CN117788395A - System and method for extracting root phenotype parameters of pinus massoniana seedlings based on images - Google Patents
System and method for extracting root phenotype parameters of pinus massoniana seedlings based on images Download PDFInfo
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Abstract
The invention discloses an image-based system and method for extracting phenotype parameters of a pinus massoniana seedling root system, comprising the following steps: collecting an image of a root system of a pinus massoniana seedling and correcting the image; preprocessing the corrected image; performing image refinement operation on the processed image by using an improved ZhangSuen skeleton extraction algorithm; searching endpoints of pixel points in the root system skeleton diagram; performing turning point search on pixel points in the root skeleton diagram; dividing main roots and primary lateral roots of the root systems of the pinus massoniana seedlings by adopting a priority path searching algorithm; extracting root system phenotype parameters of the pinus massoniana seedlings based on the main roots and the primary lateral roots of the segmented pinus massoniana seedling roots; the system and the method can rapidly and accurately measure the root phenotype parameters of the pinus massoniana seedlings, avoid high cost and high error of manual measurement, and do not need to rely on expensive detection equipment.
Description
Technical Field
The invention relates to the field of intelligent forestry, in particular to an image-based system and method for extracting phenotype parameters of a pinus massoniana seedling root system.
Background
The pinus massoniana is one of forestation pioneer tree species in subtropical regions of China, has rapid growth, high yield and good quality, is a good raw material for building and industrial production, is popular in the market, and has good economic effect. The root phenotype data of the seedlings are necessary conditions for judging the strong seedlings of the improved varieties, so that the root phenotype parameter extraction method of the pinus massoniana seedlings is researched to meet the rapid and efficient detection requirement of the quality evaluation of the seedlings in the forest farm, and the method has very important theoretical and practical significance.
The traditional method for manually measuring root system parameters is high in cost, large in error and low in efficiency, and the effect of the existing three-dimensional reconstruction equipment in a general laboratory on detecting finer root systems is difficult to meet the evaluation requirement, so that a pinus massoniana seedling quality evaluation system is difficult to construct accurately.
Disclosure of Invention
The invention aims to solve the technical problem of providing a system and a method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on images with low cost aiming at the defects of the prior art.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
an image-based method for extracting phenotype parameters of a pinus massoniana seedling root system comprises the following steps:
step 1: collecting an image of a root system of a pinus massoniana seedling and correcting the image;
step 2: preprocessing the corrected image;
step 3: carrying out image refinement operation on the processed image by using an improved ZhangSuen skeleton extraction algorithm to realize root skeleton extraction of pinus massoniana seedlings;
step 4: constructing root skeleton end point judging conditions based on the root skeleton diagram obtained in the step 3, searching end points of pixel points in the root skeleton diagram, judging the pixel points meeting the judging conditions as end points of a root system, and realizing the end point positioning of the root system of the pinus massoniana seedlings;
step 5: constructing a root system skeleton turning point judgment condition based on the root system skeleton diagram obtained in the step 3, carrying out turning point search on pixel points in the root system skeleton diagram, judging the pixel points meeting the judgment condition as turning points of a root system, and realizing the positioning of the turning points of the root system of the pinus massoniana seedlings;
step 6: based on the end points and the turning points of the root system skeleton of the pinus massoniana seedlings positioned in the step 4 and the step 5, dividing the main root and the primary lateral root of the root system of the pinus massoniana seedlings by adopting a priority path searching algorithm with priority directivity;
step 7: and extracting root system phenotype parameters of the pinus massoniana seedlings based on the main roots and the primary lateral roots of the segmented pinus massoniana seedling roots.
As a further improved technical scheme of the invention, the step 1 specifically comprises the following steps:
step 1.1, digging pinus massoniana seedlings from soil, cleaning soil impurities on a pinus massoniana seedling root system by water flow, and spreading the cleaned pinus massoniana seedling root system on a white background plate which is in clear contrast with a black root system;
and 1.2, placing a 3X 4 checkerboard calibration plate at a fixed position on a white background plate, wherein the size of the checkerboard is 20X 20mm, and the checkerboard calibration plate is used for calibrating a camera and acquiring internal and external parameters of the camera, and correcting root system images according to the acquired internal and external parameters of the camera after acquiring images of the root systems of pinus massoniana seedlings.
As a further improved technical scheme of the present invention, the step 2 specifically includes:
step 2.1, performing gray level transformation operation on the corrected root system image by a weighted average method, and converting the three channels into single channels, wherein the weighted average method has the formula as follows:
where G (x, y) is the pixel value of the output image, R is the pixel value of the R channel in the color image, G is the pixel value of the G channel in the color image, B is the pixel value of the B channel in the color image, w R To give weight to the R component, w G To give weight to the G component, w B To give a weight value to the B component;
step 2.2, executing a global thresholding binarization operation on the gray level image, wherein the global threshold range is (125,255), and the global thresholding formula is as follows:
wherein T is the selected threshold value, and b (x, y) is the pixel point of the output image;
step 2.3, removing noise which is not eliminated or enhanced after image binarization by using median filtering, wherein the size of a filtering kernel of a median filtering algorithm is 5 multiplied by 5;
and 2.4, filling and eliminating gaps and small holes on the root system image by using a closed operation in morphological operation, and keeping the integral integrity of the root system skeleton, wherein the size of a closed operation kernel is 5 multiplied by 5.
As a further improved technical scheme of the present invention, the step 3 specifically includes:
step 3.1, based on the preprocessed masson pine seedling root system image, setting a pixel point with a pixel value of 255 in the root system image as 1, using an improved ZhangSuen skeleton extraction algorithm for the root system image, reducing the image skeleton from a multi-pixel width to a unit pixel width, and keeping the basic shape and trend of the skeleton;
step 3.2, wherein the improved zhangsuin algorithm mainly adjusts the calculation area of the root system image to the minimum bounding box range of the pinus massoniana seedling root system, and the minimum bounding box range of the seedling root system is updated once after each iteration calculation; adjusting the priority of a judgment condition of image calculation, wherein the judgment condition of image calculation is that if the point currently traversed is a boundary point, the point is deleted from the root point set, and the condition is set as priority judgment; when there are no points that can be deleted continuously, the algorithm ends, generating a skeleton of the calculation region.
