Intelligent monitoring method and device for landscaping maintenance and storage medium

Abstract

The invention relates to the technical field of image processing, in particular to a landscaping maintenance intelligent monitoring method and device and a storage medium, and the method comprises the steps: carrying out the type division of each pixel block; generating a garden protection strategy based on the near-infrared band reflectivity acquired in the monitoring period, the gray level image contrast of the plant canopy image and the type of each pixel block; acquiring soil parameters, meteorological parameters and plant physiological parameters in the monitoring period; standardizing the acquired soil parameters, meteorological parameters and plant physiological parameters, and updating the generation process of the garden protection strategy by environmental pressure; counting the number of sound anomaly events based on the sound pulse signals acquired in the monitoring period, calculating a sound anomaly proportion, and determining an adjustment weight; and constructing a health index based on the insect pest index, the environmental factor and the sound anomaly proportion, and performing graded early warning to the user. According to the invention, the monitoring efficiency of landscaping maintenance is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method, device and storage medium for intelligent monitoring of landscaping maintenance. Background Art

[0002] Traditional landscaping maintenance relies primarily on manual inspections and single-parameter monitoring, resulting in low efficiency, limited coverage, and poor real-time performance. Existing technologies fail to integrate multidimensional environmental data and lack the ability to dynamically adjust strategies. Furthermore, existing technologies fail to adequately analyze the synergistic impacts of soil health, meteorological stress, and plant physiological responses, leading to one-sided maintenance strategies. Therefore, a landscaping maintenance monitoring solution that integrates multi-source data, enables dynamic assessment, and enables intelligent decision-making is urgently needed to address the limitations of traditional methods. Summary of the Invention

[0003] The object of the present invention is to provide a method, device and storage medium for intelligent monitoring of landscaping maintenance to solve at least one of the problems existing in the prior art.

[0004] To achieve the above object, the present invention adopts the following technical solutions: A landscaping maintenance intelligent monitoring method, comprising: Obtaining a grayscale image of the plant canopy image, dividing the grayscale image into blocks to obtain pixel blocks, and classifying each pixel block into different types; Based on the acquired near-infrared band reflectance, the grayscale image contrast of the plant canopy image, and the type of each pixel block, an insect pest index is constructed, and the insect pest status is determined, and a garden protection strategy is generated based on the insect pest status; Standardize the acquired soil parameters, meteorological parameters, and plant physiological parameters to quantify soil health, meteorological stress, and plant physiological responses, and determine environmental stress, thereby updating the generation process of garden protection strategies; The number of acoustic abnormality events is counted based on the acquired acoustic pulse signals to calculate the acoustic abnormality ratio and determine the adjustment weight.

[0005] Optionally, the grayscale image is divided into blocks of n×n pixels each, and the variance of the pixel values of each pixel in the i-th pixel block is denoted as σ2i. If σ2i is greater than the variance threshold, the pixel block is marked as a diseased area, otherwise, it is marked as a normal area.

[0006] Optionally, a pest index D is constructed based on the near-infrared band reflectance Rn, the red light reflectance reference value Rr, the grayscale image contrast Ct of the plant canopy image, the texture contrast reference value Cr, and the type of each pixel block. When the pest index D is greater than the pest discrimination factor d1, the pest state is determined to be an infected state; otherwise, the pest state is determined to be a healthy state. When the pest status is infected, set the spray rate to PY and use the spray rate as a garden protection strategy.

[0007] Optionally, the method further includes: obtaining soil parameters, meteorological parameters and plant physiological parameters within a monitoring period; The acquired soil parameters, meteorological parameters and plant physiological parameters were standardized to obtain the standardized soil moisture Sm, soil electrical conductivity Cs, ambient temperature Ht, light intensity Gm, leaf surface temperature Yt and plant transpiration rate Et.

[0008] Alternatively, the standardized soil moisture Sm and soil electrical conductivity Cs are fused to quantify soil health KA, the standardized ambient temperature Ht and light intensity Gm are fused to quantify meteorological stress QA, and the standardized leaf surface temperature Yt and plant transpiration rate Et are fused to quantify plant physiological response ZA.

