CN117079140A - Landscape plant planting management method - Google Patents
Landscape plant planting management method Download PDFInfo
- Publication number
- CN117079140A CN117079140A CN202311321580.XA CN202311321580A CN117079140A CN 117079140 A CN117079140 A CN 117079140A CN 202311321580 A CN202311321580 A CN 202311321580A CN 117079140 A CN117079140 A CN 117079140A
- Authority
- CN
- China
- Prior art keywords
- information
- plant
- landscape
- planting
- image information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007726 management method Methods 0.000 title claims abstract description 20
- 230000001575 pathological effect Effects 0.000 claims abstract description 57
- 238000000034 method Methods 0.000 claims abstract description 49
- 238000012423 maintenance Methods 0.000 claims abstract description 26
- 230000012010 growth Effects 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 11
- 241000196324 Embryophyta Species 0.000 claims description 188
- 230000035800 maturation Effects 0.000 claims description 7
- 230000010198 maturation time Effects 0.000 claims description 7
- 230000008635 plant growth Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 4
- 201000010099 disease Diseases 0.000 description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 4
- 239000003086 colorant Substances 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000003672 processing method Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000000465 moulding Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008636 plant growth process Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 1
- 230000016776 visual perception Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/54—Extraction of image or video features relating to texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30188—Vegetation; Agriculture
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Emergency Management (AREA)
- Tourism & Hospitality (AREA)
- Geometry (AREA)
- Human Resources & Organizations (AREA)
- Radiology & Medical Imaging (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Economics (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Image Analysis (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The application is applicable to the technical field of intelligent planting, and provides a landscape plant planting management method, which comprises the following steps: receiving a landscape display picture, climate information and display time uploaded by a user, wherein the landscape display picture comprises a plurality of landscape areas, and each landscape area comprises color information and size information; determining a plurality of plant types according to the color information, the climate information and the display time, wherein each plant type corresponds to a landscape area; determining the planting quantity of plant types in each landscape area according to the size information; the planting maintenance information and the pathological characteristic information of each plant type are called; image information in the growth process of landscape plants is collected, and whether corresponding pathological feature information exists in the image information is identified. According to the application, a user can directly know which plants are planted in each landscape area, and can perform correct planting operation according to planting maintenance information, so that the final plant landscape presentation effect is ensured.
Description
Technical Field
The application relates to the technical field of intelligent planting, in particular to a landscape plant planting management method.
Background
In order to further improve the ornamental value of landscape plants, various types of landscape plants with various colors can be planted on a large scale in places such as parks, gardens and the like, so that the landscape plants in pieces can form unique shapes. At present, in order to make landscape plants plant the molding, often need to hire professional gardener to instruct and select which plant types, instruct how to go to plant, the cost is higher, and the flexibility is relatively poor. Accordingly, there is a need to provide a landscape plant planting management method, which aims to solve the above problems.
Disclosure of Invention
Aiming at the defects in the prior art, the application aims to provide a landscape plant planting management method for solving the problems in the background art.
The application is realized in such a way that a landscape plant is planted and managed, the method comprises the following steps:
receiving a landscape display picture, climate information and display time uploaded by a user, wherein the landscape display picture comprises a plurality of landscape areas, and each landscape area comprises color information and size information;
determining a plurality of plant types according to the color information, the climate information and the display time, wherein each plant type corresponds to a landscape area;
determining the planting quantity of plant types in each landscape area according to the size information;
the planting maintenance information and the pathological characteristic information of each plant type are called;
image information in the growth process of landscape plants is collected, each image information corresponds to a plant type, whether corresponding pathological characteristic information exists in the image information is identified, and when the corresponding pathological characteristic information exists, early warning information is generated.
As a further scheme of the application: the step of determining a plurality of plant species according to the color information, the climate information and the display time specifically comprises the following steps:
inputting color information, climate information and display time into a landscape plant library, wherein the landscape plant library comprises a plurality of plant types, and each plant type is correspondingly marked with color, growth climate, maturation time, maturation height and unit price;
outputting plant types with the same color, growth climate and maturation time, classifying the output plant types according to the landscape area, and marking maturation height and unit price on each plant type;
and receiving plant type selection information input by a user, and determining a plurality of plant types.
