CN118077534A - Intelligent precise identification system for heat resistance of rice and construction method thereof - Google Patents
Intelligent precise identification system for heat resistance of rice and construction method thereof Download PDFInfo
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Abstract
The invention discloses an intelligent accurate identification system for heat resistance of rice and a construction method thereof, comprising the following steps: obtaining rice materials to be identified, transplanting and cultivating, processing tillers, and reserving main tillers based on preset conditions; selecting a preset number of rice materials in a flowering and heading period to perform high-temperature stress, judging anther morphological characteristics under high temperature and moderate temperature conditions, and determining and comparing antioxidant enzyme activity and endogenous hormone content and difference; obtaining the setting rate under the condition of proper temperature and high temperature, identifying the heat resistance of the rice, and analyzing the correlation between the heat resistance of the rice and the physiological and biochemical indexes corresponding to the morphological characteristics of anthers, the antioxidant enzyme activity and the endogenous hormone content; and constructing a heat resistance identification system by using the correlation screening physiological and biochemical indexes as evaluation indexes to perform rapid heat resistance evaluation. The invention starts from physiological and biochemical indexes of tillering, screens high characterization indexes of heat resistance of rice, improves the convenience degree of heat resistance of the rice and meets the requirement of short time period.
Description
Technical Field
The invention relates to the technical field of rice heat resistance identification, in particular to an intelligent accurate identification system for rice heat resistance and a construction method thereof.
Background
In recent years, global climate is warmed, extremely high Wen Tianqi frequently occurs, and grain safety is seriously endangered. Rice is one of important grain crops, and the high temperature directly affects stable yield and high yield of the rice. The temperature of 25.0-30.0 ℃ is suitable for the seedling stage of the rice, when the rice is in the flowering stage, the ambient temperature rises to 37.0-42.0 ℃, and the fruiting rate of the rice can be greatly reduced. The rice is stressed by high temperature in the tillering stage, and the tillering number and the effective spike number are reduced. The heading and flowering period is most easily affected by high temperature, and the high temperature stress can cause the reduction of glume fertility, anther cracking, abnormal growth of pollen tubes and the like, so that the rice seed setting rate, thousand seed weight and yield are reduced.
At present, the heat resistance of rice is identified by adopting a method which is to cultivate rice varieties in a rice pot under natural environment, mark the snapping seeds at the same flowering period when the rice is in the flowering period, then move the snapping seeds into a high-temperature environment of an artificial climatic chamber for treatment, move the rice out of the climatic chamber after the treatment is finished, cultivate the rice in a normal environment until the rice is mature, examine the seed setting rate of the marked snapping seeds after the high-temperature treatment, and judge the heat resistance of the rice. However, the method has the defects of large regional limitation, uncontrollable temperature, poor repeatability and the like. And through intelligent regulation and control of environmental parameters such as temperature, humidity, illumination and the like, the test environment of natural weather conditions is simulated, so that the heat resistance identification of rice is not limited by geography and seasons, and the research period of variety heat resistance can be shortened. Therefore, an intelligent precise identification system for the heat resistance of the rice is constructed based on intelligent regulation and control temperature so as to meet the current short time period requirement.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent accurate identification system for heat resistance of rice and a construction method thereof.
The first aspect of the invention provides a construction method of an intelligent precise identification system for heat resistance of rice, which comprises the following steps:
obtaining a rice material to be identified, setting variety labels, selecting similar rice plants according to rice plant characteristics, transplanting and cultivating, processing tillers, and retaining main tillers based on preset conditions;
Selecting a preset number of rice materials in a flowering and heading period for high-temperature stress, taking the tillers as samples, judging anther morphological characteristics under high temperature and moderate temperature conditions, and measuring and comparing the antioxidant enzyme activity and endogenous hormone content and difference;
after the high-temperature stress is finished, all rice materials are placed under natural conditions to grow to maturity, the maturing rate under the conditions of proper temperature and high temperature is obtained to identify the heat resistance of the rice, and the correlation of the heat resistance of the rice with the physiological and biochemical indexes corresponding to the morphological characteristics of anthers, the antioxidant enzyme activity and the endogenous hormone content is analyzed;
And (3) screening physiological and biochemical indexes by utilizing the correlation, constructing a heat resistance identification system according to the screened physiological and biochemical indexes as evaluation indexes and combining index weights, and carrying out rapid heat resistance assessment on the heat resistance of the rice material to be identified.
In the scheme, similar rice plants are selected according to the rice plant characteristics to be transplanted and cultivated, tillers are treated, and main tillers are reserved based on preset conditions, specifically:
Planting rice materials to be identified of different varieties, obtaining plant height and leaf characteristics of the rice to be identified in the planting process as plant characteristics, monitoring average plant characteristics of a sowing area and judging whether transplanting standards are met;
If the conditions reach, constructing a cultivation environment by using the soil of the pretreated rice planting area, obtaining rice plants with similar plant characteristics by using similarity calculation according to target plants of different varieties, marking, transplanting a preset number of marked plants to the cultivation environment for each variety, and continuously monitoring the plant characteristics of the rice materials;
And generating a plant characteristic sequence by combining the plant characteristic with a time stamp, analyzing the tillering condition according to the plant characteristic sequence, analyzing the heading characteristics of tillers in single plants, identifying the main tillers of different rice materials, selecting and retaining by utilizing the heading characteristics of the main tillers, and removing unselected redundant tillers.