[0009] Optionally, the quantified soil health KA, meteorological stress QA and plant physiological response ZA are data coupled to construct the environmental factor H; The environmental factor H is compared with the environmental discrimination factor h1 to judge the environmental pressure. If the environmental factor H is less than or equal to the environmental discrimination factor h1, the environmental pressure is judged to be in a normal state. If the environmental factor H is greater than the environmental discrimination factor h1, the environmental pressure is judged to be in a stress state. At the same time, the generation process of the garden protection strategy is updated, and the pest discrimination factor is updated to d2. The expression of d2 is d2=d1×{1-adjustment weight×exp[3×(H-h1)-3]}.

[0010] Optionally, the number of times the internal pulse signal is greater than the sound discrimination factor during the monitoring period is counted as the number of sound abnormality events, and the signal ratio of the sound abnormality number is calculated as the sound abnormality ratio SY. When the sound abnormality ratio SY is greater than the preset ratio L, the adjustment weight is determined to be M1. When the sound abnormality ratio SY is less than or equal to the preset ratio L, the adjustment weight is determined to be M2.

[0011] Optionally, it also includes: constructing a health index based on the pest index, environmental factors and the proportion of sound anomalies, and issuing graded warnings to users; setting the health index as ET, and comparing the health index ET with the health discrimination factors jp1 and jp2 to issue graded warnings to users. If ET is less than jp1, no health warning is issued to the user; if ET is greater than or equal to jp1 and ET is less than or equal to jp2, a first-level health warning is issued to the user; if ET is greater than jp2, a second-level health warning is issued to the user.

[0012] According to another aspect of the present application, there is provided an intelligent monitoring device for landscaping maintenance, comprising: An image processing unit is used to obtain a grayscale image of the plant canopy image, and to perform block processing on the grayscale image to obtain pixel blocks, and to classify each pixel block into a type; A strategy generation unit is used to construct an insect pest index based on the acquired near-infrared band reflectance, the grayscale image contrast of the plant canopy image, and the type of each pixel block, and to determine the insect pest status and generate a garden protection strategy based on the insect pest status; Environmental information acquisition unit, used to obtain soil parameters, meteorological parameters and plant physiological parameters within the monitoring period, The environmental factor construction unit is used to standardize the acquired soil parameters, meteorological parameters, and plant physiological parameters to quantify soil health, meteorological stress, and plant physiological responses, and to couple the quantified results to construct environmental factors, thereby judging environmental pressure and updating the generation process of garden protection strategies; The acoustic anomaly analysis unit is used to count the number of acoustic anomaly events based on the acoustic pulse signals obtained during the monitoring period, calculate the acoustic anomaly ratio, and determine the adjustment weight; The monitoring unit is used to construct a health index based on the pest index, environmental factors and the proportion of sound anomalies, and provide graded warnings to users.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, which stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the intelligent monitoring method for landscaping maintenance during operation.

[0014] The beneficial effects of the present invention are as follows: Through multi-dimensional data collection, intelligent analysis, and dynamic strategy adjustment, the present invention constructs a fully closed-loop monitoring system for landscaping maintenance. Image segmentation and pest index technology solve the problems of low efficiency and high error rate of traditional manual inspections; multi-parameter fusion and standardization technology breaks through data silos and achieves accurate quantification of environmental pressure; acoustic pulse monitoring expands the monitoring dimension and fills the gap in underground disease detection; health index and graded early warning mechanism promote the maintenance strategy from "passive response" to "active prevention." The overall solution significantly reduces manpower and resource consumption, improves the accuracy of pest control and environmental adaptation, and provides strong support for the intelligent and refined transformation of garden maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 Schematic diagram of the flow of the intelligent monitoring method for landscaping maintenance in this embodiment.

[0017] Figure 2 Schematic diagram of the flow of the method for generating a garden protection strategy in this embodiment.

[0018] Figure 3 Schematic diagram of the flow of the method for determining the environmental pressure in this embodiment.