As a further improved technical scheme of the present invention, the step 4 specifically includes:
step 4.1, setting all pixel points with the pixel value of 255 in the image as 1 based on the pinus massoniana seedling root system thinning skeleton diagram obtained in the step 3, wherein the pixel value of a point on a root system skeleton line in the image is 1, and the pixel value of a non-skeleton point is 0;
step 4.2, constructing judging conditions of root system endpoints of pinus massoniana seedlings, traversing each pixel value non-zero point on a root system skeleton diagram, carrying out marker search on a 3X 3 neighborhood range of the pinus massoniana seedling by taking a current point as a center, marking the pixel Points meeting the judging conditions in the neighborhood as the root system endpoints, and adding the pixel Points into an endpoint set end_points; wherein the non-zero point of the pixel value is called as non-zero pixel point for short;
judging conditions of the root system end points of the pinus massoniana seedlings are as follows:
judgment condition (1): within the 3 x 3 neighborhood of the current point, there is and only one non-zero pixel point;
judgment condition (2): within the 3×3 neighborhood of the current point, there are two adjacent non-zero pixel points;
if the judgment condition (1) or the judgment condition (2) is satisfied, the current point is judged as an endpoint.
As a further improved technical scheme of the present invention, the step 5 specifically includes:
step 5.1, setting all pixel points with the pixel value of 255 in the image as 1 based on the pinus massoniana seedling root system thinning skeleton diagram obtained in the step 3;
step 5.2, constructing judging conditions of root system Turning Points of pinus massoniana seedlings, traversing all non-zero pixel Points in a root system skeleton diagram, performing marker search on a 3X 3 neighborhood range of a current point, marking the pixel Points in the neighborhood which meet the judging conditions as the root system Turning Points, and adding the pixel Points into a turning_points set;
the judging conditions of the root system turning points of the pinus massoniana seedlings are as follows:
judgment condition (1): in the 3X 3 neighborhood of the current point, at least three pixel points in P2, P4, P6 and P8 are non-zero pixel points;
judgment condition (2): in the 3X 3 neighborhood of the current point, at least three pixel points in P3, P5, P7 and P9 are non-zero pixel points;
judgment condition (3): three non-zero pixel points are arranged in the 3X 3 neighborhood of the current point, and are not adjacent to each other;
judgment condition (4): within the 3 x 3 neighborhood of the current point, there are four non-zero pixel points, of which at most two are adjacent;
if the judging condition (1), the judging condition (2), the judging condition (3) or the judging condition (4) is met, judging that the current point is a turning point;
wherein, P2-P9 represent 3X 3 neighborhood of the current point, P2 is located right above the current point, P3 is located right above the current point, P4 is located right side of the current point, P5 is located right below the current point, P6 is located right below the current point, P7 is located left below the current point, P8 is located left side of the current point, and P9 is located left above the current point.
As a further improved technical scheme of the present invention, the step 6 specifically includes:
step 6.1, setting a pixel point with a pixel value of 255 in a masson pine seedling root system skeleton diagram as 1, firstly setting an endpoint A at the top of the root system skeleton diagram, namely a first endpoint A in an endpoint set end_points as a current starting point based on the root system endpoints and the turning Points positioned in the step 4 and the step 5, setting the pixel value of the current point as 0, and deleting the current point from the endpoint set end_points;
step 6.2, judging whether non-zero pixel points exist in a 3×3 neighborhood of the current point, if the non-zero pixel points exist in P2, P4, P6 and P8, preferentially selecting the non-zero pixel point with the minimum index value in the 3×3 neighborhood as a next judging traversal point, and if the non-zero pixel points do not exist in P2, P4, P6 and P8, selecting the non-zero pixel points with the minimum index values in the 3×3 neighborhood of P3, P5, P7 and P9 as the next judging traversal points;
step 6.3, if the current traversing point is neither an endpoint nor a turning point, setting the pixel value of the point to 0, and jumping back to the step 6.2 to continue to execute the loop;
step 6.4, if the current traversing point is a Turning point, setting the pixel value of the point to 0, adding the pixel value to a passing Turning point set path_turning_points, and jumping back to the step 6.2 to continue to execute the loop;
step 6.5:
6.5.1, if the current traversing point is an endpoint, setting the endpoint pixel value to 0, deleting the endpoint pixel value from the endpoint set end_points, and storing a root system path from the starting point to the endpoint;
6.5.2 then, jumping the current traversal point back to the last point in the set of Points through path_turning_points;
6.5.3, judging whether non-zero pixel points exist in the 3 multiplied by 3 neighborhood of the jumping-back point;
6.5.4, if the non-zero pixel points exist in the 3 multiplied by 3 neighborhood of the jumping-back point, continuing to execute the loop in the jumping-back step 6.2;
6.5.5 if there is no non-zero pixel point in the 3×3 neighborhood of the skipped point, deleting the skipped point from path_turning_points, and skipping the current traversal point back to the last point in the updated path_turning_points set, returning to 6.5.3 for executing the loop;
step 6.6, ending the algorithm when the endpoint set end_points is an empty set;
step 6.7, calculating the Euclidean distance d of all the stored root system paths, wherein the root system path corresponding to the longest Euclidean distance is the main root of the root system of the pinus massoniana seedlings, and the Euclidean distance of the main root is recorded as d M ；
Where n is the total number of points on the root path; x is x i ，y i Coordinates of points;
step 6.8, the turning point on the root system main root is a first-stage turning point, and the longest root stretched out by taking the first-stage turning point as a starting point is a first-stage lateral root; therefore, deleting the points on the main root from the original skeleton diagram, and repeatedly and circularly executing the steps 6.2 to 6.6 by taking each primary turning point on the main root as a current starting point, so as to further store root system path information below all primary lateral roots;
step 6.9, respectively calculating Euclidean distance d of root system paths starting from each primary turning point, wherein the root system path corresponding to the longest Euclidean distance starting from each primary turning point is the primary lateral root of the pinus massoniana seedling root system, and the Euclidean distance of the primary lateral root is recorded as d L 。
As a further improved technical scheme of the present invention, the step 7 specifically includes:
step 7.1, calculating a scaling factor mu of a root system by using a calibration block, and correcting the main root length and the primary side root length calculated in the steps 6.7 and 6.9 by using the scaling factor to obtain respective theoretical lengths;
d MR ＝d M ×μ (4)；
d LR ＝d L ×μ (5)；
wherein d MR Theoretical length of principal root, d LR Is the theoretical length of the primary lateral root;
step 7.2, calculating the number of endpoints after the endpoints of the main root are removed, namely the number of lateral roots of the root system of the pinus massoniana seedlings;
Num Lateral_Roots ＝len(End_Points)-2 (6)；
wherein Num is Lateral_Roots The number of lateral roots of the root system of the seedling; len () represents the total number of Points in the set and End Points represents the set of endpoints established in step 4.
