CN117730655A - Quantitative analysis method, device, equipment and storage medium for vigor of rice seeds - Google Patents
Quantitative analysis method, device, equipment and storage medium for vigor of rice seeds Download PDFInfo
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Abstract
The invention discloses a quantitative analysis method, a device, equipment and a storage medium for rice seed vitality, which are characterized in that a conversion relation from a multispectral image of a sample rice seed to an nCDA image is analyzed to generate an nCDA image generation model and the nCDA image generation model is configured in an image acquisition device for warehousing detection, so that a warehousing detection personnel can realize warehousing detection judgment of the rice seed according to the rice seed color in an nCDA image photo of the warehousing rice seed shot by the image acquisition device, the vitality value of the rice seed is not required to be calculated, and the warehousing detection efficiency and the detection result recognition degree are improved; when the precise calculation of the seed vitality value is needed for the rice seeds after the rice seeds are placed in the warehouse, the vitality index of the batch of rice seeds can be precisely calculated by detecting the color value input vitality index of the rice seeds in the nCDA image photo and inputting a relation model of the color value of the nCDA image, and data support is provided for the quality fine classification of the rice seeds and the subsequent purchase of the rice seeds.
Description
Technical Field
The invention relates to the technical field of geological disaster prediction, in particular to a quantitative analysis method, a quantitative analysis device, quantitative analysis equipment and a quantitative analysis storage medium for vigor of rice seeds.
Background
The vigor of rice seeds is one of important indexes for measuring the quality of the seeds, and is a comprehensive reflection of the germination rate and the growth quantity of the seeds. The high-activity rice seeds have obvious growth advantages and production potential, and have extremely important significance for seed management and agricultural production. The activity of the rice seeds is highest when the seeds are physiologically mature, natural irreversible seed aging occurs along with the extension of the storage time, and the activity of the corresponding seeds is gradually reduced. Traditional rice seed activity detection is based on enzyme activity measurement, adenosine Triphosphate (ATP) content measurement, seedling growth measurement, germination speed measurement, hyperspectral imaging technology and the like, but has the defects of complex operation, long time consumption, damage to rice seeds and incapability of meeting nondestructive detection and quantitative detection. Although, there have been proposed methods for detecting seed vigor based on multispectral image analysis (for example, patent of the invention filed by the applicant in advance and published under the publication number CN116941380 a).
However, in practical applications of seed management and agricultural production, since rice seed purchasing has seasonal characteristics, there are generally large purchasing amount and purchasing frequency in a rice seed purchasing period (for example, a scenario in which a large number of rice planting individual households are subjected to rice seed purchasing in a country or a rice seed planting area), and under such a scenario, the in-house vigor detection of rice seeds needs to satisfy: the method is suitable for the requirements of different types of rice seeds for detection, high detection speed, strong identification of detection results, deliverable circulation of the detection results, storage of stubbles of the detection results and the like.
In the seed vitality detection scheme based on the multispectral image, which is proposed in the prior art, the detection of the seed vitality is mainly realized by directly calculating the seed vitality value, and the method has the following defects: (1) The detection time consumption is unnecessary in the detection of the warehousing activity, the detection of the warehousing activity of the rice seeds does not need the high-precision detection result such as the activity value of the seeds, and only the simple activity detection is needed for the sample of the rice seeds in the warehouse to judge whether to purchase the batch of rice seeds (the high-precision detection of the activity value of the seeds is needed when the quality of the rice seeds is finely divided after the purchase and the quality of the purchased rice seeds is evaluated to provide data support for the subsequent purchase and other links); (2) The identification of the seed vitality value as a detection result is not high, the warehousing vitality detection behavior performed during rice seed purchase is often performed by non-professional staff, and the purchase judgment is performed by the seed vitality value, so that the judgment error condition caused by the low identification degree exists; (3) When different types of rice seeds are detected, the different types of rice seeds have different standard seed vitality values, so that the judgment difficulty is further improved; (4) The method of directly calculating the seed vitality value cannot realize the deliverable circulation of the detection result and the storage of the stub of the detection result, so that the seed vitality value of the batch of rice seeds cannot be accurately detected and verified in the follow-up process.
Therefore, how to improve the adaptability of rice seed vitality detection to the demands of purchasing in different periods in practical scenes such as seed management, agricultural production and the like, namely, ensure the high-precision detection of selectivity after warehousing while ensuring the detection speed and the detection result discrimination of the warehousing vitality detection, is a technical problem to be solved urgently.
