CN117232651A - Method for rapidly identifying moisture content and moisture distribution condition of corn seeds - Google Patents
Method for rapidly identifying moisture content and moisture distribution condition of corn seeds Download PDFInfo
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Abstract
A method for rapidly identifying the moisture content and moisture distribution of corn seeds comprises the steps of obtaining corn seeds, hyperspectral imaging of corn seeds, hyperspectral imaging image analysis, obtaining imaging data and visualizing the imaging data; and (3) grouting corn seeds until corn plants are withered and yellow, harvesting a batch of corn seeds of the same corn variety every 10 days, performing near infrared imaging by using a Scanalyzer HTS phenotype analysis platform to obtain imaging images of each batch of corn seeds, qualitatively analyzing the moisture content and the distribution condition of the corn seeds by the color depth of the corn seeds in the images, and further analyzing the hyperspectral imaging images of each batch by using a plant color image analysis system to obtain specific imaging data. The invention provides a key technology for digitizing and visualizing the internal water distribution condition of the corn seeds, and can more intuitively display the internal water distribution condition of the corn seeds with different maturity.
Description
Technical Field
The invention belongs to the technical field of image analysis, and particularly relates to a method for rapidly identifying the moisture content and moisture distribution of corn seeds, which is applied to quality and quality inspection of crop varieties and seeds.
Background
Corn (Zea mays L.) is one of the main food crops in China, and is used as an important crop for both grain, menstruation and feeding, thereby having great influence on the development of national economy. The seeds are the basis of crops, the moisture content is one of important indexes for evaluating the quality of the seeds, and is also an important basis for whether the seeds can normally sprout, germinate and emerge. The research on the water content and the distribution of the seeds is carried out, and the method plays an important role in improving the seedling formation and strengthening of the seeds and completing variety identification and high yield and efficiency, and researches on the water content and the distribution rule of the seeds, so that the related control mechanism of the quality of the seeds is clarified, and the purpose of controlling the quality of the seeds is achieved.
The traditional research method of seed moisture is only based on the germination rate of quantity statistics, so that the change condition of the moisture is considered from the time aspect, or the migration and distribution condition of the moisture in the seed are analyzed by physiological dissection of the seed, the method is destructive, the moisture state in the seed can be disturbed, the moisture change process of the same seed cannot be continuously monitored, and the moisture distribution and dynamic migration process in the seed cannot be directly researched. With the development of phenotyping and image analysis technology, the image information and the spectrum information contained in the system not only reflect morphological characteristics of the measured sample from the appearance, but also reflect physical structures and chemical components of the measured sample from the inside, thereby realizing comprehensive evaluation of the inside and the outside of the measured object.
The hyperspectral imaging is used as a novel spectrum measuring method, has remarkable advantages compared with the traditional spectrum measuring technology, and can obtain the spectrum information, namely the space information, of the measured sample. The application field of the hyperspectral imaging technology is expanded to the fields of agriculture, industry, medical science and the like. Hyperspectral imaging can be classified into raman, ultraviolet, visible, infrared and near infrared hyperspectral imaging according to the difference between the measurement spectral band and the measurement principle (Bai Wenming, etc., the near infrared hyperspectral imaging technology has been developed in drug analysis [ J ]. Journal of drug analysis, 2018,38 (10): 1661-1667; zhang Tianliang, et al, hyperspectral imaging to identify early lodging resistance of maize varieties [ J ]. Spectroscopy and Spectroscopy analysis 2022,42 (4): 1229-1234).
The phenotype image analysis technology is basically mature at present, scientific research institutions and companies with conditions can develop the technology, and compared with the traditional research method, the technology has the characteristics of no invasion and no damage, and can rapidly, accurately and intuitively monitor the structural characteristics and the water distribution in different organs of crops, so that the technology can be used for monitoring dynamic distribution change and transportation process of water in different growth periods in living plants, change rules of water at each part of the plant body of the crops and the like, and has successful application.
