CN112986156A - Cereal seed frostbite characterization and identification method - Google Patents

Abstract

The invention discloses a representation and identification method for cereal seed frostbite. Placing the cereal seeds under different conditions to obtain results of different frostbite conditions; measuring physical and chemical parameters, and performing freeze injury characterization and classification on the grain seeds according to the microscopic characteristic conditions of seed coats and seed embryos; collecting spectral data and preprocessing; processing the preprocessed spectrum by using a two-dimensional correlation analysis method and a waveband extraction method to obtain two characteristic wavebands and determine a final characteristic waveband; and establishing a classification model of the grain seeds and carrying out detection and identification. The method can characterize the frozen grain seeds and recognize the frozen grain seed damage condition, has visual and clear characterization of grain seed damage, and has the advantages of clear grain seed damage mechanism, good nondestructive measurement and classification effect and the like.

Description

Cereal seed frostbite characterization and identification method

Technical Field

The invention relates to a seed damage characterization and identification method in the technical field of agricultural product (grain seed) quality detection, in particular to a method for identifying the freezing injury condition of grain seeds by using related physicochemical parameters and microscopic images to characterize the seed quality and using a two-dimensional related synchronous spectrum combined with a related characteristic waveband extraction method to extract a characteristic waveband of spectral data.

Background

The seed frostbite is one of agricultural disasters, and internal component substances of the seeds subjected to low-temperature frostbite can change, so that the subsequent germination of the seeds, the growth of root systems and the growth and development are greatly influenced. The frostbite of the seeds has a great relationship with the water content of the seeds, the temperature and the duration of the frostbite, and researches show that: the higher the moisture content of the seed, the lower the temperature and the longer the duration of the frostbite, the more severe the frostbite of the seed. Therefore, it is very necessary to detect seed frostbite and to detect its frostbite condition.

By referring to data, the detection of the seed frostbite does not have a unified standard, most of the detection is judged by the height of the seed germination rate, the germination rate is used as an index for determining the seed frostbite by patent Chinese No. CN 108444928B, and the conductivity is used as an index for determining the seed frostbite by Zhang et al (Classification of Frozen Corn seed Using Hyperspectral VIS/NIR reflecting Imaging, 2019).

The two-dimensional correlation analysis method is used for analyzing the spectrums under different interferences to obtain a synchronous two-dimensional correlation spectrum and an asynchronous two-dimensional correlation spectrum, is simple, convenient and quick, does not need to separate a sample to be detected, and has certain practical significance for the identification and research of a complex system.

Disclosure of Invention

In order to solve the problems in the background art, the invention aims to provide a cereal seed frostbite characterization and identification method, the quality of the cereal seeds frostbitten is characterized by using related physicochemical parameters and microscopic images, the internal change mechanism of the cereal seeds is explored, the cereal seeds are classified into three types, namely normal, slight frostbitten and severe frostbitten, the cereal seeds have the advantages of no damage, good classification effect and the like, and the classification detection research on the cereal seed frostbitten condition is realized by collecting the spectral data of the cereal seeds to be detected and carrying out related data processing.

The method adopting the following technical scheme comprises the following steps:

1) placing the cereal seeds with a certain water content at different frostbite temperatures and frostbite for different time to obtain the cereal seeds with different frostbite conditions;

2) measuring physical and chemical parameters of the grain seeds with different frostbite conditions, performing frostbite characterization on the grain seeds according to microscopic characteristic conditions of seed coats and seed embryos, and classifying the grain seeds into three categories of normal frostbite, slight frostbite and severe frostbite according to the characterized conditions;

3) collecting spectrum data of grain seeds with different frostbite conditions, and preprocessing the spectrum data by a preprocessing method to obtain a preprocessing spectrum;

4) on one hand, a two-dimensional correlation analysis method is used for analyzing the preprocessed spectrum to obtain a two-dimensional correlation synchronous spectrum, and then a plurality of characteristic wave bands extracted according to a diagonal matrix of the two-dimensional correlation synchronous spectrum are used as first characteristic wave bands; on the other hand, a characteristic waveband of the preprocessed spectrum is extracted by a waveband extraction method to be used as a second characteristic waveband; determining a final characteristic wave band by combining the first characteristic wave band and the second characteristic wave band;

5) and establishing a classification model of the grain seeds and carrying out detection and identification.

