Precise intelligent identification platform for heat resistance and cold resistance of rice

Abstract

The invention discloses a rice heat resistance and cold resistance accurate intelligent identification platform, and belongs to the technical field of rice breeding stress resistance identification, and the rice heat resistance and cold resistance accurate intelligent identification platform comprises the following steps: S1, constructing an intelligent temperature, humidity, illumination and climate regulation simulation environment as a rice heat resistance and cold resistance identification indoor platform; s2, identifying a rice sample at any time in the environment; s3, the rice sample comes from a to-be-identified rice variety cultivation area in the field or a cultivation area in other places; s4, the rice variety cultivation area is composed of an outdoor field cultivation pool and a field rice planting area; s5, cultivating a to-be-identified rice variety in the field cultivation pool; and S6, sowing rice varieties to be identified in the field rice planting area, and carrying out planting observation contrast experiments. According to the invention, stable and repeatable experimental conditions are provided, the influence of external factors on experimental results is reduced, the reliability and accuracy of the experimental results are remarkably improved, the molecular mechanism of heat resistance and cold resistance of rice is disclosed, and the research and development process of new varieties of rice is accelerated.

Description

Technical Field

[0001] The present invention belongs to the technical field of stress resistance identification in rice breeding, especially a precise and intelligent identification platform for rice heat resistance and cold resistance. Background Art

[0002] Rice is a temperature-sensitive crop, and its growth and development are closely related to temperature. The heat resistance and cold resistance of rice refer to its adaptability under high-temperature and low-temperature conditions. Rice is a thermophilic crop. Within a certain range, as the temperature rises, the growth rate of rice accelerates. However, when the temperature exceeds a certain limit, high temperature will start to have a negative impact on rice. Although rice is a tropical and subtropical crop, it is also widely planted in temperate regions. Low temperature will also have an adverse effect on rice, especially during the seedling stage after spring sowing and the filling stage in autumn.

[0003] There are some limitations in traditional intelligent platforms for identifying rice heat resistance and cold resistance. For example, most laboratories use simple incubators or natural environments for experiments, lacking advanced automatic control systems. For instance, ordinary incubators have limited space and can only conduct small-scale experiments, unable to perform large-scale experimental identifications. Under natural environmental conditions, it is difficult to ensure continuous high-temperature or low-temperature weather, often resulting in the failure to smoothly complete a batch of experiments, or it is difficult to guarantee the repeatability and accuracy of the experiments. The experimental results of different batches may vary significantly, affecting subsequent analysis and decision-making.

[0004] In addition, without the assistance of artificial intelligence (AI), researchers need to rely on their own experience and limited data analysis tools for experimental design, data interpretation, and result evaluation. This mode is not only time-consuming and laborious but also prone to subjective biases or omission of important information, thus affecting the quality and speed of scientific research decisions. At the same time, without the support of a cloud computing platform, laboratories usually rely on local servers or personal computers for data analysis. For large-scale data sets, such as high-throughput sequencing data or long-time series environmental monitoring data, local computing resources are often insufficient to support fast and efficient data processing requirements, which will lead to an extended data analysis cycle and even the inability to complete complex data mining tasks. Summary of the Invention

[0005] The purpose of the present invention is to provide a precise and intelligent identification platform for rice heat resistance and cold resistance to solve the problems raised in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A precise and intelligent identification platform for rice heat resistance and cold resistance, including the following steps:

[0007] S1: Construct an intelligent temperature, humidity, and light climate simulation environment as an indoor platform for identifying rice heat resistance and cold resistance;

[0008] S2: Identify rice samples at any time in the above environment;

[0009] S3: The rice samples are from the cultivation area of the rice variety to be identified in the field or the cultivation area in other places;

[0010] S4: The rice variety cultivation area consists of an outdoor field cultivation pool and a field rice planting area;

[0011] S5: The field cultivation pool is used to cultivate the growth of the rice variety to be identified and the control rice variety for identification;

[0012] S6: The field rice planting area is used to sow the rice variety to be identified and conduct planting observation and comparison experiments;

[0013] S7: The intelligent Internet of Things platform system includes an Internet of Things management platform, a cloud platform, sensors such as temperature, humidity, and light, a monitoring system, a data acquisition system, and a control system.

[0014] Preferably, in this solution, the intelligent regulation of temperature, humidity, light, and climate simulation environment in S1 ensures that the indoor temperature control accuracy reaches within ±1°C, and the spatial temperature distribution is uniform.