In order to achieve the technical purpose, the invention adopts another technical scheme that:
an image-based system for extracting phenotype parameters of a pinus massoniana seedling root system, comprising:
the image acquisition module is used for acquiring root system images of the pinus massoniana seedlings and correcting the root system images;
the image preprocessing module is used for preprocessing the corrected root system image;
the root system skeleton refining module is used for refining the root system image to realize extraction of the root system skeleton of the pinus massoniana seedlings;
the root system end point positioning module is used for searching the end points of the pixel points in the extracted root system skeleton diagram, judging the pixel points meeting the conditions as the end points of the root system, and realizing the end point positioning of the root system of the pinus massoniana seedlings;
the root system turning point positioning module is used for carrying out turning point search on pixel points in the extracted root system skeleton diagram, judging the pixel points meeting the conditions as turning points of root systems, and realizing the turning point positioning of the root systems of the pinus massoniana seedlings;
the root system segmentation module is used for segmenting main roots and primary lateral roots of the root system by adopting a priority path search algorithm with priority directivity according to the positioned end points and turning points of the root system of the pinus massoniana seedlings;
the root system phenotype parameter extraction module is used for extracting root system phenotype parameters of the pinus massoniana seedlings, wherein the root system phenotype parameters comprise the theoretical length of main roots, the theoretical length of primary lateral roots and the number of lateral roots.
The beneficial effects of the invention are as follows:
(1) According to the invention, the running speed of the ZhangSuen skeleton extraction algorithm is optimized, the calculation area of the root system image is adjusted to the minimum bounding box range of the pinus massoniana seedling root system, and the minimum bounding box range of the seedling root system is updated once after each iteration calculation. Meanwhile, the judging condition priority of image calculation is adjusted, the condition (mark deletion if the current calculation point is a boundary point) is set as the priority judgment, and unnecessary calculation is reduced.
(2) According to the invention, judging conditions of the end points and the turning points of the root system skeleton of the pinus massoniana seedlings are constructed, the end points and the turning points are searched for pixel points in the root system skeleton diagram, the pixel points meeting the conditions are judged to be the end points or the turning points of the root system, the positioning of the end points and the turning points of the root system of the pinus massoniana seedlings is realized, and a foundation is provided for the segmentation of the main roots and the lateral roots of the root system of the seedlings;
(3) The Preferential Path Search Algorithm (PPSA) with preferential directivity can accurately find out the main root and the primary side root while ensuring the correct trend and branches of the root system skeleton, does not have the condition of wrong recognition on the recognition of the multi-stage side root, and ensures the continuity and the integrity of the path. Therefore, the PPSA algorithm shows excellent accuracy and reliability in root skeleton segmentation and multi-stage lateral root recognition.
(4) Aiming at the problems that the traditional manual measurement root system has large error and low efficiency, and the existing three-dimensional reconstruction equipment in a laboratory cannot meet the evaluation requirement on the detection effect of finer root systems, the invention designs a low-cost and high-efficiency image-based pinus massoniana seedling root system phenotype information detection method. The method saves the measurement cost and improves the measurement precision of the phenotype parameters of the root system of the pinus massoniana seedlings. Meanwhile, the method and the flow are also suitable for other forest seedlings, and an effective and accurate method is provided for rapidly and accurately measuring the phenotype parameters of the root system of the seedling.
(5) The system and the method can rapidly and accurately measure the root phenotype parameters of the pinus massoniana seedlings, avoid high cost and high error of manual measurement, and do not need to rely on expensive detection equipment; the root phenotype parameters measured by the method can meet the quality evaluation requirements of the nursery stocks in the forest farm.
Drawings
FIG. 1 is a flow chart of a method for extracting phenotype parameters of a pinus massoniana seedling root system based on images.
Fig. 2 is a numbering diagram of points in a 3×3 neighborhood of pixels.
Fig. 3 is an explanatory diagram of judging conditions of the end points of the root system skeleton of the pinus massoniana seedlings.
Fig. 3 (a) is an explanatory diagram of the judgment condition (1) of the end points of the root system skeleton of the pinus massoniana seedling.
Fig. 3 (b) is an explanatory diagram of the judgment condition (2) of the end points of the root system skeleton of the pinus massoniana seedling.
Fig. 4 is an explanatory diagram of the judgment conditions of the point of the root system skeleton of the pinus massoniana.
FIG. 4 (a) is an explanatory diagram of the judgment condition (1) of the point of the root system skeleton of the Pinus massoniana.