In the scheme, a preset number of rice materials are selected to carry out high temperature stress in the flowering and heading period, and the method specifically comprises the following steps:
In a cultivation environment, regulating and controlling the flowering period of the transplanted rice material to be consistent through water and fertilizer, temperature and humidity, and selecting half of the preset number of transplanted rice materials to perform high-temperature stress when the first glume flowers of the rice material bloom according to the plant characteristics;
The method comprises the steps of obtaining heat damage data of a rice planting area by utilizing data retrieval, carrying out structural pretreatment on the retrieved heat damage data, obtaining temperature information in the pretreated heat damage data by keyword extraction, removing outliers of the temperature information, and generating a temperature interval;
Acquiring the severity degree of heat injury data corresponding to different temperatures, generating a label of the heat injury data by using the severity degree, clustering the heat injury data by using the label, and acquiring temperature distribution corresponding to the heat injury data under different severity degrees according to a clustering result;
And dividing the temperature distribution in the temperature interval to generate temperature intervals of different degrees of heat stress, regulating and controlling the temperature intervals of different degrees of heat stress by utilizing automatic temperature control, and monitoring whether the temperature conditions reach corresponding high-temperature identification standards in real time.
In the scheme, the morphological characteristics of anthers under high temperature and moderate temperature conditions are judged, and the activities of antioxidase and the contents and differences of endogenous hormones are measured and compared, specifically:
Acquiring microscopic image data of anthers of rice materials in flowering and heading periods under proper temperature cultivation and high temperature stress cultivation through visual equipment, and respectively acquiring microscopic image sequences of the anthers under proper temperature and high temperature conditions by utilizing monitoring time stamps;
Preprocessing the anther microscopic image sequence, training a feature extraction model based on deep learning, obtaining a rice anther image set containing a segmentation coordinate label, and training an attention input module of the model by utilizing the segmentation coordinate label in the rice anther image set;
the preprocessed anther microscopic image sequence is utilized to acquire the attention information of the pixel points by an attention input module, the pixel points are weighted by the attention information, a background area and a target anther area are distinguished, and the target anther area is enhanced;
Reading the characteristics of the enhanced anther microscopic image by utilizing a residual network structure, acquiring deep characteristics by convolution of residual blocks with different sizes, introducing a progressive characteristic pyramid structure after a first residual block, and acquiring characteristic diagrams with different scales as shallow characteristics;
combining the deep features and the shallow features and converting the deep features and the shallow features into one-dimensional vectors, and respectively obtaining anther morphological feature sequences under the conditions of proper temperature and high temperature;
Taking tillering at high temperature and moderate temperature as a sample, obtaining the antioxidant enzyme activity and the endogenous hormone content in the sample by measuring, constructing data matrixes corresponding to different types of antioxidant enzymes and endogenous hormones, and obtaining the data matrix deviation at the moderate temperature and the high temperature.
In the scheme, obtaining the setting percentage under the conditions of proper temperature and high temperature to identify the heat resistance of the rice comprises the following specific steps:
obtaining rice materials under the conditions of proper temperature and high temperature, performing seed setting rate test, calculating a heat resistance index, a heat sensitivity index and a grain weight heat sensitivity index based on the seed setting rate, respectively using the heat resistance index, the heat sensitivity index and the grain weight heat sensitivity index as evaluation indexes to identify the heat resistance of the rice, and judging and outputting grading evaluation results of the heat resistance of the rice after obtaining an average value of the evaluation indexes.
In the scheme, the correlation between the heat resistance of rice and physiological and biochemical indexes corresponding to the morphological characteristics of anthers, the activity of antioxidant enzymes and the content of endogenous hormones is analyzed, and the method specifically comprises the following steps:
Carrying out fine granularity analysis according to the anther morphological feature sequences under the conditions of proper temperature and high temperature, obtaining the DTW distance of the two anther morphological feature sequences by utilizing dynamic time regularity, presetting a distance threshold, obtaining anther morphological features with the DTW distance larger than the preset distance threshold, and recording the time of the flowering heading period for feature labeling;
reading the types of the antioxidant enzymes and the types of the endogenous hormones which are larger than a preset deviation threshold according to the deviation of the data matrix of the antioxidant enzyme activity and the endogenous hormone content under the conditions of proper temperature and high temperature, and constructing corresponding physiological and biochemical indexes according to the obtained anther morphological characteristics, the types of the antioxidant enzymes and the types of the endogenous hormones;
And calculating pearson correlation coefficients of the heat resistance grading evaluation result of the rice and different physiological and biochemical indexes, and screening the physiological and biochemical indexes meeting the preset correlation standard by utilizing the pearson correlation coefficient to represent the correlation degree.