[0015] Preferably, the platform formulates local standards for the heat tolerance identification of middle-season rice for the required regions. This standard takes into account local climate characteristics and agricultural practices, and uses the cloud platform for big data processing and artificial intelligence model training to ensure that the formulation of local standards is more scientific and reasonable. The formula is as follows:

[0016] CS = α·LC + β·FF + γ·AI

[0017] Among them, CS represents the comprehensive local standard score, LC is the local climate condition score, which measures the impact of climate change on rice growth, FF is the feedback score of scientific research personnel, which measures the importance of scientific research personnel's opinions on standard adjustment, AI is the artificial intelligence model score, which measures the model training effect and prediction accuracy, and α, β, and γ are weight coefficients, which reflect the importance of each factor for the formulation of local standards.

[0018] Preferably, an online database is constructed in the platform, which stores a large amount of genetic resource information on rice heat tolerance and cold tolerance, including but not limited to gene sequences, mutant libraries, and phenotypic data.

[0019] Preferably, the platform implements a long-term tracking research plan, and updates the rice heat tolerance and cold tolerance evaluation methods and technologies at least once a year, so that the platform is always at the forefront of the industry.

[0020] Compared with the prior art, the technical effects and advantages of the present invention:

[0021] This precise and intelligent identification platform for the heat and cold tolerance of rice can accurately adjust parameters such as temperature and humidity through automatic temperature control and intelligent environment simulation, ensuring that rice grows in a controlled environment. Through the sensor network installed in the experimental environment, these parameters are monitored in real-time and the data is fed back to the control system for automated adjustment, providing stable and repeatable experimental conditions, reducing the impact of external factors on experimental results, and significantly improving the reliability and accuracy of experimental results.

[0022] By using LED lights or dimmable fluorescent lights in conjunction with light sensors, the light brightness and color are automatically adjusted according to a preset schedule or external meteorological data to simulate the lighting conditions at different times such as sunrise, noon, and sunset, mimicking the natural light cycle changes, getting closer to the actual agricultural production environment, which helps to study the growth characteristics of rice under natural conditions and improves the application value of research results.

[0023] The sensor network records data once an hour and sends it to the central server via wireless network; the imaging device scans the experimental area at regular intervals every day to capture changes in the growth status of rice. All the collected data is input into a pre-trained machine learning model after cleaning and preprocessing to predict the response of rice to specific environmental conditions, achieving a full range of monitoring from environmental parameters to physiological indicators and then to morphological characteristics, providing rich data support, enabling researchers to understand the growth mechanism of rice more deeply, and accelerating the development process of new varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 Steps for constructing the precise and intelligent identification platform for the heat and cold tolerance of rice of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some well-known technical features in the art are not described.

[0027] Unless otherwise defined, the directions such as up, down, left, right, front, back, inside and outside involved in this article are based on the up, down, left, right, front, back, inside and outside directions in the figures shown in the present invention. This is hereby explained together.

[0028] This embodiment provides a precise intelligent identification platform for rice heat tolerance and cold tolerance as shown in Figure 1 the following steps:

[0029] S1: Construct an intelligent control climate simulation environment of temperature, humidity, light, etc. as an indoor platform for identifying rice heat tolerance and cold tolerance;

[0030] S2: Rice samples can be identified at any time in the above environment;

[0031] S3: The rice samples are from the rice variety cultivation area to be identified in the field or the cultivation area in other places;

[0032] S4: The rice variety cultivation area consists of an outdoor field cultivation pool and a field rice planting area;

[0033] S5: The field cultivation pool is used for cultivating the growth of the rice variety to be identified and the control rice variety for identification;

[0034] S6: The field rice planting area is used for sowing the rice variety to be identified and conducting planting observation and comparison experiments;

[0035] S7: An intelligent Internet of Things platform system; mainly including an Internet of Things management platform, a cloud platform, sensors such as temperature, humidity, and light, a monitoring system, a data acquisition system, a control system, etc.;

[0036] S8: The software system of the rice heat tolerance identification experiment management platform mainly includes identification experiment management software systems such as rice materials, varieties, samples, standards, processes, methods, etc., a big data analysis and processing system, a user management system, an identification report management system, etc.;

[0037] S9: The above-mentioned indoor identification room, outdoor field cultivation pool, field rice planting area, and Internet of Things intelligent control system together constitute a hardware identification platform for rice heat tolerance and cold tolerance;

[0038] S10: Based on the above hardware platform, a set of standardized software systems for rice heat tolerance and cold tolerance identification experiment management and identification data analysis are developed;

[0039] S11: Establish a long-term tracking research mechanism for rice heat tolerance and cold tolerance, and continuously improve and upgrade the identification platform system.