Fig. 4 (b) is an explanatory diagram of the judgment condition (2) of the point of the root system skeleton of the pinus massoniana.
FIG. 4 (c) is an explanatory diagram of the judgment condition (3) of the point of the root system skeleton of the Pinus massoniana.
FIG. 4 (d) is an explanatory diagram of the judgment condition (4) of the point of the root system skeleton of the Pinus massoniana.
Fig. 5 is an algorithm flow diagram of a Prioritized Path Search Algorithm (PPSA).
Fig. 6 is a graph showing the effect of extracting a pinus massoniana seedling root skeleton using the modified zhangsuin algorithm.
FIG. 7 is a graph showing the effect of locating the end points and turning points of the root system skeleton of Pinus massoniana.
Fig. 7 (a) is a graph showing the effect of locating the end points of the root system skeleton of the pinus massoniana seedling.
Fig. 7 (b) is a graph showing the effect of locating the point of the root system skeleton of the pinus massoniana.
Fig. 8 is an effect diagram of dividing the root system of the pinus massoniana seedling by the Preferential Path Search Algorithm (PPSA).
Detailed Description
The following is a further description of embodiments of the invention, with reference to the accompanying drawings:
an image-based method for extracting phenotype parameters of a pinus massoniana seedling root system, the flow of which is shown in figure 1, comprises the following steps:
step 1: collecting root system images of pinus massoniana seedlings and correcting the root system images;
step 2: preprocessing the corrected masson pine root system image;
step 3: carrying out image refinement operation on the processed root system image by using an improved ZhangSuen skeleton extraction algorithm to realize root system skeleton extraction of pinus massoniana seedlings;
step 4: constructing a root skeleton endpoint judgment condition based on the root skeleton graph obtained in the step 3, searching endpoints of pixel points in the root skeleton graph, judging the pixel points meeting the condition as endpoints of a root system, and realizing the positioning of root endpoints of pinus massoniana seedlings;
step 5: constructing a root system skeleton turning point judging condition based on the root system skeleton diagram obtained in the step 3, carrying out turning point searching on pixel points in the root system skeleton diagram, judging the pixel points meeting the condition as turning points of a root system, and realizing the positioning of the turning points of the root system of the pinus massoniana seedlings;
step 6: based on the end points and the turning points of the root system skeleton of the pinus massoniana seedlings positioned in the step 4 and the step 5, dividing the main root and the primary lateral root of the root system of the pinus massoniana seedlings by adopting a priority path searching algorithm (Priority Path Search Algorithm, PPSA for short) with priority directivity;
step 7: and extracting root system phenotype parameters of the pinus massoniana seedlings based on the root system skeleton segmented by the PPSA algorithm.
The step 1 specifically comprises the following steps:
step 1.1, digging pinus massoniana seedlings from soil, and cleaning soil impurities on a root system by using water flow; the cleaned masson pine root system is laid on a white background plate which is in sharp contrast with a black root system, so that the subsequent image processing of root system images is facilitated;
and 1.2, placing a 3X 4 checkerboard calibration plate at a fixed position on a white background plate, wherein the size of the checkerboard is 20X 20mm, and the checkerboard calibration plate is used for calibrating a camera and acquiring internal and external parameters of the camera. After the images of the root systems of the pinus massoniana seedlings are collected, the root system images are corrected according to the obtained internal and external parameters of the camera.
The step 2 specifically includes:
step 2.1, the corrected root system image is subjected to gray level transformation operation by a weighted average method, the three channels are converted into single channels, the calculated amount in image processing is reduced, and the weighted average method has the formula:
where g (x, y) is the pixel value of the output image, R, G, B is the pixel value in three channels in the color image, w, respectively R 、w G And w B The three components of RGB are given different weight values, respectively. To achieve the accurate segmentation purpose, give w R 、w G And w B Assigned respectively, w R ＝0.299，w G ＝0.587，w B ＝0.114；
Step 2.2, performing a global threshold method binarization operation on the gray level image according to the obvious difference between the target root system and the background brightness in the gray level image, wherein the global threshold range is (125,255), and the global threshold method has the following formula:
wherein T is the selected threshold value, and b (x, y) is the pixel point of the output image;
step 2.3, removing noise which is not eliminated or enhanced after image binarization by using median filtering, wherein the size of a filtering kernel of a median filtering algorithm is 5 multiplied by 5;
and 2.4, aiming at the masson pine seedling root system image subjected to median filtering, generating a part of gaps and a plurality of small holes, filling and eliminating the gaps and the small holes on the masson pine seedling root system skeleton diagram by using a closing operation in morphological operation, and keeping the integral integrity of the root system skeleton, wherein the size of a closing operation kernel is 5 multiplied by 5.
The step 3 specifically includes:
step 3.1, based on the preprocessed masson pine seedling root system image, setting a pixel point with a pixel value of 255 in the root system image as 1, using an improved ZhangSuen skeleton extraction algorithm for the root system image, reducing the image skeleton from a multi-pixel width to a unit pixel width, and keeping the basic shape and trend of the skeleton;
step 3.2, the embodiment adopts an improved zhangsuin skeleton extraction algorithm, and the improvement point of the improved zhangsuin skeleton extraction algorithm mainly comprises that the calculation area of the root system image is adjusted to the minimum bounding box range of the pinus massoniana seedling root system, and the minimum bounding box range of the seedling root system is updated once after each iterative calculation. Meanwhile, the judging condition priority of image calculation is adjusted, the condition (if the current calculation point is a boundary point, the mark is deleted, namely the calculation point is deleted from the whole root point set) is set as the priority judgment, and unnecessary calculation is reduced. When there are no points that can be deleted continuously, the algorithm ends, generating a skeleton of the calculation region. An effect diagram of extracting a pinus massoniana seedling root system skeleton by using the improved zhangsuin algorithm is shown in fig. 6.
The step 4 specifically includes:
step 4.1, setting all pixel points with the pixel value of 255 in the image as 1 based on the pinus massoniana seedling root system thinning skeleton diagram obtained in the step 3; in the image, the pixel value of a point on a root system skeleton line is 1 (skeleton point), and the pixel value of a non-skeleton point is 0 (background point);
step 4.2, traversing each pixel value non-zero point on the root system skeleton diagram, carrying out marker search on a 3 multiplied by 3 neighborhood range by taking the pixel value non-zero point as a center, marking the pixel Points meeting judgment conditions in the neighborhood as root system endpoints, and adding the pixel Points into an endpoint set end_points; as shown in FIG. 2, P2-P9 represent a 3×3 neighborhood range centered on P1;
the end point is the end point of the root system, so the judgment conditions for constructing the end point of the root system of the pinus massoniana seedlings are as follows:
judgment condition (1): within the 3 x 3 neighborhood of the current point, there is and only one non-zero pixel point;
judgment condition (2): within the 3 x 3 neighborhood of the current point, there are two adjacent non-zero pixel points.
Sum (n) =1 judgment condition (1);
wherein n represents a 3×3 neighborhood range of the current point; sum represents the total number of non-zero pixel points in the 3 x 3 neighborhood of the current point; index (i) represents an Index in the 3×3 neighborhood of the i-th non-zero pixel; the current point can be judged to be an endpoint when the judging condition (1) and the judging condition (2) meet one of the judging conditions; in the condition explanatory diagrams shown in (a) and (b) of fig. 3, black squares represent non-zero pixel points, and white squares represent zero pixel values.
The step 5 specifically includes:
step 5.1, setting all pixel points with the pixel value of 255 in the image as 1 based on the pinus massoniana seedling root system thinning skeleton diagram obtained in the step 3;
step 5.2, traversing all non-zero pixel Points in the root skeleton diagram, carrying out marker search on a 3 multiplied by 3 neighborhood range of the current point, marking the pixel Points meeting the judgment conditions in the neighborhood as root system Turning Points, and adding the pixel Points into a Turning point set turning_points;
the turning point is a bifurcation point in the root system of the pinus massoniana seedlings, so the judgment conditions for constructing the turning point of the root system of the pinus massoniana seedlings are as follows:
judgment condition (1): in the 3X 3 neighborhood of the current point, at least three pixel points in P2, P4, P6 and P8 are non-zero points;
judgment condition (2): in the 3X 3 neighborhood of the current point, at least three pixel points in P3, P5, P7 and P9 are non-zero points;
judgment condition (3): three non-zero pixel points are arranged in the 3X 3 neighborhood of the current point, and are not adjacent to each other;
judgment condition (4): within the 3 x 3 neighborhood of the current point, there are four non-zero pixel points, of which at most two are adjacent; .
Sum (P2, P4, P6, P8) is more than or equal to 3, judging the condition (1);
sum (P3, P5, P7, P9) is more than or equal to 3, judging the condition (2);
wherein n represents a 3×3 neighborhood range of the current point; sum represents the total number of non-zero pixel points in the 3 x 3 neighborhood of the current point; index (i) represents the Index of the ith non-zero pixel point in the 3×3 neighborhood; sum (x=m) represents the total number of elements equal to m in the set x; judging condition (1), judging condition (2), judging condition (3) and judging condition (4) meet one of them to judge the current point as turning point; as in the conditional explanatory diagrams of (a) - (d) in fig. 4, black squares represent non-zero pixel points, and white squares represent zero pixel values.
As shown in fig. 7, fig. 7 (a) is a diagram of the effect of locating the end points of the root system skeleton of the pinus massoniana, and fig. 7 (b) is a diagram of the effect of locating the turning points of the root system skeleton of the pinus massoniana.
The step 6 is specifically shown in fig. 5, and includes:
step 6.1, setting a pixel point with a pixel value of 255 in a masson pine seedling root system skeleton diagram as 1; based on the root system End Points and the turning Points positioned in the step 4 and the step 5, firstly taking the End point A positioned at the top of the root system skeleton diagram as a starting point, setting the pixel value of the current point as 0, and deleting the current point from the End point set End_points;
step 6.2, judging whether non-zero points exist in the 3×3 neighborhood of the current point, if non-zero pixel points exist in P2, P4, P6 and P8, preferentially selecting the non-zero point P with the minimum index value in the 3×3 neighborhood i (i=2, 4,6, 8) as the next decision point, if P2, P4, P6, P8 are all zero pixel points, selecting the non-zero point P with the smallest index value of P3, P5, P7, P9 in the 3×3 neighborhood i (i=3, 5,7, 9) as the next decision point;
step 6.3, if the current traversing point is neither an endpoint nor a turning point, setting the pixel value of the point to 0, and jumping back to the step 6.2 to continue to execute the loop;
step 6.4, if the current traversing point is a Turning point, setting the pixel value of the point to 0, adding the pixel value to a passing Turning point set path_turning_points, and jumping back to the step 6.2 to continue to execute the loop;
step 6.5, if the current traversing point is an endpoint, setting the pixel value of the point to 0, deleting the pixel value from the endpoint set end_points, and storing the root system path from the starting point to the endpoint; then jumping the current traversal point back to the last point in the set of Points through path_turn_points; if no non-zero point exists around the jumping-back point, deleting the jumping-back point from the path_turning_points, and then jumping back the current traversal point to the last point in the updated path_turning_points set; then jumping back to the step 6.2 to continue the execution loop;
step 6.6, ending the algorithm when the endpoint set end_points is an empty set, and finally storing the root system path information of all the seedlings;
step 6.7, calculating the Euclidean distance d of all the stored root system paths, wherein the root system path with the longest Euclidean distance is the main root of the root system of the pinus massoniana seedling, and the Euclidean distance of the main root is recorded as d M ；
Where n is the total number of points on the root path; x is x i ，y i Coordinates of points;
step 6.8, marking the turning point on the root system main root as a first-stage turning point, and taking the longest root stretched out by taking the turning point as a starting point as a first-stage lateral root; deleting points on the main root from the original skeleton diagram, and repeatedly and circularly executing the steps 6.2 to 6.6 by taking each primary turning point on the main root as a starting point, so as to further store root system path information below all primary lateral roots;
step 6.9, respectively calculating Euclidean distance d of root system paths starting from each primary turning point, wherein the root corresponding to the longest Euclidean distance starting from each primary turning point is the primary lateral root of the root system of the Pinus massoniana seedling, and the Euclidean distance of the primary lateral root is recorded as d L 。
Similarly, if the secondary lateral root is to be stored, the turning point on the primary lateral root of the root system is marked as a secondary turning point, and the longest root stretched out by taking the turning point as a starting point is the secondary lateral root; and 6.8-6.9, storing root system path information of all the secondary lateral roots, and calculating Euclidean distance of the secondary lateral roots.
