of the growth of bayberry trees.

[0068] S300, analyzing the bayberry tree based on the growth data of the bayberry tree to obtain analysis data; wherein the analysis data is used to reflect the thermal imaging conditions and insect impact conditions of the bayberry tree.

[0069] It can be understood that the thermal imaging situation refers to the distribution and intensity of metabolic hot spots of the bayberry tree shown in the metabolic thermal imaging image, and the insect impact situation refers to the damage or potential threat caused by insect activities to the bayberry tree.

[0070] For example, the bayberry tree can be analyzed based on the growth data of the bayberry tree using thermal imaging technology and insect monitoring technology; thermal imaging technology generates metabolic thermal imaging by capturing infrared radiation emitted from the surface of the bayberry tree, which can intuitively display the distribution and intensity of metabolic hotspots of the bayberry tree, thereby reflecting the physiological activities and health status of the bayberry tree. Metabolic hotspots are usually associated with active growth areas, water evaporation, or pest and disease infection sites of the bayberry tree. By observing metabolic thermal imaging, possible health problems of the bayberry tree, such as local water loss, pest and disease infection, or nutritional imbalance, can be discovered in a timely manner. At the same time, insect monitoring technology can be achieved by monitoring the insect activities around the bayberry tree, which can be achieved by setting up insect audio monitoring equipment near the bayberry tree. The collected insect samples or audio data can be used to identify the insect species, quantity, and activity patterns, thereby judging the extent of the impact of insects on the bayberry tree. For example, the large-scale appearance of certain types of insects may indicate that the bayberry tree is facing a serious threat of pests and diseases.

[0071] With this setting, by combining thermal imaging and insect impact conditions, we can obtain analytical data that comprehensively reflects the growth status of bayberry trees, which will help growers to more deeply understand the health status, physiological needs and potential risks of bayberry trees, and thus formulate more scientific and reasonable planting strategies and management measures.

[0072] In one possible implementation, see Figure 6 S300: Analyze the bayberry tree based on the growth data of the bayberry tree to obtain analysis data, including: S310, performing metabolic analysis on the bayberry tree based on the growth data of the bayberry tree to obtain metabolic data; wherein the metabolic data is used to reflect the image condition of the metabolic thermal imaging of the bayberry tree.

[0073] For example, the metabolic analysis of bayberry trees is performed by thermal imaging technology, which captures the infrared radiation emitted from the surface of the bayberry tree and generates metabolic thermal imaging images. Based on the metabolic thermal imaging images, the distribution and intensity of metabolic hot spots of the bayberry tree can be observed. The distribution of metabolic hot spots corresponds to the active growth area, water evaporation area or potential infection site of pests and diseases of the bayberry tree. In the metabolic analysis process, the growth data of the bayberry tree, such as planting time, growth rate, environmental conditions, etc., can also be combined to improve the accuracy and reliability of the analysis. It can also serve as an important basis for subsequent management and treatment, guiding growers to take targeted measures to improve the health of bayberry trees and increase yields.

[0074] In one possible implementation, see Figure 7 , S310, performing metabolic analysis on the bayberry tree based on the growth data of the bayberry tree to obtain metabolic data, including: S311, performing surface temperature analysis on the leaves of the bayberry tree to obtain a distribution diagram of the surface temperature field on the leaves of the bayberry tree.

[0075] For example, the surface temperature analysis of the leaves of the bayberry tree can be performed by scanning the leaves of the bayberry tree with a thermal imager to obtain the temperature distribution on the surface of the leaves. Since the metabolic activity of the leaves generates heat, the distribution map of the surface temperature field can reflect the metabolic hotspots of the leaves, that is, the areas where the metabolic activity is more concentrated. These metabolic hotspots are related to physiological processes such as active growth of the leaves, water evaporation, or infection by pests and diseases.

[0076] S312, performing chlorophyll fluorescence analysis on the leaves of the bayberry tree to obtain the fluorescence intensity of chlorophyll on the leaves of the bayberry tree.

