WO2017215206A1 - Procédé et système d'identification automatique de plante - Google Patents

Procédé et système d'identification automatique de plante Download PDF

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Publication number
WO2017215206A1
WO2017215206A1 PCT/CN2016/108821 CN2016108821W WO2017215206A1 WO 2017215206 A1 WO2017215206 A1 WO 2017215206A1 CN 2016108821 W CN2016108821 W CN 2016108821W WO 2017215206 A1 WO2017215206 A1 WO 2017215206A1
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plant
information
image
module
identified
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PCT/CN2016/108821
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English (en)
Chinese (zh)
Inventor
张贯京
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深圳市易特科信息技术有限公司
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Publication of WO2017215206A1 publication Critical patent/WO2017215206A1/fr

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    • 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
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • 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
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation

Definitions

  • the present invention relates to the field of plant identification technology, and in particular, to an automatic plant identification system and method.
  • the main object of the present invention is to provide an automatic plant identification system and method, which aims to solve the problem that the plant non-professionals cannot quickly identify plants without knowing the name of the plant in the prior art, which is not conducive to plant non-professionals.
  • the present invention provides an automatic plant identification system.
  • the plant automatic identification system runs in a smart terminal, and the system includes a plant image information acquisition module
  • An image preprocessing module configured to acquire image information of the plant to be identified;
  • the image preprocessing module is configured to remove noise in image information of a plant to be identified
  • the plant feature extraction and matching module is configured to extract a plant wheel from image information subjected to denoising processing Profile characteristics and color features, and matching the extracted plant outline features and color features with the plant outline features and color feature information in the pre-established database, respectively, to obtain the matching degree between the plants to be identified and the existing plant information in the database;
  • the plant information output module is configured to output plant information with the greatest matching degree as information of the plant to be identified
  • the digitizing sub-module is configured to sample and quantize original image information of the plant to be identified into a digital image
  • the normalization sub-module is configured to retain a specific feature of the digital image by a transform process
  • the smoothing processing sub-module is configured to cancel noise in the digital image
  • an image complex atom module for correcting pixel degradation of the digital image
  • the image enhancement sub-module is configured to perform image enhancement processing on the information in the digital image to improve the visual effect of the digital image.
  • the plant feature extraction and matching module includes a contour feature extraction sub-module, a color feature extraction sub-module, and a matching degree calculation sub-module, wherein:
  • the contour feature extraction sub-module is configured to extract contour features in image information by using a Hu moment matching algorithm, a contour tree matching algorithm, or a pairwise geometric histogram matching algorithm;
  • the color feature extraction sub-module is configured to extract color features in the image information by using a color moment algorithm or a color set algorithm
  • the matching degree calculation sub-module is configured to match the contour feature and the color feature in the extracted image information with the plant outline feature and the color feature information in the pre-established database to obtain the plant to be identified and the database.
  • the degree of matching of plant information already exists.
  • the image information of the plant to be identified includes at least two kinds of information of leaves, neck, flowers and fruits of the plant to be identified.
  • the information of the plant to be identified includes at least the name, picture, growth habit and function of the plant.
  • the present invention also provides a method for automatically identifying plants.
  • the automatic plant identification method runs in a smart terminal, and the method includes the following steps: [0024] acquiring image information of the plant to be identified;
  • the plant information having the largest matching degree is output as the information of the plant to be identified.
  • the step of removing noise in the image information of the plant to be identified includes:
  • the plant outline feature and the color feature are extracted from the image information subjected to the denoising process, and the extracted plant outline feature and the color feature are respectively associated with the plant outline feature and the color feature information in the pre-established database.
  • the steps of matching to obtain the matching degree of the plant information to be recognized in the database and the database include:
  • contour feature and the color feature in the extracted image information are matched with the plant outline feature and the color feature information in the pre-established database to obtain the matching degree between the plant to be identified and the existing plant information in the database.
  • the image information of the plant to be identified includes at least two kinds of information of leaves, neck, flowers and fruits of the plant to be identified.
  • the information of the plant to be identified includes at least the name, picture, growth habit and function of the plant.