The Scanalyzer HTS phenotype analysis platform (research and development unit: lemnaTec company, germany, commercial product) is a laboratory edition high-throughput phenotype measurement system Scanalyzer HTS, which has a multifunctional phenotype system with a function of collecting phenotype data of miniature plants and other sample materials in a high throughput manner, and can conduct deep phenotype data collection on miniature plants or other sample materials of different varieties and different life periods, different configuration versions can be selected according to the number of measured samples, various sensors such as Infrared (IR), near Infrared (NIR) and the like and light sources can be selected, imaging can be conducted under the condition of Near Infrared (NIR) light sources, and the water content of seeds can be qualitatively measured. However, the Scanalyzer HTS phenotyping platform has the disadvantage that further deep analysis of the imaged images, colors and the like is not possible.
The moisture content and the moisture distribution condition in the crop variety change with time to form the moisture migration in the seeds. The distribution change condition can influence various proteases in the seeds, which influence the storage and germination of the seeds, and the distribution rule of the moisture in the seeds can be known and mastered, so that the distribution of the moisture in the seeds can be known, the movement of the moisture in the seeds can be realized, and the influence on the enzyme activity can be caused, so that the storage and germination of the seeds are influenced, and the moisture content in the seeds and the distribution of the moisture in the seeds are one of key points of the quality of the seeds.
Disclosure of Invention
In order to accurately, quickly and conveniently identify the moisture content and the distribution condition of the moisture in the corn seeds, the invention provides a method for quickly identifying the moisture content and the distribution condition of the moisture in the corn seeds.
The technical scheme of the invention is as follows:
a method for rapidly identifying the moisture content and the moisture distribution condition in corn seeds comprises the steps of (1) obtaining corn seeds, (2) hyperspectral imaging of the corn seeds, (3) hyperspectral imaging image analysis, (4) obtaining imaging data and (5) visualizing the imaging data; the method is characterized in that:
in the step (1) of obtaining corn seeds, grouting is started from corn kernels until corn plants are withered and yellow, and a batch of corn seeds are harvested every 10 days for the same corn variety, so that corn seeds of different batches of the same corn variety are obtained in a mode, namely, corn seeds of different maturity are obtained, the corn seeds are used as different maturity gradients of the corn seeds, the corn seeds of each batch of each corn variety are selected to be positioned in the middle of corn ears, and the corn seeds are hermetically preserved by using a vacuum packaging bag to prevent water loss;
in the hyperspectral imaging of the corn seeds in the step (2), selecting a near infrared light source for direct shooting by using a Scanalyzer HTS phenotype analysis platform for each batch of corn seeds of the same corn variety obtained in the step (1) to carry out near infrared imaging to obtain an imaging image of the corn seeds of the batch;
in the hyperspectral imaging image analysis of the step (3), imaging images of corn seeds of each batch, which are obtained from the same corn variety, are sequentially discharged together from left to right according to the time for obtaining the corn seeds of each batch, and a qualitative analysis is carried out on the moisture content and the moisture distribution condition in the corn seeds by carrying out the qualitative analysis on the color depth of the corn seeds in the images, wherein the qualitative analysis is as follows: the darker the color of the corn seeds in the imaging image, the higher the moisture content in the corn seeds, the lighter the color of the corn seeds and the lower the moisture content in the corn seeds, so that a trend of overall change of the moisture content in the corn seeds with different maturity of the same corn variety is obtained;
in the step (4) of obtaining imaging data, further analyzing each batch of hyperspectral imaging images of the same corn variety arranged in the step (3) by using a plant color image analysis system, wherein the plant color image analysis system is divided into a basic setting module, an image cutting module, a color data acquisition module and a result processing module; according to the Munsell color system, firstly, cutting and preprocessing an image background: namely, the image of corn kernels is reserved, and the background is removed; then restoring the color space corresponding to the color data, uniformly restoring the color data into RHS color card numbers, and clustering according to the color system divided by the RHS color card numbers to obtain a percentage image of the color area represented by each RHS color card number, wherein the percentage image is the obtained imaging data, and the percentage image is the whole image area after the image background is cut and preprocessed;
in the imaging data visualization of the step (5), the images of different color systems in each batch of the same corn variety in the step (4) are transversely arranged together through conventional image processing software, and then the images of each batch which are transversely arranged together are spliced together from top to bottom according to the sequence of each batch, so that the visualized images of the moisture content and the moisture distribution condition in corn seeds of different maturity of the same corn variety are obtained.