As a further scheme of the application: the step of determining the planting number of the plant species in each landscape area according to the size information specifically comprises the following steps:
determining the planting area of each landscape area according to the size information;
the planting density of each plant species is retrieved, and the planting quantity of each plant species is determined according to the planting area and the planting density.
As a further scheme of the application: the step of calling the planting maintenance information and the pathological characteristic information of each plant type specifically comprises the following steps:
inputting plant types and climate information into a planting and curing library, wherein the planting and curing library comprises a plurality of plant types, each plant type corresponds to one pathological characteristic information and a plurality of planting and curing information, and each planting and curing information corresponds to a climate characteristic;
and outputting planting maintenance information and pathological characteristic information matched with the plant types and the climate information.
As a further scheme of the application: the step of identifying whether the corresponding pathological characteristic information exists in the image information specifically comprises the following steps:
determining the pathological characteristic information of the plant type corresponding to the image information, wherein the pathological characteristic information comprises color characteristics, texture characteristics and shape characteristics;
determining color information of pixel points in image information, and judging whether color features exist in the image information according to the color information;
extracting actual texture information of image information, and judging whether texture features exist in the image information according to the actual texture information;
extracting actual shape information of image information, and judging whether the image information has shape characteristics according to the actual shape information;
when color features, texture features or shape features exist in the image information, judging that corresponding pathological feature information exists in the image information; otherwise, no pathological characteristic information exists.
As a further scheme of the application: the method for extracting the actual texture information of the image information includes, but is not limited to, a signal processing method, a geometric method and a model method.
As a further scheme of the application: the method for extracting the actual shape information of the image information includes, but is not limited to, a boundary feature method, a fourier shape descriptor method and a geometric parameter method.
Another object of the present application is to provide a landscape plant planting management system, the system comprising:
the display picture uploading module is used for receiving landscape display pictures, climate information and display time uploaded by a user, wherein the landscape display pictures comprise a plurality of landscape areas, and each landscape area comprises color information and size information;
the plant species determining module is used for determining a plurality of plant species according to the color information, the climate information and the display time, and each plant species corresponds to one landscape area;
a planting number determining module for determining the planting number of the plant species in each landscape area according to the size information;
the planting maintenance information module is used for retrieving planting maintenance information and pathological characteristic information of each plant type;
the plant growth monitoring module is used for collecting image information in the growth process of landscape plants, each image information corresponds to a plant type, identifying whether corresponding pathological characteristic information exists in the image information, and generating early warning information when the corresponding pathological characteristic information exists.
Compared with the prior art, the application has the beneficial effects that:
according to the application, a plurality of plant types can be determined according to the color information, the climate information and the display time, so that a user can directly know which plants are planted in each landscape area; the planting quantity of the plant types in each landscape area can be determined, the quantitative purchasing is convenient for a user, and meanwhile, the planting maintenance information and the pathological characteristic information of each plant type can be called, so that the user can perform correct planting operation according to the planting maintenance information, and the presentation effect of the final plant landscape is ensured to a certain extent; in addition, image information in the growth process of landscape plants is acquired, whether the pathological characteristic information exists in the image information is identified, and when the pathological characteristic information exists, early warning information is generated to remind a user to remedy in time. The application has low use cost and high flexibility, and does not need to apply professional gardeners.
Drawings
FIG. 1 is a flow chart of a method of landscape plant planting management.
FIG. 2 is a flow chart of a landscape plant species determination method.
FIG. 3 is a flow chart of a method for landscape plant planting management for determining a planting number of plant species.
FIG. 4 is a flow chart of a landscape plant planting management method for retrieving planting and maintenance information and pathological feature information.
Fig. 5 is a flowchart of identifying image information in a landscape plant growing management method.
Fig. 6 is a schematic structural diagram of a landscape plant planting management system.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Specific implementations of the application are described in detail below in connection with specific embodiments.
As shown in fig. 1, an embodiment of the present application provides a landscape plant planting management method, which includes the following steps:
s100, receiving landscape display pictures, climate information and display time uploaded by a user, wherein the landscape display pictures comprise a plurality of landscape areas, and each landscape area comprises color information and size information;
s200, determining a plurality of plant types according to the color information, the climate information and the display time, wherein each plant type corresponds to a landscape area;
s300, determining the planting quantity of plant types in each landscape area according to the size information;
s400, planting maintenance information and pathological characteristic information of each plant type are called;
s500, collecting image information in the growth process of landscape plants, wherein each image information corresponds to a plant type, identifying whether corresponding pathological feature information exists in the image information, and generating early warning information when the corresponding pathological feature information exists.