  • the automatic plant identification system and method provided by the present invention removes irrelevant information in an image by removing noise in image information of a plant to be identified, and recovers useful real information, and is preprocessed by extraction.
  • the contour features and color features of the image information are matched with the plant outline features and color feature information in the pre-established database to obtain the matching degree of the plant information in the database to be identified and the database, and the plant information with the largest matching degree is output.
  • the plant non-professionals can provide a quick way to identify plant information without knowing the name of the plant, which is beneficial to the survival of plant non-professionals in the wild.
  • FIG. 1 is a schematic diagram of functional modules of a preferred embodiment of the automatic plant identification system of the present invention
  • FIG. 2 is a schematic diagram of sub-function modules of an image pre-processing module in the automatic plant identification system of the present invention
  • FIG. 3 is a schematic diagram of a sub-function module of a plant feature extraction and matching module in the automatic plant identification system of the present invention
  • FIG. 4 is a schematic flow chart of a preferred embodiment of the automatic plant identification method of the present invention.
  • step S20 in FIG. 4 is a schematic flowchart of the refinement of step S20 in FIG. 4;
  • FIG. 6 is a schematic diagram showing the refinement process of step S30 in FIG. 4.
  • the present invention provides an automatic plant identification system and method for recovering useful real information by extracting noise in image information of a plant to be identified, eliminating irrelevant information in the image, by extracting
  • the contour features and color features of the preprocessed image information are matched with the plant outline features and color feature information in the pre-established database to obtain the matching degree of the plant information in the database to be identified and the database, and the output matching degree is the largest.
  • the plant information is for the user's reference for the information of the plant to be identified, and provides a quick identification plant letter for plant non-professionals without knowing the name of the plant. The way of interest is conducive to the survival of plant non-professionals in the wild.
  • FIG. 1 is a schematic diagram of functional modules of a preferred embodiment of the automatic plant identification system of the present invention.
  • the automatic plant identification system 10 runs in the smart terminal 1.
  • the smart terminal 1 is a microcomputer that is convenient to carry and has communication and information processing capabilities, such as a smart phone, an intelligent handheld terminal, or a smart tablet.
  • the intelligent terminal 1 further includes a storage unit 12, a processing unit 14, and a communication unit 16.
  • the storage unit 12 may be a read only storage unit ROM, an electrically erasable storage unit EEPRO M, a flash storage unit FLASH or a solid hard disk.
  • the processing unit 14 may be a central processing unit (CPU), a microcontroller (MCU), a data processing chip, or an information processing unit having data processing functions.
  • the communication unit 16 is a wireless communication interface or a wired interface with remote communication functions, for example, wireless or communication technologies supporting GSM, GPRS, WCDMA, CDMA, TD-SCDMA, WiMAX, TD-LTE, FDD-LTE, etc. Wired communication interface
  • the automatic plant identification system 10 includes a plant image information acquisition module 100, an image preprocessing module 102, a plant feature extraction and matching module 104, and a plant information output module 106.
  • the module referred to in the present invention refers to a series of computer program instruction segments that can be executed by the processing unit 14 and that are capable of performing fixed functions, which are stored in the storage unit 12.
  • the plant image information acquiring module 100 is configured to acquire image information of the plant to be identified, and the image information of the plant to be identified includes at least two kinds of information of leaves, necks, flowers, and fruits of the plants to be identified.
  • the information of the leaves includes at least the color and shape of the blade;
  • the information of the neck includes at least the color and diameter of the neck;
  • the information of the flower includes at least the shape and color of the flower;
  • the information of the fruit includes at least the color, shape and size of the fruit.
  • the image preprocessing module 102 is configured to remove noise in image information of the plant to be identified.
  • the main purpose of image preprocessing is to eliminate irrelevant information in the image, restore useful real information, enhance the detectability of the information and minimize the data, thereby improving the reliability of feature extraction, image segmentation, matching and recognition.
  • the image preprocessing process generally includes steps of digitization, normalization, smoothing, restoration, and enhancement.
  • FIG. 2 is a schematic diagram of a sub-function module of an image pre-processing module in the automatic plant identification system of the present invention.
  • the image preprocessing module 102 includes a digitization submodule 1020, a normalization submodule 1022, a smoothing submodule 1024, an image complex atom module 1026, and an image enhancement submodule.