In the step 1), the water content of the cereal seeds is required to be 20-35%, the frostbite temperature is-5 ℃ to-20 ℃, and the frostbite time is 2-10 h.

The step 2) is specifically as follows:

the physicochemical parameters comprise the activity of seed related enzyme (catalase and peroxidase), the content of protein and the like;

the grain seeds are corn seeds;

according to the physicochemical property change condition of the seeds, the freezing injury condition of the seeds is divided into different categories, which specifically comprises the following steps:

classifying cereal seeds with catalase activity in the range of 10-25U/g, peroxidase activity in the range of 15-30U/g and protein content in the range of 1.3-1.8mg/g into normal categories;

classifying the cereal seeds with catalase activity in the range of 30-55U/g, peroxidase activity in the range of 50-70U/g and protein content in the range of 0.85-1.3mg/g into a slight frostbite category;

cereal seeds with seed-related enzyme activities in the >55U/g range and peroxidase activities in the >70U/g range and protein contents in the range <0.85mg/g were classified as severe frostbite category.

The microscopic characteristics of the seed coat are collected by a scanning electron microscope and determined according to the texture uniformity and the texture depth of the surface of the seed coat; microscopic characteristics of the seed embryo are collected by a transmission electron microscope and determined according to the integrity of the seed embryo cell wall, cell membrane and cell nucleus;

as seen from the electron microscope image, the seed coat surface texture is regular, and the seed embryo cell membrane, the cell wall and the nucleus structure are complete and are in normal category;

the occasional disordered change of the surface texture of the seed coat (15% -35% of the texture of a connecting line appears between two or more textures in a scanning electron microscope image of the seed coat, and unevenness appears on the seed coat tissue between-10% -30% of the textures), the seed embryo cell membrane, the cell wall (the partial plasmolysis of the cell membrane and the cell wall structure occurs, the definition between the cell tissue and the cell wall decreases, the phospholipid molecule arrangement space increases in the cell to cause 15% -30% loss), and the nucleus (the integrity of the nucleus structure is between 70% -85%, the change of the structural integrity mainly occurs in the distortion of the shape of the nuclear membrane part of the nucleus and the reduction of the nuclear content by 10% -30%) are partially broken and are in the category of slight frostbite;

the seed coat surface texture is extremely disordered and changed (more than 70% of the textures in a scanning image of a seed coat electron microscope are changed, not only the overlapping coincidence condition of the textures of more than 30% and the unevenness of the seed coat tissue appear, but also the situation that more than 50% of the textures are not clearly disappeared and the distance between two or more textures is irregular), the seed embryo cell membrane, the cell wall (the cell membrane and the cell wall structure are seriously separated from each other/the contour between the cell tissue and the cell wall is fuzzy/and more than 70% of phospholipid molecules in the cell are lacked) and most of the structure of the cell nucleus (the integrity of the cell nucleus is less than 30%, the nuclear membrane disappears and more than 70% of the cell nucleus is cracked) is cracked or cracked, and the structure is in a.

In the step 3), the spectrum data of the grain seeds are obtained under a unified condition, specifically, under the condition that parameters such as the exposure time of a spectrometer, the distance between the grain seeds and the spectrometer and the like are unified, the pretreatment method adopts a 5-3 smoothening method.

In the step 4), the two-dimensional correlation synchronization spectrum diagonal matrix is a matrix formed by focusing elements in the two-dimensional correlation synchronization spectrum, each element in the two-dimensional correlation synchronization spectrum diagonal matrix forms a spectral line, and a wave band corresponding to a peak or a trough on the spectral line is taken as a first characteristic wave band.