It should be noted that, at present, in order to make landscape plants plant the molding, often need to apply professional gardeners to guide which plant types are selected, guide how to plant, and have higher cost and poorer flexibility.
In the embodiment of the application, firstly, a user needs to draw an effect diagram which is finally wanted to be presented, namely, the user needs to determine a landscape display picture, and determine the display time, for example, 8 months to 9 months, and the user also needs to determine the climate information of a planting area, wherein the landscape display picture comprises a plurality of landscape areas, and each landscape area corresponds to color information and size information; then, the embodiment of the application automatically determines a plurality of plant types according to the color information, the climate information and the display time, wherein each plant type corresponds to one landscape area, so that a user can directly know which plants are planted in each landscape area; then, the embodiment of the application also determines the planting quantity of the plant types in each landscape area according to the size information, is convenient for users to quantitatively purchase, and can simultaneously call the planting maintenance information and the pathological characteristic information of each plant type, so that the users can perform correct planting operation according to the planting maintenance information, and the presentation effect of the final plant landscape is ensured to a certain extent; in addition, a camera is required to be installed in each landscape area, image information in the growth process of landscape plants is acquired through the camera, each image information corresponds to a plant type, the embodiment of the application can automatically call the pathological characteristic information corresponding to the plant type in the image information, identify whether the pathological characteristic information exists in the image information, and when the pathological characteristic information exists, the situation that the plants in the landscape area are likely to have problems in the growth process is indicated, early warning information is generated, the early warning information is sent to a user terminal, and a user is reminded of timely remedying.
As shown in fig. 2, as a preferred embodiment of the present application, the step of determining a plurality of plant species according to the color information, the climate information and the display time specifically includes:
s201, inputting color information, climate information and display time into a landscape plant library, wherein the landscape plant library comprises a plurality of plant types, and each plant type is correspondingly marked with color, growth climate, maturation time, maturation height and unit price;
s202, outputting plant types with the same colors, growth climates and maturity time, classifying the output plant types according to the landscape area, and marking maturity height and unit price on each plant type;
s203, receiving plant type selection information input by a user, and determining a plurality of plant types.
In the embodiment of the application, a landscape plant library is established in advance, the landscape plant library comprises a large number of plant types, and each plant type is correspondingly marked with a color, a growing climate, a maturation time, a maturation height and a unit price, wherein the color refers to the color of the plant when maturing or the color of the plant in the flowering period. The color information, the climate information and the display time are input into a landscape plant library, the plant types conforming to the color, the growth climate and the maturity time are automatically screened out according to the parameters, the output plant types are classified according to landscape areas, each landscape area corresponds to a plurality of plant types, and each plant type is marked with a maturity height and a unit price, so that a user can further select a proper plant type according to the maturity height and the unit price, at the moment, the user needs to input plant type selection information, and the embodiment of the application can finally determine a plurality of plant types according to the plant type selection information. It is readily understood that landscape plant libraries require maintenance and renewal by the relevant staff.
As shown in fig. 3, as a preferred embodiment of the present application, the step of determining the number of plants of the plant species in each landscape area according to the size information specifically includes:
s301, determining the planting area of each landscape area according to the size information;
s302, the planting density of each plant type is called, and the planting quantity of each plant type is determined according to the planting area and the planting density.
In the embodiment of the application, the planting area of each landscape area is determined according to the dimension information marked on the landscape area, specifically, the outline and the dimension information of the landscape area can be transferred into mechanical drawing software, and the planting area of the landscape area can be directly obtained; the planting density of each plant species is then retrieved, the number of plants per plant species is determined based on the planting area and the planting density, and the landscape plant library further includes the planting density of each plant species.