[0040] In this embodiment, by precisely controlling experimental conditions and implementing efficient lighting management, researchers can simulate multiple growth cycles in a short period of time and quickly evaluate the performance of different rice varieties under various environments. This not only improves research efficiency but also significantly shortens the development time of new varieties, enabling new heat-resistant and cold-resistant varieties to enter the market more quickly. The comprehensive data collection and analysis system ensures the integrity and accuracy of the data; the user-friendly interface and long-term tracking research mechanism promote communication and cooperation among global researchers. The high-quality data provides a solid foundation for subsequent research, while international cooperation in turn promotes data sharing and technological progress, forming a virtuous cycle. Standardized operating procedures and technical specifications ensure the consistency of experiments; after introducing an artificial intelligence-assisted decision-making system, it can provide optimization suggestions based on data analysis results. The AI system can not only help researchers make more scientific decisions but also automatically adjust experimental parameters, further optimizing the experimental design and improving research efficiency.

[0041] In this embodiment, the intelligent regulation of temperature, humidity, and lighting in the climate simulation environment in S1, rather than the traditional wet curtain cooling system, ensures that the indoor temperature control accuracy reaches within ±1°C, and the spatial temperature distribution is uniform, thus significantly improving the accuracy and reliability of the identification results. In addition, the air conditioning control system is equipped with an energy recovery device, which reduces energy consumption and realizes green and energy-saving operation. By installing a variable frequency compressor, temperature sensors, and a PLC controller, writing a temperature control program, and using the PID algorithm to maintain a constant set temperature with an accuracy of ±1°C, the initial temperature of the laboratory is confirmed and set, and the system continuously monitors the indoor temperature and compares it with the set value to dynamically adjust the refrigeration / heating power. For example, when the detected temperature rises, the refrigeration effect is immediately enhanced to restore to the set temperature. The energy recovery device is a plate or tube heat exchanger installed in the exhaust duct. When cold air is exhausted, it transfers part of the heat to the incoming fresh air, reducing the heating requirement. When the exhaust system operates, the heat exchanger is automatically activated to raise the temperature of the incoming fresh air, thus saving energy. The energy recovery device reduces energy consumption, while the advanced air conditioning control system ensures high-precision control of the indoor temperature (±1°C). The green and energy-saving measures reduce operating costs, while the high-precision temperature control improves the reliability of experimental results. The two complement each other and jointly promote sustainable development.

[0042] In this embodiment, a local standard for the heat tolerance identification of mid-season rice is formulated for the required region (such as a certain province). This standard takes into account the local climate characteristics and agricultural practices, making the localized heat tolerance evaluation both scientific and practical. At the same time, the platform has also established a feedback mechanism to regularly collect the opinions and suggestions of front-line scientific research personnel, and timely adjust and improve the content of the local standard to keep it always up-to-date. In order to improve the intelligence level of local standard formulation, a cloud platform is used for big data processing and artificial intelligence model training to ensure that the formulation of local standards is more scientific and reasonable. The formula is as follows:

[0043] CS = α·LC + β·FF + γ·AI

[0044] Among them, CS represents the comprehensive score of the local standard, LC is the score of local climate conditions, which measures the impact of climate change on rice growth, FF is the score of scientific research personnel feedback, which measures the importance of scientific research personnel's opinions on standard adjustment, AI is the score of the artificial intelligence model, which measures the model training effect and prediction accuracy, and α, β, and γ are weight coefficients, reflecting the importance of each factor for the formulation of local standards. Collect local meteorological data and scientific research personnel feedback, understand local climate characteristics and agricultural practices, use the cloud platform and AI model training to generate a scientific and reasonable standard document, substitute it into the formula for calculation, and comprehensively consider climate conditions (LC), scientific research personnel feedback (FF), and AI model score (AI) to ensure the scientificity and reasonableness of the standard. For example, if high temperatures are frequent in a certain place in summer, the standard may particularly emphasize heat tolerance evaluation. Use the cloud platform to store and process a large amount of data to provide a basis for standard formulation. Apply AI technology to optimize the standard content to make it more in line with actual needs. For example, through AI analysis of historical data, predict future climate change trends and adjust key parameters in the standard. Combine local climate characteristics and agricultural practices to formulate local standards, and conduct intelligent optimization through the cloud platform and AI model training. The local standard is both scientific and practical, meeting the needs of specific regions; intelligent support ensures the dynamic adjustment and continuous improvement of the standard, enhancing applicability and forward-lookingness.