In the step 4), calculating average spectra of spectrum data of all the grain seeds under the same frostbite condition, integrating the average spectra of the grain seeds under different frostbite conditions, namely, connecting the average spectra together, obtaining a synchronous two-dimensional correlation synchronization spectrum by using a two-dimensional correlation analysis method, and simultaneously extracting N1 first characteristic wave bands in a diagonal matrix of the two-dimensional correlation synchronization spectrum;

extracting N2 second characteristic wave bands of the preprocessed spectrum in the step 3) by using a continuous projection algorithm as a wave band extraction method;

and simultaneously combining and merging the N1 first characteristic wave bands and the N2 second characteristic wave bands to obtain a characteristic wave band of the union set as a final characteristic wave band.

N1 first eigenbands and N2 second eigenbands form N1+ N2 eigenbands, and if there are S repeated bands in N1 and N2, N1+ N2-S eigenbands need to be formed.

The classification model in the step 5) adopts an LDA classification model.

And 5) specifically, processing the grain seeds with known frostbite condition classification according to the steps to obtain a final characteristic wave band, inputting a spectral value corresponding to the final characteristic wave band and the known frostbite condition classification into a classification model for training, then processing the final characteristic wave band of the grain seeds to be tested by using the trained classification model, and outputting the frostbite condition classification of the grain seeds to be tested.

The method comprises the steps of measuring related physicochemical parameters, observing microscopic characteristic pictures of seed coats and seed embryos to realize characterization of frozen grain seeds, performing spectral data acquisition and a series of processing on the frozen grain seeds, extracting characteristic wave bands by using a two-dimensional correlation method and an SPA algorithm, creatively combining the wave bands extracted by the two characteristic wave band extraction methods, and finally establishing a grain seed freezing condition classification model to obtain a grain seed freezing condition detection result.

The invention represents and identifies the quality of the frozen grain seeds by using the related physicochemical parameters and the microscopic image, explores the internal change mechanism of the frozen grain seeds, analyzes the acquired spectral data by using the synchronous two-dimensional related spectrum, acquires a plurality of characteristic wave bands in a diagonal matrix, fuses the characteristic wave bands with the characteristic wave bands obtained by the SPA algorithm, inputs the characteristic wave bands into a model to realize the classification of the frozen seeds, and realizes the more visual and clear representation of the frozen seeds and the better spectral detection of the frozen seeds.

The invention has the beneficial effects that:

aiming at the problem that the freezing injury mechanism of the existing freezing injury cereal seeds is not clear, the characterization of the freezing injury cereal seeds is realized by measuring related physicochemical parameters and observing microscopic characteristic pictures of seed coats and seed embryos, and the cereal seeds are divided into three categories of normal, slight freezing injury and severe freezing injury according to the height of the physicochemical parameter values and the integrity of related biological devices in the microscopic characteristic pictures;

aiming at the necessity of identifying and analyzing frostbite seeds, the classification detection of the frostbite condition of the grain seeds is realized by collecting the spectral data of different frostbite grain seeds, combining the characteristic wave bands obtained by synchronous two-dimensional correlation spectrum and the characteristic wave bands extracted by SPA algorithm and using the spectral values under the characteristic wave bands as variables.

In the embodiment, the characterization of the frozen grain seeds is visual and clear, and the damage mechanism of the grain seeds can be determined; the method has the advantages that the classification model is established to classify the frozen injury of the seeds by comparing the results of the characteristic extraction of the synchronous two-dimensional correlation spectrum or the SPA algorithm which is independently used and combining the characteristic wave band extracted by the synchronous two-dimensional correlation spectrum and the wave band extracted by the SPA algorithm, so that the nondestructive measurement and the good classification effect are realized.

Drawings

FIG. 1 is a flow chart of a method of the present invention;

FIG. 2(a) is a graph of the activity of each class of corn seed catalase (CAT enzyme);

FIG. 2(b) is a diagram showing the activity of peroxidase (POD enzyme) in corn seeds of each class;

FIG. 2(c) is a graph of the protein content of various classes of maize seeds.