[0019] Figure 4 This is a schematic diagram of the structure of the intelligent monitoring device for landscaping maintenance in this embodiment.

[0020] Figure 5 Schematic diagram of the structure of the electronic device of this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the following detailed description is illustrative rather than restrictive and should not be used to limit the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] Specifically, the intelligent monitoring method for garden greening maintenance described in this embodiment is applied to the field of health management of urban green spaces, parks and landscape plant communities to achieve real-time monitoring and dynamic evaluation of plant status; the monitoring method used in this application is based on the integration of pest index construction, environmental factor coupling analysis, acoustic abnormality event statistics and health index graded warning as the core, and through the collaborative processing and dynamic adjustment of weights of multi-source parameters (plant canopy characteristics, soil-meteorological indicators, acoustic pulse signals), it solves the problems of delayed pest identification, insufficient quantification of environmental pressure and lack of underground abnormality perception in traditional maintenance, thereby realizing graded prevention and control of garden plant health risks and intelligent generation of precise protection strategies.

[0024] Please continue reading Figure 1 As shown, the landscaping maintenance intelligent monitoring method further includes: Step S101 : obtaining a grayscale image of a plant canopy image within a monitoring period, dividing the grayscale image into blocks to obtain pixel blocks, and classifying each pixel block into different types.

[0025] Specifically, the grayscale image of the plant canopy image can be obtained by using a drone (such as the DJI Phantom series) equipped with a camera to take a vertical aerial photo of the plant canopy, with the height controlled at 1-3 meters from the top of the canopy, and converting the color image into a grayscale image through image processing software (such as ENVI, Photoshop or Python OpenCV library); this embodiment does not specifically limit the method for obtaining the grayscale image of the plant canopy image, and those skilled in the art can freely set it according to needs.

[0026] Specifically, the grayscale image is divided into blocks of n×n pixels each, and the variance of the pixel values of each pixel in the i-th pixel block is recorded as σ 2 i, if σ 2 If i is greater than the variance threshold, the pixel block is marked as a diseased area, otherwise, it is marked as a normal area.

[0027] Specifically, the variance of the pixel values of each pixel point in the i-th pixel block can be obtained through ImageJ. In this embodiment, there is no specific limitation on the method for obtaining the variance of the pixel values of each pixel point in the i-th pixel block, and those skilled in the art can freely set it according to needs.

[0028] For example, in this embodiment, the variance threshold may be set to 0.05, and n may be set to 16. In this embodiment, the settings of the variance threshold and n are not specifically limited, and those skilled in the art may freely set them according to needs.

[0029] Please continue reading Figure 1 As shown, the landscaping maintenance intelligent monitoring method further includes: Step S102 : constructing an insect pest index based on the near-infrared band reflectance, the grayscale image contrast of the plant canopy image and the type of each pixel block obtained during the monitoring period, judging the insect pest status, and generating a garden protection strategy based on the insect pest status.

[0030] Exemplarily, the near-infrared band reflectance can be obtained by a multispectral camera or a near-infrared dedicated sensor, and the grayscale image contrast of the plant canopy image can be obtained by Agisoft Metashape or ImageJ. In this embodiment, there is no specific limitation on the method of obtaining the near-infrared band reflectance and the grayscale image contrast of the plant canopy image, and those skilled in the art can freely set them according to their needs.

[0031] Specifically, by integrating multi-source data such as near-infrared reflectance and grayscale image contrast to construct a pest index, we can break through the limitations of traditional single parameter detection, achieve dynamic quantitative assessment of pest status, and provide a scientific basis for precise prevention and control.

[0032] See also Figure 2 As shown, the method for generating the garden protection strategy includes: Step S201 : constructing an insect pest index based on the near-infrared band reflectance, the grayscale image contrast of the plant canopy image, and the type of each pixel block, and judging the insect pest status.