As shown in fig. 4, as a preferred embodiment of the present application, the step of retrieving the planting and curing information and the pathological feature information of each plant species specifically includes:
s401, inputting plant types and climate information into a planting and curing library, wherein the planting and curing library comprises a plurality of plant types, each plant type corresponds to one pathological characteristic information and a plurality of planting and curing information, and each planting and curing information corresponds to a climate characteristic;
s402, outputting planting and maintenance information and pathological characteristic information matched with the plant types and the climate information.
In the embodiment of the application, a planting and curing library is established in advance, the planting and curing library comprises a large number of plant types, each plant type corresponds to one pathological characteristic information and a plurality of planting and curing information, and each planting and curing information corresponds to a climatic characteristic, namely the planting and curing information of the same plant under different climatic environments is different. Plant species and climate information are input into a planting and curing warehouse, planting and curing information and pathological characteristic information matched with the plant species and climate information can be automatically output, and the method is scientific and accurate.
As shown in fig. 5, as a preferred embodiment of the present application, the step of identifying whether the corresponding pathological feature information exists in the image information specifically includes:
s501, determining the pathological characteristic information of the plant type corresponding to the image information, wherein the pathological characteristic information comprises color characteristics, texture characteristics and shape characteristics;
s502, determining color information of pixel points in the image information, and judging whether color features exist in the image information according to the color information;
s503, extracting actual texture information of the image information, and judging whether texture features exist in the image information according to the actual texture information;
s504, extracting actual shape information of the image information, and judging whether the image information has shape features according to the actual shape information;
s505, when color features, texture features or shape features exist in the image information, judging that corresponding pathological feature information exists in the image information; otherwise, no pathological characteristic information exists.
In the embodiment of the application, in order to determine whether a plant has a disease state problem in the growth process, the color characteristics, texture characteristics and shape characteristics of the plant type corresponding to the image information are required to be determined, then the color information of the pixel points in the image information is determined, and whether the image information has the color characteristics is determined according to the color information; meanwhile, the actual texture information of the image information is extracted, whether the image information has texture features is judged according to the actual texture information, and the method for extracting the actual texture information of the image information comprises, but is not limited to, a signal processing method, a geometric method and a model method, wherein the signal processing method can be used: gray level co-occurrence matrix, tamura texture feature, autoregressive texture model and the like, wherein the extraction and matching of the gray level co-occurrence matrix feature mainly depend on four parameters of energy, inertia, entropy and relativity; tamura texture features propose 6 attributes, namely roughness, contrast, orientation, line image, regularity and coarseness, based on human visual perception psychology study of texture; an autoregressive texture model is an example of an application of the Markov random field model. The geometric method is a texture feature analysis method based on the theory of texture elements (basic texture elements), and in the geometric method, two algorithms are commonly used: voroni tessellation and structuring. The model method is based on a structural model of an image, and adopts parameters of the model as texture features. The embodiment of the application also extracts the actual shape information of the image information, judges whether the image information has the shape feature according to the actual shape information, and the method for extracting the actual shape information of the image information comprises, but is not limited to, a boundary feature method, a Fourier shape descriptor method and a geometric parameter method, wherein the boundary feature method acquires the shape parameter of the image through the description of the boundary feature, and the Hough transformation detection parallel straight line method and the boundary direction histogram method are classical methods. The basic idea of the fourier shape descriptor method is to use fourier transform of object boundaries as shape description, convert two-dimensional problem into one-dimensional problem by using the closeness and periodicity of region boundaries, and derive three shape expressions from boundary points, namely curvature function, centroid distance and complex coordinate function. The geometric parameter method is characterized by simpler region characterization method.
As shown in fig. 6, an embodiment of the present application further provides a landscape plant planting management system, the system including:
the display picture uploading module 100 is configured to receive a landscape display picture, climate information and display time uploaded by a user, where the landscape display picture includes a plurality of landscape areas, and each landscape area includes color information and size information;
a plant species determining module 200, configured to determine a plurality of plant species according to the color information, the climate information and the display time, where each plant species corresponds to a landscape area;
a planting number determining module 300 for determining a planting number of plant species in each landscape area according to the size information;
a plant maintenance information module 400 for retrieving plant maintenance information and pathological feature information of each plant species;
the plant growth monitoring module 500 is configured to collect image information in a landscape plant growth process, each image information corresponds to a plant type, identify whether corresponding pathological feature information exists in the image information, and generate early warning information when the corresponding pathological feature information exists.
