CN111833342A - Bee colony structure composition and health condition determination method based on machine vision - Google Patents
Bee colony structure composition and health condition determination method based on machine vision Download PDFInfo
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Abstract
The invention belongs to the technical field of bee breeding and discloses a bee colony structure composition and health condition determination method based on machine vision. Firstly, acquiring a honeycomb image, performing gray level histogram processing on the image, and determining a threshold segmentation point of the gray level image, the average area of a single bee metamorphosis and the radius of the average; performing single threshold segmentation on the preprocessed image, and judging whether sub-images obtained by segmentation have an overlapping phenomenon; then extracting color parameters of each bee by adopting a wavelet extreme value edge detection method to obtain color information of each bee in healthy and unhealthy states of bee eggs, larvae and adult bees; and finally, performing data analysis on the extracted color characteristic information, and evaluating the health condition of the bees. The method of the invention can apply the machine vision and image processing technology to the actual research and production process, and improve the accuracy and timeliness of the bee colony composition and health condition determination.
Description
Technical Field
The invention relates to the technical field of bee breeding, in particular to a bee colony structure composition and health condition determination method based on machine vision.
Background
At present, the domestic bee industry is in a key stage of transformation and upgrading, the traditional bee breeding mode is gradually replaced by the intelligent breeding technology, and the intelligent bee field is a necessary trend for the development of the modern bee industry. The number and health condition of bee eggs, larvae, pupae and adult bees in the bee colony are important indexes reflecting the quality of the bee colony, so that the measurement of the number of the eggs, the larvae, the pupae and the adult bees in the bee colony is a basic experimental requirement in the biological research of the bees. The existing method is to measure the area of larvae by using a plastic transparent plate consisting of square grids, then convert the area into the number of larvae, and directly observe the data of the adult bees by naked eyes and estimate the data by taking spleen as a unit. The prior art is adopted before scientific research personnel have no better method and is also a method approved by the same company, but the method adopts an area conversion mode, and the data accuracy is insufficient; and collecting basic data of 6 boxes of bees, which usually needs 4-6 hours; in addition, the honeycomb is taken out of the beehive for a long time, so that the probability of low-temperature disease of the larva is increased, and the accuracy, the rapidness and the like are not sufficient.
With the increasingly wide application of computer technology in agriculture, the machine vision and image processing technology is discussed and used to quickly and accurately identify the characteristics of different bee metamorphoses, so that the automation and intellectualization of bee colony composition and health condition determination are realized, and the method is undoubtedly a new way to reduce the influence of artificial factors and improve the determination efficiency. Therefore, the bee colony structure composition and health condition determination method based on machine vision is applied to the actual production process, has important significance for realizing the technical development of bee colony quality detection, is beneficial to improving the accuracy and timeliness of determination of eggs, larvae, pupae and adult bees in the bee colony, is beneficial to improving the identification accuracy of the bee colony health condition, and promotes the development of the work of genetic breeding and propagation of bees, prevention and control of bee diseases and the like in China.
Disclosure of Invention
The invention aims to overcome the defects of the prior art that the accuracy, the rapidness and the like are still insufficient, and provides a bee colony structure composition and health condition determination method based on machine vision. The method of the invention can apply the machine vision and image processing technology to the actual research and production process, and improve the accuracy and timeliness of the bee colony composition and health condition determination.