Further, the number of corn seeds taken in the middle of the corn ear per batch in step (1) is determined based on the imaging window of the Scanalyzer HTS phenotyping platform capable of acquiring imaging images of all corn seeds taken.
Further, the corn seeds taken in step (1) are 20 grains per batch.
Further, the specific specification of an imaging module for near infrared imaging of each batch of corn seeds is as follows: a focal length of 50mm, a 1450nm bandpass filter, a field of view of 18 DEG x14 DEG, a working distance of 540mm.
Further, the conventional image processing software described in step (5) is Photoshop software.
Further, when the images of each batch transversely arranged together are spliced together from top to bottom according to the sequence of each batch in the step (5), color blocks with the color area smaller than 1% in each batch are removed, and the visualized images without the moisture content and the moisture distribution condition in corn seeds with different maturity of the same corn variety and with the color area smaller than 1% are obtained.
The invention also provides an image of the moisture content and the moisture distribution in the corn seeds formed by the method for rapidly identifying the moisture content and the moisture distribution of the corn seeds according to any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a key technology for digitizing and visualizing the internal moisture distribution of corn seeds. The invention sets up the corn seeds (kernels) to be grouted from the corn kernels to the maize plant withered and yellow (i.e. the mature ears are normally harvested) and harvest at different time, and a batch of samples are harvested for imaging analysis at intervals of 10 days, so as to observe and analyze the whole process of dynamic change of the moisture in the seeds due to different maturity, realize the analysis and research on the moisture condition in the seeds, which cannot be realized by the traditional technical method, and lay the technical foundation for influencing the storage, germination and the like of the seeds due to the influence on enzyme activity.
2. According to the invention, the independently developed plant color image analysis system (software) is utilized to perform color analysis on the hyperspectral imaging image, so that the water distribution conditions in corn seeds with different maturity are more intuitively displayed, and compared with the prior art, the method is more time-saving and labor-saving, and has the characteristics of rapidness, no damage, accuracy, high repeatability and the like.
3. The method of the invention is equally applicable to other plant seeds or living organs of other plants.
Drawings
Fig. 1: and (3) displaying images of corn seeds of the same corn variety with different maturity after hyperspectral near infrared imaging. Wherein 10d represents corn seeds harvested from corn kernel beginning to 10 th day, 20d represents corn seeds harvested from corn kernel beginning to 20 th day, 30d represents corn seeds harvested from corn kernel beginning to 30 th day, 40d represents corn seeds harvested from corn kernel beginning to 40 th day, 50d represents corn seeds harvested from corn kernel beginning to 50 th day, 60d represents corn seeds harvested from corn kernel beginning to 60 th day corn plant withered and yellow (i.e., normal harvest of mature corn ears) plant.