[0033] Specifically, the pest index D is constructed based on the near-infrared band reflectance Rn, the red light reflectance reference value Rr, the grayscale image contrast Ct of the plant canopy image, the texture contrast reference value Cr, and each pixel block type. D is set as (Rn-Rr) / (Rn+Rr)×ln(1+Ct / Cr)×(k0 / k1), where k0 is the number of pixel blocks marked as diseased areas and k1 is the total number of pixel blocks. When the pest index D is greater than the pest discrimination factor d1, the pest state is determined to be an infected state; otherwise, the pest state is determined to be a healthy state.

[0034] For example, the red light reflectance reference value Rr can be set to 0.2, the texture contrast reference value Cr can be set to 0.5, and the pest discrimination factor can be set to 0.5; in this embodiment, there is no specific limitation on the setting of the red light reflectance reference value, the texture contrast reference value and the pest discrimination factor, and those skilled in the art can freely set them according to their needs.

[0035] Specifically, by combining the near-infrared band reflectance and the red light reflectance benchmark values, the spectral characteristic changes of plants affected by pests can be effectively captured, thereby enhancing the sensitivity of pest identification. By introducing the grayscale image contrast and texture contrast benchmark values, the degree of pest damage can be quantified through the differences in image features, avoiding the one-sidedness of relying solely on a single indicator.

[0036] Please continue reading Figure 2 As shown, the method for generating the garden protection strategy further includes: Step S202: Generate a garden protection strategy based on the pest status.

[0037] Specifically, when the pest state is an infected state, the spraying amount is set to PY, and the expression of PY is PY=p0×(D-d1), where p0 is the preset spraying amount, and the spraying amount is used as a garden protection strategy.

[0038] For example, for common garden plants (such as trees and shrubs), the preset spraying amount can be set to 2.0 mL / m², and for herbaceous plants (such as lawns), it can be reduced to 1.5 mL / m²; this embodiment does not specifically limit the setting of the preset spraying amount, and those skilled in the art can freely set it according to their needs.

[0039] Specifically, the amount of pesticide sprayed by the plant protection drone can be dynamically adjusted according to the pest index to achieve on-demand application of pesticides, reducing pesticide waste and environmental pollution.

[0040] See also Figure 1 As shown, it is a flow chart of the intelligent monitoring method for landscaping maintenance of this embodiment, including: Step S103, obtaining soil parameters, meteorological parameters and plant physiological parameters within the monitoring period, wherein the soil parameters include soil moisture and soil electrical conductivity, the meteorological parameters include ambient temperature and light intensity, and the plant physiological parameters include plant transpiration rate and leaf surface temperature.

[0041] Specifically, by integrating the real-time collection of soil, meteorological and plant physiological parameters, a multi-dimensional monitoring network is constructed to comprehensively reflect the growth environment and health status of plants, providing data support for comprehensive decision-making.

[0042] Specifically, the soil parameters can be collected by using a capacitive soil moisture sensor (buried at a depth of 20 cm, sampling frequency of 1 Hz), and soil conductivity can be simultaneously measured to assess the risk of salinization; the meteorological parameters can be collected and obtained through intelligent sensors, and the plant physiological parameters can be obtained by monitoring the plant transpiration rate through a stem flow sensor (heat dissipation method), and the leaf surface temperature can be obtained in combination with an infrared thermometer; this embodiment does not specifically limit the method of obtaining each parameter, and those skilled in the art can freely set it according to their needs.

[0043] Please continue reading Figure 1 As shown, the landscaping maintenance intelligent monitoring method includes: In step S104, the acquired soil parameters, meteorological parameters, and plant physiological parameters are standardized to quantify soil health, meteorological stress, and plant physiological responses, and the quantified results are data-coupled to construct environmental factors, thereby judging environmental pressure and updating the generation process of garden protection strategies.

[0044] Specifically, standardization is used to eliminate dimensional differences in multiple parameters, and soil health, meteorological stress and plant physiological response data are integrated to construct a unified environmental factor and enhance the comprehensive analysis capability of complex environmental pressures.

[0045] See also Figure 2 As shown, the method for determining the environmental pressure includes: Step S301: standardize the acquired soil parameters, meteorological parameters and plant physiological parameters.

[0046] Specifically, the obtained soil moisture is ratio analyzed with the humidity threshold to obtain the standardized soil moisture Sm, the obtained soil conductivity is ratio analyzed with the conductivity threshold to obtain the standardized soil conductivity Cs, the obtained ambient temperature is ratio analyzed with the temperature threshold to obtain the standardized ambient temperature Ht, the obtained light intensity is ratio analyzed with the light threshold to obtain the standardized light intensity Gm, the leaf surface temperature threshold is ratio analyzed with the leaf temperature threshold to obtain the standardized leaf surface temperature Yt, and the obtained plant transpiration rate is ratio analyzed with the transpiration rate threshold to obtain the standardized plant transpiration rate Et.

[0047] Specifically, parameters of different dimensions are converted into standardized ratios to facilitate horizontal comparison and weight allocation, thereby improving data comparability.

[0048] Specifically, the soil parameters, meteorological parameters, and plant physiological parameters obtained in this embodiment are average values of the data collected during the monitoring period.

[0049] For example, the soil moisture threshold can be set to 80%, the conductivity threshold can be set to 2dS / m, the ambient temperature threshold can be set to 50°C, the light intensity threshold can be set to 1200μmol / m² / s, the leaf surface temperature threshold can be set to 55°C, and the plant transpiration rate threshold can be set to 10mmol / m² / s.

[0050] See also Figure 2 As shown, the method for determining the environmental pressure further includes: Step S302 , integrating standardized soil moisture and soil electrical conductivity to quantify soil health, integrating standardized ambient temperature and light intensity to quantify meteorological stress, and integrating standardized leaf surface temperature and plant transpiration rate to quantify plant physiological responses.

[0051] Specifically, the standardized soil moisture Sm and soil electrical conductivity Cs are integrated to quantify soil health KA, which is expressed as: ; The α is the weight factor, z1 is the humidity deviation coefficient, and bs is the conductivity saturation coefficient; The standardized ambient temperature Ht and light intensity Gm are integrated to quantify the meteorological stress QA. The expression of QA is QA=Ht / lg(1+Gm); The standardized leaf surface temperature Yt and plant transpiration rate Et are integrated to quantify the plant physiological response ZA. The expression of ZA is ZA=ln(3Yt+1) / ln4+Et / (Yt+η), where η is the gain coefficient.

[0052] For example, in this embodiment, the weight factor can be set to 0.6, the humidity deviation coefficient can be set to 0.3, the conductivity saturation coefficient can be set to 0.86, and the gain coefficient can be set to 0.8. In this embodiment, there is no specific limitation on the setting of the weight factor, humidity deviation coefficient, conductivity saturation coefficient and gain coefficient, and those skilled in the art can freely set them according to their needs.

[0053] Specifically, soil moisture and electrical conductivity are integrated to quantify soil health, soil fertility and salinization level are comprehensively assessed to avoid misleading decision-making by a single parameter, temperature and light intensity are combined to quantify meteorological stress, and the negative impact of extreme weather on plants is dynamically identified. Plant physiological responses are quantified through leaf temperature and transpiration rate to intuitively reflect the plant's ability to adapt to the environment.

[0054] Please continue reading Figure 2 As shown, the method for determining the environmental pressure further includes: Step S303 : Data coupling of the quantified soil health, meteorological stress, and plant physiological response is performed to construct environmental factors, thereby determining environmental pressure and updating the generation process of the garden protection strategy.

[0055] Specifically, the quantified soil health KA, meteorological stress QA, and plant physiological response ZA are data coupled to construct the environmental factor H. The expression of H is H=w1×KA+w2×QA+w3×ZA, where w1 is the soil weight, w2 is the meteorological weight, and w3 is the plant physiological weight, and w1+w2+w3=1; The environmental factor H is compared with the environmental discrimination factor h1 to judge the environmental pressure. If the environmental factor H is less than or equal to the environmental discrimination factor h1, the environmental pressure is judged to be in a normal state. If the environmental factor H is greater than the environmental discrimination factor h1, the environmental pressure is judged to be in a stress state. At the same time, the generation process of the garden protection strategy is updated, and the pest discrimination factor is updated to d2. The expression of d2 is d2=d1×{1-adjustment weight×exp[3×(H-h1)-3]}.