In order to achieve the purpose of the invention, the bee colony structure composition and health condition determination method based on machine vision comprises the following steps:
s1: collecting structural images of bees at different development stages in a bee colony by adopting an industrial CCD camera combined method, and reading the collected 24-bit RGB images into a computer to obtain clear and original honeycomb images;
s2: on the basis of collecting an original honeycomb image, preprocessing the image by adopting a gray level histogram method and a contour area detection method to obtain an accurate image segmentation threshold, the average area of a single bee and the length and the short diameter of the average; determining the central point of the bee by using morphological asymmetric corrosion operation, and obtaining images of different metamorphosis types of single ovum, larva, pupa and adult bee by combining length and diameter;
s3: performing primary segmentation on the honeycomb image by adopting a single threshold value method to obtain a plurality of sub-images of each image, detecting the sub-images by adopting an area comparison method, and determining the number of eggs, larvae, pupae and adult bees of the bees included in the sub-images;
s4: for the situation that the number of the sub-images is more than 1, namely the bee overlapping phenomenon occurs, an asymmetric morphological processing method is adopted to corrode the sub-images for multiple times to obtain the distribution center of the image of the bee, the center is taken as a circular point, the effective area of the bee is defined by taking the mean radius of a single bee as a radius, and the overlapping bee is segmented to obtain the image of each bee;
s5: obtaining an accurate distribution area of each bee metamorphosis by adopting a gray level image extreme value wavelet edge detection method, extracting color parameters, namely extracting RGB color parameters in the area, and obtaining HIS color parameters and Lab color parameters on the basis of the RGB color parameters;
s6: establishing the characteristic parameters of the bee health condition according to the RGB color parameters, the HIS color parameters and the Lab color parameters, and realizing the measurement of the bee health condition by performing data analysis on the extracted color characteristic information of the bee in a good health state and a disease state.
Further, in some embodiments of the present invention, in the step S5, the color parameter extraction is performed by:
s5.1: firstly, carrying out gray processing on an image to enable the image to lose color information and enhance the gray of the image;
s5.2: weakening the salt and pepper noise by adopting a median filtering technology;
s5.3: performing pixel-by-pixel access in the edge detection range, extracting the RGB value of each pixel point, and determining the HIS and Lab color information size of each pixel point according to the RGB value;
s5.4: and averaging the RGB, HIS and Lab of each pixel point to obtain the color information of each bee.
Further, in some embodiments of the present invention, the specific steps of determining the health status of the bees in S6 are:
s6.1: establishing a relation model of bee health and RGB color parameters, HIS color parameters and Lab color parameters;
s6.2: according to the relation model established in S6.1, determining the characteristic parameters measured in the bee health state and the disease state, establishing the characteristic vectors of the bee health measurement, and obtaining the weight regulation method of each component;
s6.3: determining the bee health according to the feature vector of the bee health determination;
s6.4: evaluating the measuring accuracy of the bee health condition measuring method based on machine vision by adopting a laboratory microscopic examination method and a visual observation method.
By the bee colony structure composition and health condition determination method based on machine vision, the speed and the precision of image acquisition, image processing and color feature extraction can be improved, and different metamorphosis feature determination and health condition determination of bees are better realized. Meanwhile, the parameters of each module are adjusted, so that the method can be suitable for evaluating the health condition of the bees under different conditions, and the evaluation error caused by the change of external factors is reduced. The method can improve the accuracy and timeliness of bee colony composition and health condition determination, and promote the development of bee genetic breeding work in China.
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Fig. 1 is a flow chart of the bee colony structure composition and health condition determination method based on machine vision.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention. It is to be understood that the following description is only illustrative of the present invention and is not to be construed as limiting the present invention.
The terms "comprises," "comprising," "includes," "including," "has," "having," "contains," "containing," or any other variation thereof, as used herein, are intended to cover a non-exclusive inclusion. For example, a composition, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such composition, process, method, article, or apparatus.
Furthermore, the description below of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily for the same embodiment or example. Further, the technical features of the embodiments of the present invention may be combined with each other as long as they do not conflict with each other.
The bee colony structure composition and health condition measuring method provided by the invention comprises the steps of firstly, collecting a honeybee comb image, carrying out gray level histogram processing on the image to obtain the gray level distribution condition of the honeybee comb image, and determining the threshold segmentation point of the gray level image, the average area and the average radius of the single bee metamorphosis; performing single threshold segmentation on the preprocessed image, judging whether sub-images obtained by segmentation have an overlapping phenomenon, and performing segmentation processing on an overlapping region with the area larger than the average area of a single bee by adopting asymmetric morphological erosion operation to obtain a completely separated single bee; then extracting color parameters of each bee by adopting a wavelet extreme value edge detection method, and counting the characteristic mean values of three sets of color systems, namely RGB, HIS and Lab, in the edge outline of a single bee to obtain color information of each bee egg, larva and adult bee in healthy and unhealthy states; and finally, performing data analysis on the extracted color characteristic information of the bees in a good health state and a disease state to obtain measurement parameters of the healthy bees and the disease bees, and evaluating the health condition of the bees.
Example 1
As shown in figure 1, the method for measuring the bee colony structure composition and the health condition comprises the following steps: firstly, collecting bee images; preprocessing the bee image; thirdly, object segmentation and extraction of the bee preprocessed image; fourthly, collecting the color characteristics of the bee larvae under different health conditions; measuring the color characteristics of the health condition of the bees.
(1) Bee image acquisition
The method comprises the steps of collecting images of a honeycomb with and without a honeycomb, which is put forward from a beehive, by using an industrial CCD camera, and reading the collected 24-bit RGB images into a computer according to a time sequence and storing the images.
(2) Image pre-processing
The method mainly comprises the steps of processing bee images acquired by a bee colony structure composition and health condition determination method based on machine vision by using software programming, preprocessing the acquired bee images by using a gray histogram method and a contour area detection method, wherein the gray histogram represents the number of pixels with each gray level in the images and reflects the frequency of each gray level in the images. The gray histogram operation can be used effectively for image enhancement, providing useful image statistics, and is easy to calculate in software. And (3) obtaining the gray distribution condition of the bee image through gray histogram processing, and determining the threshold segmentation point of the gray image, the average area and the average radius of a single bee.
(3) Object segmentation and extraction of bee preprocessed images
Performing single threshold segmentation on the preprocessed image to obtain segmented subimages, performing area comparison on the segmented subimages by using programming, and determining the number of bee eggs, larvae, pupae and bee-forming amount and the overlapping condition of bee-forming in each subimage: for the situation that the number of bees in the sub-image is more than 1, the bee overlapping phenomenon occurs. And (3) performing repeated corrosion on the subimages by adopting an asymmetric morphological processing method, determining the central point of the bee by using morphological asymmetric corrosion operation, and combining the mean value long and short diameters to obtain a single bee image. The central point and the mean value major-minor diameter of the bee are determined by using morphological asymmetric corrosion operation, so that the bee range is determined, and a single bee image can be obtained more accurately. And obtaining the distribution center of the image of the bee by multiple times of corrosion, using the center as a circular point, using the mean length and the length of a single bee to define the effective area of the bee, and performing overlapping bee segmentation to obtain each bee image so as to realize accurate segmentation of the bee image.
(4) Bee larva color feature Collection
The image is grayed by adopting a gray level image extreme value wavelet edge detection method, so that the image loses color information and is beneficial to gray level image enhancement. The median filtering technique is adopted to weaken the noise in consideration of the existence of salt and pepper noise. And accessing the pixels one by one in the edge detection range, extracting the RGB value of each pixel point, and determining the HIS and Lab color information of each pixel point according to the RGB value. And averaging the RGB, HIS and Lab of each pixel point to obtain the color information of the bees in different developmental stages. Extracting RGB color parameters in the region, obtaining HIS color parameters and Lab color parameters based on the RGB color parameters, and counting the characteristic mean values of three sets of RGB, HIS and Lab color systems in the edge contour of a single bee larva.
(5) Determination of bee larva health color characteristics
Establishing a relation model of healthy bee larva activity, RGB color parameters, HIS color parameters and Lab color parameters, determining characteristic parameters of healthy bees and diseased bees, establishing characteristic vectors of the healthy bees, giving a weight adjusting method of each component, evaluating the health condition of the bee larvae according to the characteristic vectors of the healthy bees, and finally evaluating the quantity of the bees based on machine vision and the measuring accuracy of the health condition measuring method by adopting a laboratory microscopic examination method and a visual observation method.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (3)
1. A bee colony structure composition and health condition determination method based on machine vision is characterized by comprising the following steps of:
s1: collecting structural images of bees at different development stages in a bee colony by adopting an industrial CCD camera combined method, and reading the collected 24-bit RGB images into a computer to obtain clear and original honeycomb images;
s2: on the basis of collecting an original honeycomb image, preprocessing the image by adopting a gray level histogram method and a contour area detection method to obtain an accurate image segmentation threshold, the average area of a single bee and the length and the short diameter of the average; determining the central point of the bee by using morphological asymmetric corrosion operation, and obtaining images of different metamorphosis types of single ovum, larva, pupa and adult bee by combining length and diameter;
s3: performing primary segmentation on the honeycomb image by adopting a single threshold value method to obtain a plurality of sub-images of each image, detecting the sub-images by adopting an area comparison method, and determining the number of eggs, larvae, pupae and adult bees of the bees included in the sub-images;
s4: for the situation that the number of the sub-images is more than 1, namely the bee overlapping phenomenon occurs, an asymmetric morphological processing method is adopted to corrode the sub-images for multiple times to obtain the distribution center of the image of the bee, the center is taken as a circular point, the effective area of the bee is defined by taking the mean radius of a single bee as a radius, and the overlapping bee is segmented to obtain the image of each bee;
s5: obtaining an accurate distribution area of each bee metamorphosis by adopting a gray level image extreme value wavelet edge detection method, extracting color parameters, namely extracting RGB color parameters in the area, and obtaining HIS color parameters and Lab color parameters on the basis of the RGB color parameters;
s6: establishing the characteristic parameters of the bee health condition according to the RGB color parameters, the HIS color parameters and the Lab color parameters, and realizing the measurement of the bee health condition by performing data analysis on the extracted color characteristic information of the bee in a good health state and a disease state.
2. The machine vision-based honeybee colony structural composition and health status measuring method as claimed in claim 1, wherein in step S5, the color parameter extraction is performed by the following steps:
s5.1: firstly, carrying out gray processing on an image to enable the image to lose color information and enhance the gray of the image;
s5.2: weakening the salt and pepper noise by adopting a median filtering technology;
s5.3: performing pixel-by-pixel access in the edge detection range, extracting the RGB value of each pixel point, and determining the HIS and Lab color information size of each pixel point according to the RGB value;
s5.4: and averaging the RGB, HIS and Lab of each pixel point to obtain the color information of each bee.
3. The method for determining bee colony structural composition and health status based on machine vision as claimed in claim 1, wherein the step of determining the health status of the bees in step S6 comprises the following steps:
s6.1: establishing a relation model of bee health and RGB color parameters, HIS color parameters and Lab color parameters;
s6.2: according to the relation model established in S6.1, determining the characteristic parameters measured in the bee health state and the disease state, establishing the characteristic vectors of the bee health measurement, and obtaining the weight regulation method of each component;
s6.3: determining the bee health according to the feature vector of the bee health determination;
s6.4: evaluating the measuring accuracy of the bee health condition measuring method based on machine vision by adopting a laboratory microscopic examination method and a visual observation method.
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| CN112734736A (en) * | 2021-01-15 | 2021-04-30 | 山东农业大学 | Accurate measuring and calculating method and device for swarm vigor based on computer vision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102288606A (en) * | 2011-05-06 | 2011-12-21 | 山东农业大学 | Pollen viability measuring method based on machine vision |
| CN107589769A (en) * | 2017-08-31 | 2018-01-16 | 西安理工大学 | A kind of intelligent beehive control system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102288606A (en) * | 2011-05-06 | 2011-12-21 | 山东农业大学 | Pollen viability measuring method based on machine vision |
| CN107589769A (en) * | 2017-08-31 | 2018-01-16 | 西安理工大学 | A kind of intelligent beehive control system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112734736A (en) * | 2021-01-15 | 2021-04-30 | 山东农业大学 | Accurate measuring and calculating method and device for swarm vigor based on computer vision |
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