Fig. 2: and (3) specific imaging data results after visualization of hyperspectral near infrared imaging images of the moisture distribution and moisture content change conditions in corn seeds with different maturity of the same corn variety. Wherein 10d represents corn seeds harvested from corn kernel beginning to 10 th day, 20d represents corn seeds harvested from corn kernel beginning to 20 th day, 30d represents corn seeds harvested from corn kernel beginning to 30 th day, 40d represents corn seeds harvested from corn kernel beginning to 40 th day, 50d represents corn seeds harvested from corn kernel beginning to 50 th day, 60d represents corn seeds harvested from corn kernel beginning to 60 th day corn plant withered and yellow (i.e., normal harvest of mature ears) plant. Each row of color patches from left to right, different color patches representing color patches of different color systems of the seed imaging image, the number within each color patch representing the percentage of area occupied by the color patch area in the imaged image area of the batch (the color patch area ratio thereof decreasing from left to right), such as 98C (128A), 128A (D53.3), 120A (D10.0), 140A (D53.6), 140A (D55.4) color patch color [98C (128A) representing the color patch number of the RHS color card number, i.e., the color patch number, the remainder, in the order of from left to right, the number within each color patch representing the percentage of area occupied by the color patch area in the imaged image area of the batch (e.g., the number 29.64% in the first color patch in 10D represents 29.64% of the color patch area in fig. 2, the remainder, etc.). The batch of corn seeds harvested on day 20 is, in order from left to right, the color of the color patch of 96C (D8.4), 120A (D10.0), 128A (D53.5), 140A (D53.7), 140A (D55.8) RHS color card number, and the numbers within each color patch from left to right represent the percentage of the area of the color patch to the imaged image area of the batch. The batch of corn seeds harvested on day 30 is color of color patches of 113B (D5.1), 128A (D55.8), 140A (D54.4), 128A (D49.4), 12A (D8.8) RHS color card number in order from left to right, the number within each color patch from left to right representing the percentage of the area of the color patch to the imaged image area of the batch. The batch of corn seeds harvested on day 40 is color of color patches of 125B (D19.1), 130A (D55.3), 140A (D54.1), 101B (D4.2), 24A (D5.5) RHS color card number in order from left to right, the numbers within each color patch from left to right representing the percentage of the area of the color patch to the imaged image area of the batch. The batch of corn seeds harvested on day 50 is color of color patches of 125B (D19.5), 140A (D53.8), 140A (D55.8), 128A (D55.5), 150A (D22.5) RHS color card number in that order from left to right, with the numbers within each color patch from left to right representing the percentage of area occupied by the area of the color patch in the imaged image area of the batch. The batch of corn seeds harvested on day 60 is color of color patches of 128A (D53.6), 118A (D6.6), 140A (D53.4), 140A (D55.8), 149A (D35.9) RHS color card number in order from left to right, the number within each color patch from left to right representing the percentage of the area of the color patch to the imaged image area of the batch. The RHS color block number appears as a code of color in the imaged image, distinct from other color blocks.
Detailed Description
Terminology:
RHS color card number: the RHS color card number is the color number of the reference standard color card for identifying the plant color.
Example 1
(1) Corn seed harvesting
Collecting corn seeds of different corn varieties, then planting in a field, wherein the planting mode is a hill planting mode, the planting specification is 40 plants in each district, the row spacing is 70cm, the plant spacing is 30cm, sowing is carried out in the middle ten days or the late ten days of 5 months, then harvesting corn seeds of the same corn variety every 10 days in the middle ten days of 8 months when the corn seeds begin to be grouted, sampling until the corn plants are withered and yellow (namely the corn seeds are fully mature), namely, the normal harvesting of the corn ears is carried out for 10 months, the maturity of the corn seeds harvested in different time periods is different, the corn seeds are used as different maturity gradients of the corn seeds, 20 corn seeds in the middle part of each corn ear are selected in each corn variety in batches (the quantity of the corn seeds obtained in each batch can be determined according to imaging images of all the corn seeds obtained in the corn ears by an imaging window of a ScAnalyzer phenotype analysis platform), and the embodiment can also be used for taking seeds at the upper part or the lower part of the corn ears, and sealing and preserving the corn ears by using a vacuum packaging bag to prevent water loss.
(2) Hyperspectral imaging of corn seeds
After sample corn seeds are harvested, placing each batch of corn seeds (according to batches) of the same corn variety harvested in the step (1) into imaging equipment similar to a box body by utilizing a Scanalyzer HTS phenotype analysis platform, closing a box door after placing neatly, selecting a system software near infrared light source of the Scanalyzer HTS phenotype analysis platform to conduct near infrared imaging, directly shooting to obtain hyperspectral imaging images (namely image data) of each batch of corn seeds, wherein the specific specification of an imaging module for near infrared imaging of each batch of corn seeds is near infrared: a band-pass filter with a focal length of 50mm and 1450 nm; the field of view is 18 deg. x14 deg., working distance 540mm.
(3) Hyperspectral imaging image analysis
The hyperspectral image analysis mainly comprises visual qualitative analysis of the content of water in the seeds and the water distribution situation through the color depth of the seeds, wherein the color depth of the seeds in an imaging image represents the content of water in the seeds, the darker the color of the corn seeds in the imaging image is, the higher the content of water in the corn seeds is, the lighter the color of the corn seeds is, and the lower the content of water in the corn seeds is.
In the hyperspectral imaging image analysis of the step (3), the corn seed imaging images obtained in each batch are sequentially discharged together from left to right according to the time for obtaining the corn seeds (figure 1), the water content in the corn seeds and the water distribution situation are visually and qualitatively analyzed through the color depth of the corn seeds in the images, the water content difference between the seeds with different maturity and different parts of the seeds with the same maturity (such as the upper part, the middle part or the lower part of corn ears is obtained in the test), for example, 10d in figure 1 is a hyperspectral near infrared imaging image of 20 corn seeds obtained from the beginning of grouting of corn seeds to the 10 th day, the darker the color of each corn seed is, the higher the water content of each corn seed is, the shallower the color of each corn seed is, the water content of each corn seed is lower, and the like, so that the total variation trend of the water content in corn seeds with different maturity of each batch of the same corn variety, namely the same corn variety can be obtained; the whole continuous dynamic moisture content change process of the corn variety seeds from the beginning of the grouting to the termination of the maize plant's withered and yellow (the maize seeds are fully mature) was obtained (fig. 1). The usual imaging and analysis of the image ends up, so far only an apparent image is formed, and no specifically quantified values are obtained. The specific quantified values thereof are completed by the following steps.
(4) Acquisition of imaging data
The above results are based on visual observation, but the relative ratio of water represented by a certain color cannot be known, so that more visual and accurate results can be obtained after analysis by using image color analysis software. The plant color image analysis system (software) independently developed by the subject group is utilized to analyze each hyperspectral near infrared imaging image, and the occupation ratio (area percentage) of each color area in the whole imaging image area is calculated so as to specifically quantify different colors representing the moisture content and obtain specific values.
In the step (4) of obtaining imaging data, a plant color image analysis system (software) is used for further analyzing each batch of hyperspectral imaging images of the same corn variety arranged in the step (3), the plant color image analysis system is divided into a basic setting module, an image cutting module, a color data obtaining module and a result processing module, according to a Munsell color system, firstly cutting and preprocessing an image background, wherein the cutting and preprocessing of the image background is to retain images of corn kernels, and the background is removed; and then restoring the color space corresponding to the color data, uniformly restoring the color data into RHS color card numbers, and clustering according to the color system divided by the RHS color card numbers to finally obtain a percentage condition image (figure 2) of the color area represented by each RHS color card number, which occupies the whole image area after the image background is cut and preprocessed, namely the obtained specific imaging data.
(5) Imaging data visualization
And (3) transversely arranging images of different color systems in each batch of the same corn variety in the step (4) together through conventional image processing software Photoshop software, and splicing the transversely arranged images of each batch of the same corn variety together from top to bottom according to the sequence of each batch (figure 2), so as to obtain a visual image (figure 2) of the moisture content in corn seeds of different maturity of the same corn variety and the moisture change condition of the seeds of different seed maturity (of each time period).
Since the color area (divided by color system) is too small, the moisture content is low (without reference value), further, color blocks with the color area smaller than 1% in each batch are removed, and a visual image (figure 2) without the moisture content and the moisture distribution condition in corn seeds with different maturity of the same corn variety and with the color area smaller than 1% is obtained, so that a more visual and quantized image analysis result (figure 2) is formed.