[0056] For example, in this embodiment, the soil weight, meteorological weight, and plant physiological weight can be set to 0.4, 0.3, and 0.3, respectively, and the environmental discrimination factor can be set to 0.36.

[0057] Specifically, the pest identification threshold is dynamically adjusted based on environmental factors to make the protection strategy more in line with actual environmental changes and enhance the adaptability of the system.

[0058] Please continue reading Figure 1 As shown, the landscaping maintenance intelligent monitoring method further includes: Step S105 : counting the number of acoustic abnormality events based on the acoustic pulse signals acquired during the monitoring period to calculate the acoustic abnormality ratio and determine the adjustment weight.

[0059] Specifically, the acoustic pulse signal can be captured by a piezoelectric ceramic sensor (buried at a depth of 30 cm). In this embodiment, there is no specific limitation on the acquisition of the acoustic pulse signal, and those skilled in the art can freely configure it according to their needs.

[0060] Specifically, the number of times the pulse signal generated during the monitoring period is greater than the sound discrimination factor is counted as the number of sound abnormal events, and the signal ratio of the sound abnormality times is calculated as the sound abnormality ratio SY. When the sound abnormality ratio SY is greater than the preset ratio L, the adjustment weight is determined to be M1, and the expression of M1 is m1=m0×[1+γ×(SY-L) 2 ], m0 is the preset adjustment weight, γ is the correction factor, when the acoustic abnormality proportion SY is less than or equal to the preset proportion L, the adjustment weight is determined to be M2, and the expression of M2 is M2=m0.

[0061] For example, in this embodiment, the preset ratio L may be set to 0.15, the correction factor may be set to 0.5, and the preset adjustment weight may be set to 0.2.

[0062] Specifically, piezoelectric sensors are used to capture acoustic signals of root activity or pests gnawing in the soil to identify potential underground threats; the weights of environmental factors are dynamically corrected according to the proportion of acoustic anomalies, so that the system can still maintain high-precision judgment in complex environments.

[0063] Please continue reading Figure 1 As shown, the landscaping maintenance intelligent monitoring method further includes: Step S106: construct a health index based on the pest index, environmental factors and the proportion of abnormal sound, and issue a graded warning to the user.

[0064] Specifically, the health index is set to ET, and the expression of ET is ET=q1×D+q2×H+q3×SY, where q1 is the pest weight, q2 is the environment weight, and q3 is the sound abnormality weight, q1+q2+q3=1; The health index ET is compared with the health discrimination factors jp1 and jp2 to provide graded warnings to the user. If ET is less than jp1, no health warning is given to the user. If ET is greater than or equal to jp1 and ET is less than or equal to jp2, a first-level health warning is given to the user. If ET is greater than jp2, a second-level health warning is given to the user.

[0065] For example, in this embodiment, the pest weight may be set to 0.5, the environment weight may be set to 0.3, the sound abnormality weight may be set to 0.2, the health discrimination factor jp1 may be set to 0.3, and the health discrimination factor jp2 may be set to 0.5.

[0066] Specifically, by integrating pest, environmental and acoustic data to generate a health index, and based on a graded early warning mechanism, it helps managers prioritize high-risk areas and optimize resource allocation and emergency response efficiency.

[0067] See also Figure 4 As shown, the landscaping maintenance intelligent monitoring device includes: The image processing unit 401 is used to obtain a grayscale image of the plant canopy image, and perform block processing on the grayscale image to obtain pixel blocks, and classify each pixel block into a type; A strategy generating unit 402 is configured to construct an insect pest index based on the acquired near-infrared band reflectance, the grayscale image contrast of the plant canopy image, and the type of each pixel block, determine the insect pest status, and generate a garden protection strategy based on the insect pest status; The environmental information acquisition unit 403 is used to obtain soil parameters, meteorological parameters and plant physiological parameters within the monitoring period. The environmental factor construction unit 404 is used to standardize the acquired soil parameters, meteorological parameters, and plant physiological parameters to quantify soil health, meteorological stress, and plant physiological response, and to couple the quantified results to construct environmental factors, thereby determining environmental pressure and updating the generation process of garden protection strategies; The acoustic anomaly analysis unit 405 is used to count the number of acoustic anomaly events based on the acoustic pulse signals obtained during the monitoring period, calculate the acoustic anomaly ratio, and determine the adjustment weight; The monitoring unit 406 is used to construct a health index based on the pest index, environmental factors and the proportion of abnormal sounds, and to provide graded warnings to users.

