Method for sequentially extracting phenotypic flux of multiple samples based on CT tomography

Abstract

The application discloses a kind of based on CT tomography multi-sample seed phenotypic flux sequential extraction method, it is related to crop detection field.According to the order of being loaded into different models of mould according to seed size, CT scanning is carried out to obtain spatial data;Positioning anchor point is set in different models of mould;According to the spatial data obtained by CT scanning, mold combination is carried out;Using spatial algorithm, cooperate with automatic extraction algorithm and phenotype algorithm, sequentially carry out phenotypic analysis to single seed, finally realize multi-sample seed phenotypic flux sequential extraction.The application designs special seed mould, improves seed phenotype extraction flux.According to the difference of seed morphology, size, three kinds of seed moulds are designed for different crop seeds, i.e.five layers 100 grain mould, single layer 20 grain mould and single layer 2 grain mould, respectively can be wheat, soybean, corn, peanut, peanut pod and other phenotypic extraction, compared with original method, flux is increased by 100 times, 24 times and 2 times respectively.

Description

Technical Field

[0001] This invention relates to the field of crop detection, and more specifically to a method for sequential extraction of multi-sample seed phenotypic throughput based on CT tomography. Background Technology

[0002] Phenotype refers to the measurable external manifestations of an organism, including shape, structure, size, and color, resulting from the interaction between its genetic characteristics and its environment. Most crops are harvested as seeds, and the quality of seed phenotypic traits directly affects their marketability, promotional value, and the design and matching of sowing, harvesting, and processing equipment. These are characteristics of utmost concern to breeders, large-scale growers, and related enterprises. With the development of transmission imaging technology, CT tomography has become an important method for analyzing the internal and external structures of organisms. In recent years, as the types of substances that can be analyzed in organisms have gradually increased and the analytical precision has gradually improved from the micrometer level to the nanometer level, CT tomography technology has been increasingly applied in analyzing crop plant architecture, root distribution, and seed morphology.

[0003] For a long time, the extraction of crop seed phenotypes has mainly relied on measurements using rulers and balances. However, with the development of modern medical imaging and artificial intelligence technologies, the size and precision of related instruments have become more controllable. Furthermore, the increasing demand for more refined phenotypic parameters in crop science research has led to the gradual application of CT tomography in seed phenotype determination. However, while companies like Fraunhofer in Germany, a world-leading developer of plant phenotype equipment, have achieved the application of CT technology in plant research, the determination of seed phenotypes (such as length and width) still primarily relies on two-dimensional manual measurements, failing to fully leverage the advantages of CT in three-dimensional space. In 2020, Jessica Schmidt et al. published a paper in the academic journal *Plant Methods*, using CT scanning technology to determine wheat seed traits; in 2021, Zhao Huan et al. published a paper in the academic journal *Smart Agriculture*, using CT scanning technology to determine maize seed phenotypes. However, both of these reports used methods that involved scanning one seed at a time or scanning a batch of seeds of the same type and calculating the average, neither of which solved the problem of one-to-one correspondence between individuals in multi-sample throughput measurements. Insufficient throughput has a significant impact on the efficiency of crop seed phenotypic extraction, and has become one of the main limiting factors restricting crop germplasm resource research and the breeding of suitable new processing varieties. Summary of the Invention

[0004] In view of this, the present invention provides a multi-sample seed phenotype throughput sequential extraction method based on CT tomography.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A multi-sample seed phenotype throughput sequential extraction method based on CT tomography includes the following steps:

[0007] Seeds are placed into molds of different sizes according to their size, and spatial data is obtained by CT scanning.

[0008] Positioning anchor points are set in molds of different models;

[0009] Based on the spatial data obtained from CT scans, the mold is assembled;

[0010] By using spatial algorithms, combined with automatic extraction and phenotypic algorithms, phenotypic analysis is performed on individual seeds sequentially, ultimately achieving sequential extraction of phenotypic throughput from multiple seed samples.

[0011] Optionally, positioning anchor points can be set in different models of molds for positioning and sorting. The 100-hole mold is a 5-layer mold group with 20 holes per layer, with a blank positioning anchor point in the upper left corner; the 24-hole mold is a single-layer mold, with a blank positioning anchor point in the second position of the upper left corner of the 5 rows × 5 columns; the 2-hole mold is a single-layer mold, with a blank positioning anchor point on the left side between the two holes.

[0012] Optionally, it also includes using a sorting algorithm to ensure that rotating or reversing the mold does not change the order of seed phenotypic extraction within the mold.