Claims (7)
1. A method for rapidly identifying the moisture content and the moisture distribution condition in corn seeds comprises the steps of (1) obtaining corn seeds, (2) hyperspectral imaging of the corn seeds, (3) hyperspectral imaging image analysis, (4) obtaining imaging data and (5) visualizing the imaging data; the method is characterized in that:
in the step (1) of obtaining corn seeds, grouting is started from corn kernels until corn plants are withered and yellow, and a batch of corn seeds are harvested every 10 days for the same corn variety, so that corn seeds of different batches of the same corn variety are obtained in a mode, namely, corn seeds of different maturity are obtained, the corn seeds are used as different maturity gradients of the corn seeds, the corn seeds of each batch of each corn variety are selected to be positioned in the middle of corn ears, and the corn seeds are hermetically preserved by using a vacuum packaging bag to prevent water loss;
in the hyperspectral imaging of the corn seeds in the step (2), selecting a near infrared light source for direct shooting by using a Scanalyzer HTS phenotype analysis platform for each batch of corn seeds of the same corn variety obtained in the step (1) to carry out near infrared imaging to obtain an imaging image of the corn seeds of the batch;
in the hyperspectral imaging image analysis of the step (3), imaging images of corn seeds of each batch, which are obtained from the same corn variety, are sequentially discharged together from left to right according to the time for obtaining the corn seeds of each batch, and a qualitative analysis is carried out on the moisture content and the moisture distribution condition in the corn seeds by carrying out the qualitative analysis on the color depth of the corn seeds in the images, wherein the qualitative analysis is as follows: the darker the color of the corn seeds in the imaging image, the higher the moisture content in the corn seeds, the lighter the color of the corn seeds and the lower the moisture content in the corn seeds, so that a trend of overall change of the moisture content in the corn seeds with different maturity of the same corn variety is obtained;
in the step (4) of obtaining imaging data, further analyzing each batch of hyperspectral imaging images of the same corn variety arranged in the step (3) by using a plant color image analysis system, wherein the plant color image analysis system is divided into a basic setting module, an image cutting module, a color data acquisition module and a result processing module; according to the Munsell color system, firstly, cutting and preprocessing an image background: namely, the image of corn kernels is reserved, and the background is removed; then restoring the color space corresponding to the color data, uniformly restoring the color data into RHS color card numbers, and clustering according to the color system divided by the RHS color card numbers to obtain a percentage image of the color area represented by each RHS color card number, wherein the percentage image is the obtained imaging data, and the percentage image is the whole image area after the image background is cut and preprocessed;
in the imaging data visualization of the step (5), the images of different color systems in each batch of the same corn variety in the step (4) are transversely arranged together through conventional image processing software, and then the images of each batch which are transversely arranged together are spliced together from top to bottom according to the sequence of each batch, so that the visualized images of the moisture content and the moisture distribution condition in corn seeds of different maturity of the same corn variety are obtained.
2. The method of claim 1, wherein the amount of corn seeds collected in the middle of the corn ear for each batch in step (1) is determined from the image of all corn seeds collected in the image window of the Scanalyzer HTS phenotyping platform.
3. The method of claim 2, wherein the corn seeds obtained in step (1) are 20 corn seeds per batch.
4. The method for quickly identifying moisture content and moisture distribution in corn seeds according to claim 1, wherein the specific specifications of the imaging module used for near infrared imaging of each batch of corn seeds are as follows: a focal length of 50mm, a 1450nm bandpass filter, a field of view of 18 DEG x14 DEG, a working distance of 540mm.
5. The method of claim 1, wherein the conventional image processing software in step (5) is Photoshop software.
6. The method for quickly identifying the moisture content and the moisture distribution in the corn seeds according to claim 1, wherein when the images of each batch transversely arranged together are spliced together from top to bottom according to the sequence of each batch in the step (5), color blocks with the color area smaller than 1% in each batch are removed, and the visual images of the moisture content and the moisture distribution in the corn seeds with different maturity of the same corn variety without the color area smaller than 1% are obtained.
7. An image of moisture content and moisture distribution in corn seeds formed by the method of rapidly identifying moisture content and moisture distribution in corn kernels according to any one of claims 1-6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117730655A (en) * | 2024-02-20 | 2024-03-22 | 云南省农业科学院质量标准与检测技术研究所 | Quantitative analysis method, device, equipment and storage medium for vigor of rice seeds |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117730655A (en) * | 2024-02-20 | 2024-03-22 | 云南省农业科学院质量标准与检测技术研究所 | Quantitative analysis method, device, equipment and storage medium for vigor of rice seeds |
| CN117730655B (en) * | 2024-02-20 | 2024-05-14 | 云南省农业科学院质量标准与检测技术研究所 | Quantitative analysis method, device, equipment and storage medium for vigor of rice seeds |
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