FIG. 3(a) is a scanning electron microscope image of seed coats of various corn seeds, wherein a thick black solid line box represents a position where unevenness occurs, a thin black dotted line box represents a position where obvious line texture appears between two textures, and a thick dotted line box represents a position where the texture interval changes;

FIG. 3(b) is a transmission electron micrograph of the cell wall and cell membrane of the germ cell of each category of corn seeds;

FIG. 3(c) is a transmission electron micrograph of the nuclei of germ cells of various classes of maize seeds;

FIG. 4(a) is a graph of raw average spectra for various categories of corn seeds;

FIG. 4(b) is a graph of the average spectrum of pretreatment of various types of corn seeds;

FIG. 5(a) is a synchronized two-dimensional correlation spectrum of a two-dimensional correlation method;

FIG. 5(b) is a schematic diagram of a diagonal matrix diagram of a synchronous two-dimensional correlation spectrum and the positions of extracted characteristic bands thereof;

fig. 6 is a schematic diagram of the positions of the characteristic bands extracted by the SPA algorithm.

Detailed Description

The present invention will be described in further detail with reference to the following drawings and specific embodiments. But the embodiments of the present invention are not limited thereto.

As shown in fig. 1, an embodiment of the present invention is as follows:

the corn seeds are used as research objects in the embodiment of the invention.

The corn seeds used in the invention are Vitaceae 702 corn seeds which are just harvested from the field, the initial water content is determined to be 32% according to Chinese national standard GB/T3543.6-19955 crop seed inspection regulation-moisture determination, and then the corn seeds are respectively placed in constant temperature refrigerators at minus 10 ℃ and minus 20 ℃ for frostbite for 10h to obtain the frostbitten corn seeds.

The corn seeds with different frostbite conditions are measured according to a related physicochemical parameter test method (provided by Nanjing institute of bioengineering), wherein the method comprises the following steps: activity of seed-related enzymes (catalase, peroxidase), protein content, etc. The detected physical and chemical parameters of the corn seeds are shown in figures 2(a) to (c): according to the variation of the related physicochemical indexes of the seeds, the cold injury of the seeds is classified into different categories, the activity of CAT enzyme measured by the corn seeds which are not subjected to cold injury is between 14.63 and 17.43U/g, the activity of POD enzyme is between 21.74 and 27.68U/g, the protein content is between 1.45 and 1.73mg/g, and the corn seeds belong to the normal category 1; the activity of CAT enzyme measured by the corn seeds which are frozen at 10 ℃ for 10h is between 40.42 and 47.63U/g, the activity of POD enzyme is between 60.75 and 66.42U/g, the protein content is between 0.91 and 1.21mg/g, and the corn seeds belong to the category 2 of slight freezing injury; the activity of CAT enzyme measured by the corn seeds which are frozen at the temperature of 20 ℃ below zero for 10h is between 62.63 and 68.48U/g, the activity of POD enzyme is between 81.52 and 82.80U/g, the protein content is between 0.53 and 0.83mg/g, and the corn seeds belong to the severe freezing category 3.

Processing and observation of seed coats and embryos, FIG. 3(a) is a scanning electron microscope observation of seed coats of various types of seeds, and FIGS. 3(b) to (c) are an observation of cell membrane and cell wall structures and a nuclear structure of seed embryos of various types. The surface texture of the seed coat of the corn seed which is not subjected to the frostbite is neat, and the structures of the seed embryo cell membrane, the cell wall and the cell nucleus are complete; the surface texture of the seed coat of the corn seed frozen at 10 ℃ for 10h is occasionally disordered by about 20 percent, the condition that the two textures at the upper part and the middle part are uneven can be found in a visual field diagram, the cross-linked texture is formed between the two parallel textures, but good parallelism is maintained between the textures, the definition of the boundary contour of the cell membrane and the cell wall of a seed embryo is reduced, gaps are formed among the phospholipid molecules in an arrangement mode, partial 10 percent of the phospholipid molecules are lost, and the nuclear membrane outside the nuclear structure is partially distorted and cracked; the surface texture of the seed coat of the corn seed frozen at the temperature of minus 20 ℃ for 10h is changed by about 70 percent of extreme disorder, the color of the texture is lightened in a visual field image, almost every texture is cut off, the space and the parallelism between the textures at the lower left corner are changed seriously, the serious overlapping between the textures is generated, the cell membrane and the cell wall of the embryo disappear, more than 90 percent of phospholipid molecules disappear, and most of the nucleus structure is broken.

According to the characteristic changes, the internal change mechanism of the corn suffering from the frostbite can be obtained as follows: increasing the activities of CAT enzyme and POD enzyme to protect seed cells from being damaged by active oxygen along with the increase of the degree of the frostbite; the protein content is reduced due to processes such as proteolysis. Through microscopic observation, the seed coat and the seed embryo of the corn seed are found to be changed to different degrees along with the increase of the degree of the frostbite. For the seed coat, the uniformity and the definition of the texture on the surface of the seed coat are poorer and poorer; for seed embryos, as freezing increases, the internal structure of the embryo, the integrity of the cell membrane, the arrangement of cells, cellular organelles and other contents change.

In this embodiment, the collected spectrum is collected by using a hyperspectral imaging device, and the average spectrum data is finally obtained by performing methods such as image processing on the collected hyperspectral data. When the hyperspectral imaging system works, the exposure time of the near-infrared camera is set to be 3.7ms, the moving speed of the mobile platform is set to be 6.5mm/s, the distance between the sample and the camera lens is 25cm, and after the collected hyperspectral image is acquired, the average spectral range of the sample is 434 and 1041nm through methods such as image processing and the like, and 473 wave bands are totally obtained, as shown in fig. 4 (a). The raw average spectrum obtained was preprocessed by 5-3 smoothening, and the preprocessed average spectrum obtained is shown in fig. 4 (b).

A two-dimensional correlation analysis method is used to obtain a synchronous two-dimensional correlation spectrum as shown in fig. 5(a), a diagonal matrix is obtained, a diagonal matrix picture is obtained as shown in fig. 5(b), the characteristic wave bands of the diagonal matrix are extracted, and the preferred wave bands are 3 characteristic wave bands of 592.59nm, 629.62nm and 926.36nm in total.

The spectral curve composed of 473 bands is extracted by SPA algorithm with characteristic bands, as shown in FIG. 6, the preferred bands are total 11 characteristic bands of 454.95nm, 483.30nm, 504.42nm, 836.01nm, 883.15nm, 911.96nm, 926.36nm, 962.98nm, 986.50nm, 1007.36nm and 1021.69 nm.

The invention finds that 926.36nm in the two acquired characteristic bands is a repetition band, and finally acquires 3+ 11-1-13 characteristic bands as the input of the model.

The classified corn seeds with known frostbite conditions are divided into a plurality of batches, the steps are repeated in each batch to obtain 13 characteristic wave band spectrum values of seed spectrums of 3 categories of the corn seeds, and an LDA frostbite identification classification model is obtained through input.

And repeating the steps of processing the to-be-identified cereal seeds with unknown frostbite conditions to obtain characteristic wave bands and spectral values thereof, and inputting the characteristic wave bands and the spectral values into the classification model established by the LDA to obtain the frostbite conditions of the to-be-identified cereal seeds. The results of the LDA frostbite recognition classification model are shown in table 1 by comparing whether preprocessing is used, whether a feature extraction algorithm is used, or used alone or in combination:

according to the results in table 1, in the embodiment, the spectral data of the cereal seeds are obtained by utilization, the data are preprocessed, the characteristic wave bands of the spectrum are extracted by utilizing a two-dimensional correlation analysis method and an SPA algorithm, and when damage detection is performed on the frostbite corn seeds, the effect of establishing a classification model on classifying the frostbite seeds is good, the precision is high, and finally the classification accuracy of the damage degrees of the three classes can reach more than 94%.

The method can characterize the frozen grain seeds and recognize the frozen grain seed damage condition, has visual and clear characterization of grain seed damage, has definite grain seed damage mechanism, and has the advantages of no damage, good classification effect, high reliability, strong practicability and the like.

The method of the present invention is described in the above embodiments, but not limited to the present invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention, so that all equivalent technical solutions also belong to the scope of the invention, and the scope of the invention should be defined by the claims.

Claims (8)

1. A millet freezing injury characterization and identification method is characterized by comprising the following steps:

1) placing the cereal seeds with a certain water content at different frostbite temperatures and frostbite for different time to obtain the cereal seeds with different frostbite conditions;

2) measuring physical and chemical parameters of the grain seeds with different frostbite conditions, performing frostbite characterization on the grain seeds according to the microscopic characteristic conditions of seed coats and seed embryos, and classifying the grain seeds into three categories of normal frostbite, slight frostbite and severe frostbite;

3) collecting spectrum data of grain seeds with different frostbite conditions, and preprocessing the spectrum data by a preprocessing method to obtain a preprocessing spectrum;

4) on one hand, a two-dimensional correlation analysis method is used for analyzing the preprocessed spectrum to obtain a two-dimensional correlation synchronous spectrum, and then a plurality of characteristic wave bands extracted according to a diagonal matrix of the two-dimensional correlation synchronous spectrum are used as first characteristic wave bands; on the other hand, a characteristic waveband of the preprocessed spectrum is extracted by a waveband extraction method to be used as a second characteristic waveband; determining a final characteristic wave band by combining the first characteristic wave band and the second characteristic wave band;

5) and establishing a classification model of the grain seeds and carrying out detection and identification.

2. The cereal species frostbite characterization and identification method of claim 1, wherein: in the step 1), the water content of the cereal seeds is required to be 20-35%, the frostbite temperature is-5 ℃ to-20 ℃, and the frostbite time is 2-10 h.

3. The cereal species frostbite characterization and identification method of claim 1, wherein: the step 2) is specifically as follows: the physical and chemical parameters comprise the activity of seed-related enzyme, the content of protein and the like; the microscopic characteristics of the seed coat are collected by a scanning electron microscope and determined according to the texture uniformity and the texture depth of the surface of the seed coat; microscopic features of the seed embryo were collected by transmission electron microscopy and determined by the biological integrity of the seed embryo cell wall, cell membrane and nucleus.

4. The cereal species frostbite characterization and identification method of claim 1, wherein:

in the step 3), the spectrum data of the grain seeds are obtained under a unified condition, specifically, under the condition that parameters such as the exposure time of a spectrometer, the distance between the grain seeds and the spectrometer and the like are unified, the pretreatment method adopts a 5-3 smoothening method.

5. The cereal species frostbite characterization and identification method of claim 1, wherein:

in the step 4), the two-dimensional correlation synchronization spectrum diagonal matrix is a matrix formed by focusing elements in the two-dimensional correlation synchronization spectrum, each element in the two-dimensional correlation synchronization spectrum diagonal matrix forms a spectral line, and a wave band corresponding to a peak or a trough on the spectral line is taken as a first characteristic wave band.

6. The cereal species frostbite characterization and identification method of claim 1, wherein:

in the step 4), calculating average spectrums of spectrum data of all the cereal seeds under the same frostbite condition, integrating the average spectrums of the cereal seeds under different frostbite conditions, obtaining a synchronous two-dimensional correlation synchronization spectrum by using a two-dimensional correlation analysis method, and simultaneously extracting N1 first characteristic wave bands in a diagonal matrix of the two-dimensional correlation synchronization spectrum;

extracting N2 second characteristic wave bands of the preprocessed spectrum in the step 3) by using a continuous projection algorithm as a wave band extraction method;

and simultaneously combining and merging the N1 first characteristic wave bands and the N2 second characteristic wave bands to obtain a characteristic wave band of the union set as a final characteristic wave band.

7. The cereal species frostbite characterization and identification method of claim 1, wherein:

the classification model in the step 5) adopts an LDA classification model.

8. The cereal species frostbite characterization and identification method of claim 1, wherein:

and 5) specifically, processing the grain seeds with known frostbite condition classification according to the steps to obtain a final characteristic wave band, inputting a spectral value corresponding to the final characteristic wave band and the known frostbite condition classification into a classification model for training, then processing the final characteristic wave band of the grain seeds to be tested by using the trained classification model, and outputting the frostbite condition classification of the grain seeds to be tested.

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