As a preferred embodiment of the present application, the plant species determination module 200 includes:
the related information input unit is used for inputting color information, climate information and display time into the landscape plant library, wherein the landscape plant library comprises a plurality of plant types, and each plant type is correspondingly marked with color, growth climate, maturation time, maturation height and unit price;
a plant species output unit for outputting plant species conforming to colors, growth climates, and maturity times, classifying the output plant species according to a landscape area, each plant species being marked with a maturity height and a unit price;
and the plant type determining unit is used for receiving plant type selection information input by a user and determining a plurality of plant types.
As a preferred embodiment of the present application, the planting number determining module 300 includes:
a planting area determining unit for determining a planting area of each landscape area according to the size information;
and the planting quantity determining unit is used for retrieving the planting density of each plant type and determining the planting quantity of each plant type according to the planting area and the planting density.
As a preferred embodiment of the present application, the planting and curing information module 400 includes:
the plant species and climate information input unit is used for inputting plant species and climate information into the plant maintenance library, the plant maintenance library comprises a plurality of plant species, each plant species corresponds to one pathological characteristic information and a plurality of plant maintenance information, and each plant maintenance information corresponds to a climate characteristic;
and the maintenance information output unit is used for outputting planting maintenance information and pathological characteristic information matched with the plant types and the climate information.
As a preferred embodiment of the present application, the plant growth monitoring module 500 includes:
the disease state feature determining unit is used for determining disease state feature information of plant types corresponding to the image information, wherein the disease state feature information comprises color features, texture features and shape features;
the color feature judging unit is used for determining color information of pixel points in the image information and judging whether the image information has color features or not according to the color information;
the texture feature judging unit is used for extracting actual texture information of the image information and judging whether the image information has texture features or not according to the actual texture information;
the shape feature judging unit is used for extracting actual shape information of the image information and judging whether the image information has shape features or not according to the actual shape information;
the image processing device comprises a pathological feature judging unit, a processing unit and a processing unit, wherein the pathological feature judging unit is used for judging that corresponding pathological feature information exists in image information when color features, texture features or shape features exist in the image information; otherwise, no pathological characteristic information exists.
The foregoing description of the preferred embodiments of the present application should not be taken as limiting the application, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (7)
1. A landscape plant planting management method, characterized in that the method comprises the following steps:
receiving a landscape display picture, climate information and display time uploaded by a user, wherein the landscape display picture comprises a plurality of landscape areas, and each landscape area comprises color information and size information;
determining a plurality of plant types according to the color information, the climate information and the display time, wherein each plant type corresponds to a landscape area;
determining the planting quantity of plant types in each landscape area according to the size information;
the planting maintenance information and the pathological characteristic information of each plant type are called;
image information in the growth process of landscape plants is collected, each image information corresponds to a plant type, whether corresponding pathological characteristic information exists in the image information is identified, and when the corresponding pathological characteristic information exists, early warning information is generated.
2. The landscape plant growing management method according to claim 1 wherein the step of determining a number of plant species according to the color information, climate information and display time, specifically comprises:
inputting color information, climate information and display time into a landscape plant library, wherein the landscape plant library comprises a plurality of plant types, and each plant type is correspondingly marked with color, growth climate, maturation time, maturation height and unit price;
outputting plant types with the same color, growth climate and maturation time, classifying the output plant types according to the landscape area, and marking maturation height and unit price on each plant type;
and receiving plant type selection information input by a user, and determining a plurality of plant types.
3. The landscape plant growing management method according to claim 1, wherein the step of determining the growing number of the plant species in each landscape area based on the size information, specifically comprises:
determining the planting area of each landscape area according to the size information;
the planting density of each plant species is retrieved, and the planting quantity of each plant species is determined according to the planting area and the planting density.
4. The method according to claim 1, wherein the step of retrieving the planting maintenance information and the pathological feature information for each plant species comprises:
inputting plant types and climate information into a planting and curing library, wherein the planting and curing library comprises a plurality of plant types, each plant type corresponds to one pathological characteristic information and a plurality of planting and curing information, and each planting and curing information corresponds to a climate characteristic;
and outputting planting maintenance information and pathological characteristic information matched with the plant types and the climate information.
