WO2017215206A1

WO2017215206A1 - Automatic plant identification system and method

Info

Publication number: WO2017215206A1
Application number: PCT/CN2016/108821
Authority: WO
Inventors: 张贯京
Original assignee: 深圳市易特科信息技术有限公司
Priority date: 2016-06-17
Filing date: 2016-12-07
Publication date: 2017-12-21
Also published as: CN106096563A

Abstract

An automatic plant identification system and method. The method comprises the following steps of: acquiring image information about a plant to be identified (S10); removing noise from the image information about the plant to be identified (S20); extracting a plant contour feature and a colour feature from the image information subjected to de-noising processing, and respectively matching the extracted plant contour feature information and colour feature with plant contour feature and colour feature information in a pre-established database, so as to obtain a degree of matching between the plant to be identified and the plant information existing in the database (S30); and outputting plant information with the maximum degree of matching as information about the plant to be identified (S40). By means of the method, a path for quickly identifying plant information is provided where a plant name is unknown to a plant non-specialist, wherein same facilitates the field survival of the plant non-specialist.

Description

Automatic plant identification system and method

Technical field

[0001] The present invention relates to the field of plant identification technology, and in particular, to an automatic plant identification system and method.

Background technique

[0002] It is estimated that there are approximately 220,000 to 420,000 different types of plants on Earth. The classification and identification of plants is a huge and complex task. Traditional plant identification methods rely mainly on the corresponding botanists to use their own expertise to conduct research and analysis on plant shapes, skins, leaves, etc., to identify plant types. However, for those who often need to conduct field operations or work, without professional botany knowledge, it is difficult to quickly determine the classification and name of the plant, and not know its growth and habits. In fact, many institutions have established a very complete electronic plant knowledge encyclopedia, such as Baidu Encyclopedia, just enter the exact name of the plant to find the detailed information of the plant, but without knowing the name of the plant, it can not Get detailed information about the plant. Therefore, the main shortcomings of the prior art are as follows: Plant non-professionals cannot quickly identify plant information without knowing the name of the plant, which is not conducive to its survival in the wild.

technical problem

[0003] 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. Technical problems of personnel survival in the wild.

Problem solution

Technical solution

In order to achieve the above object, the present invention provides an automatic plant identification system.

[0005] The plant automatic identification system runs in a smart terminal, and the system includes a plant image information acquisition module

An image preprocessing module, a plant feature extraction and matching module, and a plant information output module, wherein: the plant image information acquisition module is configured to acquire image information of the plant to be identified;

[0007] the image preprocessing module is configured to remove noise in image information of a plant to be identified;

[0008] 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;

[0009] the plant information output module is configured to output plant information with the greatest matching degree as information of the plant to be identified

[0010] Further, the digitizing sub-module is configured to sample and quantize original image information of the plant to be identified into a digital image;

[0011] the normalization sub-module is configured to retain a specific feature of the digital image by a transform process;

[0012] the smoothing processing sub-module is configured to cancel noise in the digital image;

[0013] an image complex atom module for correcting pixel degradation of the digital image;

[0014] 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.

[0015] Further, 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:

[0016] 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;

[0017] 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;

[0018] 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.

[0019] Preferably, 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.

[0020] Preferably, the information of the plant to be identified includes at least the name, picture, growth habit and function of the plant.

[0021]

[0022] The present invention also provides a method for automatically identifying plants.

[0023] 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;

[0025] removing noise in image information of the plant to be identified;

Extracting plant outline features and color features from the image information subjected to denoising processing, and matching the extracted plant outline features and color features with plant outline features and color feature information in a pre-established database, respectively, to be obtained Identify the degree of match between the plant and the existing plant information in the database;

[0027] The plant information having the largest matching degree is output as the information of the plant to be identified.

[0028] Further, the step of removing noise in the image information of the plant to be identified includes:

[0029] sampling and quantizing the original image information of the plant to be identified to obtain a digital image;

[0030] retaining specific features of the digital image by transform processing;

[0031] eliminating noise in the digital image;

Correcting pixel degradation of the digital image;

[0033] performing image enhancement processing on the information in the digital image to improve the visual effect of the digital image.

[0034] Further, 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:

[0035] 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;

[0036] extracting color features in the image information by using a color moment algorithm or a color set algorithm;

[0037] The 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.

[0038] Preferably, 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.

[0039] Preferably, the information of the plant to be identified includes at least the name, picture, growth habit and function of the plant.