[0077] It can be understood that chlorophyll fluorescence analysis is carried out by exciting chlorophyll molecules in the leaves and measuring the fluorescence intensity emitted. In addition, chlorophyll fluorescence intensity is closely related to the photosynthetic efficiency of the leaves, which reflects the ability of the leaves to convert light energy into chemical energy. Through chlorophyll fluorescence analysis, the photosynthetic state of the leaves can be evaluated, and then the growth vitality and health of the bayberry tree can be judged. For example, a decrease in chlorophyll fluorescence intensity may mean that the photosynthesis of the leaves has been inhibited, which may be caused by water loss, insufficient nutrients, or infection by pests and diseases. Combined with the distribution map of the surface temperature field, a more comprehensive understanding of the metabolic activity and health of the leaves can be obtained, providing a scientific basis for subsequent planting management.

[0078] S313, constructing a photosynthesis efficiency heat map according to the distribution map of the surface temperature field on the leaves of the bayberry tree and the fluorescence intensity of chlorophyll on the leaves of the bayberry tree.

[0079] Exemplarily, the distribution map of the surface temperature field and the fluorescence intensity of chlorophyll are directly superimposed to construct a photosynthetic efficiency heat map. The photosynthetic efficiency heat map can intuitively display the photosynthetic efficiency and metabolic activity status of bayberry leaves in different areas, and indicate the level of photosynthetic efficiency by color depth or different patterns. Areas with darker colors or specific patterns indicate higher photosynthetic efficiency and more vigorous metabolic activity; while areas with lighter colors or different patterns may indicate lower photosynthetic efficiency and relatively weak metabolic activity. By observing the photosynthetic efficiency heat map, areas with active metabolism and potential problems in bayberry leaves can be quickly located, such as metabolic hotspots, areas of water loss, or sites of disease and insect infection.

[0080] S314, obtaining metabolic data according to the constructed photosynthesis efficiency heat map.

[0081] Exemplarily, the photosynthetic efficiency and metabolic activity states of different regions displayed in the photosynthetic efficiency heat map are determined as metabolic data.

[0082] S320, identifying all insects on the bayberry tree to obtain insect data; wherein the insect data is used to reflect the impact of the insects on the bayberry tree on the bayberry tree.

[0083] It can be understood that all insects on the bayberry tree can be identified by using insect audio monitoring equipment, insect image recognition equipment or insect radar monitoring equipment to monitor and identify insects on the bayberry tree in real time. The insect audio monitoring equipment can capture the sounds made by insects and identify the types and quantity of insects by sound characteristics; the insect image recognition equipment can take images of insects around the bayberry tree and use image recognition technology to determine the types and distribution of insects; the insect radar monitoring equipment can emit radar signals and receive signals reflected by insects, thereby realizing the monitoring and positioning of insects. By analyzing these insect data, we can understand the types, quantities, activity patterns and potential threats of insects. For example, the large-scale appearance of certain insects may indicate that the bayberry tree is facing a serious threat of pests and diseases, which helps growers to take targeted prevention and control measures in a timely manner to protect the healthy growth of bayberry trees.

[0084] In one possible implementation, see Figure 8 , S320, identify and analyze all insects on the bayberry tree to obtain insect data, including: S321, obtaining voiceprint data and flowering phenological data of the bayberry tree; wherein the voiceprint data is used to reflect the voiceprint conditions detected by the voiceprint sensor array deployed on the bayberry tree, and the flowering phenological data is used to indicate the expected flowering time of the bayberry tree.

[0085] It can be understood that the expected flowering time of the bayberry tree can be understood as the flowering time under ideal conditions.

[0086] Exemplarily, the voiceprint data is obtained by detecting multiple voiceprints through a voiceprint sensor array deployed on the bayberry tree. The flowering period phenology data can be calculated based on the growth record of the bayberry tree.

[0087] S322, collecting the voiceprint features of the insect based on the voiceprint data; wherein the voiceprint features include the insect's wing flapping frequency, duration and amplitude.

[0088] Exemplarily, voiceprint features are collected by using a voiceprint sensor array. Voiceprint features mainly include the frequency, duration and amplitude of insect wingbeats, which can reflect the type, size and flight state of insects. By comparing with the known insect voiceprint feature library, the insect species on the bayberry tree can be quickly and accurately identified. At the same time, combined with the flowering phenological data, the relationship between the activity pattern of insects and the growth cycle of bayberry trees can be further analyzed. For example, some insects may be more active before and after the bayberry tree blooms, thus posing a greater threat to the bayberry tree. The insect voiceprint feature library can be obtained through historical or previous accumulation of voiceprints.

[0089] S323, analyzing the insect's wingbeat frequency, duration, and amplitude included in the soundprint features to obtain the insect type; wherein the insect type includes a pollinating insect or a pest.

[0090] Exemplarily, the analysis method for obtaining the soundprint features of the insect's wingbeat frequency, duration, and amplitude can be to compare it with a known insect soundprint feature library to accurately identify the type of insect. For example, the wingbeat frequency and amplitude characteristics of some insects may indicate that they are pollinating insects, which are beneficial to the growth of bayberry trees; while the soundprint characteristics of other insects may show that they are pests and pose a threat to bayberry trees. By accurately identifying the type of insect, corresponding management measures can be taken more targeted to effectively prevent and control pests and protect the healthy growth of bayberry trees; at the same time, measures can be taken to attract and protect pollinating insects to improve the pollination efficiency and fruit quality of bayberry trees.

[0091] S324, if the expected flowering time of the bayberry tree indicated by the flowering phenology data is the same as the flowering time of the bayberry tree after planting, and the insect type is a pollinating insect, obtain the pollination status; wherein the pollination status includes pollination duration, flower visit frequency and stigma contact area.

[0092] Exemplarily, if pollinating insects are active during the flowering period of bayberry trees, and their flower visit frequency is high, the pollination duration is long, and the stigma contact area is large, it indicates that the pollination condition is good, which is conducive to the efficient pollination of bayberry trees and the increase of fruit yield. On the contrary, if the pollinating insects have a low flower visit frequency, a short pollination duration, or a small stigma contact area, it may lead to insufficient pollination, affecting the fruit setting rate and fruit quality of bayberry trees. The pollinating insect database records the types of insects that can pollinate. To determine whether the insect type is a pollinating insect, the obtained insect can be compared with all the pollinating insects in the pollinating insect database, and then it can be determined whether the obtained insect is a pollinating insect.

[0093] With this setup, through detailed monitoring of pollinating insects, the pollination status of bayberry trees can be grasped in real time, potential pollination problems can be discovered in time, and appropriate measures can be taken to intervene so that the bayberry trees can be pollinated normally, thereby increasing the yield and quality of the fruit and better recording and monitoring the growth of the bayberry trees.

[0094] S325, obtaining insect data according to the pollination status. The insect data is used to reflect the influence of insects on the bayberry tree on the bayberry tree. For example, key parameters such as pollination duration, flower visit frequency, and stigma contact area in the pollination status are determined as insect data, wherein the longer the pollination duration, the higher the flower visit frequency, and the larger the stigma contact area, the more significant the pollination effect of the insect on the bayberry tree, and the more beneficial it is to the growth and fruit yield of the bayberry tree.

[0095] In a possible implementation, the method further includes: If the expected flowering time of the bayberry tree indicated by the flowering phenological data is the same as or different from the flowering time of the bayberry tree after planting, and the insect type is a pest, the insect repellent situation is obtained; wherein the insect repellent situation includes the insect repellent time, insect repellent drugs and droplet size.

[0096] For example, if the expected flowering time of the bayberry tree indicated by the flowering phenology data is the same or different from the flowering time of the planted bayberry tree, and the insect type is not a pollinating insect in the pollinating insect database, the insect type is determined as a pest, and then the determined pest is matched with the pest database to match the corresponding pest, and the cleaning method of the pest is obtained (the cleaning method is the amount of medicine, concentration, etc.), and then the pest is expelled according to the cleaning method and the expelling time, expelling medicine and droplet size indicated by the expelling situation. Pests and pest databases are databases that record pests, and the pest database includes pests and their information and cleaning methods.

[0097] With this setup, through detailed monitoring and timely cleaning of pests, the number and range of pests can be effectively controlled to prevent pests from causing serious damage to bayberry trees. According to the type of pests and cleaning methods, appropriate insect repellent drugs and droplet particle sizes can be selected to improve the insect repellent effect and reduce the impact on the environment. By combining the analysis of the insect repellent situation, a more scientific and reasonable pest management strategy can be formulated to protect the health of bayberry trees and better record and monitor the growth of bayberry trees.

[0098] S330, obtaining analysis data according to the metabolic data and the insect data.

[0099] Exemplarily, the distribution, intensity and photosynthesis efficiency of metabolic hot spots of the bayberry tree reflected by the metabolic data, and the types, quantity and activity patterns of insects on the bayberry tree reflected by the insect data are determined as analysis data. This setting helps determine whether the bayberry tree is threatened by pests and diseases. By integrating these two types of data, we can have a deeper understanding and monitor the growth of bayberry trees and identify potential health problems and risk points. For example, if the metabolic data shows that the metabolic activity in a certain area of ​​the bayberry tree is abnormal, and the insect data shows that there is pest activity in the area, then the grower can quickly locate the problem area and take targeted prevention and control measures to protect the healthy growth of the bayberry tree.

[0100] S400, obtaining monitoring data of the bayberry tree according to the detection data and the analysis data; wherein the monitoring data is used to reflect the growth condition of the bayberry tree obtained under monitoring conditions.

[0101] Exemplarily, the physiological indicators and chemical indicators of the bayberry tree indicated by the monitoring data and the thermal imaging conditions and insect impact conditions of the bayberry tree reflected by the analysis data are determined as the monitoring data of the bayberry tree.

[0102] To sum up, it is helpful to comprehensively evaluate the health status of bayberry trees, solve the situations that threaten the healthy growth of bayberry trees as much as possible in the early stage, reduce the problem of processing lag, and improve or optimize the monitoring methods of the growth of bayberry trees.

[0103] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0104] Corresponding to the bayberry tree growth monitoring method described in the above embodiment, the embodiment of the present application also provides a bayberry tree growth monitoring system, and each unit of the system can implement each step of the bayberry tree growth monitoring method. Fig. 9 A structural block diagram of a bayberry tree growth monitoring system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0105] Reference Fig. 9 , the bayberry tree growth monitoring system includes: An acquisition unit, used to acquire the growth data of the current bayberry tree when the monitoring state is detected to be on; wherein the growth data is used to indicate the planting time, growth time and growth condition of the current bayberry tree; A detection unit, used to detect the bayberry tree based on the growth data of the bayberry tree to obtain detection data; wherein the detection data is used to indicate the physiological indexes and chemical indexes of the bayberry tree; An analysis unit, used for analyzing the bayberry tree based on the growth data of the bayberry tree to obtain analysis data; wherein the analysis data reflects the thermal imaging and pollination conditions of the bayberry tree; The result unit is used to obtain monitoring data of the bayberry tree according to the detection data and the analysis data; wherein the monitoring data is used to reflect the growth condition of the bayberry tree obtained under the monitoring condition.

[0106] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0107] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0108] The embodiment of the present application also provides a device for monitoring the growth of bayberry trees. Fig.10 This is a schematic diagram of the structure of a bayberry tree growth monitoring device provided in one embodiment of the present application. Fig.10 As shown, the bayberry tree growth monitoring device 6 of this embodiment includes: at least one processor 60 ( Fig.10 Only one is shown), at least one memory 61 ( Fig.10 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the bayberry tree growth monitoring device 6 implements the steps in any of the above-mentioned bayberry tree growth monitoring method embodiments, or implements the functions of each module / unit in the above-mentioned system embodiments.

[0109] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the bayberry tree growth monitoring device 6.

[0110] The bayberry tree growth monitoring device 6 can be a computing device such as a desktop computer or a notebook. The bayberry tree growth monitoring device can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Fig.10This is only an example of the bayberry tree growth monitoring device 6 and does not constitute a limitation on the bayberry tree growth monitoring device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, buses, etc.

[0111] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0112] In some embodiments, the memory 61 may be an internal storage unit of the bayberry tree growth monitoring device 6, such as a hard disk or memory of the bayberry tree growth monitoring device 6. In other embodiments, the memory 61 may also be an external storage device of the bayberry tree growth monitoring device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the bayberry tree growth monitoring device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the bayberry tree growth monitoring device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0113] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0114] An embodiment of the present application provides a computer program product. When the computer program product is run on a bayberry tree growth monitoring device, the bayberry tree growth monitoring device implements the steps in any of the above method embodiments.

[0115] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the bayberry tree growth monitoring device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0116] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0118] In the embodiments provided in the present application, it should be understood that the disclosed bayberry tree growth monitoring system, device and method can be implemented in other ways. For example, the bayberry tree growth monitoring system and device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring the growth of bayberry trees, characterized in that: include: When it is detected that the monitoring state is turned on, the growth data of the current bayberry tree is obtained; wherein the growth data is used to indicate the current planting time, growth time and current growth status of the bayberry tree; Detecting the bayberry tree based on the growth data of the bayberry tree to obtain detection data; wherein the detection data is used to indicate the physiological indicators and chemical indicators of the bayberry tree; Analyzing the bayberry tree based on the growth data of the bayberry tree to obtain analysis data; wherein the analysis data is used to reflect the thermal imaging situation and insect impact situation of the bayberry tree; The monitoring data of the bayberry tree is obtained according to the detection data and the analysis data; wherein the monitoring data is used to reflect the growth condition of the bayberry tree obtained under the monitoring device.

2. The method for monitoring the growth of bayberry trees as claimed in claim 1, wherein: When the monitoring state is detected to be turned on, before obtaining the growth data of the current bayberry tree, the method includes: Acquire an ideal bayberry tree model that meets various planting indicators, and extract indicators from the ideal bayberry tree model to obtain ideal data; wherein the ideal data is used to indicate various indicator values ​​of the ideal bayberry tree model; Extracting indicators from all the bayberry trees to be planted to obtain indicator data of each bayberry tree; wherein the indicator data is used to indicate various indicator values ​​of the bayberry trees to be planted; Comparing the various index values ​​indicated by the ideal data with the various index values ​​indicated by the index data to obtain comparison results of the various index values; The comparison results of the index values ​​are weighted to obtain satisfaction data; wherein the satisfaction data is used to indicate the bayberry trees to be planted that meet the planting requirements among all the bayberry trees to be planted.

3. The method for monitoring the growth of bayberry trees as claimed in claim 1, wherein: The step of detecting the bayberry tree based on the growth data of the bayberry tree to obtain detection data includes: Based on the growth data of the bayberry tree, the physiological signals of the bayberry tree are monitored to obtain monitoring data; wherein the monitoring data is used to reflect the epidermis condition of the trunk of the bayberry tree; Analyze the secretions of the bayberry tree to obtain secretion data; wherein the secretion data is used to reflect the influence of the secretions secreted by the root system of the bayberry tree on the bayberry tree; The detection data is obtained according to the monitoring data and the secretion data.

4. The method for monitoring the growth of bayberry trees as claimed in claim 3, characterized in that: The monitoring of physiological signals of the bayberry tree based on the growth data of the bayberry tree to obtain monitoring data includes: Acquiring electrode array data; wherein the electrode array data is used to indicate a flexible biological electrode array deployed on the epidermis of a tree trunk; collecting spontaneous or induced electrical signals generated by ion flow in the body of the bayberry tree according to the state of the flexible biological electrode array indicated by the electrode array data; Performing characteristic analysis on the spontaneous or induced electric signals generated by the ion flow in the body of the bayberry tree to obtain signal characteristic data; wherein the signal characteristic data is used to indicate frequency, amplitude or waveform; Obtain stress response data according to the frequency, amplitude or waveform indicated by the signal characteristic data and the planting time and the growth time of the bayberry tree indicated by the growth data; wherein the stress response data is used to indicate that the trunk epidermis of the bayberry tree is in drought and / or in the early stage of pests and diseases; Monitoring data is obtained based on the stress response data.