[0068] The embodiment of the present application also provides an electronic device for executing the intelligent monitoring method for landscaping maintenance. Figure 5As shown, the electronic device includes: a processor 501 , a memory 502 , a communication interface 503 and a system bus 504 . The processor includes at least one of a central processing unit (CPU), a graphics processing unit (GPU) or a field programmable gate array (FPGA), and is configured to call computer programs and multi-source monitoring data (including plant canopy images, environmental parameters and acoustic pulse signals) stored in a memory, execute the algorithm flow of grayscale image block processing, pest index calculation and health index fusion analysis, and generate garden protection strategy control instructions; the memory includes random access memory (RAM) and / or non-volatile memory (NVM), and the NVM includes high-speed flash memory or solid-state drive (SSD) for storing computer programs, image processing intermediate data (such as pixel block variance calculation results) and historical monitoring parameter sets (including soil-meteorological time series and pest index dynamic records); the communication interface includes a wired communication module and a wireless communication module, the wired communication module supports Modbus or RS-485 protocol and is used to connect to soil moisture sensors, multispectral cameras and acoustic sensor networks; the wireless communication module supports LoRa, NB-IoT or 4G / 5G cellular communication protocols and is used to transmit pest index warning information to a remote garden management platform and receive drone control instructions at the same time; the system bus adopts PCIe Gen4 or AXI4 bus architecture enables parallel data exchange and real-time synchronization between processors, multi-channel image acquisition cards and communication interfaces.

[0069] The present application also provides a computer-readable storage medium having computer program code stored thereon. When the program code is loaded into a processor via an embedded system carrier board and written to a cache area via a system bus, the processor executes the code to perform the following functions: plant canopy image segmentation and lesion detection, cross-modal fusion of near-infrared reflectance and grayscale contrast, standardized coupled calculation of soil, meteorological, and physiological parameters, and generation of graded warning signals based on health index thresholds.

[0070] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A landscaping maintenance intelligent monitoring method, characterized in that: include: Obtaining a grayscale image of the plant canopy image, dividing the grayscale image into blocks to obtain pixel blocks, and classifying each pixel block into different types; Based on the acquired near-infrared band reflectance, the grayscale image contrast of the plant canopy image, and the type of each pixel block, an insect pest index is constructed, and the insect pest status is determined, and a garden protection strategy is generated based on the insect pest status; Standardize the acquired soil parameters, meteorological parameters, and plant physiological parameters to quantify soil health, meteorological stress, and plant physiological responses, and determine environmental stress, thereby updating the generation process of garden protection strategies; The number of acoustic abnormality events is counted based on the acquired acoustic pulse signals to calculate the acoustic abnormality ratio and determine the adjustment weight.

2. The intelligent monitoring method for landscaping maintenance according to claim 1, characterized in that: The grayscale image is divided into blocks of n×n pixels each, and the variance of the pixel values of each pixel in the i-th pixel block is denoted as σ 2 i, if σ 2 If i is greater than the variance threshold, the pixel block is marked as a diseased area, otherwise, it is marked as a normal area.

3. The intelligent monitoring method for landscaping maintenance according to claim 2, characterized in that: The pest index D is constructed based on the near-infrared band reflectance Rn, the red light reflectance reference value Rr, the grayscale image contrast Ct of the plant canopy image, the texture contrast reference value Cr, and the type of each pixel block. When the pest index D is greater than the pest discrimination factor d1, the pest state is judged to be infected; otherwise, the pest state is judged to be healthy. When the pest status is infected, set the spray rate to PY and use the spray rate as a garden protection strategy.