  • Block 1028 Block 1028:
  • the digitizing sub-module 1020 is configured to sample and quantize the original image information of the plant to be identified to obtain a digital image that the processing unit 14 can process.
  • the gray value of the original image information (i.e., the original image) of a plant to be identified is a continuous function of a spatial variable (continuous value of position).
  • the original image is grayscaled and quantized on the MxN lattice (classified as one of 2b grayscales) to obtain a digital image that the computer can process.
  • the magnitudes of the M, N, and b values are preset.
  • the sampling period is equal to or less than half of the minimum detail period in the original image
  • the spectrum of the reconstructed image is equal to the spectrum of the original image, so the reconstructed image can be identical to the original image.
  • the product of M, N and b determines the storage amount of an image in the intelligent terminal
  • the appropriate M, N and b values are selected according to the different properties of the image under the condition of a certain amount of storage to obtain the best. Processing effect.
  • the normalization sub-module 1022 is configured to preserve a particular feature of the digital image by a transform process such that a particular feature of the digital image has a constant property under a given transform. Certain features of the digital image, such as the area and perimeter of the object, have constant properties for changes in coordinate rotation. In general, the influence of certain factors or transformations on some properties of the image can be eliminated or reduced by normalization and can be selected as the basis for measuring the image. For example, for remote sensing images with uncontrollable illumination, the normalization of grayscale histograms is necessary for image analysis. Gray Normalization, Geometric Normalization, and Transformation Normalization is the three normalization methods for obtaining the invariant properties of an image.
  • the smoothing sub-module 1024 is for canceling noise in the digital image.
  • Commonly used smoothing methods are median method, local averaging method and k-nearest neighbor averaging method.
  • the local area size can be fixed or it can be changed point by point with the gray value.
  • the image complex atom module 1026 is for correcting pixel degradation of the digital image such that the reconstructed or estimated digital image is as close as possible to the ideal digital image without degradation.
  • Image pixel degradation often occurs in practical applications. For example, disturbances in large airflow, aberrations in the optical system, and relative motion of the camera and objects can degrade the remote sensing image.
  • the basic restoration technique is to treat the acquired degraded image g(x, y) as a convolution of the degenerate function h(x, y) and the ideal image f(x, y). Their Fourier transform relationship
  • the image enhancement sub-module 1028 is configured to selectively perform image enhancement processing on information in the digital image to improve a visual effect of the digital image, or convert the digital image to be more suitable for the processing unit 14
  • the form of processing to facilitate data extraction or identification can highlight the outline of an image with a high-pass filter, allowing the machine to measure the shape and perimeter of the outline.
  • Contrast broadening, logarithmic transformation, density stratification, and histogram equalization can all be used to change image grays and highlight details.
  • the plant feature extraction and matching module 104 is configured to extract plant outline features and color features from the denoised processed image information, and extract the extracted plant outline features and color features respectively with plants in a pre-established database.
  • the contour feature and the color feature information are matched to obtain the matching degree between the plant to be identified and the existing plant information in the database.
  • FIG. 3 is a schematic diagram of sub-function modules of the plant feature extraction and matching module in the automatic plant identification system of the present invention.
  • the plant feature extraction and matching module 104 includes a contour feature extraction sub-module 1040, a color feature extraction sub-module 1042, and a matching degree calculation sub-module 1044.
  • the contour feature extraction sub-module 1040 is configured to extract contour features in the image information by using a Hu moment matching algorithm, a contour tree matching algorithm, or a pairwise geometric histogram matching algorithm.
  • the contour can be obtained by extracting and matching the contour features to obtain the shape, size, etc. of the elements of the plant image (for example: leaves, neck, flowers, fruits). Since the Hu moment of the contour can be invariant to changes including scaling, rotation, and mirror mapping, contour matching is performed through a common Hu moment matching algorithm.
  • the cvMatchShapes function makes it easy to match between two contours.
  • the contour tree matching algorithm compares two contours in the form of a tree.
  • the cvMa tchContourTrees function enables comparison of contour trees.
  • the pairwise geometric histogram matching algorithm refers to the use of histogram contrast method to perform contour matching after the paired geometric histograms of the contours are obtained.
  • the color feature extraction sub-module 1042 is configured to extract color features in the image information by using a color moment algorithm or a color set algorithm.
  • the color histogram is used to reflect the composition distribution of the image color, that is, the probability of occurrence of various colors.
  • the color moment algorithm uses the concept of moments in linear algebra to represent the color distribution in an image as a color moment. The color distribution is described by the color first moment (average Average), the color second moment (variance Variance), and the color third moment (skewness Skewness). Unlike color histograms, using color moments Line image descriptions do not require quantization of image features.
  • the color set algorithm implements retrieval of large-scale images based on color.
  • the method converts the color into the HSV color space
  • the image is divided into several regions according to the color information thereof, and the color is divided into multiple bins, and each region performs color space quantization to establish a color cable I, thereby establishing a binary image.
  • Color index table To speed up the search, you can also construct a binary search tree for feature retrieval.
  • the matching degree calculation sub-module 1044 is configured to match the contour feature and the color feature in the extracted image information with the plant outline feature and the color feature information in the pre-established database to obtain the plant and database to be identified.
  • the degree of matching of plant information already exists.
  • the pre-established database is run in a remote server, and the smart terminal 1 establishes a communication connection with the remote server through the communication unit 16, and reads pre-stored plant outline features and color feature information from the database of the remote server and matches them. .
  • the plant information output module 106 is configured to output plant information with the greatest matching degree as information of the plant to be identified.
  • the information of the plant to be identified includes at least the name, picture, growth habit and function of the plant for reference by the user, and provides a means for the plant non-professional to provide quick identification of plant information without knowing the name of the plant, which is beneficial to the plant non- Professionals survive in the wild.
  • the image matching and/or comparison process of the present invention is an image processing technology in the prior art, which is not limited and described herein.
  • the automatic plant identification system removes noise in the image information of the plant to be identified through the image preprocessing module, eliminates irrelevant information in the image, restores useful real information, and extracts through the plant feature extraction and matching module.
  • the contour feature and the color feature of the processed image information are matched with the plant outline feature and the color feature information in the pre-established database, and the matching degree of the plant information to be recognized in the database and the database is obtained, and the plant with the largest matching degree is outputted.
  • the information is for the reference of the information of the plants to be identified, and provides a way for plant non-professionals to quickly identify plant information without knowing the name of the plant, which is beneficial to the survival of plant non-professionals in the wild.
  • FIG. 4 is a schematic flow chart of a preferred embodiment of the automatic plant identification method of the present invention.
  • the automatic plant identification method runs on the smart In device 1, the method includes the following steps:
  • S10 acquiring image information of the plant to be identified
  • the plant image information acquiring module 100 acquires image information of the plant to be identified, and the image information of the plant to be identified includes at least two kinds of information of a leaf, a neck, a flower, and a fruit of the plant to be identified.
  • the information of the leaves includes at least the color and shape of the blade;
  • the information of the neck includes at least the color and diameter of the neck;
  • the information of the flower includes at least the shape and color of the flower;
  • the information of the fruit includes at least the color, shape and size of the fruit.
  • S20 removing noise in image information of the plant to be identified
  • the image pre-processing module 102 removes noise in the image information of the plant to be identified.
  • the main purpose of image preprocessing is to eliminate irrelevant information in the image, restore useful real information, enhance the detectability of the information and minimize the data, thereby improving the reliability of feature extraction, image segmentation, matching and recognition.
  • the image preprocessing process generally includes steps of digitization, normalization, smoothing, restoration, and enhancement.
  • FIG. 5 is a schematic diagram of the refinement process of step S20 in FIG. Step S20 includes the following steps
  • S201 Raw image information of the plant to be identified is sampled and quantized to obtain a digital image.
  • the digitizing sub-module 1020 samples and quantizes the original image information of the plant to be identified to obtain a digital image that the processing unit 14 can process.
  • the gray value of the original image information (i.e., the original image) of a plant to be identified is a continuous function of a spatial variable (continuous value of position).
  • the original image is grayscaled and quantized on the MxN lattice (classified as one of 2b grayscales) to obtain a digital image that the computer can process.
  • the magnitudes of the M, N, and b values are preset.
  • the sampling period is equal to or less than half of the minimum detail period in the original image
  • the spectrum of the reconstructed image is equal to the spectrum of the original image, so the reconstructed image can be identical to the original image.
  • the product of M, N and b determines the storage amount of an image in the intelligent terminal
  • the appropriate M, N and b values are selected according to the different properties of the image under the condition of a certain amount of storage to obtain the best. Processing effect.
  • S202 retaining a specific feature of the digital image by a transform process.
  • the normalization sub-module 1022 preserves certain features of the digital image by transform processing such that certain features of the digital image have invariant properties under a given transformation. Certain features of the image, such as the area and perimeter of the object, have invariant properties for coordinate rotation transformations. In general, the influence of certain factors or transformations on some properties of the image can be eliminated or attenuated by normalization, which can be selected as the basis for measuring the image. For example, for remote sensing images with uncontrollable illumination, the normalization of gray histograms is necessary for image analysis. Gray normalization, geometric normalization, and transform normalization are three normalization methods for obtaining the invariant properties of an image.
  • S203 Eliminate noise in the digital image.
  • the smoothing processing sub-module 1024 eliminates random noise in the digital image.
  • Commonly used smoothing methods are median method, local averaging method and k-nearest neighbor averaging method.
  • the size of the local area can be fixed or it can be changed point by point with the size of the gray value.
  • the image complex atom module 1026 corrects pixel degradation of the digital image caused by various causes, so that the reconstructed or estimated image is as close as possible to the ideal image field without degradation.
  • Image pixel degradation often occurs in practical applications. For example, disturbances in large airflow, aberrations in the optical system, and relative motion of the camera and objects can degrade the remote sensing image.
  • the basic restoration technique is to treat the acquired degraded image g(x, y) as a convolution of the degenerate function h(x, y) and the ideal image f(x, y).
  • F (u, v) After determining the degradation function according to the degradation mechanism, F (u, v) can be obtained from this relation. Using the inverse Fourier transform to find f(x, y), usually called the inverse filter. When there is no noise, the Wiener filter becomes the ideal inverse filter.
  • S205 Perform image enhancement processing on the information in the digital image to improve the visual effect of the digital image.
  • the image enhancement sub-module 1028 selectively enhances and suppresses information in the digital image to improve the visual effect of the digital image, or to transform the image into a form more suitable for processing by the processing unit 14.
  • an image enhancement system can highlight the outline of an image through a high-pass filter, enabling the machine to measure the shape and perimeter of the outline.
  • image enhancement techniques Contrast broadening, logarithmic transformation, density stratification, and histogram equalization can all be used to change the graph. Like gray tones and highlighting details.
  • S30 extracting plant outline features and color features from the image information after denoising processing, and matching the extracted plant outline features and color features with the plant outline features and color feature information in a pre-established database, respectively, The degree of matching between the plants to be identified and the existing plant information in the database is obtained.
  • FIG. 6 is a schematic flowchart of the refinement of step S30 in FIG. Step S30 includes the following steps:
  • S301 extracting contour features in the image information by using a Hu moment matching algorithm, a contour tree matching algorithm, or a pairwise geometric histogram matching algorithm.
  • the contour feature extraction sub-module extracts contour features in the image information by using a Hu moment matching algorithm, a contour tree matching algorithm, or a pairwise geometric histogram matching algorithm.
  • the contour can be extracted and matched by contour features to obtain the shape, size, etc. of the elements of the plant image (for example: leaves, neck, flowers, fruits). Since the Hu moment of the contour can be invariant to changes including scaling, rotation, and mirror mapping, contour matching is performed through a common Hu moment matching algorithm.
  • the cvMatchShapes function makes it easy to match between two contours.
  • the contour tree matching algorithm compares two contours in the form of a tree.
  • the cvMatchC ontourTrees function enables the comparison of contour trees.
  • the pairwise geometric histogram matching algorithm refers to the use of histogram contrast method to perform contour matching after obtaining the paired geometric histogram of the contour.
  • S302 Extracting color features in the image information by using a color moment algorithm or a color set algorithm.
  • the color feature extraction sub-module 1042 extracts color features in the image information using a color moment algorithm or a color set algorithm.
  • the color histogram is used to reflect the composition distribution of the image color, that is, the probability of occurrence of various colors.
  • the color moment algorithm uses the concept of moments in linear algebra to represent the color distribution in an image as a color moment. The color distribution is described by the color first moment (average Average), the color second moment (variance Variance), and the color third moment (skewness). Unlike color histograms, image description using color moments does not require quantization of image features.
  • the color set algorithm implements the retrieval of large-scale images based on color.
  • the method converts the color into the HSV color space
  • the image is divided into several regions according to the color information thereof, and the color is divided into multiple bins, and each region performs color space quantization to establish a color cable I, thereby establishing a binary image.
  • Color index table To speed up the search, you can also construct a binary search tree for feature retrieval.
  • S303 The contour feature and the color feature in the extracted image information are followed by a pre-established database.
  • the plant outline feature and the color feature information are matched to obtain the matching degree of the plant information to be identified and the existing plant information in the database.
  • the matching degree calculation sub-module 1044 matches the contour feature and the color feature in the extracted image information with the plant outline feature and the color feature information in the pre-established database to obtain the plant to be identified and the database.
  • the degree of matching of plant information already exists.
  • the pre-established database is run in a remote server, and the smart terminal 1 establishes a communication connection with the remote server through the communication unit 16, and reads pre-stored plant outline features and color feature information from the database of the remote server and matches them. .
  • S40 Output plant information with the greatest matching degree as information of the plant to be identified.
  • the plant information output module 106 outputs the plant information having the greatest matching degree as the information of the plant to be identified.
  • the information of the plant to be identified includes at least the name, picture, growth habit and function of the plant
  • the image matching and/or comparison process of the present invention is an image processing technology in the prior art, which is not limited and described herein.
  • the automatic plant identification method removes the noise in the image information of the plant to be identified, eliminates irrelevant information in the image, restores useful real information, and extracts contour features and color features of the preprocessed image information. And matching with the plant outline feature and color feature information in the pre-established database, obtaining the matching degree of the existing plant information in the database to be identified, and outputting the plant information with the largest matching degree as the information of the plant to be identified for the user's reference. It provides a way for plant non-professionals to quickly identify plant information without knowing the name of the plant, which is beneficial to the survival of plant non-professionals in the wild.
  • the automatic plant identification system and method provided by the present invention removes the plant to be identified Noise in the image information, eliminating irrelevant information in the image, restoring useful real information, by extracting contour features and color features of the preprocessed image information, and with plant contour features and color feature information in a pre-established database Matching is performed to obtain the matching degree of the plant information in the database to be identified, and the plant information with the largest matching degree is the information of the plant to be identified for the user's reference, and the plant non-professionals provide the fast without knowing the plant name.
  • the way to identify plant information is beneficial to the survival of plant non-professionals in the wild.

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Abstract

La présente invention concerne un système et un procédé d'identification automatique de plante. Le procédé comprend les étapes consistant : à acquérir des informations d'image concernant une plante à identifier (S10) ; à éliminer le bruit des informations d'image concernant la plante à identifier (S20) ; à extraire une caractéristique de contour de plante et une caractéristique de couleur des informations d'image soumises à un traitement de dé-bruitage, et à mettre en correspondance respectivement les informations de caractéristique de contour de plante extraites et la caractéristique de couleur avec une caractéristique de contour de plante et des informations de caractéristique de couleur dans une base de données préétablie, de façon à obtenir un degré de correspondance entre la plante à identifier et les informations de plantes présentes dans la base de données (S30) ; à fournir les informations de plante avec le degré maximal de correspondance en tant qu'informations concernant la plante à identifier (S40). Le procédé permet d'identifier rapidement des informations sur les plantes, un nom de plante n'étant pas connu par un non-spécialiste de plante, ce qui facilite la survie sur le terrain du non-spécialiste de plante.
PCT/CN2016/108821 2016-06-17 2016-12-07 Procédé et système d'identification automatique de plante WO2017215206A1 (fr)

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Cited By (5)

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CN109558883A (zh) * 2018-12-03 2019-04-02 宁夏智启连山科技有限公司 叶片特征提取方法及装置
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