[0013] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method for sequential extraction of multi-sample seed phenotype throughput based on CT tomography. Based on CT tomography technology, it achieves sequential extraction of multi-sample seed phenotype throughput by designing a specialized seed-containing mold, using blank positioning markers, and a sorting algorithm. Compared with the original method, it has the following advantages:

[0014] (1) Design specialized seed molds to improve the throughput of seed phenotypic extraction. Based on the different seed shapes and sizes, three types of seed molds were designed for different crop seeds: a five-layer 100-seed mold, a single-layer 20-seed mold, and a single-layer 2-seed mold. These molds can be used for phenotypic extraction of wheat, soybeans, corn, peanuts, and peanut pods, respectively. Compared with the original method, the throughput is increased by 100 times, 24 times, and 2 times, respectively.

[0015] (2) Blank positioning markers and sorting algorithms are used to solve the problem of sequential correspondence analysis of seeds in multiple samples. Seedless blanks or empty slots are set at specific positions in the seed mold. The blank areas are used to mark the starting point of the seed sequence in the mold. Combined with the sorting algorithm, the seed phenotype extraction order in the mold is not changed regardless of whether the mold is rotated up, down, left, right, forward, or backward, ensuring the accuracy of one-to-one correspondence between seed phenotypes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating the working principle of the present invention;

[0018] Figure 2 This is a drawing of a 100-hole mold.

[0019] Figure 3 This is a drawing of a 24-hole mold.

[0020] Figure 4 This is a diagram of a 2-hole mold. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] This invention discloses a multi-sample seed phenotype throughput sequential extraction method based on CT tomography, such as... Figure 1 As shown, it includes the following steps:

[0023] Seeds are placed into molds of different sizes according to their size, and spatial data is obtained by CT scanning.

[0024] Positioning anchor points are set in molds of different models;

[0025] Based on the spatial data obtained from CT scans, the mold is assembled;

[0026] By using spatial algorithms, combined with automatic extraction and phenotypic algorithms, phenotypic analysis is performed on individual seeds sequentially, ultimately achieving sequential extraction of phenotypic throughput from multiple seed samples.

[0027] like Figures 2-4As shown, seeds are sequentially placed into specialized molds of different sizes (100-well, 24-well, and 2-well) according to their size, and spatial data is obtained through CT scanning. To achieve positioning and sorting, the 100-well mold consists of 5 layers of 20-well molds each, with a blank positioning anchor point in the upper left corner; the 24-well mold is a single-layer mold with a blank positioning anchor point in the second position from the upper left corner of a 5x5 column mold; the 2-well mold is a single-layer mold with a blank positioning anchor point on the left side between the two wells. The placement of these anchor points ensures accurate sample sorting based on the spatial data obtained, regardless of the mold (or mold group) placement. The molds can be further combined according to the scanning space range of the CT equipment. Using spatial algorithms (Euclidean distance between anchor points and blank orientation), combined with automatic extraction algorithms and other phenotypic algorithms, phenotypic analysis is performed on individual seeds sequentially, ultimately achieving sequential extraction of phenotypic throughput from multiple seed samples.

[0028] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0029] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for sequential extraction of seed phenotypes from multiple samples based on CT tomography, characterized in that, Includes the following steps: Seeds are placed into molds of different sizes according to their size, and spatial data is obtained by CT scanning. Positioning anchor points are set in molds of different models; Based on the spatial data obtained from CT scans, the mold is assembled; By using spatial algorithms, combined with automatic extraction and phenotypic algorithms, phenotypic analysis is performed on individual seeds sequentially, ultimately achieving sequential extraction of phenotypic throughput from multiple seed samples.

2. The method for sequential extraction of multi-sample seed phenotypes based on CT tomography according to claim 1, characterized in that, Positioning anchors are set in different types of molds to achieve positioning and sorting. The 100-hole mold is a 5-layer mold group with 20 holes per layer, with a blank positioning anchor in the upper left corner; the 24-hole mold is a single-layer mold, with a blank positioning anchor in the second position of the upper left corner of the 5 rows × 5 columns; the 2-hole mold is a single-layer mold, with a blank positioning anchor in the left side between the two holes.

3. The method for sequential extraction of multi-sample seed phenotypes based on CT tomography according to claim 2, characterized in that, It also includes using sorting algorithms to ensure that rotating or reversing the mold does not change the order of seed phenotypic extraction within the mold.

Images