4. The intelligent monitoring method for landscaping maintenance according to claim 3, characterized in that: Also includes: Obtain soil parameters, meteorological parameters and plant physiological parameters within the monitoring period; The acquired soil parameters, meteorological parameters and plant physiological parameters were standardized to obtain the standardized soil moisture Sm, soil electrical conductivity Cs, ambient temperature Ht, light intensity Gm, leaf surface temperature Yt and plant transpiration rate Et.

5. The intelligent monitoring method for landscaping maintenance according to claim 4, characterized in that: Standardized soil moisture Sm and soil electrical conductivity Cs were integrated to quantify soil health KA, standardized ambient temperature Ht and light intensity Gm were integrated to quantify meteorological stress QA, and standardized leaf surface temperature Yt and plant transpiration rate Et were integrated to quantify plant physiological response ZA.

6. The intelligent monitoring method for landscaping maintenance according to claim 1, characterized in that: The quantified soil health KA, meteorological stress QA and plant physiological response ZA are coupled to construct the environmental factor H; The environmental factor H is compared with the environmental discrimination factor h1 to judge the environmental pressure. If the environmental factor H is less than or equal to the environmental discrimination factor h1, the environmental pressure is judged to be in a normal state. If the environmental factor H is greater than the environmental discrimination factor h1, the environmental pressure is judged to be in a stress state. At the same time, the generation process of the garden protection strategy is updated, and the pest discrimination factor is updated to d2. The expression of d2 is d2=d1×{1-adjustment weight×exp[3×(H-h1)-3]}.

7. The intelligent monitoring method for landscaping maintenance according to claim 6, characterized in that: The number of times the internal pulse signal is greater than the sound discrimination factor during the monitoring period is counted as the number of sound abnormality events, and the signal ratio of the sound abnormality number is calculated as the sound abnormality ratio SY. When the sound abnormality ratio SY is greater than the preset ratio L, the adjustment weight is determined to be M1. When the sound abnormality ratio SY is less than or equal to the preset ratio L, the adjustment weight is determined to be M2.

8. The intelligent monitoring method for landscaping maintenance according to claim 7, characterized in that: Also includes: Build a health index based on pest index, environmental factors and the proportion of abnormal sound, and provide users with graded warnings; The health index is set to ET, and the health index ET is compared with the health discrimination factors jp1 and jp2 to provide a graded warning to the user. If ET is less than jp1, no health warning is given to the user. If ET is greater than or equal to jp1 and ET is less than or equal to jp2, a first-level health warning is given to the user. If ET is greater than jp2, a second-level health warning is given to the user.

9. An intelligent monitoring device for landscaping maintenance, characterized in that: include: An image processing unit is used to obtain a grayscale image of the plant canopy image, and to perform block processing on the grayscale image to obtain pixel blocks, and to classify each pixel block into a type; A strategy generation unit is used to construct an insect pest index based on the acquired near-infrared band reflectance, the grayscale image contrast of the plant canopy image, and the type of each pixel block, and to determine the insect pest status and generate a garden protection strategy based on the insect pest status; Environmental information acquisition unit, used to obtain soil parameters, meteorological parameters and plant physiological parameters within the monitoring period, The environmental factor construction unit is used to standardize the acquired soil parameters, meteorological parameters, and plant physiological parameters to quantify soil health, meteorological stress, and plant physiological responses, and to couple the quantified results to construct environmental factors, thereby judging environmental pressure and updating the generation process of garden protection strategies; The acoustic anomaly analysis unit is used to count the number of acoustic anomaly events based on the acoustic pulse signals obtained during the monitoring period, calculate the acoustic anomaly ratio, and determine the adjustment weight; The monitoring unit is used to construct a health index based on the pest index, environmental factors and the proportion of sound anomalies, and provide graded warnings to users.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the landscaping maintenance intelligent monitoring method according to any one of claims 1 to 8 during operation.

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