In the scheme, a heat resistance identification system is constructed according to the screened physiological and biochemical indexes as evaluation indexes and combining index weights, and the heat resistance identification system specifically comprises the following components:
The screened physiological and biochemical indexes are used as evaluation indexes of a heat resistance identification system, the related knowledge of the heat resistance of the rice is obtained through a big data retrieval means, and the related knowledge of the heat resistance of the rice is used for being connected into a corresponding knowledge graph in a related manner;
positioning the physiological and biochemical indexes in a knowledge graph, obtaining the number of nodes directly connected with the physiological and biochemical indexes in the knowledge graph, and setting the initial weight of the physiological and biochemical indexes according to the number of the nodes;
Acquiring weight information of physiological and biochemical indexes by a analytic hierarchy process, combining the weight information with initial weights to obtain index weights, constructing a heat resistance identification system by fuzzy comprehensive evaluation, presetting heat resistance grades of rice heat resistance identification, judging membership of corresponding factors of the physiological and biochemical indexes to each heat resistance grade according to preset membership functions, and obtaining a membership matrix;
and calculating a fuzzy comprehensive evaluation result in the target layer according to the membership matrix and the index weight, outputting a heat resistance identification result of the rice material to be identified, and verifying by utilizing a grading evaluation result of the heat resistance of the rice.
The invention provides a rice heat resistance intelligent accurate identification system, which is obtained by a construction method of the rice heat resistance intelligent accurate identification system and comprises a transplanting cultivation unit, a high temperature stress unit, a biochemical physiological monitoring unit, a data evaluation unit and a data storage unit;
The transplanting cultivation unit constructs a cultivation environment, selects rice materials to be identified by utilizing plant characteristics for transplanting cultivation, and processes tillering of the rice materials;
the high-temperature stress unit realizes different degrees of heat stress on the transplanted rice material by utilizing automatic temperature control, and carries out high-temperature identification standard monitoring;
the biochemical and physiological monitoring unit judges the morphological characteristics of anthers under the conditions of high temperature and proper temperature, determines and compares the activities of antioxidant enzymes and the contents and differences of endogenous hormones, and determines index parameters of physiological and biochemical indexes;
The data evaluation unit performs weight analysis of index parameters through analytic hierarchy process, presets heat resistance grade of rice heat resistance identification, constructs a fuzzy judgment matrix, forms a fuzzy comprehensive evaluation result through index weight evaluation, and outputs heat resistance identification result of rice materials to be identified;
And the data storage unit stores the heat resistance identification result of the rice material to be identified after the evaluation and index parameters of the physiological and biochemical indexes.
The invention discloses an intelligent accurate identification system for heat resistance of rice and a construction method thereof, comprising the following steps: obtaining rice materials to be identified, transplanting and cultivating, processing tillers, and reserving main tillers based on preset conditions; selecting a preset number of rice materials in a flowering and heading period to perform high-temperature stress, judging anther morphological characteristics under high temperature and moderate temperature conditions, and determining and comparing antioxidant enzyme activity and endogenous hormone content and difference; obtaining the setting rate under the condition of proper temperature and high temperature, identifying the heat resistance of the rice, and analyzing the correlation between the heat resistance of the rice and the physiological and biochemical indexes corresponding to the morphological characteristics of anthers, the antioxidant enzyme activity and the endogenous hormone content; and constructing a heat resistance identification system by using the correlation screening physiological and biochemical indexes as evaluation indexes to perform rapid heat resistance evaluation. The invention starts from physiological and biochemical indexes of tillering, screens high characterization indexes of heat resistance of rice, improves the convenience degree of heat resistance of the rice and meets the requirement of short time period.
Drawings
FIG. 1 shows a flow chart of a method for constructing an intelligent precise identification system for heat resistance of rice;
FIG. 2 shows a flow chart of the present invention for obtaining anther morphology, antioxidant enzyme activity and endogenous hormone content;
FIG. 3 shows a flow chart of the present invention for constructing a heat resistance identification system by fuzzy comprehensive evaluation;
FIG. 4 shows a block diagram of an intelligent precise identification system for heat resistance of rice.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a construction method of an intelligent precise identification system for heat resistance of rice.
As shown in FIG. 1, the first aspect of the invention provides a construction method of an intelligent precise identification system for heat resistance of rice, which comprises the following steps:
S102, obtaining a rice material to be identified and setting variety labels, selecting similar rice plants according to rice plant characteristics for transplanting cultivation, processing tillers, and retaining main tillers based on preset conditions;
s104, selecting a preset number of rice materials to carry out high temperature stress in a flowering and heading period, taking the tillers as samples, judging anther morphological characteristics under high temperature and moderate temperature conditions, and measuring and comparing the activities of antioxidase and the contents and differences of endogenous hormones;
S106, after the high temperature stress is finished, all rice materials are placed under natural conditions to grow to maturity, the maturing rate under the conditions of proper temperature and high temperature is obtained to identify the heat resistance of the rice, and the correlation of the heat resistance of the rice with the physiological and biochemical indexes corresponding to the morphological characteristics of anthers, the antioxidant enzyme activity and the endogenous hormone content is analyzed;
s108, screening physiological and biochemical indexes by utilizing the correlation, constructing a heat resistance identification system according to the screened physiological and biochemical indexes as evaluation indexes and combining index weights, and carrying out rapid heat resistance assessment on heat resistance of the rice material to be identified.