Fig. 8 is an effect diagram of dividing the root system of the pinus massoniana seedling by the Preferential Path Search Algorithm (PPSA) of step 6.
The step 7 specifically includes:
step 7.1, calculating a scaling factor mu of the root system by using a calibration block, and correcting the lengths of the main root and the primary side root calculated in the step 6.7 and the step 6.9 by using the scaling factor to obtain respective theoretical lengths:
d MR ＝d M ×μ (4)；
d LR ＝d L ×μ (5)；
wherein d MR ，d LR The theoretical length of the main root and the theoretical length of the primary side root are respectively;
step 7.2, calculating the number of endpoints after the endpoints of the main root are removed, namely the number of lateral roots of the root system of the pinus massoniana seedlings:
Num Lateral_Roots ＝len(End_Points)-2 (6)；
wherein Num is Lateral_Roots The number of lateral roots of the root system of the seedling; len () represents the total number of Points in the set and End Points represents the set of endpoints established in step 4.
The embodiment also provides an image-based method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings, which comprises the following steps:
the image acquisition module is used for acquiring images of root systems of the pinus massoniana seedlings and correcting the images;
the image preprocessing module is used for enhancing root system characteristics, removing irrelevant noise and filling gaps and holes on root systems;
the root system skeleton refining module is used for refining the root system image to realize extraction of the root system skeleton of the pinus massoniana seedlings;
the root system end point positioning module is used for carrying out end point search on the pixel points in the root system skeleton diagram, judging the pixel points meeting the conditions as the end points of the root system, and realizing the end point positioning of the root system of the pinus massoniana seedlings;
the root system turning point positioning module is used for carrying out turning point search on pixel points in the root system skeleton diagram, judging the pixel points meeting the conditions as turning points of root systems, and realizing the positioning of the root system turning points of pinus massoniana seedlings;
the root system segmentation module is used for segmenting main roots and primary lateral roots of the root system by adopting a priority path search algorithm with priority directivity for the root systems of the pinus massoniana seedlings;
the root system phenotype parameter extraction module is used for extracting root system phenotype parameters of the pinus massoniana seedlings, wherein the root system phenotype parameters comprise main root length, primary side root length and side root number.
According to the embodiment, the running speed of the ZhangSuen skeleton extraction algorithm is optimized, the calculation area of the root system image is adjusted to the minimum bounding box range of the pinus massoniana seedling root system, and the minimum bounding box range of the seedling root system is updated once after each iteration calculation. Meanwhile, the judging condition priority of image calculation is adjusted, the condition (mark deletion if the current calculation point is a boundary point) is set as the priority judgment, and unnecessary calculation is reduced.
According to the embodiment, judging conditions of the end points and the turning points of the root system skeleton of the pinus massoniana, searching the end points and the turning points of the pixel points in the root system skeleton diagram, judging the pixel points meeting the conditions as the end points or the turning points of the root system, positioning the end points and the turning points of the root system of the pinus massoniana, and providing a foundation for dividing the main root and the lateral root of the root system of the seedling;
the Preferential Path Search Algorithm (PPSA) with preferential directionality can accurately find out the main root and the primary side root while ensuring the correct trend and branches of the root system skeleton, and in the multi-stage side root recognition, the situation of recognition errors does not occur, and the continuity and the integrity of paths are ensured. Therefore, the PPSA algorithm shows excellent accuracy and reliability in root skeleton segmentation and multi-stage lateral root recognition.
Aiming at the problems that the traditional manual measurement root system has large error and low efficiency, and the existing three-dimensional reconstruction equipment in a laboratory cannot meet the evaluation requirement on the detection effect of finer root systems, the embodiment designs the image-based pinus massoniana seedling root system phenotype information detection method with low cost and high efficiency. The method saves the measurement cost and improves the measurement precision of the phenotype parameters of the root system of the pinus massoniana seedlings. Meanwhile, the method and the flow are also suitable for other forest seedlings, and an effective and accurate method is provided for rapidly and accurately measuring the phenotype parameters of the root system of the seedling.
The scope of the present invention includes, but is not limited to, the above embodiments, and any alterations, modifications, and improvements made by those skilled in the art are within the scope of the invention.
Claims (9)
1. An image-based method for extracting phenotype parameters of a pinus massoniana seedling root system is characterized by comprising the following steps:
step 1: collecting an image of a root system of a pinus massoniana seedling and correcting the image;
step 2: preprocessing the corrected image;
step 3: carrying out image refinement operation on the processed image by using an improved ZhangSuen skeleton extraction algorithm to realize root skeleton extraction of pinus massoniana seedlings;
step 4: constructing root skeleton end point judging conditions based on the root skeleton diagram obtained in the step 3, searching end points of pixel points in the root skeleton diagram, judging the pixel points meeting the judging conditions as end points of a root system, and realizing the end point positioning of the root system of the pinus massoniana seedlings;
step 5: constructing a root system skeleton turning point judgment condition based on the root system skeleton diagram obtained in the step 3, carrying out turning point search on pixel points in the root system skeleton diagram, judging the pixel points meeting the judgment condition as turning points of a root system, and realizing the positioning of the turning points of the root system of the pinus massoniana seedlings;
step 6: based on the end points and the turning points of the root system skeleton of the pinus massoniana seedlings positioned in the step 4 and the step 5, dividing the main root and the primary lateral root of the root system of the pinus massoniana seedlings by adopting a priority path searching algorithm with priority directivity;
step 7: and extracting root system phenotype parameters of the pinus massoniana seedlings based on the main roots and the primary lateral roots of the segmented pinus massoniana seedling roots.