Disclosure of Invention
The invention mainly aims to provide a quantitative analysis method, a quantitative analysis device, quantitative analysis equipment and a quantitative analysis storage medium for the vigor of rice seeds, and aims to solve the problems that the detection speed and the detection result of the in-warehouse vigor detection are low in discrimination degree, the vigor detection result delivery and transfer after the in-warehouse vigor detection and the storage of stubbles are difficult and the vigor detection precision is low in the current practical scenes such as seed management and agricultural production.
In order to achieve the above purpose, the invention provides a quantitative analysis method for the vigor of rice seeds, which comprises the following steps:
acquiring multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds;
carrying out regression analysis by using the vitality indexes of the rice seeds with different samples and the corresponding nCDA images, and establishing a relation model based on the vitality indexes and the color values of the nCDA images;
taking nCDA image pictures of rice seeds to be detected, placing the nCDA image pictures and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by using an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images;
and inputting the nCDA image color value into a relation model based on the vitality index and the nCDA image color value, and obtaining the vitality index of the rice seeds to be detected, which is output by the relation model.
Optionally, obtaining multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds, wherein the steps specifically comprise:
acquiring multispectral images of a plurality of groups of sample rice seeds; wherein each group of sample rice seeds comprises a plurality of rice seeds, and each group of sample rice seeds has different aging degrees;
placing a plurality of groups of sample rice seeds with different aging degrees into an incubator, and counting germination conditions of the sample rice seeds to generate vitality indexes of each group of sample rice seeds;
and converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vitality index of each group of sample rice seeds.
Optionally, the step of converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vitality index of each group of sample rice seeds specifically includes:
acquiring a first multispectral image corresponding to a first group of sample rice seeds with highest vitality indexes and a second multispectral image corresponding to a second group of sample rice seeds with lowest vitality indexes, and respectively endowing a first color value and a second color value to nCDA images corresponding to the first multispectral image and the second multispectral image;
extracting spectral features of the first multispectral image and the second multispectral image for normalization to be used as color discrimination standard features, obtaining multispectral images to be analyzed corresponding to a plurality of groups of sample rice seeds with other vitality indexes, extracting the spectral features, and performing normalization treatment to be used as color discrimination actual features;
based on the positions of a first color corresponding to the color discrimination standard feature of the first multispectral image and a second color corresponding to the color discrimination standard feature of the second multispectral image in a preset color map respectively, performing discrimination analysis on the target position of a target color corresponding to the color discrimination actual feature of the multispectral image to be analyzed in the preset color map;
and taking the target color corresponding to the target position of each multispectral image to be analyzed in the preset color map as the nCDA image color of the corresponding group of sample rice seeds, and generating the nCDA image of each group of sample rice seeds.
Optionally, performing regression analysis by using the vitality indexes of the rice seeds with different samples and corresponding nCDA images, and establishing a relationship model based on the vitality indexes and the color values of the nCDA images, which specifically comprises the following steps:
obtaining vitality indexes and corresponding nCDA images of rice seeds with different samples;
and extracting a color value L and a color value a of CIELAB values of the nCDA images of each group of sample rice seeds, carrying out regression analysis, and establishing a relationship model based on the vitality index and related to the color value L and the color value a.
Optionally, after the step of converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vigor index of each group of sample rice seeds, the method further includes:
extracting spectral features of multispectral images corresponding to each group of sample rice seeds to form feature vectors, and extracting target positions of target colors corresponding to each group of sample rice seeds in a preset color map;
and respectively inputting the characteristic vector corresponding to each group of sample rice seeds and the target position of the target color in the preset color map as training data and training labels of training samples into a neural network model for training, and obtaining an nCDA image generation model after training is completed.
Optionally, the image acquisition device is configured with multispectral image acquisition equipment and an nCDA image generation controller, and the nCDA image generation controller stores an nCDA image generation model corresponding to the rice seed to be detected; taking nCDA image pictures of rice seeds to be detected, placing the nCDA image pictures and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by utilizing an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images, wherein the method specifically comprises the following steps:
driving multispectral image acquisition equipment in the image acquisition device to acquire a multispectral image to be detected of the rice seed to be detected, and driving an nCDA image generation controller in the image acquisition device to generate an nCDA image photo of the rice seed to be detected according to the multispectral image to be detected by using an nCDA image generation model;
placing the nCDA image photo and the standard color card into an image acquisition room, and acquiring a first color measurement image and a second color measurement image which contain the nCDA image photo and the standard color card and are positioned at two opposite positions in the image acquisition room;
according to the first RGB value of each color block of the standard color card and the CIEXYZ value of the color block in the first color measurement image and the second color measurement image, a color value conversion matrix is established, according to the second RGB value of the nCDA image in the first color measurement image and the second color measurement image and the color value conversion matrix, the CIEXYZ value of the nCDA image of the rice seed to be detected is determined, and the CIEXYZ value of the nCDA image is converted into the CIELAB value of the nCDA image.
Optionally, the step of inputting the color value of the nCDA image into a relationship model based on the vigor index and the color value of the nCDA image to obtain the vigor index of the rice seed to be measured output by the relationship model specifically includes:
extracting a color value L and a color value a in an nCDA image CIELAB value of the rice seed to be detected;
and inputting the color value L and the color value a into a relation model based on the vitality index and the nCDA image color value to obtain the vitality index of the rice seed to be detected.
In addition, in order to achieve the above purpose, the present invention also provides a quantitative analysis device for vigor of rice seeds, comprising:
the acquisition module is used for acquiring multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds;
the establishing module is used for carrying out regression analysis by utilizing the vitality indexes of the rice seeds with different samples and the corresponding nCDA images, and establishing a relation model based on the vitality indexes and the color values of the nCDA images;
the shooting module is used for shooting nCDA image photos of rice seeds to be detected, placing the nCDA image photos and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by using an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images;
and the output module is used for inputting the nCDA image color value into a relation model based on the vitality index and the nCDA image color value, and obtaining the vitality index of the rice seed to be detected, which is output by the relation model.
In addition, in order to achieve the above object, the present invention also provides a quantitative analysis apparatus for vigor of rice seeds, comprising: the quantitative analysis method for the vigor of the rice seeds comprises a memory, a processor and a quantitative analysis program for the vigor of the rice seeds, wherein the quantitative analysis program for the vigor of the rice seeds is stored in the memory and can run on the processor, and the quantitative analysis program for the vigor of the rice seeds is implemented by the processor.
In addition, in order to achieve the above object, the present invention also provides a storage medium, on which a quantitative analysis program for vigor of rice seeds is stored, which when executed by a processor, implements the steps of the quantitative analysis method for vigor of rice seeds described above.
The invention has the beneficial effects that: the invention provides a quantitative analysis method, a device, equipment and a storage medium for rice seed vitality, which are characterized in that a nCDA image generation model is generated by analyzing the conversion relation of a multispectral image of a sample rice seed to nCDA image conversion, and the nCDA image generation model is configured in an image acquisition device for warehousing detection, so that a warehousing detector can realize warehousing detection judgment of the rice seed according to the rice seed color in a nCDA image photo of the warehousing rice seed shot by the image acquisition device, and the rice seed vitality value is not required to be calculated, thereby improving the warehousing detection efficiency and the detection result identification; after that, the nCDA image photo and the rice seeds in the warehouse are circulated together, when the accurate calculation of the seed vitality value is needed for the rice seeds, the vitality index of the batch of rice seeds can be accurately calculated by detecting the color value of the rice seeds in the nCDA image photo and inputting the color value into a relation model of the vitality index and the nCDA image color value, and data support is provided for the quality fine classification of the rice seeds and the subsequent purchase of the rice seeds, so that the adaptability of the rice seed vitality detection to the needs of purchase in different periods in actual scenes such as seed management, agricultural production and the like is improved.
Drawings
FIG. 1 is a schematic diagram of a device structure of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of the quantitative analysis method for the vigor of rice seeds according to the present invention;
fig. 3 is a block diagram of a quantitative analysis device for vigor of rice seeds according to an embodiment of the invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of an apparatus structure of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the arrangement of the apparatus shown in fig. 1 is not limiting and may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a rice seed vigor quantitative analysis program may be included in the memory 1005 as a computer storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call the quantitative analysis program for vigor of rice seeds stored in the memory 1005, and perform the following operations:
acquiring multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds;
carrying out regression analysis by using the vitality indexes of the rice seeds with different samples and the corresponding nCDA images, and establishing a relation model based on the vitality indexes and the color values of the nCDA images;
taking nCDA image pictures of rice seeds to be detected, placing the nCDA image pictures and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by using an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images;
and inputting the nCDA image color value into a relation model based on the vitality index and the nCDA image color value, and obtaining the vitality index of the rice seeds to be detected, which is output by the relation model.
The specific embodiment of the present invention applied to the apparatus is basically the same as the following embodiments of the quantitative analysis method for the vigor of rice seeds, and will not be described herein.
The embodiment of the invention provides a quantitative analysis method for the vigor of rice seeds, and referring to fig. 2, fig. 2 is a flow chart of an embodiment of the quantitative analysis method for the vigor of rice seeds.
In this embodiment, the quantitative analysis method for the vigor of the rice seeds comprises the following steps:
s100: acquiring multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds;
s200: carrying out regression analysis by using the vitality indexes of the rice seeds with different samples and the corresponding nCDA images, and establishing a relation model based on the vitality indexes and the color values of the nCDA images;
s300: taking nCDA image pictures of rice seeds to be detected, placing the nCDA image pictures and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by using an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images;
s400: and inputting the nCDA image color value into a relation model based on the vitality index and the nCDA image color value, and obtaining the vitality index of the rice seeds to be detected, which is output by the relation model.
It should be noted that, in the seed vitality detection scheme based on the multispectral image provided in the prior art, the problems of low recognition degree of detection speed and detection result of the in-warehouse vitality detection during the vitality detection of rice seeds in actual scenes such as seed management, agricultural production and the like, difficult delivery and diversion of the vitality detection result after in-warehouse, low storage difficulty of stubbles, low vitality detection precision and the like exist. In order to solve the above problems, in this embodiment, by analyzing the conversion relationship from the multispectral image of the sample rice seed to the nCDA image to generate an nCDA image generation model, the nCDA image generation model is configured in the image acquisition device for the in-house detection, so that the in-house detection personnel can realize in-house detection judgment of the rice seed according to the rice seed color in the nCDA image photograph of the in-house rice seed taken by the image acquisition device, without calculating the vitality value of the rice seed, and improving the in-house detection efficiency and the detection result identification; after that, the nCDA image photo and the rice seeds in the warehouse are circulated together, when the accurate calculation of the seed vitality value is needed for the rice seeds, the vitality index of the batch of rice seeds can be accurately calculated by detecting the color value of the rice seeds in the nCDA image photo and inputting the color value into a relation model of the vitality index and the nCDA image color value, and data support is provided for the quality fine classification of the rice seeds and the subsequent purchase of the rice seeds, so that the adaptability of the rice seed vitality detection to the needs of purchase in different periods in actual scenes such as seed management, agricultural production and the like is improved.
In a preferred embodiment, a multispectral image of a plurality of groups of sample rice seeds is obtained, and the multispectral image is converted into an nCDA image according to the vitality index of each group of sample rice seeds, which specifically comprises the following steps: acquiring multispectral images of a plurality of groups of sample rice seeds; wherein each group of sample rice seeds comprises a plurality of rice seeds, and each group of sample rice seeds has different aging degrees; placing a plurality of groups of sample rice seeds with different aging degrees into an incubator, and counting germination conditions of the sample rice seeds to generate vitality indexes of each group of sample rice seeds; and converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vitality index of each group of sample rice seeds.
In this embodiment, when obtaining sample rice seeds with different aging degrees, the sample rice seeds may be grouped and placed into an aging device to perform aging treatment with different durations, so that several groups of sample rice seeds have sample rice seeds with different rice seed vigor indexes.
In a preferred embodiment, the step of converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vigor index of each group of sample rice seeds specifically includes: acquiring a first multispectral image corresponding to a first group of sample rice seeds with highest vitality indexes and a second multispectral image corresponding to a second group of sample rice seeds with lowest vitality indexes, and respectively endowing a first color value and a second color value to nCDA images corresponding to the first multispectral image and the second multispectral image; extracting spectral features of the first multispectral image and the second multispectral image for normalization to be used as color discrimination standard features, obtaining multispectral images to be analyzed corresponding to a plurality of groups of sample rice seeds with other vitality indexes, extracting the spectral features, and performing normalization treatment to be used as color discrimination actual features; based on the positions of a first color corresponding to the color discrimination standard feature of the first multispectral image and a second color corresponding to the color discrimination standard feature of the second multispectral image in a preset color map respectively, performing discrimination analysis on the target position of a target color corresponding to the color discrimination actual feature of the multispectral image to be analyzed in the preset color map; and taking the target color corresponding to the target position of each multispectral image to be analyzed in the preset color map as the nCDA image color of the corresponding group of sample rice seeds, and generating the nCDA image of each group of sample rice seeds.
In this embodiment, the multispectral image corresponding to each group of sample rice seeds is converted into an nCDA image by adopting a special nCDA (normalized canonical discriminant analysis, normalized standard discriminant analysis) processing method, mainly by acquiring multispectral images of two extreme values of the vigor index, after corresponding color values are given to the nCDA images of the two extreme values, the color values of the nCDA images of other sample rice seeds between the two extreme values can be distinguished according to the differences of the spectral features in the multispectral images, for example, when linear distinction is adopted, the colors of the nCDA images of the two extreme values are at two end positions in the color map, and the color values of the nCDA images of other sample rice seeds can be obtained by determining the positions corresponding to the linear proportions on the connecting line between the two end points according to the linear proportions of the multispectral features.
In a preferred embodiment, regression analysis is performed by using the vigor index of the rice seeds with different samples and corresponding nCDA images, and a relation model step based on the vigor index and the color value of the nCDA images is established, which specifically includes: obtaining vitality indexes and corresponding nCDA images of rice seeds with different samples; and extracting a color value L and a color value a of CIELAB values of the nCDA images of each group of sample rice seeds, carrying out regression analysis, and establishing a relationship model based on the vitality index and related to the color value L and the color value a.
In this embodiment, after obtaining the vigor index and the corresponding nCDA image of the rice seed with different samples, regression analysis is performed by extracting the color value L and the color value a of the CIELAB value of the nCDA image, so as to obtain a relationship model based on the vigor index with respect to the color value L and the color value a. In the specific example of regression analysis, the analysis results obtained were as follows:
| Multiple R | 0.999185374 |
| R Square | 0.998371411 |
| Adjusted R Square | 0.995114234 |
table 1: regression effect parameters
Wherein, multiplex R, R Square and Adjusted R Square are 3 evaluation indexes of regression equation, 3 values are between 0 and 1, and the closer to 1, the better the effect.
| Coefficients | Standard error of | t Stat | P-value | |
| Intercept | 1.049608636 | 0.074677431 | 14.05523233 | 0.045217953 |
| L | -0.009678807 | 0.000847545 | -11.41981926 | 0.055605085 |
| a | -0.007616526 | 0.00033428 | -22.78483949 | 0.027922579 |
Table 2: regression analysis results
Here, intersett is a constant term, L is a coefficient of the color value L, and a is a coefficient of the color value a. Wherein, the P value of the constant term and a is less than 0.05, and the difference is obvious.
Thus, the expression of the relationship model based on the vitality index with respect to the color value L and the color value a in the specific embodiment is obtained as follows: vitality index = -0.009679L-0.00757a+1.0496.
In a preferred embodiment, after the step of converting the multispectral image corresponding to each set of sample rice seeds into an nCDA image according to the vigor index of each set of sample rice seeds, the method further comprises: extracting spectral features of multispectral images corresponding to each group of sample rice seeds to form feature vectors, and extracting target positions of target colors corresponding to each group of sample rice seeds in a preset color map; and respectively inputting the characteristic vector corresponding to each group of sample rice seeds and the target position of the target color in the preset color map as training data and training labels of training samples into a neural network model for training, and obtaining an nCDA image generation model after training is completed.
On the basis, the image acquisition device is provided with multispectral image acquisition equipment and an nCDA image generation controller, and the nCDA image generation controller stores an nCDA image generation model corresponding to the rice seeds to be detected; taking nCDA image pictures of rice seeds to be detected, placing the nCDA image pictures and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by utilizing an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images, wherein the method specifically comprises the following steps: driving multispectral image acquisition equipment in the image acquisition device to acquire a multispectral image to be detected of the rice seed to be detected, and driving an nCDA image generation controller in the image acquisition device to generate an nCDA image photo of the rice seed to be detected according to the multispectral image to be detected by using an nCDA image generation model; placing the nCDA image photo and the standard color card into an image acquisition room, and acquiring a first color measurement image and a second color measurement image which contain the nCDA image photo and the standard color card and are positioned at two opposite positions in the image acquisition room; according to the first RGB value of each color block of the standard color card and the CIEXYZ value of the color block in the first color measurement image and the second color measurement image, a color value conversion matrix is established, according to the second RGB value of the nCDA image in the first color measurement image and the second color measurement image and the color value conversion matrix, the CIEXYZ value of the nCDA image of the rice seed to be detected is determined, and the CIEXYZ value of the nCDA image is converted into the CIELAB value of the nCDA image.
The step of inputting the color value of the nCDA image into a relation model based on the vitality index and the color value of the nCDA image to obtain the vitality index of the rice seed to be detected, which is output by the relation model, specifically comprises the following steps: extracting a color value L and a color value a in an nCDA image CIELAB value of the rice seed to be detected; and inputting the color value L and the color value a into a relation model based on the vitality index and the nCDA image color value to obtain the vitality index of the rice seed to be detected.
In the embodiment, through analyzing the conversion relation of the multispectral image of the sample rice seeds to the nCDA image conversion, an nCDA image generation model is generated, and the nCDA image generation model is configured in an image acquisition device for warehousing detection, so that a warehousing detection personnel can realize warehousing detection judgment of the rice seeds according to the rice seed color in the nCDA image photo of the warehousing rice seeds shot by the image acquisition device, and the calculation of the vitality value of the rice seeds is not needed, so that the warehousing detection efficiency and the detection result discrimination are improved; after that, the nCDA image photo and the rice seeds in the warehouse are circulated together, when the accurate calculation of the seed vitality value is needed for the rice seeds, the vitality index of the batch of rice seeds can be accurately calculated by detecting the color value of the rice seeds in the nCDA image photo and inputting the color value into a relation model of the vitality index and the nCDA image color value, and data support is provided for the quality fine classification of the rice seeds and the subsequent purchase of the rice seeds, so that the adaptability of the rice seed vitality detection to the needs of purchase in different periods in actual scenes such as seed management, agricultural production and the like is improved.
The detection method for the color value of the rice seeds in the nCDA image photo adopts the following modes: placing the nCDA image photo and the standard color card into an image acquisition room, wherein a shooting light source is arranged in the image acquisition room, the whole sealing design is adopted, the external light source is prevented from entering the image acquisition room, the influence caused by environmental change in the rice seed color measurement process is reduced, and the image acquisition room comprises a first color measurement image and a second color measurement image which are of the nCDA image photo and the standard color card and are positioned at two opposite positions in the image acquisition room; taking the average value of the first RGB value of each color block in the first color measurement image and the first RGB value of each color block in the second color measurement image as the RGB value of each color block in the standard color card for removing environmental interference, establishing a color value conversion matrix according to the RGB value and the CIEXYZ value of the color block, determining the CIEXYZ value of the nCDA image of the rice seed to be measured according to the second RGB value of the nCDA image in the first color measurement image and the second color measurement image and the color value conversion matrix, and converting the CIEXYZ value of the nCDA image into the CIELAB value of the nCDA image; therefore, the high-precision detection of the rice seed color value in the nCDA image photo is realized, the problem of low detection precision caused by a human eye observation mode is avoided, and the vitality index of the rice seeds to be detected can be determined according to the rice seed color value detected with high precision, so that data support is provided for fine classification of the quality of the rice seeds and subsequent rice seed purchase.
According to the embodiment, the primary detection of the rice seeds in the warehouse and the accurate detection after the warehouse are separated, so that the problems that the detection speed and the detection result of the warehouse-in vitality detection are low in discrimination degree, the vitality detection result delivery and the storage of stubbles are difficult and the vitality detection precision is low in the existing detection of the vitality of the rice seeds in actual scenes such as seed management and agricultural production are solved, and the method has good scene adaptability.
In addition, in order to achieve the above purpose, the present invention also provides a quantitative analysis device for vigor of rice seeds, comprising:
referring to fig. 3, fig. 3 is a block diagram showing the structure of an embodiment of the quantitative analyzer for the vigor of rice seeds according to the present invention.
As shown in fig. 3, the quantitative analysis device for vigor of rice seeds according to the embodiment of the invention includes:
the acquisition module 10 is used for acquiring multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds;
the establishing module 20 is configured to perform regression analysis by using the vitality indexes of the rice seeds with different samples and the corresponding nCDA images, and establish a relationship model based on the vitality indexes and the color values of the nCDA images;
the shooting module 30 is configured to shoot an nCDA image photo of a rice seed to be detected, put the nCDA image photo and a standard color card into an image acquisition room, acquire a color measurement image in the image acquisition room by using an image acquisition device, and determine an nCDA image color value of the rice seed to be detected based on the color measurement image;
and the output module 40 is used for inputting the color value of the nCDA image into a relation model based on the vitality index and the color value of the nCDA image to obtain the vitality index of the rice seed to be tested, which is output by the relation model.
Other embodiments or specific implementation manners of the quantitative analysis device for vigor of rice seeds can refer to the above method embodiments, and are not repeated here.
In addition, the invention also provides quantitative analysis equipment for the vigor of the rice seeds, which comprises the following components: the quantitative analysis method for the vigor of the rice seeds comprises a memory, a processor and a quantitative analysis program for the vigor of the rice seeds, wherein the quantitative analysis program for the vigor of the rice seeds is stored in the memory and can run on the processor, and the quantitative analysis program for the vigor of the rice seeds is implemented by the processor.
The specific implementation manner of the quantitative analysis device for the activity of the rice seeds is basically the same as that of each embodiment of the quantitative analysis method for the activity of the rice seeds, and is not repeated here.
In addition, the invention also provides a readable storage medium, which comprises a computer readable storage medium, and the quantitative analysis program of the vigor of the rice seeds is stored on the computer readable storage medium. The readable storage medium may be a Memory 1005 in the terminal of fig. 1, or may be at least one of a ROM (Read-Only Memory)/RAM (Random Access Memory ), a magnetic disk, and an optical disk, and the readable storage medium includes a plurality of instructions for causing a quantitative analysis device for rice seed vigor having a processor to perform the quantitative analysis method for rice seed vigor according to the embodiments of the present invention.
The specific implementation manner of the readable storage medium is basically the same as the above embodiments of the quantitative analysis method for the vigor of rice seeds, and is not repeated here.
It is appreciated that in the description herein, reference to the terms "one embodiment," "another embodiment," "other embodiments," or "first through nth embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The quantitative analysis method for the vigor of the rice seeds is characterized by comprising the following steps of:
acquiring multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds;
carrying out regression analysis by using the vitality indexes of the rice seeds with different samples and the corresponding nCDA images, and establishing a relation model based on the vitality indexes and the color values of the nCDA images;
taking nCDA image pictures of rice seeds to be detected, placing the nCDA image pictures and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by using an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images;
and inputting the nCDA image color value into a relation model based on the vitality index and the nCDA image color value, and obtaining the vitality index of the rice seeds to be detected, which is output by the relation model.
2. The quantitative analysis method for vigor of rice seeds according to claim 1, wherein the step of obtaining multispectral images of a plurality of groups of sample rice seeds and converting the multispectral images into nCDA images according to the vigor index of each group of sample rice seeds comprises the steps of:
acquiring multispectral images of a plurality of groups of sample rice seeds; wherein each group of sample rice seeds comprises a plurality of rice seeds, and each group of sample rice seeds has different aging degrees;
placing a plurality of groups of sample rice seeds with different aging degrees into an incubator, and counting germination conditions of the sample rice seeds to generate vitality indexes of each group of sample rice seeds;
and converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vitality index of each group of sample rice seeds.
3. The quantitative analysis method for vigor of rice seeds according to claim 2, wherein the step of converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vigor index of each group of sample rice seeds comprises the following steps:
acquiring a first multispectral image corresponding to a first group of sample rice seeds with highest vitality indexes and a second multispectral image corresponding to a second group of sample rice seeds with lowest vitality indexes, and respectively endowing a first color value and a second color value to nCDA images corresponding to the first multispectral image and the second multispectral image;
extracting spectral features of the first multispectral image and the second multispectral image for normalization to be used as color discrimination standard features, obtaining multispectral images to be analyzed corresponding to a plurality of groups of sample rice seeds with other vitality indexes, extracting the spectral features, and performing normalization treatment to be used as color discrimination actual features;
based on the positions of a first color corresponding to the color discrimination standard feature of the first multispectral image and a second color corresponding to the color discrimination standard feature of the second multispectral image in a preset color map respectively, performing discrimination analysis on the target position of a target color corresponding to the color discrimination actual feature of the multispectral image to be analyzed in the preset color map;
and taking the target color corresponding to the target position of each multispectral image to be analyzed in the preset color map as the nCDA image color of the corresponding group of sample rice seeds, and generating the nCDA image of each group of sample rice seeds.
4. The quantitative analysis method for vigor of rice seeds according to claim 3, wherein the step of establishing a relationship model based on the vigor index and the color value of the nCDA image by performing regression analysis using the vigor index of the rice seeds of different samples and the corresponding nCDA image specifically comprises:
obtaining vitality indexes and corresponding nCDA images of rice seeds with different samples;
and extracting a color value L and a color value a of CIELAB values of the nCDA images of each group of sample rice seeds, carrying out regression analysis, and establishing a relationship model based on the vitality index and related to the color value L and the color value a.