The method is characterized in that rice materials to be identified of different varieties are planted, plant heights and leaf characteristics of the rice to be identified in the planting process are obtained to serve as plant characteristics, average plant characteristics of a sowing area are monitored, and whether transplanting standards are met or not is judged, wherein transplanting is usually carried out in about 5-leaf period; if the conditions are met, sampling naturally air-dried paddy field soil, applying compound fertilizer to perform soil pretreatment, constructing a potting cultivation environment by utilizing the pretreated paddy field soil, obtaining paddy plants with similar plant characteristics by utilizing similarity calculation according to target plants of different varieties to mark, transplanting a preset number of marked plants to the potting cultivation environment for each variety, and continuously monitoring the plant characteristics of the paddy materials; generating a plant characteristic sequence by combining the plant characteristic with a timestamp, analyzing the tillering condition according to the plant characteristic sequence, analyzing the tillering heading characteristics of tillers in a single plant, identifying main tillers of different rice materials, selecting and reserving the main tillers by utilizing the heading characteristics of the main tillers, removing unselected redundant tillers, preferably applying a base fertilizer in the transplanting and cultivating process, not applying a tillering fertilizer, and inhibiting rice tillering.
Regulating and controlling the flowering period of the transplanted rice material to be consistent through water and fertilizer, temperature and humidity in a cultivation environment, selecting half of the preset number of transplanted rice materials to carry out high-temperature stress for 5 days or 8 days when the first glume flowers of the rice material bloom according to the plant characteristics, and growing the other half of the transplanted rice materials under natural conditions to serve as a control; the rice material pots are randomly arranged, and the pots are randomly arranged every day to eliminate errors caused by positions. In order to avoid the instant damage of mechanical forced drainage to materials nearby the wind path, the high temperature stress is matched with the opening and closing of windows around the greenhouse to control. The method comprises the steps of obtaining heat damage data of a rice planting area by utilizing data retrieval, carrying out structural pretreatment on the retrieved heat damage data, obtaining temperature information in the pretreated heat damage data by keyword extraction, removing outliers of the temperature information, and generating a temperature interval; acquiring the severity degree of heat injury data corresponding to different temperatures, generating a label of the heat injury data by using the severity degree, clustering the heat injury data by using the label, and acquiring temperature distribution corresponding to the heat injury data under different severity degrees according to a clustering result; and dividing the temperature distribution in the temperature interval to generate temperature intervals of different degrees of heat stress, regulating and controlling the temperature intervals of different degrees of heat stress by utilizing automatic temperature control, and monitoring whether the temperature conditions reach corresponding high-temperature identification standards in real time.
FIG. 2 shows a flow chart of the present invention for obtaining anther morphology, antioxidant enzyme activity and endogenous hormone content.
According to the embodiment of the invention, the morphological characteristics of anthers under high temperature and moderate temperature conditions are judged, and the activities of antioxidase and the contents and differences of endogenous hormones are measured and compared, specifically:
s202, acquiring microscopic image data of anthers of rice materials in flowering and heading periods under proper temperature cultivation and high temperature stress cultivation through visual equipment, and respectively acquiring microscopic image sequences of the anthers under proper temperature and high temperature conditions by utilizing a monitoring time stamp;
s204, preprocessing the anther microscopic image sequence, training a feature extraction model based on deep learning, obtaining a rice anther image set containing segmentation coordinate labels, and training an attention input module of the model by utilizing the segmentation coordinate labels in the rice anther image set;
S206, the preprocessed anther microscopic image sequence is utilized to acquire the attention information of the pixel points by using an attention input module, the pixel points are weighted by the attention information, a background area and a target anther area are distinguished, and the target anther area is enhanced;
S208, reading the characteristics of the enhanced anther microscopic image by utilizing a residual network structure, acquiring deep characteristics by convolution of residual blocks with different sizes, introducing a progressive characteristic pyramid structure after a first residual block, and acquiring characteristic diagrams with different scales as shallow characteristics;
S210, combining the deep features and the shallow features, converting the deep features and the shallow features into one-dimensional vectors, and respectively obtaining anther morphological feature sequences under the conditions of proper temperature and high temperature;
S212, taking tillering at high temperature and under a proper temperature condition as a sample, obtaining the antioxidant enzyme activity and the endogenous hormone content in the sample through measurement, constructing data matrixes corresponding to different types of antioxidant enzymes and endogenous hormones, and obtaining the data matrix deviation at the proper temperature and the high temperature condition.
The stable anther wall and pollen development are helpful for enhancing the high temperature resistance of the rice, the high temperature easily causes the structure of the cuticle of the anther epidermis of the rice to become compact, the cell is not degraded, the fusion medicine chamber is not formed, the anther wall is thickened, the Ubbelohde body is unevenly distributed, the cracking rate of the anther is reduced, and the anther powder is poor, so that the morphological characteristics of the anther of the rice can reflect the heat resistance of the rice to a certain extent. Carrying out pretreatment such as normalization, cutting and data expansion on the anther microscopic image sequence, constructing a feature extraction model by using ResNet network as a main network, using an attention input module of a segmentation coordinate label training model concentrated by rice anther images, using an attention mechanism to inhibit a background area, highlighting the anther area, increasing the extraction accuracy of morphological features, setting 4 residual blocks with different sizes, extracting deep features by using the residual blocks in the feature extraction model on one hand, carrying out coding fusion on the convolution features output by a first residual block by using a progressive feature pyramid structure, reserving the space information of a feature map, obtaining feature vectors to generate shallow features, and using the shallow features to enable the model to pay more attention to key features such as colors, morphologies and the like of the anther.
The antioxidant enzyme activity and the endogenous hormone content in a sample are measured by ultraviolet visible light spectrometry, high performance liquid chromatograph measurement and other modes, and enzyme antioxidant substances comprise peroxide separation enzyme (SOD), peroxidase (POD), ascorbate Peroxidase (APX), catalase (CAT), glutathione Reductase (GR), ascorbate reductase (DHAR), mono-dehydroascorbate reductase (MDHAR), glutathione-S transferase (GST) and Glutathione Peroxidase (GPX), wherein SOD, POD, APX, CAT and GR have higher correlation degree with the heat resistance of plants, and high temperature stress has different degrees of influence on a plurality of physiological metabolic processes of rice, so that the endogenous hormone level of the rice is induced to change during the heading period of the rice, and the yield is further influenced, and therefore, part of antioxidant enzyme activity and endogenous hormone content are obviously related to heat resistance.
Carrying out fine granularity analysis according to the anther morphological feature sequences under the conditions of proper temperature and high temperature, obtaining the DTW distance of the two anther morphological feature sequences by utilizing dynamic time regularity, presetting a distance threshold, obtaining anther morphological features with the DTW distance larger than the preset distance threshold, and recording the time of the flowering heading period for feature labeling; reading the types of the antioxidant enzymes and the types of the endogenous hormones which are larger than a preset deviation threshold according to the deviation of the data matrix of the antioxidant enzyme activity and the endogenous hormone content under the conditions of proper temperature and high temperature, and constructing corresponding physiological and biochemical indexes according to the obtained anther morphological characteristics, the types of the antioxidant enzymes and the types of the endogenous hormones; and calculating pearson correlation coefficients of the heat resistance grading evaluation result of the rice and different physiological and biochemical indexes, and screening the physiological and biochemical indexes meeting the preset correlation standard by utilizing the pearson correlation coefficient to represent the correlation degree.
The method comprises the steps of obtaining rice materials under the conditions of moderate temperature and high temperature, performing seed test on the seed setting rate, calculating a heat resistance index (high-temperature seed setting rate/moderate-temperature seed setting rate), a heat sensitivity index (difference between moderate-temperature seed setting rate and high-temperature seed setting rate/moderate-temperature seed setting rate) and a grain weight heat sensitivity index (difference between thousand grain weight at moderate temperature and thousand grain weight at high temperature/thousand grain weight at moderate temperature) based on the seed setting rate, respectively serving as evaluation indexes to identify the heat resistance of the rice, and judging and outputting grading evaluation results of the heat resistance of the rice after an average value of the evaluation indexes is obtained.
FIG. 3 shows a flow chart of the construction of a heat resistance identification system by fuzzy comprehensive evaluation according to the present invention.
According to the embodiment of the invention, a heat resistance identification system is constructed according to the screened physiological and biochemical indexes as evaluation indexes and combining index weights, specifically:
S302, taking the screened physiological and biochemical indexes as evaluation indexes of a heat resistance identification system, acquiring relevant knowledge of the heat resistance of the rice by a big data retrieval means, and accessing a corresponding knowledge graph by using the relevant knowledge of the heat resistance of the rice in a correlated way;
s304, positioning the physiological and biochemical indexes in a knowledge graph, obtaining the number of nodes directly connected with the physiological and biochemical indexes in the knowledge graph, and setting the initial weight of the physiological and biochemical indexes according to the number of nodes;
s306, acquiring weight information of the physiological and biochemical indexes by a analytic hierarchy process, combining the weight information with the initial weight to obtain index weight, constructing a heat resistance identification system by fuzzy comprehensive evaluation, presetting heat resistance grades of rice heat resistance identification, and judging membership of corresponding factors of the physiological and biochemical indexes to each heat resistance grade according to preset membership functions to obtain a membership matrix;
s308, calculating a fuzzy comprehensive evaluation result in the target layer according to the membership matrix and the index weight, outputting a heat resistance identification result of the rice material to be identified, and verifying by utilizing the grading evaluation result of the heat resistance of the rice.
The method is characterized in that the corresponding knowledge patterns are connected by utilizing the related knowledge of the heat resistance of the rice, the more the number of nodes directly connected with the nodes in the knowledge patterns, the more important the nodes are, the initial weight is generated by judging the importance of different physiological and biochemical index nodes in heat resistance evaluation, so that a heat resistance system pays more attention to important index parameters. The method comprises the steps of constructing a physiological and biochemical index hierarchical structure, generating weights of all physiological and biochemical indexes according to a judgment matrix of each layer in the hierarchical structure, calculating membership degrees of index layers and target layers through membership degrees and weights of indexes of the next layer, determining the heat resistance of rice to be 5 grades through fuzzy comprehensive evaluation construction heat resistance identification system, wherein the grade is strong, general, weak and weak respectively, corresponds to different membership degrees, and can be flexibly modified according to actual conditions to perform phenotype observation and description.
The method comprises the steps of constructing a rice heat resistance database, storing heat resistance identification matching variety labels of different varieties of rice into the rice heat resistance database, utilizing historical rice planting data and historical heat damage data of a target area, presetting expected yield, analyzing the rice heat resistance requirement of the target area, constructing an environment image of the target area based on the rice heat resistance requirement, searching in the rice heat resistance database according to the environment image, utilizing similarity to calculate and screen rice varieties meeting the rice heat resistance requirement, and selecting a preset number of rice varieties to realize the planting recommendation of heat-resistant rice resources of the target area.
FIG. 4 shows a block diagram of an intelligent precise identification system for heat resistance of rice.
The second aspect of the invention provides a rice heat resistance intelligent accurate identification system 4, which is obtained by a construction method of the rice heat resistance intelligent accurate identification system and comprises a transplanting cultivation unit 41, a high temperature stress unit 42, a biochemical and physiological monitoring unit 43, a data evaluation unit 44 and a data storage unit 45;
The transplanting cultivation unit 41 constructs a cultivation environment, selects rice materials to be identified by utilizing plant characteristics for transplanting cultivation, and processes tillering of the rice materials;
the high temperature stress unit 42 realizes different degrees of heat stress on the transplanted rice material by utilizing automatic temperature control, and performs high temperature identification standard monitoring;
The biochemical and physiological monitoring unit 43 judges the morphological characteristics of anthers under high temperature and moderate temperature conditions, determines and compares the activities of antioxidant enzymes and the contents and differences of endogenous hormones, and determines index parameters of physiological and biochemical indexes;
the data evaluation unit 44 performs weight analysis of index parameters through analytic hierarchy process, presets heat resistance grades of rice heat resistance identification, constructs a fuzzy judgment matrix, forms a fuzzy comprehensive evaluation result through index weight evaluation, and outputs heat resistance identification results of rice materials to be identified;
the data storage unit 45 stores the heat resistance identification result and the index parameters of the physiological and biochemical index of the rice material to be identified after the evaluation.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, etc., which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. The construction method of the intelligent precise identification system for the heat resistance of the rice is characterized by comprising the following steps of:
obtaining a rice material to be identified, setting variety labels, selecting similar rice plants according to rice plant characteristics, transplanting and cultivating, processing tillers, and retaining main tillers based on preset conditions;
Selecting a preset number of rice materials in a flowering and heading period for high-temperature stress, taking the tillers as samples, judging anther morphological characteristics under high temperature and moderate temperature conditions, and measuring and comparing the antioxidant enzyme activity and endogenous hormone content and difference;
after the high-temperature stress is finished, all rice materials are placed under natural conditions to grow to maturity, the maturing rate under the conditions of proper temperature and high temperature is obtained to identify the heat resistance of the rice, and the correlation of the heat resistance of the rice with the physiological and biochemical indexes corresponding to the morphological characteristics of anthers, the antioxidant enzyme activity and the endogenous hormone content is analyzed;
And (3) screening physiological and biochemical indexes by utilizing the correlation, constructing a heat resistance identification system according to the screened physiological and biochemical indexes as evaluation indexes and combining index weights, and carrying out rapid heat resistance assessment on the heat resistance of the rice material to be identified.
2. The method for constructing the intelligent precise identification system for heat resistance of rice according to claim 1, wherein similar rice plants are selected according to rice plant characteristics for transplanting cultivation, tillering is processed, and main tillers are reserved based on preset conditions, specifically:
Planting rice materials to be identified of different varieties, obtaining plant height and leaf characteristics of the rice to be identified in the planting process as plant characteristics, monitoring average plant characteristics of a sowing area and judging whether transplanting standards are met;
If the conditions reach, constructing a cultivation environment by using the soil of the pretreated rice planting area, obtaining rice plants with similar plant characteristics by using similarity calculation according to target plants of different varieties, marking, transplanting a preset number of marked plants to the cultivation environment for each variety, and continuously monitoring the plant characteristics of the rice materials;
And generating a plant characteristic sequence by combining the plant characteristic with a time stamp, analyzing the tillering condition according to the plant characteristic sequence, analyzing the heading characteristics of tillers in single plants, identifying the main tillers of different rice materials, selecting and retaining by utilizing the heading characteristics of the main tillers, and removing unselected redundant tillers.
3. The method for constructing an intelligent precise identification system for heat resistance of rice according to claim 1, wherein a preset number of rice materials are selected to carry out high temperature stress in a flowering and heading period, specifically:
In a cultivation environment, regulating and controlling the flowering period of the transplanted rice material to be consistent through water and fertilizer, temperature and humidity, and selecting half of the preset number of transplanted rice materials to perform high-temperature stress when the first glume flowers of the rice material bloom according to the plant characteristics;
The method comprises the steps of obtaining heat damage data of a rice planting area by utilizing data retrieval, carrying out structural pretreatment on the retrieved heat damage data, obtaining temperature information in the pretreated heat damage data by keyword extraction, removing outliers of the temperature information, and generating a temperature interval;
Acquiring the severity degree of heat injury data corresponding to different temperatures, generating a label of the heat injury data by using the severity degree, clustering the heat injury data by using the label, and acquiring temperature distribution corresponding to the heat injury data under different severity degrees according to a clustering result;
And dividing the temperature distribution in the temperature interval to generate temperature intervals of different degrees of heat stress, regulating and controlling the temperature intervals of different degrees of heat stress by utilizing automatic temperature control, and monitoring whether the temperature conditions reach corresponding high-temperature identification standards in real time.
4. The method for constructing an intelligent precise identification system for heat resistance of rice according to claim 1, wherein the method is characterized by judging morphological characteristics of anthers under high temperature and moderate temperature conditions, and determining and comparing antioxidant enzyme activity and endogenous hormone content and difference, and specifically comprises the following steps:
Acquiring microscopic image data of anthers of rice materials in flowering and heading periods under proper temperature cultivation and high temperature stress cultivation through visual equipment, and respectively acquiring microscopic image sequences of the anthers under proper temperature and high temperature conditions by utilizing monitoring time stamps;
Preprocessing the anther microscopic image sequence, training a feature extraction model based on deep learning, obtaining a rice anther image set containing a segmentation coordinate label, and training an attention input module of the model by utilizing the segmentation coordinate label in the rice anther image set;
the preprocessed anther microscopic image sequence is utilized to acquire the attention information of the pixel points by an attention input module, the pixel points are weighted by the attention information, a background area and a target anther area are distinguished, and the target anther area is enhanced;
Reading the characteristics of the enhanced anther microscopic image by utilizing a residual network structure, acquiring deep characteristics by convolution of residual blocks with different sizes, introducing a progressive characteristic pyramid structure after a first residual block, and acquiring characteristic diagrams with different scales as shallow characteristics;
combining the deep features and the shallow features and converting the deep features and the shallow features into one-dimensional vectors, and respectively obtaining anther morphological feature sequences under the conditions of proper temperature and high temperature;
Taking tillering at high temperature and moderate temperature as a sample, obtaining the antioxidant enzyme activity and the endogenous hormone content in the sample by measuring, constructing data matrixes corresponding to different types of antioxidant enzymes and endogenous hormones, and obtaining the data matrix deviation at the moderate temperature and the high temperature.
5. The method for constructing an intelligent precise identification system for heat resistance of rice according to claim 1, wherein obtaining the setting rate under the conditions of moderate temperature and high temperature is used for identifying the heat resistance of the rice, and the method is specifically as follows:
obtaining rice materials under the conditions of proper temperature and high temperature, performing seed setting rate test, calculating a heat resistance index, a heat sensitivity index and a grain weight heat sensitivity index based on the seed setting rate, respectively using the heat resistance index, the heat sensitivity index and the grain weight heat sensitivity index as evaluation indexes to identify the heat resistance of the rice, and judging and outputting grading evaluation results of the heat resistance of the rice after obtaining an average value of the evaluation indexes.
6. The method for constructing an intelligent precise identification system for heat resistance of rice according to claim 1, wherein the method is characterized by analyzing the correlation of the heat resistance of rice with the physiological and biochemical indexes corresponding to the morphological characteristics of anthers, the antioxidant enzyme activity and the endogenous hormone content, and specifically comprises the following steps:
Carrying out fine granularity analysis according to the anther morphological feature sequences under the conditions of proper temperature and high temperature, obtaining the DTW distance of the two anther morphological feature sequences by utilizing dynamic time regularity, presetting a distance threshold, obtaining anther morphological features with the DTW distance larger than the preset distance threshold, and recording the time of the flowering heading period for feature labeling;
reading the types of the antioxidant enzymes and the types of the endogenous hormones which are larger than a preset deviation threshold according to the deviation of the data matrix of the antioxidant enzyme activity and the endogenous hormone content under the conditions of proper temperature and high temperature, and constructing corresponding physiological and biochemical indexes according to the obtained anther morphological characteristics, the types of the antioxidant enzymes and the types of the endogenous hormones;
And calculating pearson correlation coefficients of the heat resistance grading evaluation result of the rice and different physiological and biochemical indexes, and screening the physiological and biochemical indexes meeting the preset correlation standard by utilizing the pearson correlation coefficient to represent the correlation degree.