5. The landscape plant growing management method according to claim 1, wherein the step of identifying whether the corresponding pathological feature information exists in the image information specifically includes:
determining the pathological characteristic information of the plant type corresponding to the image information, wherein the pathological characteristic information comprises color characteristics, texture characteristics and shape characteristics;
determining color information of pixel points in image information, and judging whether color features exist in the image information according to the color information;
extracting actual texture information of image information, and judging whether texture features exist in the image information according to the actual texture information;
extracting actual shape information of image information, and judging whether the image information has shape characteristics according to the actual shape information;
when color features, texture features or shape features exist in the image information, judging that corresponding pathological feature information exists in the image information; otherwise, no pathological characteristic information exists.
6. The method of landscape plant growth management according to claim 5 wherein the method of extracting the actual texture information of the image information includes but is not limited to signal processing, geometry and modeling.
7. The landscape plant growing management method according to claim 5 wherein the method of extracting the actual shape information of the image information includes but is not limited to boundary feature method, fourier shape descriptor method and geometric parameter method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311321580.XA CN117079140B (en) | 2023-10-13 | 2023-10-13 | Landscape plant planting management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311321580.XA CN117079140B (en) | 2023-10-13 | 2023-10-13 | Landscape plant planting management method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117079140A true CN117079140A (en) | 2023-11-17 |
| CN117079140B CN117079140B (en) | 2024-01-23 |
Family
ID=88717266
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311321580.XA Active CN117079140B (en) | 2023-10-13 | 2023-10-13 | Landscape plant planting management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117079140B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106871971A (en) * | 2017-04-10 | 2017-06-20 | 南京乐仲智能化工程有限公司 | One plant growth management system |
| CN107295310A (en) * | 2017-07-31 | 2017-10-24 | 深圳前海弘稼科技有限公司 | Planting monitoring method and planting monitoring device |
| CN109255779A (en) * | 2018-08-17 | 2019-01-22 | 南京邮电大学 | Service platform is planted in trustship based on Internet of Things |
| CN109840549A (en) * | 2019-01-07 | 2019-06-04 | 武汉南博网络科技有限公司 | A kind of pest and disease damage recognition methods and device |
| CN109918531A (en) * | 2019-03-06 | 2019-06-21 | 长光禹辰信息技术与装备(青岛)有限公司 | A kind of the seeking method, apparatus and computer readable storage medium of mother drug plants |
| WO2020056148A1 (en) * | 2018-09-12 | 2020-03-19 | PlantSnap, Inc. | Systems and methods for electronically identifying plant species |
| CN112116514A (en) * | 2020-09-10 | 2020-12-22 | 深圳文科园林股份有限公司 | Plant planting recommendation method, device, equipment and computer-readable storage medium |
| WO2021223607A1 (en) * | 2020-05-07 | 2021-11-11 | 杭州睿琪软件有限公司 | Method and system for evaluating state of plant, and computer readable storage medium |
| CN113807132A (en) * | 2020-06-12 | 2021-12-17 | 广州极飞科技股份有限公司 | Method and device for identifying irrigation state of plant growing area and storage medium |
| CN114568265A (en) * | 2022-03-22 | 2022-06-03 | 武汉鸿榛园林绿化工程有限公司 | Afforestation maintenance intelligent monitoring management system based on artificial intelligence |
| CN114722168A (en) * | 2022-03-15 | 2022-07-08 | 珠海格力电器股份有限公司 | Plant recommendation method and device, electronic equipment and storage medium |
| CN114792399A (en) * | 2022-06-23 | 2022-07-26 | 深圳市海清视讯科技有限公司 | Plant monitoring method, device and equipment |
| CN115563684A (en) * | 2022-10-18 | 2023-01-03 | 浙江汉宇设计有限公司 | Landscape design system based on scene simulation |
-
2023
- 2023-10-13 CN CN202311321580.XA patent/CN117079140B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106871971A (en) * | 2017-04-10 | 2017-06-20 | 南京乐仲智能化工程有限公司 | One plant growth management system |