[0045] In this embodiment, an online database is constructed in the platform, which stores a large amount of genetic resource information on the heat tolerance and cold tolerance of rice, including but not limited to gene sequences, mutant libraries, and phenotypic data.

[0046] It should be noted that, in this document, relational terms such as "one" and "two" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0047] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A precise and intelligent identification platform for the heat tolerance and cold tolerance of rice, characterized in that, It includes the following steps: S1: Construct an intelligent temperature, humidity, light, and climate simulation environment as an indoor platform for identifying the heat tolerance and cold tolerance of rice; S2: Identify rice samples at any time in the above environment; S3: The rice samples are from the breeding areas of rice varieties to be identified in the field or other breeding areas; S4: The rice variety breeding area consists of an outdoor field cultivation pool and a field rice planting area; S5: The field cultivation pool is used for the growth cultivation of rice varieties to be identified and control rice varieties for identification; S6: The field rice planting area is used to sow rice varieties to be identified and conduct planting observation and comparison experiments; S7: An intelligent Internet of Things platform system, including an Internet of Things management platform, a cloud platform, sensors such as temperature, humidity, and light, a monitoring system, a data acquisition system, and a control system.

2. The precise and intelligent identification platform for rice heat tolerance and cold tolerance according to claim 1, characterized in that: Among them, the intelligent temperature, humidity, light, and climate simulation environment in S1 ensures that the indoor temperature control accuracy reaches within ±1°C, and the spatial temperature distribution is uniform.

3. The precise and intelligent identification platform for rice heat tolerance and cold tolerance according to claim 2, characterized in that: The platform uses the phenotypic traits and seed setting rate of rice varieties to deeply analyze the phenotypic traits of varieties under high-temperature or low-temperature stress conditions, as the basis for screening breeding materials, and provides experimental support for biological breeding.

4. The precise and intelligent identification platform for rice heat tolerance and cold tolerance according to claim 3, characterized in that: The platform integrates color sorter technology to distinguish hybrid seeds from male and female parents during the mechanized hybrid seed production process, ensuring that the purity of hybrid seeds is not less than 98%.

5. The precise and intelligent identification platform for rice heat tolerance and cold tolerance according to claim 4, characterized in that: The platform applies a combination of unmanned aerial vehicle (UAV) remote sensing technology and ground sensors for large-area farmland monitoring, collecting data on crop health conditions. The UAV is equipped with multi-spectral cameras and infrared cameras, while the ground sensors are responsible for capturing more detailed local information.

6. The precise and intelligent identification platform for rice heat tolerance and cold tolerance according to claim 1, characterized in that: The platform formulates local standards for identifying the heat tolerance of middle-season rice for the required regions. This standard takes into account local climate characteristics and agricultural practices, and uses the cloud platform for big data processing and artificial intelligence model training to ensure that the formulation of local standards is more scientific and reasonable. The formula is as follows: CS = α·LC + β·FF + γ·AI Where CS represents the comprehensive local standard score, LC is the local climate condition score, which measures the impact of climate change on rice growth, FF is the feedback score of scientific researchers, which measures the importance of scientific researchers' opinions for standard adjustment, AI is the artificial intelligence model score, which measures the model training effect and prediction accuracy, and α, β, and γ are weight coefficients, reflecting the importance of each factor for the formulation of local standards.

7. The precise and intelligent identification platform for rice heat and cold tolerance according to claim 6, characterized in that: An online database is constructed in the platform, storing a large amount of genetic resource information on the heat tolerance and cold tolerance of rice, including but not limited to gene sequences, mutant libraries, and phenotypic data.

8. The precise and intelligent identification platform for rice heat tolerance and cold tolerance according to claim 7, characterized in that: The platform implements a long-term tracking research plan, updating the evaluation methods and technologies for the heat tolerance and cold tolerance of rice at least once a year, so that the platform always remains at the forefront of the industry.

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