Advantageous effects of the invention

Beneficial effect Compared with the prior art, 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. For the information of the plants to be identified for the user's reference, 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.

Brief description of the drawing

DRAWINGS

1 is a schematic diagram of functional modules of a preferred embodiment of the automatic plant identification system of the present invention;

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;

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;

4 is a schematic flow chart of a preferred embodiment of the automatic plant identification method of the present invention;

5 is a schematic flowchart of the refinement of step S20 in FIG. 4;

6 is a schematic diagram showing the refinement process of step S30 in FIG. 4.

BEST MODE FOR CARRYING OUT THE INVENTION

The specific embodiments, structures, features and functions of the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0048] In order to achieve the object of the present invention, 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.

Referring to FIG. 1, 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 provided by the present invention 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.

[0051] 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

In the present embodiment, 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.

[0053] 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; and the information of the fruit includes at least the color, shape and size of the fruit.

[0054] 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. Specifically, referring to FIG. 2, 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:

[0055] 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. Specifically, 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. In order to enable the digital image to reconstruct the original image, the magnitudes of the M, N, and b values are preset. Within the spatial and grayscale resolution capabilities of the receiving device, the larger the values of M, N, and b, the better the quality of the reconstructed image. When 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. Since 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.

[0056] 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.

[0057] 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. In addition, there is a method of applying a spatial frequency domain bandpass filtering method.

[0058] 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

G(u, v=H(u, v)F(u, v). After determining the degradation function according to the degradation mechanism, we can find F from this relation. (u, v), then find the f(x, y) by the inverse Fourier transform, which is usually called the inverse filter. When there is no noise, the Wiener filter becomes the ideal inverse filter.

[0059] 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. For example, an image enhancement system can highlight the outline of an image with a high-pass filter, allowing the machine to measure the shape and perimeter of the outline. There are several ways to enhance image enhancement. Contrast broadening, logarithmic transformation, density stratification, and histogram equalization can all be used to change image grays and highlight details.

[0060] 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. Specifically, referring to FIG. 3, 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.

[0061] 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.

[0062] 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. After 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.

[0063] 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. Specifically, 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. .

[0064] 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.

[0066] The automatic plant identification system provided by the invention 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.

[0067]

In another aspect of the invention, a method corresponding to the plant automatic identification system described above is provided.

[0069] Referring to FIG. 4, FIG. 4 is a schematic flow chart of a preferred embodiment of the automatic plant identification method of the present invention.

[0070] In the embodiment, as shown in FIG. 1, FIG. 2 and FIG. 3, the automatic plant identification method runs on the smart In device 1, the method includes the following steps:

[0071] S10: acquiring image information of the plant to be identified;

Specifically, 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; and the information of the fruit includes at least the color, shape and size of the fruit.

[0073] S20: removing noise in image information of the plant to be identified;

[0074] Specifically, 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.

Referring to FIG. 5, FIG. 5 is a schematic diagram of the refinement process of step S20 in FIG. Step S20 includes the following steps

[0076] S201: Raw image information of the plant to be identified is sampled and quantized to obtain a digital image.

[0077] Specifically, 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. Specifically, 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. In order to enable the digital image to reconstruct the original image, the magnitudes of the M, N, and b values are preset. Within the spatial and grayscale resolution capabilities of the receiving device, the larger the values of M, N, and b, the better the quality of the reconstructed image. When 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. Since 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.

[0078] S202: retaining a specific feature of the digital image by a transform process. [0079] Specifically, 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.

[0080] S203: Eliminate noise in the digital image.

[0081] Specifically, 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. In addition, there is a method for applying spatial frequency domain bandpass filtering

[0082] S204: Correcting pixel degradation of the digital image.

[0083] Specifically, 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). Their Fourier transform has a relationship G(u, v=H(u, v)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.

[0084] S205: Perform image enhancement processing on the information in the digital image to improve the visual effect of the digital image.

[0085] Specifically, 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. For data extraction or identification. For example, 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. There are several ways to enhance 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.

[0086] 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.

Specifically, referring to FIG. 6, FIG. 6 is a schematic flowchart of the refinement of step S30 in FIG. Step S30 includes the following steps:

[0088] 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.

[0089] Specifically, 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.

[0090] S302: Extracting color features in the image information by using a color moment algorithm or a color set algorithm.

[0091] Specifically, 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. After 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.

[0092] 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.

[0093] Specifically, 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. Specifically, 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. .

[0094] S40: Output plant information with the greatest matching degree as information of the plant to be identified.

[0095] Specifically, 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

For the reference of users, 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.

[0097] The automatic plant identification method provided by the invention 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.

[0098]

The above are only the preferred embodiments of the present invention, and are not intended to limit the scope of the invention, and the equivalent structure or equivalent function transformations made by the description of the invention and the drawings are directly or indirectly applied to other related The technical field is equally included in the scope of patent protection of the present invention.

Industrial applicability

[0100] Compared with the prior art, 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.

Claims

claims

[Claim 1] A plant automatic identification system, characterized in that the plant automatic identification system runs in an intelligent terminal, and the system includes a plant image information acquisition module, an image preprocessing module, a plant feature extraction and matching module, and a plant Information output module, wherein: the plant image information acquisition module is used to obtain image information of plants to be identified; the image preprocessing module is used to remove noise in the image information of plants to be identified; the plant feature extraction and matching module Used to extract plant outline features and color features from denoised image information, and match 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 plant outline features and color features to be identified The matching degree between the plant and the existing plant information in the database; the plant information output module is used to output the plant information with the largest matching degree as the information of the plant to be identified.

[Claim 2] The plant automatic identification system according to claim 1, characterized in that the image preprocessing module includes a digitization sub-module, a normalization sub-module, a smoothing sub-module, an image complex atom module and an image enhancement sub-module, wherein: the digitization sub-module is used to sample and quantify the original image information of the plant to be identified to obtain a digital image; the normalization sub-module is used to retain the specific characteristics of the digital image through transformation processing; the The smoothing sub-module is used to eliminate noise in the digital image; the image complex atom module is used to correct the pixel degradation of the digital image; the image enhancement sub-module is used to perform image enhancement processing on the information in the digital image to improve The visual effects of the digital image.

[Claim 3] The plant automatic identification system according to claim 1, wherein 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 used to extract contour features in image information using Hu moment matching algorithm, contour tree matching algorithm or paired geometric histogram matching algorithm; the color feature extraction sub-module is used to use color moment algorithm or color set algorithm Extract the color features in the image information; the matching degree calculation sub-module is used to match the contour features and color features in the extracted image information with the plant contour features and color feature information in the pre-established database to obtain the desired Match the identified plants with existing plant information in the database.

[Claim 4] The automatic plant identification system of claim 1, wherein the image information of the plant to be identified includes at least two types of information of leaves, necks, flowers and fruits of the plant to be identified.

[Claim 5] The automatic plant identification system of claim 1, wherein the information about the plant to be identified includes at least the name, picture, growth habit and function of the plant.

[Claim 6] A plant automatic identification method, characterized in that the plant automatic identification method is run in an intelligent terminal, and the method includes the following steps: Obtaining the image information of the plant to be identified; Removing the image information of the plant to be identified noise; extract plant outline features and color features from the denoised image information, and match the extracted plant outline features and color features with plant outline features and color feature information in the pre-established database, respectively, to obtain the desired The matching degree between the identified plants and the existing plant information in the database; the plant information with the largest matching degree is output as the information of the plant to be identified.

[Claim 7] The automatic identification method of plants according to claim 6, wherein the step of removing noise from the image information of the plant to be identified includes: sampling and quantifying the original image information of the plant to be identified to obtain a digital Image; retain specific characteristics of the digital image through transformation processing; eliminate noise in the digital image; correct pixel degradation of the digital image; perform image enhancement processing on the information in the digital image to improve the digital image visual effects.

[Claim 8] The automatic plant identification method according to claim 6, characterized in that: extracting plant outline features and color features from the image information that has undergone denoising processing, and using the extracted plant outline features and color features Match the plant outline features and color feature information in the pre-established database respectively. The steps to obtain the matching degree of the plant to be identified and the existing plant information in the database include: using Hu moment matching algorithm, contour tree matching algorithm or pairwise geometry The histogram matching algorithm extracts the contour features in the image information; the color moment algorithm or the color set algorithm is used to extract the color features in the image information; the extracted contour features and color features in the image information are compared with the plants in the pre-established database The contour features and color feature information are matched to obtain the matching degree between the plant to be identified and the existing plant information in the database.

[Claim 9] The automatic plant identification method of claim 6, wherein the image information of the plant to be identified includes at least two types of information of leaves, necks, flowers and fruits of the plant to be identified.

[Claim 10] The automatic plant identification method according to claim 6, wherein the information about the plant to be identified at least includes the name, picture, growth habit and function of the plant.