7. The method for constructing an intelligent precise identification system for heat resistance of rice according to claim 1, wherein the heat resistance identification system is constructed by combining index weights according to the screened physiological and biochemical indexes as evaluation indexes, specifically:
The screened physiological and biochemical indexes are used as evaluation indexes of a heat resistance identification system, the related knowledge of the heat resistance of the rice is obtained through a big data retrieval means, and the related knowledge of the heat resistance of the rice is used for being connected into a corresponding knowledge graph in a related manner;
positioning the physiological and biochemical indexes in a knowledge graph, obtaining the number of nodes directly connected with the physiological and biochemical indexes in the knowledge graph, and setting the initial weight of the physiological and biochemical indexes according to the number of the nodes;
Acquiring weight information of physiological and biochemical indexes by a analytic hierarchy process, combining the weight information with initial weights to obtain index weights, constructing a heat resistance identification system by fuzzy comprehensive evaluation, presetting heat resistance grades of rice heat resistance identification, judging membership of corresponding factors of the physiological and biochemical indexes to each heat resistance grade according to preset membership functions, and obtaining a membership matrix;
and calculating a fuzzy comprehensive evaluation result in the target layer according to the membership matrix and the index weight, outputting a heat resistance identification result of the rice material to be identified, and verifying by utilizing a grading evaluation result of the heat resistance of the rice.
8. An intelligent accurate identification system for heat resistance of rice, which is obtained by the construction method of the intelligent accurate identification system for heat resistance of rice according to any one of claims 1-7, and is characterized by comprising a transplanting cultivation unit, a high-temperature stress unit, a biochemical and physiological monitoring unit, a data evaluation unit and a data storage unit;
The transplanting cultivation unit constructs a cultivation environment, selects rice materials to be identified by utilizing plant characteristics for transplanting cultivation, and processes tillering of the rice materials;
the high-temperature stress unit realizes different degrees of heat stress on the transplanted rice material by utilizing automatic temperature control, and carries out high-temperature identification standard monitoring;
the biochemical and physiological monitoring unit judges the morphological characteristics of anthers under the conditions of high temperature and proper temperature, determines and compares the activities of antioxidant enzymes and the contents and differences of endogenous hormones, and determines index parameters of physiological and biochemical indexes;
The data evaluation unit performs weight analysis of index parameters through analytic hierarchy process, presets heat resistance grade of rice heat resistance identification, constructs a fuzzy judgment matrix, forms a fuzzy comprehensive evaluation result through index weight evaluation, and outputs heat resistance identification result of rice materials to be identified;
And the data storage unit stores the heat resistance identification result of the rice material to be identified after the evaluation and index parameters of the physiological and biochemical indexes.
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116046977A (en) * | 2022-11-30 | 2023-05-02 | 湖南杂交水稻研究中心 | Method for detecting high-temperature tolerance of rice and detection kit |
US20230410280A1 (en) * | 2022-05-31 | 2023-12-21 | Zhejiang University | Method and system for monitoring rice bacterial blight in field based on multi-source data |
CN117337732A (en) * | 2023-11-10 | 2024-01-05 | 安徽省农业科学院水稻研究所 | Heat resistance identification method for rice |
CN117541423A (en) * | 2023-12-27 | 2024-02-09 | 河北省农林科学院植物保护研究所 | Aphis gossypii harm monitoring method and system based on fusion map features |
CN117745148A (en) * | 2024-02-10 | 2024-03-22 | 安徽省农业科学院烟草研究所 | Multi-source data-based rice stubble flue-cured tobacco planting quality evaluation method and system |
CN117837441A (en) * | 2023-03-30 | 2024-04-09 | 淮北师范大学 | Evaluation and identification method for heat resistance of alfalfa |
-
2024
- 2024-04-18 CN CN202410470418.2A patent/CN118077534B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20230410280A1 (en) * | 2022-05-31 | 2023-12-21 | Zhejiang University | Method and system for monitoring rice bacterial blight in field based on multi-source data |
CN116046977A (en) * | 2022-11-30 | 2023-05-02 | 湖南杂交水稻研究中心 | Method for detecting high-temperature tolerance of rice and detection kit |
CN117837441A (en) * | 2023-03-30 | 2024-04-09 | 淮北师范大学 | Evaluation and identification method for heat resistance of alfalfa |
CN117337732A (en) * | 2023-11-10 | 2024-01-05 | 安徽省农业科学院水稻研究所 | Heat resistance identification method for rice |
CN117541423A (en) * | 2023-12-27 | 2024-02-09 | 河北省农林科学院植物保护研究所 | Aphis gossypii harm monitoring method and system based on fusion map features |
CN117745148A (en) * | 2024-02-10 | 2024-03-22 | 安徽省农业科学院烟草研究所 | Multi-source data-based rice stubble flue-cured tobacco planting quality evaluation method and system |
Non-Patent Citations (2)
Title |
---|
张桂莲 等: "水稻花药对高温胁迫的生理响应", 植物生理学报, no. 09, 30 September 2013 (2013-09-30), pages 923 - 928 * |
陈刚 等: "杂交中籼水稻花穗期耐热性品种筛选及鉴定指标评", 作物杂志, no. 5, 31 October 2014 (2014-10-31), pages 80 - 85 * |
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