| CN107295310A (en) * | 2017-07-31 | 2017-10-24 | 深圳前海弘稼科技有限公司 | Planting monitoring method and planting monitoring device |
| CN109255779A (en) * | 2018-08-17 | 2019-01-22 | 南京邮电大学 | Service platform is planted in trustship based on Internet of Things |
| WO2020056148A1 (en) * | 2018-09-12 | 2020-03-19 | PlantSnap, Inc. | Systems and methods for electronically identifying plant species |
| CN109840549A (en) * | 2019-01-07 | 2019-06-04 | 武汉南博网络科技有限公司 | A kind of pest and disease damage recognition methods and device |
| CN109918531A (en) * | 2019-03-06 | 2019-06-21 | 长光禹辰信息技术与装备(青岛)有限公司 | A kind of the seeking method, apparatus and computer readable storage medium of mother drug plants |
| WO2021223607A1 (en) * | 2020-05-07 | 2021-11-11 | 杭州睿琪软件有限公司 | Method and system for evaluating state of plant, and computer readable storage medium |
| CN113807132A (en) * | 2020-06-12 | 2021-12-17 | 广州极飞科技股份有限公司 | Method and device for identifying irrigation state of plant growing area and storage medium |
| CN112116514A (en) * | 2020-09-10 | 2020-12-22 | 深圳文科园林股份有限公司 | Plant planting recommendation method, device, equipment and computer-readable storage medium |
| CN114722168A (en) * | 2022-03-15 | 2022-07-08 | 珠海格力电器股份有限公司 | Plant recommendation method and device, electronic equipment and storage medium |
| CN114568265A (en) * | 2022-03-22 | 2022-06-03 | 武汉鸿榛园林绿化工程有限公司 | Afforestation maintenance intelligent monitoring management system based on artificial intelligence |
| CN114792399A (en) * | 2022-06-23 | 2022-07-26 | 深圳市海清视讯科技有限公司 | Plant monitoring method, device and equipment |
| CN115563684A (en) * | 2022-10-18 | 2023-01-03 | 浙江汉宇设计有限公司 | Landscape design system based on scene simulation |
Non-Patent Citations (1)
| Title |
|---|
| 张青林: "植物造景在园林景观设计中的应用探讨", 《居舍》, no. 18, pages 124 - 127 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117079140B (en) | 2024-01-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Automatic grape bunch detection in vineyards with an SVM classifier | |
| US8941645B2 (en) | Comparing virtual and real images in a shopping experience | |
| Ţălu | Texture analysis methods for the characterisation of biological and medical images | |
| CN112419202B (en) | Automatic wild animal image recognition system based on big data and deep learning | |
| Zhang et al. | Estimating plant distance in maize using Unmanned Aerial Vehicle (UAV) | |
| CN114329747A (en) | Building digital twin oriented virtual and real entity coordinate mapping method and system | |
| CN114612896B (en) | Rice yield prediction method, device and equipment based on remote sensing image | |
| CN113435773B (en) | Production progress monitoring method, system and storage medium for digital factory | |
| CN109086696B (en) | Abnormal behavior detection method and device, electronic equipment and storage medium | |
| CN106030612A (en) | Providing photo heat maps | |
| CN111797831A (en) | BIM and artificial intelligence based parallel abnormality detection method for poultry feeding | |
| CN111814866A (en) | Disease and pest early warning method and device, computer equipment and storage medium | |
| CN117079140B (en) | Landscape plant planting management method | |
| Bhadra et al. | End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images | |
| EP2383680A1 (en) | Classification of objects in harvesting applications | |
| CN109523509B (en) | Method and device for detecting heading stage of wheat and electronic equipment | |
| JP6932682B2 (en) | Programs, information processing methods, and information processing equipment | |
| CN117133014A (en) | Live pig face key point detection method | |
| CN116362864A (en) | Post-credit risk assessment method and device based on aquaculture and electronic equipment | |
| Schneider et al. | Towards predicting vine yield: Conceptualization of 3d grape models and derivation of reliable physical and morphological parameters | |
| Baesso et al. | Deep learning-based model for classification of bean nitrogen status using digital canopy imaging | |
| US20240029290A1 (en) | System and method of scoring animals | |
| KR102486498B1 (en) | System and method for detecting tidal-flat burrows and constructing tidal-flat ecological environment spatial information using image-based artificial intelligence object recognition | |
| CN116844075B (en) | Tillage environment judging method and system | |
| CN117672543B (en) | Scientific and technological health big data model construction method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |