CN115294482A - Edible fungus yield estimation method based on unmanned aerial vehicle remote sensing image - Google Patents
Edible fungus yield estimation method based on unmanned aerial vehicle remote sensing image Download PDFInfo
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Abstract
The invention discloses an edible fungus yield estimation method based on an unmanned aerial vehicle remote sensing image, belonging to the technical field of image data processing; the method comprises the following steps: sequentially collecting top views of a plurality of edible fungi planted in the field along a preset route by an unmanned aerial vehicle to obtain a plurality of third communicating domains similar to the first communicating domains in shape; obtaining a plurality of triangles III similar to the triangles I in shape; splicing all adjacent two binary images in sequence to obtain a complete image of the edible fungi in the planting field; and obtaining the total weight of the edible fungi in the planting field. The invention can realize the rapid seamless splicing of images; and further estimating the yield of the edible fungi according to the area of the pileus of the edible fungi.
Description
Technical Field
The invention relates to the technical field of image data processing, in particular to an edible fungus yield estimation method based on unmanned aerial vehicle remote sensing images.
Background
The edible fungi is a large-scale fungus which can be eaten as dishes or used as a medicine for treating diseases, has the characteristics of rich nutrition, unique flavor, higher medicinal value and the like, is deeply favored by people, is gradually integrated into the ranks of natural healthy foods in the current society, enables the edible fungi to be planted outdoors in a large range artificially, and needs to be subjected to yield estimation to provide data support for subsequent picking, processing and selling. The method for estimating the crop yield by using the remote sensing technology becomes a mainstream method, and the remote sensing method for estimating the crop yield is timely and efficient in data acquisition, low in cost and wide in monitoring range. However, high-precision yield estimation is difficult to realize by satellite remote sensing, so that the unmanned aerial vehicle platform with the advantages of low cost, strong maneuverability, simplicity in operation, large observation range and the like is developed rapidly, a suitable image with high precision can be provided, and a new way is provided for farmland information acquisition and yield estimation.
However, in order to obtain a complete edible fungus planting field, each image acquired by the unmanned aerial vehicle must have an overlapping part, and in order to make yield estimation more accurate, the acquired continuous images need to be accurately spliced. The traditional image stitching algorithm, such as an image stitching technology based on an SIFT algorithm and an image stitching technology based on an SURF algorithm, has low operation efficiency in the stitching process, needs to perform operations of feature point extraction, matching and the like for each image for multiple times in a stitching mode, consumes a large amount of time, and has low robustness. Especially, when the scale transformation, the visual angle transformation and the illumination change exist in the images, the matching precision between two continuous images is poor, and splicing gaps and ghost misplacement phenomena are easy to occur.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an edible fungus yield estimation method based on an unmanned aerial vehicle remote sensing image, the method performs binarization operation on the image, and performs image splicing by using the characteristics of edible fungi in the binary image according to the acquisition sequence of the image, so that the matching precision can be ensured, the matching speed is greatly improved, the splicing ghost image dislocation phenomenon can be effectively eliminated, and the rapid seamless splicing of the image can be realized; and further estimating the yield of the edible fungi according to the area of the pileus of the edible fungi.
The invention aims to provide an edible fungus yield estimation method based on an unmanned aerial vehicle remote sensing image, which comprises the following steps:
sequentially collecting a plurality of top views of edible fungi planted in the field along a preset air route by an unmanned aerial vehicle, wherein partial areas of two adjacent top views are overlapped; carrying out binarization processing on each top view to obtain a binary image; recording two adjacent binary images as a first image and a second image;
acquiring a first communication domain of each pileus in a first image; simultaneously acquiring a second connected domain of each pileus in a second image;
acquiring a plurality of third connected domains with similar shapes to the first connected domains from all the second connected domains according to the area and the perimeter of any first connected domain and the area and the perimeter of each second connected domain;
connecting every two first connected domains with the center points of two adjacent first connected domains, wherein the center points of the two first connected domains are not on the same straight line, so as to form a triangle I; according to any third connected domain with the shape similar to that of the first connected domain, two central points which are adjacent to the third connected domain and have central points which are not on the same straight line and comprise the second connected domain and/or the third connected domain are connected in pairs to form a triangle II, and a plurality of triangles II are sequentially obtained; acquiring a plurality of triangles III similar to the shape of the triangle I from all the triangles II according to the side length in the triangle I and the side length of each triangle II;
acquiring a triangle III overlapped with the triangle I by using the number of pixel points of the three connected domains corresponding to the triangle I and the number of pixel points of the non-overlapped region when the pixel points of the three connected domains corresponding to the triangle I are overlapped with the three connected domains corresponding to each triangle III; splicing the first image and the second image based on a triangle positioning mode according to the triangle I and a triangle III superposed with the triangle I; sequentially splicing all adjacent two binary images to obtain a complete image of the edible fungi in the planting field;
obtaining a plurality of pileus with different overlooking areas, weighing the pileus, measuring the overlooking areas of the pileus, and then performing smooth curve fitting according to the overlooking areas of the pileus and corresponding weight data to obtain an area and weight function;
overlooking the area of each mushroom cap in the complete image of the edible mushrooms in the planting field, and obtaining the total weight of the edible mushrooms in the planting field through an area and weight function.
In one embodiment, a plurality of third connected components with similar shapes to the first connected component are obtained according to the following steps:
acquiring a first probability that the shape of each first connected domain is similar to that of each second connected domain according to the area and the perimeter of any first connected domain and the area and the perimeter of each second connected domain;
and acquiring a plurality of third connected domains with similar shapes to the first connected domain from all the second connected domains according to the first probability that the first connected domain is similar to each second connected domain in shape.
In one embodiment, the first probability that the first connected component and each second connected component are similar in shape is obtained according to the following steps:
acquiring first communication domains of a plurality of pileus in a first image; obtaining a first area and a first perimeter of a first communication domain of each pileus;
obtaining a first circularity of each pileus according to the first area and the first perimeter of each first connected domain;
similarly, a second area, a second perimeter and a second circularity of each pileus of a second connected domain of each pileus in the second image are obtained;
and acquiring a first probability that the shapes of the first connected domain and each second connected domain are similar according to the first area, the first circumference and the first circularity of any first connected domain and the second area, the second circumference and the second circularity of each second connected domain.
In one embodiment, a plurality of triangles iii similar in shape to the triangle i are obtained by the following steps:
acquiring two nearest and next nearest first communication domains of the first communication domain and the first communication domain which are adjacent to the first communication domain and have central points which are not on the same straight line; connecting the first connected domain with the central points of two adjacent first connected domains to form a triangle I;
acquiring any third connected domain with a shape similar to that of the first connected domain, and acquiring two nearest and second nearest second connected domains and/or third connected domains, which are adjacent to the third connected domain and have different central points on the same straight line; connecting the third connected domain with the central points of two adjacent second connected domains and/or third connected domains to form a triangle II; sequentially obtaining a plurality of triangles II;
acquiring a second probability that the shape of the triangle I is similar to that of each triangle II according to the side length in the triangle I and the side length of each triangle II; and acquiring a plurality of triangles III similar to the shape of the triangle I from all the triangles II according to the second probability that the triangle I is similar to the shape of each triangle II.
In one embodiment, the first image and the second image are stitched according to the following steps:
forming a first sub-image by using the minimum external rectangle of the three connected domains corresponding to the triangle I; forming a second sub-image by using the minimum external rectangles of the three connected domains corresponding to any one triangle III, and sequentially acquiring a plurality of second sub-images;
acquiring a second subimage superposed with the first subimage according to the quantity of the pixels in three connected domains in the first subimage when the quantity of the pixels in the three connected domains is superposed with each second subimage;
and splicing the first image and the second image based on a triangle positioning mode according to a triangle III corresponding to the second subimage superposed with the first subimage and a triangle I corresponding to the first subimage.
In an embodiment, the second sub-image coinciding with the first sub-image is obtained by:
acquiring the coincidence probability of the first sub-image and each second sub-image according to the number of the pixel points in the three connected domains in the first sub-image and the number of the pixel points in the non-coincident connected domains when the number of the pixel points in the three connected domains in the first sub-image is overlapped with each second sub-image;
and acquiring second sub-images coincident with the first sub-images according to the coincidence probability of the first sub-images and each second sub-image.
The invention has the beneficial effects that: the invention provides an edible fungus yield estimation method based on unmanned aerial vehicle remote sensing images, which comprises the steps of extracting characteristics of collected remote sensing images according to HSV color characteristics of edible fungi to obtain a connected domain of each pileus; in order to reduce the calculated amount in the algorithm and improve the running speed of the algorithm, the image is subjected to binarization operation; according to the image acquisition sequence, the optimal selected target connected domain is screened out by acquiring the two adjacent binary image similar connected domains for preliminary matching, and the workload of subsequent matching is reduced; then, triangles in two adjacent images are respectively constructed based on similar connected domains, each connected domain is regarded as a point, accurate matching is carried out in a triangular positioning mode, the accuracy of splicing the two adjacent images is effectively improved, the adjacent images are spliced, the matching speed is greatly improved while the matching accuracy is ensured, the splicing ghost image dislocation phenomenon is effectively eliminated, and rapid seamless splicing of the images can be realized; and further estimating the yield of the edible fungi according to the area of the pileus of the edible fungi.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart showing the general steps of an embodiment of the method for estimating the yield of edible fungi based on the unmanned aerial vehicle remote sensing image.
FIG. 2 is an HSV image of shiitake mushrooms in an edible fungus.
FIG. 3 is a non-uniform quantization histogram corresponding to HSV images of mushrooms in edible fungi.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method mainly estimates the yield of the edible fungi planted outdoors, but for the edible fungi with larger planting areas, the overall yield of the edible fungi is difficult to estimate by visual inspection or aerial photography; because the height of aerial photography is high when an integral image is acquired, the pileus area of each edible fungus in the photographed image is difficult to estimate. Therefore, multiple remote sensing edible fungus planting field images are shot through low-flight aerial photography, and the acquired remote sensing images need to be accurately spliced for edible fungus yield estimation; the existing image splicing method has long time and low matching precision, and splicing gaps and ghost misplacement phenomena are easy to occur.
It should be noted that the present invention is mainly directed to the yield estimation of outdoor mushroom cultivation, the production of mushrooms is almost independent growth, and the pileus of each mushroom is clearly displayed in the collected image.
According to the method, the acquired remote sensing image is processed, extraction and identification are carried out according to HSV color characteristics of edible fungi, then binarization operation is carried out on the image, and image splicing is carried out according to the acquisition sequence of the image by utilizing the characteristics of the edible fungi in the binary image, so that the matching precision is ensured, the matching speed is greatly improved, the splicing ghost image dislocation phenomenon is effectively eliminated, and the rapid seamless splicing of the image can be realized; and further estimating the yield of the edible fungi according to the area of the pileus of the edible fungi.
The invention provides an edible fungus yield estimation method based on unmanned aerial vehicle remote sensing images, which is shown in figure 1 and comprises the following steps:
s1, sequentially collecting top views of a plurality of pieces of edible fungi planted in a field along a preset air route by an unmanned aerial vehicle, wherein partial areas of two adjacent top views are overlapped; carrying out binarization processing on each top view to obtain a binary image; recording two adjacent binary images as a first image and a second image;
in this embodiment, when the top view of the edible fungi is collected, in order to avoid the influence of the irradiation intensity of the solar rays, the top view of the edible fungi planted in the field is selected to be shot by flying by using an unmanned aerial vehicle in the early morning or evening without dazzling, and the top view of the edible fungi is obtained; wherein the top view of the edible fungi contains the fungus cover of the edible fungi. Be equipped with laser range finding sensor on the unmanned aerial vehicle, can acquire its height apart from ground in real time, the camera is adjusted the height and is made the image can clearly show domestic fungus information when shooing, and carries out the altitude mark that corresponds to every high accuracy remote sensing image of gathering.
It should be noted that, when the images are acquired, the flying heights of the unmanned aerial vehicles are different, so that the scene and the actual proportion in the shot images are different, and the images are adjusted by using geometric transformation. And then carrying out binarization operation on the image, and further carrying out image splicing according to the shape and position relation of the edible fungi. Therefore, in the process of acquiring the top views, the size proportion of each top view acquired by the acquisition equipment is equal according to the height of the acquisition equipment from the planting field.
In this embodiment, the image size of gathering is unanimous, however because unmanned aerial vehicle flying height difference can lead to scenery and actual proportion different in each image, need adjust and make it unified, specifically as follows:
acquiring the proportion of the size of each image to the corresponding actual size according to the imaging parameters of the camera on the unmanned aerial vehicle and the mark height of each acquired image, and sequentially acquiring a proportion set according to the image acquisition sequence
Wherein
Is the number of images acquired. Get the set
Mean value of
The standard proportion parameter is used for unifying the proportion of the size of the scene object in each image and the actual size of the scene, and the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
in order to set the standard ratio parameter,
is as follows
The ratio of the image of the web to the actual size,
is as follows
Scaling factor of the image. According to the scaling factor of each image
The image is geometrically transformed and adjusted so that the ratio of the image size to the corresponding actual size is uniform
(ii) a It should be noted that the scaling factor is obtained according to the imaging parameters of the camera on the unmanned aerial vehicle and the mark height of each acquired image; therefore, the zoomed image is obtained, namely the top view of the edible fungi.
In this embodiment, when binarization processing is performed on each top view, binarization processing is performed on the zoomed image, where the pixel points in the pileus region of the edible fungus are 1 and the pixel points in the background region are 0.
S2, acquiring a plurality of third connected domains similar to the first connected domain in shape; the method comprises the following specific steps:
acquiring a first communication domain of each pileus in a first image; simultaneously acquiring a second connected domain of each pileus in a second image;
acquiring a plurality of third connected domains with similar shapes to the first connected domains from all the second connected domains according to the area and the perimeter of any first connected domain and the area and the perimeter of each second connected domain;
it should be noted that the color can be used for most intuitively distinguishing different types of things, the calculation amount of color feature extraction is small, and the color of the mushroom cap surface of the edible mushroom is greatly different from the surrounding environment. In this embodiment, the histogram equalization algorithm is used to enhance the image acquired by the unmanned aerial vehicle, improve the visual effect of the image, and then the bilateral filtering is used to perform denoising processing on the image. The image is then converted from the RGB color space to the HSV color space, as shown in fig. 2 and 3, this embodiment uses 16:4:4, obtaining the HSV image in fig. 2 and the non-uniform quantization histogram corresponding to the HSV image in fig. 2 in fig. 3.
The color component characteristics of the mushrooms in the current planting field are counted, a threshold range is set according to the color component characteristic values, and the edible mushroom area is judged when the color non-uniform component characteristic value of the pixel point is within the threshold range. And finally, acquiring the connected domain of each pileus by using morphological opening operation and filling operation. Therefore, a first communication domain corresponding to each pileus is obtained from the first image; and acquiring a second connected domain of each pileus from the second image.
In this embodiment, the first connected component in the first image is marked in the following way:
taking the upper left corner of the collected first image as a starting point, and traversing pixel points by pixel points at the edge of the image in a clockwise direction; the first communication domain of the pileus of the edible fungus with the closest initial traversal pixel point is taken and marked
Taking the first connected domain of the edible fungus with the shortest traversal pixel point distance, and if the connected domain is still the same
Then, the first communication domain of the edible fungus with the closest traversal pixel point distance is taken down until a new first communication domain of the edible fungus is obtained and marked as
Thereby obtaining a first set of pileus communication domains at the edge of the first image near the second image
Wherein, in the step (A),
number of first communication domains for the labeled pileus.
In this embodiment, a plurality of third connected components with similar shapes to the first connected component are obtained according to the following steps:
acquiring a first probability that the shape of each first connected domain is similar to that of each second connected domain according to the area and the perimeter of any first connected domain and the area and the perimeter of each second connected domain;
and acquiring a plurality of third connected domains with similar shapes to the first connected domain from all the second connected domains according to the first probability that the first connected domain is similar to each second connected domain in shape.
The first probability that the shape of the first connected domain is similar to that of each second connected domain is obtained according to the following steps:
acquiring first communication domains of a plurality of pileus in a first image; and obtaining a first area and a first perimeter of a first connected domain of each pileus;
obtaining a first circularity of each pileus according to the first area and the first perimeter of each first connected domain;
similarly, a second area, a second perimeter and a second circularity of each pileus of a second connected domain of each pileus in the second image are obtained;
and acquiring a first probability that the shape of the first connected domain is similar to that of each second connected domain according to the first area, the first circumference and the first circularity of any first connected domain and the second area, the second circumference and the second circularity of each second connected domain.
Note that, in the present embodiment, statistics are collected
First pileus first communication domain
Is calculated, so its first circularity E is calculated as:
in the formula (I), the compound is shown in the specification,
representing a first connected domain
A first circularity of (a);
representing a first connected domain
The area of (a) is,
representing a first connected domain
Perimeter. Wherein the first connected domain
Area of and first connected domain
The perimeter is according to the first communication domain
The number of internal pixels and the number of edge pixels. As the mushroom cap communicating area of the edible mushroom is mostly circular, the circular degree of each mushroom cap is represented by the circularity. Sequentially calculate the sets
The circularity of each first communication domain; similarly, a second area and a second perimeter of a second connected domain of each pileus in the second image and a second circularity of each pileus are obtained.
First connected domain
The first probability of similarity to each second connected component shape is calculated as follows:
in the formula (I), the compound is shown in the specification,
representing a first connected domain
A first probability of being similar in shape to any of the second connected components;
Sequentially calculating each second connected domain and each first connected domain
A first probability that the shapes of (a) and (b) are similar; set the first probability threshold of 99%, if
If the two pileus connected domains are similar, otherwise, the two pileus connected domains are not related. Sequentially acquiring the first communication domain in the second image
The number of similar second connected domains is marked as C;
if C is 0, then determine the first communication domain
Not in the overlapping region of the two images, and returning the collection
First connected domain of middle and next pileus
Calculating the first connection field
Similar connected domains; if the number of the similar connected domains still obtained is 0, the calculation is carried out until the first connected domain is calculated
Similar connectionA pass domain; wherein the content of the first and second substances,
number of first communication domains for the labeled pileus;
if C is larger than 0, the connected domains with the number of C, namely the connected domains with the first connected domain
A similar plurality of third connected domains; at least three points which are not on a straight line are needed for image splicing to be positioned, so that splicing malposition ghosting is prevented; a next determination is made to determine the most similar connected domain from the third connected domains. It should be noted that the purpose of setting the first probability threshold to 99% is to obtain the most similar connected components and avoid larger errors in subsequent triangulation.
S3, obtaining a plurality of triangles III similar to the triangle I in shape; the method comprises the following specific steps:
connecting every two first connected domains with the center points of two adjacent first connected domains, wherein the center points of the two first connected domains are not on the same straight line, so as to form a triangle I; according to any third connected domain with the shape similar to that of the first connected domain, two central points which are adjacent to the third connected domain and have central points which are not on the same straight line and comprise the second connected domain and/or the third connected domain are connected in pairs to form a triangle II, and a plurality of triangles II are sequentially obtained; obtaining a plurality of triangles III similar to the shape of the triangle I from all the triangles II according to the side length in the triangle I and the side length of each triangle II;
wherein, a plurality of triangles III similar to the triangle I in shape are obtained according to the following steps:
acquiring two first communication domains which are adjacent to the first communication domain and have central points which are not nearest and next nearest on the same straight line; connecting the central points of the first communication domains and two adjacent first communication domains to form a triangle I;
any third connected domain with the shape similar to that of the first connected domain is obtained, and the two nearest and next nearest connected domains with central points not on the same straight line are obtained and comprise the second connected domain and/or the third connected domain; connecting the third connected domain and the central points of two adjacent connected domains including the second connected domain and/or the third connected domain to form a triangle II; sequentially obtaining a plurality of triangles II;
acquiring a second probability that the shape of the triangle I is similar to that of each triangle II according to the side length in the triangle I and the side length of each triangle II; and acquiring a plurality of triangles III similar to the shape of the triangle I from all the triangles II according to the second probability that the triangle I is similar to the shape of each triangle II.
In the present embodiment, in order to obtain a plurality of triangles iii similar to the shape of the triangle i, the following are specific:
taking a first connected domain away from the pileus in the first image
Counting central points of the three first communication domains, and if the three central points are positioned on a straight line, taking the distance between the remaining first communication domains and the first communication domain
The nearest one replaces the relatively distant one of the two first communication paths until the three center points, which form a triangle i, are not in a straight line. Connecting the first communication domain
Is set as the connecting edge of the center point of the first connecting domain closest to the center point of the first connecting domain
Having a length of
Connecting the first communication domain
Is next closest to the connecting edge of the central point of the first connecting domainIs set as
Having a length of
Setting the connecting edge between the central point of the nearest first connected domain and the central point of the next nearest first connected domain as
Having a length of
。
Similarly, with the first communication domain
The third connected domain is obtained, and the nearest and second nearest two with different central points on the same straight line comprise the second connected domain and/or the third connected domain; connecting the third connected domain and the central points of two adjacent connected domains including the second connected domain and/or the third connected domain to form a triangle II; sequentially obtaining a plurality of triangles II; the connecting edge of the third connected domain and the nearest one including the center point of the second connected domain or the third connected domain is set as
Having a length of
(ii) a The third connected domain and the next nearest connected edge including the center point of the second connected domain or the third connected domain are set as
Having a length of
(ii) a The nearest center point including the second connected domain or the third connected domain is connected with the next nearest oneA connecting edge including the center point of the second connected domain or the third connected domain is set as
Having a length of
；
Acquiring a second probability that the shape of the triangle I is similar to that of each triangle II according to the side length in the triangle I and the side length of each triangle II; the calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
representing a second probability that the shape of the triangle I is similar to that of any one of the triangles II;
Similarly, in the second image, the first communication domain is acquired
All triangles II formed by each similar third connected domain and the connected domains which are adjacent to the third connected domain and the center points of which are not on the same straight line; calculating second probabilities that the shapes of the triangles II and the triangles I are similar one by one;
a second probability threshold of 99% is set,if it is
Judging that the corresponding side lengths of the triangle II and the triangle I are similar, and further knowing that the corresponding included angles are similar; otherwise it is judged not relevant. Obtaining the first communication domain from the C third communication domains in the second image
The number of third connected domains corresponding to a triangle II similar to the triangle I is marked as D;
if D is 0, all triangles II and first connected domains formed by C third connected domains in the second image are judged
The formed triangles I are dissimilar and return to the collection
First connected domain of middle and next pileus
Then sequentially calculating the first connection domain
Similar connected domains, and computing the first connected domain
The number of the third connected domains corresponding to the triangles II forming the triangles similar to the triangle I is calculated to the first connected domain in sequence if the number of the third connected domains corresponding to the triangle II still obtained is 0
；
If D is larger than 0, obtaining triangles similar to the triangle I from all the triangles II, and marking the triangles similar to the triangle I as a plurality of triangles III; then the next step of judgment is carried out, and the triangle which is the most coincident with the triangle I is selected from the plurality of triangles III.
It should be noted that the purpose of setting the second probability threshold to 99% is to obtain the most similar triangles and avoid larger errors in subsequent triangulation.
S4, sequentially splicing all adjacent two binary images to obtain a complete image of the edible fungi in the planting field; the method comprises the following specific steps:
acquiring a triangle III overlapped with the triangle I by utilizing the number of pixel points of three connected domains corresponding to the triangle I and the number of pixel points of an un-overlapped area when the three connected domains corresponding to each triangle III are overlapped; splicing the first image and the second image based on a triangle positioning mode according to the triangle I and a triangle III superposed with the triangle I; sequentially splicing all adjacent two binary images to obtain a complete image of the edible fungi in the planting field;
the first image and the second image are spliced according to the following steps:
forming a first sub-image by using the minimum circumscribed rectangle of the three connected domains corresponding to the triangle I; forming a second sub-image by using the minimum external rectangles of the three connected domains corresponding to any one triangle III, and sequentially acquiring a plurality of second sub-images;
acquiring a second sub-image superposed with the first sub-image according to the number of pixels in three connected domains in the first sub-image when the number of pixels in the three connected domains is superposed with each second sub-image;
and splicing the first image and the second image based on a triangle positioning mode according to a triangle III corresponding to the second subimage superposed with the first subimage and a triangle I corresponding to the first subimage.
It should be noted that, the second sub-image that coincides with the first sub-image is obtained according to the following steps:
acquiring the coincidence probability of the first sub-image and each second sub-image according to the number of the pixel points in the three connected domains in the first sub-image and the number of the pixel points in the non-coincident connected domains when the number of the pixel points in the three connected domains in the first sub-image is overlapped with each second sub-image;
and acquiring second sub-images coincident with the first sub-images according to the coincidence probability of the first sub-images and each second sub-image.
In this embodiment, the first sub-image formed by the minimum bounding rectangle of the three connected domains corresponding to the triangle i is recorded as
(ii) a Taking the minimum circumscribed rectangle of three connected domains corresponding to any one triangle III to form a second sub-image
；
Let the first connection region in triangle I
And the central point of (2) and the first communication area in the triangle III
The central points of the corresponding connected domains are overlapped, and the image is rotated by taking the point as the center
Make the connecting side of the triangle I and the triangle III
And with
Angle and connecting edge of
And
the sum of the included angles is minimum;
counting the number of pixel points of which the first sub-image and the second sub-image are non-overlapped regions at the moment
Then, the number of pixel points in the three connected domains selected in the first image is calculated
Obtaining the similarity probability of the two sub-images
Is as follows; the calculation formula of the coincidence probability of the first sub-image and each second sub-image is as follows:
in the formula (I), the compound is shown in the specification,
representing the coincidence probability of the first sub-image and each second sub-image;
representing the number of pixel points of a non-overlapped connected domain when the first subimage is overlapped with the second subimage;
and representing the number of pixel points in three connected domains in the first subimage. The number of pixels in which two sub-images are non-overlapped regions
The larger the more, the similar probability thereof
The smaller.
Calculating a second sub-image formed by three connected domains corresponding to each triangle III in the second image and a first sub-image formed by three connected domains corresponding to the triangle I in the first image one by one, and rotating the coincidence probability
Setting a coincidence probability threshold of 99%, if
If the two sub-images are similar, otherwise, the two sub-images are not related. It should be noted that the threshold of the coincidence probability is set to 99% mainly to avoid the occurrence of localization ghost.
And sequentially acquiring the number of all second sub-images formed in the second image and all overlapped second sub-images of the first sub-images in the first image after rotation, and recording as R.
If R is 0, judging that all the second sub-images are not coincident with the first sub-image, and returning to the collection
First connected domain of middle and next pileus
Then sequentially calculating the first communication domain
Similar connected domain, computing the first connected domain
The number of third connected domains corresponding to a triangle II similar to the triangle I is formed, the number of all second sub-images which are overlapped with the first sub-image in the first image after rotation is calculated, and if all the second sub-images which are obtained are not overlapped with the first sub-image, the number of the third connected domains is calculated from the first connected domain to the first connected domain in sequence
；
If R is larger than 0, judging that all second sub-images have second sub-images coincident with the first sub-images, and taking coincidence probability in the number of all second sub-images
Second at maximumA triangle III corresponding to the subimage is used as a triangle superposed with the triangle I;
using the central points of three connected domains corresponding to the triangle III coincident with the triangle I as positioning points, and enabling the first connected domain of the pileus in the first image to be positioned based on a triangle positioning mode
And the central point of (2) and the first communication area in the second image
The center points of the corresponding connected domains are overlapped, and then the second image is rotated to ensure that the triangle I and the connecting edge corresponding to the overlapped triangle III are connected
And with
Angle and connecting edge of
And
the sum of the included angles is minimum, the superposition is regarded as successful, and the pixel value of the pixel point corresponding to the overlapped part of the first image and the second image is set as 1; the splicing of the first image and the second image is completed; and sequentially splicing all adjacent two binary images to obtain a complete image of the edible fungi in the planting field.
The image splicing process is performed by using binary images, the values of pixel points are only 0 and 1, the calculated amount in the algorithm is greatly reduced, the running speed of the algorithm is improved, then preliminary matching is performed according to the shape characteristics of the pileus connected domain, then accurate matching is performed by using a triangular positioning mode according to the connected domain position, and the splicing ghost image dislocation phenomenon can be effectively prevented.
S5, obtaining the total weight of edible fungi in the planting field; the method comprises the following specific steps:
obtaining a plurality of pileus with different overlooking areas, weighing the pileus, measuring the overlooking areas of the pileus, and then performing smooth curve fitting according to the overlooking areas of the pileus and corresponding weight data to obtain an area and weight function;
overlooking the area of each mushroom cap in the complete image of the edible mushrooms in the planting field, and obtaining the total weight of the edible mushrooms in the planting field through an area and weight function.
In this embodiment, a complete image of the current planting field and a pileus connected domain of the edible fungi in the image are obtained, the area of the pileus connected domain is counted, and an area set is obtained
In which
The number of edible fungi in the current planted field is shown. The corresponding ratio of the image size to the actual size is known as
The actual overlook area of each cap is set
Comprises the following steps:
the method comprises the following steps of taking 50 edible fungi with different overlooking area sizes of the fungus covers, weighing the edible fungi and measuring the overlooking area of the fungus covers, and then carrying out smooth curve fitting according to the overlooking area of the 50 groups of fungus covers and corresponding weight data to obtain an area and weight function, wherein the formula is as follows:
wherein y is the weight of the edible fungi, x is the overlooking area of each actual pileus,
as a function of area and weight.
Collecting the actual overlook area of the mushroom cap
Substituting the weight function into the area to obtain the corresponding weight set of the edible fungi
(ii) a Weight set of edible fungi
The weight calculation formula of each edible fungus is as follows:
in the formula (I), the compound is shown in the specification,
shows the actual plan view area of the cap of the kth edible fungus,
as a function of the area and the weight,
is the weight of the kth edible fungus,
representing the number of edible fungi in the current planting field;
the total weight H of the edible fungi in the current planting field is obtained through the obtained weight calculation of each edible fungus, and the specific calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
is the weight of the kth edible fungus,
representing the number of the edible fungi in the current planting field, and H represents the weight estimation of the edible fungi in the current planting field; thereby calculating the total weight of the edible fungi in the planting field.
In conclusion, according to the method for estimating the yield of the edible fungi based on the unmanned aerial vehicle remote sensing image, the characteristics of the acquired remote sensing image are extracted according to HSV color characteristics of the edible fungi, and the connected domain of each pileus is obtained; in order to reduce the calculated amount in the algorithm and improve the running speed of the algorithm, the image is subjected to binarization operation; according to the image acquisition sequence, the optimal selected target connected domain is screened out by acquiring the two adjacent binary image similar connected domains for preliminary matching, and the workload of subsequent matching is reduced; then, triangles in two adjacent images are respectively constructed based on similar connected domains, each connected domain is regarded as a point, accurate matching is carried out in a triangular positioning mode, the accuracy of splicing the two adjacent images is effectively improved, the adjacent images are spliced, the matching speed is greatly improved while the matching accuracy is ensured, the splicing ghost image dislocation phenomenon is effectively eliminated, and rapid seamless splicing of the images can be realized; and further estimating the yield of the edible fungi according to the area of the pileus of the edible fungi.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An edible fungus yield estimation method based on unmanned aerial vehicle remote sensing images is characterized by comprising the following steps:
sequentially collecting a plurality of top views of edible fungi planted in the field along a preset air route by an unmanned aerial vehicle, wherein partial areas of two adjacent top views are overlapped; carrying out binarization processing on each top view to obtain a binary image; marking two adjacent binary images as a first image and a second image;
acquiring a first communication domain of each pileus in a first image; simultaneously acquiring a second connected domain of each pileus in a second image;
acquiring a plurality of third connected domains with similar shapes to the first connected domains from all the second connected domains according to the area and the perimeter of any first connected domain and the area and the perimeter of each second connected domain;
connecting every two first connected domains with the centers of two adjacent first connected domains, wherein the centers of the two first connected domains are not on the same straight line, so as to form a triangle I; according to any third connected domain with the shape similar to that of the first connected domain, two central points which are adjacent to the third connected domain and have central points which are not on the same straight line and comprise the second connected domain and/or the third connected domain are connected in pairs to form a triangle II, and a plurality of triangles II are sequentially obtained; acquiring a plurality of triangles III similar to the shape of the triangle I from all the triangles II according to the side length in the triangle I and the side length of each triangle II;
acquiring a triangle III overlapped with the triangle I by using the number of pixel points of the three connected domains corresponding to the triangle I and the number of pixel points of the non-overlapped region when the pixel points of the three connected domains corresponding to the triangle I are overlapped with the three connected domains corresponding to each triangle III; splicing the first image and the second image based on a triangle positioning mode according to the triangle I and a triangle III superposed with the triangle I; sequentially splicing all adjacent two binary images to obtain a complete image of the edible fungi in the planting field;
obtaining a plurality of pileus with different overlooking areas, weighing the pileus, measuring the overlooking area of the pileus, and then performing smooth curve fitting according to the overlooking areas of the pileus and corresponding weight data to obtain an area and weight function;
overlooking the area of each mushroom cap in the complete image of the edible mushrooms in the planting field, and obtaining the total weight of the edible mushrooms in the planting field through an area and weight function.