5. The method for monitoring the growth of bayberry trees as claimed in claim 3, characterized in that: The step of analyzing the secretions of the bayberry tree to obtain secretion data includes: Acquiring microprobe data; wherein the microprobe data is used to reflect the situation detected by the micro mass spectrometry probe buried under the root system of the bayberry tree; Based on the microprobe data, the root system of the bayberry tree is subjected to secretion identification to obtain first identification data; wherein the first identification data is used to indicate the secretion secreted by the root system of the bayberry tree; Re-identify the secretions of the root system of the bayberry tree based on the microprobe data and the preset identification cycle to obtain second identification data; wherein the second identification data is used to indicate the secretions secreted by the root system of the bayberry tree obtained under the preset identification cycle; Performing a concentration analysis of secretions on the first identification data and the second identification data to obtain change data; wherein the change data is used to reflect the change in concentration of secretions after the concentration analysis; The secretion data is obtained according to the change data.

6. The method for monitoring the growth of bayberry trees according to claim 1, wherein: The step of analyzing the bayberry tree based on the growth data of the bayberry tree to obtain analysis data includes: Performing metabolic analysis on the bayberry tree based on the growth data of the bayberry tree to obtain metabolic data; wherein the metabolic data is used to reflect the image conditions of the metabolic thermal imaging of the bayberry tree; Identify all insects on the bayberry tree to obtain insect data; wherein the insect data is used to reflect the impact of the insects on the bayberry tree on the bayberry tree; The analytical data is obtained based on the metabolic data and the insect data.

7. The method for monitoring the growth of bayberry trees according to claim 6, wherein: The performing metabolic analysis on the bayberry tree based on the growth data of the bayberry tree to obtain metabolic data includes: Performing surface temperature analysis on the leaves of the bayberry tree to obtain a distribution diagram of the surface temperature field of the leaves of the bayberry tree; Performing chlorophyll fluorescence analysis on the leaves of the bayberry tree to obtain the fluorescence intensity of chlorophyll on the leaves of the bayberry tree; Constructing a photosynthesis efficiency heat map according to the distribution map of the surface temperature field on the leaves of the bayberry tree and the fluorescence intensity of chlorophyll on the leaves of the bayberry tree; The metabolic data is obtained according to the constructed photosynthesis efficiency thermodynamic diagram.

8. The method for monitoring the growth of bayberry trees according to claim 6, wherein: The step of identifying all insects on the bayberry tree to obtain insect data includes: Acquire voiceprint data and flowering phenological data of the bayberry tree; wherein the voiceprint data is used to reflect the voiceprint conditions detected by the voiceprint sensor array deployed on the bayberry tree, and the flowering phenological data is used to indicate the expected flowering time of the bayberry tree; Collecting the soundprint features of the insect based on the soundprint data; wherein the soundprint features include the frequency, duration and amplitude of the insect's wing flapping; Analyze the wingbeat frequency, duration and amplitude of the insect included in the soundprint feature to obtain the insect type; wherein the insect type includes pollinating insects or pests; If the expected flowering time of the bayberry tree indicated by the flowering phenology data is the same as the flowering time of the bayberry tree after planting, and the insect type is the pollinating insect, the pollination status is obtained; wherein the pollination status includes pollination duration, flower visit frequency and stigma contact area; The insect data is obtained according to the pollination conditions.

9. The method for monitoring the growth of bayberry trees according to claim 8, wherein: The method further comprises: If the expected flowering time of the bayberry tree indicated by the flowering phenological data is the same as or different from the flowering time of the bayberry tree after planting, and the insect type is the pest, the insect repellent situation is obtained; wherein, the insect repellent situation includes the insect repellent time, insect repellent drugs and droplet particle size.

10. A bayberry tree growth monitoring system, characterized in that: include: An acquisition unit, used for acquiring the current growth data of the bayberry tree when the monitoring state is detected to be on; wherein the growth data is used to indicate the current planting time, growth time and growth condition of the bayberry tree; A detection unit, used to detect the bayberry tree based on the growth data of the bayberry tree to obtain detection data; wherein the detection data is used to indicate the physiological indexes and chemical indexes of the bayberry tree; An analysis unit, configured to analyze the bayberry tree based on the growth data of the bayberry tree to obtain analysis data; wherein the analysis data reflects the thermal imaging and pollination conditions of the bayberry tree; A result unit is used to obtain monitoring data of the bayberry tree according to the detection data and the analysis data; wherein the monitoring data is used to reflect the growth condition of the bayberry tree obtained under the monitoring device.

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