2. The method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on the images according to claim l, wherein the step 1 is specifically as follows:
step 1.1, digging pinus massoniana seedlings from soil, cleaning soil impurities on a pinus massoniana seedling root system by water flow, and spreading the cleaned pinus massoniana seedling root system on a white background plate which is in clear contrast with a black root system;
and 1.2, placing a 3X 4 checkerboard calibration plate at a fixed position on a white background plate, wherein the size of the checkerboard is 20X 20mm, and the checkerboard calibration plate is used for calibrating a camera and acquiring internal and external parameters of the camera, and correcting root system images according to the acquired internal and external parameters of the camera after acquiring images of the root systems of pinus massoniana seedlings.
3. The method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on the images according to claim 1, wherein the step 2 specifically comprises the following steps:
step 2.1, performing gray level transformation operation on the corrected root system image by a weighted average method, and converting the three channels into single channels, wherein the weighted average method has the formula as follows:
where G (x, y) is the pixel value of the output image, R is the pixel value of the R channel in the color image, G is the pixel value of the G channel in the color image, B is the pixel value of the B channel in the color image, w R To give weight to the R component, w G To give weight to the G component, w B To give a weight value to the B component;
step 2.2, executing a global thresholding binarization operation on the gray level image, wherein the global threshold range is (125, 255), and the global thresholding formula is as follows:
wherein T is the selected threshold value, and b (x, y) is the pixel point of the output image;
step 2.3, removing noise which is not eliminated or enhanced after image binarization by using median filtering, wherein the size of a filtering kernel of a median filtering algorithm is 5 multiplied by 5;
and 2.4, filling and eliminating gaps and small holes on the root system image by using a closed operation in morphological operation, and keeping the integral integrity of the root system skeleton, wherein the size of a closed operation kernel is 5 multiplied by 5.
4. The method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on the images according to claim 1, wherein the step 3 specifically comprises the following steps:
step 3.1, based on the preprocessed masson pine seedling root system image, setting a pixel point with a pixel value of 255 in the root system image as 1, using an improved ZhangSuen skeleton extraction algorithm for the root system image, reducing the image skeleton from a multi-pixel width to a unit pixel width, and keeping the basic shape and trend of the skeleton;
step 3.2, wherein the improved zhangsuin algorithm mainly adjusts the calculation area of the root system image to the minimum bounding box range of the pinus massoniana seedling root system, and the minimum bounding box range of the seedling root system is updated once after each iteration calculation; adjusting the priority of a judgment condition of image calculation, wherein the judgment condition of image calculation is that if the point currently traversed is a boundary point, the point is deleted from the root point set, and the condition is set as priority judgment; when there are no points that can be deleted continuously, the algorithm ends, generating a skeleton of the calculation region.
5. The method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on the images according to claim 1, wherein the step 4 specifically comprises the following steps:
step 4.1, setting all pixel points with the pixel value of 255 in the image as 1 based on the pinus massoniana seedling root system thinning skeleton diagram obtained in the step 3, wherein the pixel value of a point on a root system skeleton line in the image is 1, and the pixel value of a non-skeleton point is 0;
step 4.2, constructing judging conditions of root system endpoints of pinus massoniana seedlings, traversing each pixel value non-zero point on a root system skeleton diagram, carrying out marker search on a 3X 3 neighborhood range of the pinus massoniana seedling by taking a current point as a center, marking the pixel Points meeting the judging conditions in the neighborhood as the root system endpoints, and adding the pixel Points into an endpoint set end_points; wherein the non-zero point of the pixel value is called as non-zero pixel point for short;
judging conditions of the root system end points of the pinus massoniana seedlings are as follows:
judgment condition (1): within the 3 x 3 neighborhood of the current point, there is and only one non-zero pixel point;
judgment condition (2): within the 3×3 neighborhood of the current point, there are two adjacent non-zero pixel points;
if the judgment condition (1) or the judgment condition (2) is satisfied, the current point is judged as an endpoint.
6. The method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on the images according to claim 5, wherein the step 5 specifically comprises the following steps:
step 5.1, setting all pixel points with the pixel value of 255 in the image as 1 based on the pinus massoniana seedling root system thinning skeleton diagram obtained in the step 3;
step 5.2, constructing judging conditions of root system Turning Points of pinus massoniana seedlings, traversing all non-zero pixel Points in a root system skeleton diagram, performing marker search on a 3X 3 neighborhood range of a current point, marking the pixel Points in the neighborhood which meet the judging conditions as the root system Turning Points, and adding the pixel Points into a turning_points set;
the judging conditions of the root system turning points of the pinus massoniana seedlings are as follows:
judgment condition (1): in the 3X 3 neighborhood of the current point, at least three pixel points in P2, P4, P6 and P8 are non-zero pixel points;
judgment condition (2): in the 3X 3 neighborhood of the current point, at least three pixel points in P3, P5, P7 and P9 are non-zero pixel points;
judgment condition (3): three non-zero pixel points are arranged in the 3X 3 neighborhood of the current point, and are not adjacent to each other;
judgment condition (4): within the 3 x 3 neighborhood of the current point, there are four non-zero pixel points, of which at most two are adjacent;
if the judging condition (1), the judging condition (2), the judging condition (3) or the judging condition (4) is met, judging that the current point is a turning point;
wherein, P2-P9 represent 3X 3 neighborhood of the current point, P2 is located right above the current point, P3 is located right above the current point, P4 is located right side of the current point, P5 is located right below the current point, P6 is located right below the current point, P7 is located left below the current point, P8 is located left side of the current point, and P9 is located left above the current point.
7. The method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on the images according to claim 1, wherein the step 6 specifically comprises the following steps:
step 6.1, setting a pixel point with a pixel value of 255 in a masson pine seedling root system skeleton diagram as 1, firstly setting an endpoint A at the top of the root system skeleton diagram, namely a first endpoint A in an endpoint set end_points as a current starting point based on the root system endpoints and the turning Points positioned in the step 4 and the step 5, setting the pixel value of the current point as 0, and deleting the current point from the endpoint set end_points;
step 6.2, judging whether non-zero pixel points exist in a 3×3 neighborhood of the current point, if the non-zero pixel points exist in P2, P4, P6 and P8, preferentially selecting the non-zero pixel point with the minimum index value in the 3×3 neighborhood as a next judging traversal point, and if the non-zero pixel points do not exist in P2, P4, P6 and P8, selecting the non-zero pixel points with the minimum index values in the 3×3 neighborhood of P3, P5, P7 and P9 as the next judging traversal points;
step 6.3, if the current traversing point is neither an endpoint nor a turning point, setting the pixel value of the point to 0, and jumping back to the step 6.2 to continue to execute the loop;
step 6.4, if the current traversing point is a Turning point, setting the pixel value of the point to 0, adding the pixel value to a passing Turning point set path_turning_points, and jumping back to the step 6.2 to continue to execute the loop;
step 6.5:
6.5.1, if the current traversing point is an endpoint, setting the endpoint pixel value to 0, deleting the endpoint pixel value from the endpoint set end_points, and storing a root system path from the starting point to the endpoint;
6.5.2 then, jumping the current traversal point back to the last point in the set of Points through path_turning_points;
6.5.3, judging whether non-zero pixel points exist in the 3 multiplied by 3 neighborhood of the jumping-back point;
6.5.4, if the non-zero pixel points exist in the 3 multiplied by 3 neighborhood of the jumping-back point, continuing to execute the loop in the jumping-back step 6.2;
6.5.5 if there is no non-zero pixel point in the 3×3 neighborhood of the skipped point, deleting the skipped point from path_turning_points, and skipping the current traversal point back to the last point in the updated path_turning_points set, returning to 6.5.3 for executing the loop;
step 6.6, ending the algorithm when the endpoint set end_points is an empty set;
step 6.7, calculating the Euclidean distance d of all the stored root system paths, wherein the root system path corresponding to the longest Euclidean distance is the main root of the root system of the pinus massoniana seedlings, and the Euclidean distance of the main root is recorded as d M ；
Where n is the total number of points on the root path; x is x i ，y i Coordinates of points;
step 6.8, the turning point on the root system main root is a first-stage turning point, and the longest root stretched out by taking the first-stage turning point as a starting point is a first-stage lateral root; therefore, deleting the points on the main root from the original skeleton diagram, and repeatedly and circularly executing the steps 6.2 to 6.6 by taking each primary turning point on the main root as a current starting point, so as to further store root system path information below all primary lateral roots;
step 6.9, respectively calculating Euclidean distance d of root system paths starting from each primary turning point, wherein the root system path corresponding to the longest Euclidean distance starting from each primary turning point is the primary lateral root of the pinus massoniana seedling root system, and the Euclidean distance of the primary lateral root is recorded as d L 。
8. The method for extracting the phenotype parameters of the root system of the pinus massoniana seedlings based on the images according to claim 7, wherein the step 7 specifically comprises the following steps:
step 7.1, calculating a scaling factor mu of a root system by using a calibration block, and correcting the main root length and the primary side root length calculated in the steps 6.7 and 6.9 by using the scaling factor to obtain respective theoretical lengths;
d MR ＝d M ×μ (4)；
d LR ＝d L ×μ (5)；
wherein d MR Theoretical length of principal root, d LR Is the theoretical length of the primary lateral root;
step 7.2, calculating the number of endpoints after the endpoints of the main root are removed, namely the number of lateral roots of the root system of the pinus massoniana seedlings;
Num Lateral_Roots ＝len(End_Points)-2 (6)；
wherein Num is Lateral_Roots The number of lateral roots of the root system of the seedling; len () represents the total number of Points in the set and End Points represents the set of endpoints established in step 4.
9. An image-based pinus massoniana seedling root phenotype parameter extraction system is characterized by comprising:
the image acquisition module is used for acquiring root system images of the pinus massoniana seedlings and correcting the root system images;
the image preprocessing module is used for preprocessing the corrected root system image;
the root system skeleton refining module is used for refining the root system image to realize extraction of the root system skeleton of the pinus massoniana seedlings;
the root system end point positioning module is used for searching the end points of the pixel points in the extracted root system skeleton diagram, judging the pixel points meeting the conditions as the end points of the root system, and realizing the end point positioning of the root system of the pinus massoniana seedlings;
the root system turning point positioning module is used for carrying out turning point search on pixel points in the extracted root system skeleton diagram, judging the pixel points meeting the conditions as turning points of root systems, and realizing the turning point positioning of the root systems of the pinus massoniana seedlings;
the root system segmentation module is used for segmenting main roots and primary lateral roots of the root system by adopting a priority path search algorithm with priority directivity according to the positioned end points and turning points of the root system of the pinus massoniana seedlings;
the root system phenotype parameter extraction module is used for extracting root system phenotype parameters of the pinus massoniana seedlings, wherein the root system phenotype parameters comprise the theoretical length of main roots, the theoretical length of primary lateral roots and the number of lateral roots.
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