2. The method for estimating the yield of edible fungi based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the plurality of third connected domains with similar shapes of the first connected domain are obtained according to the following steps:
acquiring a first probability that the shape of each first connected domain is similar to that of each second connected domain according to the area and the perimeter of any first connected domain and the area and the perimeter of each second connected domain;
and acquiring a plurality of third connected domains with similar shapes to the first connected domain from all the second connected domains according to the first probability that the first connected domain is similar to each second connected domain in shape.
3. The method for estimating the yield of the edible fungi based on the unmanned aerial vehicle remote sensing image as claimed in claim 2, wherein the first probability that the shape of the first connected domain is similar to that of each second connected domain is obtained according to the following steps:
acquiring first communication domains of a plurality of pileus in a first image; and obtaining a first area and a first perimeter of a first connected domain of each pileus;
obtaining a first circularity of each pileus according to the first area and the first perimeter of each first connected domain;
similarly, a second area, a second perimeter and a second circularity of each pileus of a second connected domain of each pileus in the second image are obtained;
and acquiring a first probability that the shapes of the first connected domain and each second connected domain are similar according to the first area, the first circumference and the first circularity of any first connected domain and the second area, the second circumference and the second circularity of each second connected domain.
4. The method for estimating the yield of edible fungi based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the plurality of triangles III similar to the triangle I in shape are obtained according to the following steps:
acquiring two nearest and next nearest first communication domains of the first communication domain and the first communication domain which are adjacent to the first communication domain and have central points which are not on the same straight line; connecting the central points of the first communication domains and the central points of two adjacent first communication domains to form a triangle I;
acquiring any third connected domain with a shape similar to that of the first connected domain, and acquiring two nearest and next-nearest second connected domains and/or third connected domains which are adjacent to the third connected domain and have different central points on the same straight line; connecting the third connected domain with the central points of two adjacent second connected domains and/or third connected domains to form a triangle II; sequentially obtaining a plurality of triangles II;
acquiring a second probability that the shape of the triangle I is similar to that of each triangle II according to the side length in the triangle I and the side length of each triangle II; and acquiring a plurality of triangles III similar to the shape of the triangle I from all the triangles II according to the second probability that the triangle I is similar to the shape of each triangle II.
5. The method for estimating the yield of the edible fungi based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the first image and the second image are spliced according to the following steps:
forming a first sub-image by using the minimum circumscribed rectangle of the three connected domains corresponding to the triangle I; forming a second sub-image by using the minimum external rectangles of the three connected domains corresponding to any one triangle III, and sequentially acquiring a plurality of second sub-images;
acquiring a second sub-image superposed with the first sub-image according to the number of pixels in three connected domains in the first sub-image when the number of pixels in the three connected domains is superposed with each second sub-image;
and splicing the first image and the second image based on a triangle positioning mode according to a triangle III corresponding to a second sub-image superposed with the first sub-image and a triangle I corresponding to the first sub-image.
6. The method for estimating the yield of edible fungi based on the unmanned aerial vehicle remote sensing image according to claim 5, wherein the second sub-image which is coincident with the first sub-image is obtained according to the following steps:
acquiring the coincidence probability of the first subimage and each second subimage according to the number of the pixels in the three connected domains in the first subimage which are not coincident with the number of the pixels in the connected domains when the number of the pixels in the three connected domains in the first subimage is overlapped with each second subimage;
and acquiring second sub-images coincident with the first sub-images according to the coincidence probability of the first sub-images and each second sub-image.
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