5. The quantitative analysis method for vigor of rice seeds according to claim 3, wherein after the step of converting the multispectral image corresponding to each group of sample rice seeds into an nCDA image according to the vigor index of each group of sample rice seeds, the method further comprises:
extracting spectral features of multispectral images corresponding to each group of sample rice seeds to form feature vectors, and extracting target positions of target colors corresponding to each group of sample rice seeds in a preset color map;
and respectively inputting the characteristic vector corresponding to each group of sample rice seeds and the target position of the target color in the preset color map as training data and training labels of training samples into a neural network model for training, and obtaining an nCDA image generation model after training is completed.
6. The quantitative analysis method for vigor of rice seeds according to claim 5, wherein the image acquisition device is provided with a multispectral image acquisition device and an nCDA image generation controller, and the nCDA image generation controller stores an nCDA image generation model corresponding to rice seeds to be detected; taking nCDA image pictures of rice seeds to be detected, placing the nCDA image pictures and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by utilizing an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images, wherein the method specifically comprises the following steps:
driving multispectral image acquisition equipment in the image acquisition device to acquire a multispectral image to be detected of the rice seed to be detected, and driving an nCDA image generation controller in the image acquisition device to generate an nCDA image photo of the rice seed to be detected according to the multispectral image to be detected by using an nCDA image generation model;
placing the nCDA image photo and the standard color card into an image acquisition room, and acquiring a first color measurement image and a second color measurement image which contain the nCDA image photo and the standard color card and are positioned at two opposite positions in the image acquisition room;
according to the first RGB value of each color block of the standard color card and the CIEXYZ value of the color block in the first color measurement image and the second color measurement image, a color value conversion matrix is established, according to the second RGB value of the nCDA image in the first color measurement image and the second color measurement image and the color value conversion matrix, the CIEXYZ value of the nCDA image of the rice seed to be detected is determined, and the CIEXYZ value of the nCDA image is converted into the CIELAB value of the nCDA image.
7. The quantitative analysis method for the vigor of rice seeds according to claim 6, wherein the step of inputting the color value of the nCDA image into a relation model based on the vigor index and the color value of the nCDA image to obtain the vigor index of the rice seeds to be measured outputted by the relation model comprises the following steps:
extracting a color value L and a color value a in an nCDA image CIELAB value of the rice seed to be detected;
and inputting the color value L and the color value a into a relation model based on the vitality index and the nCDA image color value to obtain the vitality index of the rice seed to be detected.
8. A quantitative analysis device for the vigor of rice seeds, which is characterized by comprising:
the acquisition module is used for acquiring multispectral images of a plurality of groups of sample rice seeds, and converting the multispectral images into nCDA images according to the vitality index of each group of sample rice seeds;
the establishing module is used for carrying out regression analysis by utilizing the vitality indexes of the rice seeds with different samples and the corresponding nCDA images, and establishing a relation model based on the vitality indexes and the color values of the nCDA images;
the shooting module is used for shooting nCDA image photos of rice seeds to be detected, placing the nCDA image photos and a standard color card into an image acquisition room, acquiring color measurement images in the image acquisition room by using an image acquisition device, and determining nCDA image color values of the rice seeds to be detected based on the color measurement images;
and the output module is used for inputting the nCDA image color value into a relation model based on the vitality index and the nCDA image color value, and obtaining the vitality index of the rice seed to be detected, which is output by the relation model.
9. The quantitative analysis equipment for the vigor of the rice seeds is characterized by comprising the following components: a memory, a processor and a quantitative analysis program of vigor of rice seeds stored on the memory and operable on the processor, which when executed by the processor, implements the steps of the quantitative analysis method of vigor of rice seeds as claimed in any one of claims 1 to 7.
10. A storage medium, wherein a quantitative analysis program for vigor of rice seeds is stored on the storage medium, and when the quantitative analysis program for vigor of rice seeds is executed by a processor, the quantitative analysis method for vigor of rice seeds according to any one of claims 1 to 7 is implemented.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117957971A (en) * | 2024-03-28 | 2024-05-03 | 云南省农业科学院质量标准与检测技术研究所 | Quantitative detection method for vigor of rice seeds based on color image analysis |
| CN117957971B (en) * | 2024-03-28 | 2024-06-18 | 云南省农业科学院质量标准与检测技术研究所 | Quantitative detection method for vigor of rice seeds based on color image analysis |
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