CO2 concentration intelligent adjustment and control method based on agricultural intelligent platform

Abstract

The invention discloses a CO2 concentration intelligent adjustment and control method based on an agricultural intelligent connection platform. The method comprises the following steps: collecting environmental parameters of an environment where crops are located; performing data preprocessing on the environmental parameters to obtain processed data; training a maximum photosynthesis efficiency prediction model by using the processed data to obtain a trained prediction model; inputting the current environmental parameters into the trained prediction model to obtain the predicted maximum photosynthesis efficiency; searching the relationship between the maximum photosynthesis efficiency and the CO2 concentration in the corresponding environment to obtain the optimal CO2 concentration corresponding to the predicted maximum photosynthesis efficiency; the CO2 concentration of the environment where the crops are located is adjusted to be the optimal CO2 concentration. The CO2 concentration around crop leaves can be systematically analyzed and controlled, so that stable growth of crops is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of digital agriculture, and in particular to a method for intelligently regulating and controlling CO2 concentration based on an agricultural intelligent connection platform. Background Art

[0002] CO2 is an important raw material for crop growth. Under light conditions, the process in which green crops, through chloroplasts, synthesize organic matter from H2O and CO2 in the air and release O2 is called photosynthesis. Crops convert light energy into chemical energy stored in organic matter through photosynthesis, and through respiration, that is, the oxidation of carbohydrates, they provide energy for various biological or chemical reaction processes in the crop body.

[0003] The CO2 used for crop photosynthesis comes from the following three main sources: (1) CO2 in the air around the leaves; (2) CO2 produced by the respiration of tissues in the leaves; and (3) CO2 absorbed by the roots of crops. The vast majority of CO2 comes from (1) and (2), with (3) accounting for only 1%-2% of the total amount absorbed by crops. However, during the photosynthesis process of crops, CO2 is constantly absorbed by the chloroplasts of crops, so its concentration will constantly change, forming a concentration difference with the surrounding environment, resulting in uneven crop absorption and affecting crop growth.

[0004] Therefore, there is an urgent need for an intelligent CO2 concentration regulation and control method that can systematically analyze and control the CO2 concentration around crop leaves to ensure the stable growth of crops. Summary of the invention

[0005] In view of this, the purpose of the present invention is to overcome the defects in the prior art and provide an intelligent regulation and control method of CO2 concentration based on the agricultural intelligent platform, which can systematically analyze and control the CO2 concentration around crop leaves to ensure the stable growth of crops.

[0006] The CO2 concentration intelligent regulation and control method based on the agricultural intelligent connection platform of the present invention includes:

[0007] Collect environmental parameters of the environment where the crops are located; perform data preprocessing on the environmental parameters to obtain processed data;

[0008] The processed data is used to train the maximum photosynthesis efficiency prediction model to obtain a trained prediction model;

[0009] Input the current environmental parameters into the trained prediction model to obtain the predicted maximum photosynthesis efficiency;

[0010] Find the relationship between the maximum photosynthesis efficiency and CO2 concentration under the corresponding environment, and obtain the optimal CO2 concentration corresponding to the predicted maximum photosynthesis efficiency;

[0011] Adjust the CO2 concentration in the environment where the crops are located to the optimal CO2 concentration.

[0012] Furthermore, the environmental parameters include CO2 concentration, light intensity, temperature and humidity.

[0013] Further, the data processing includes data cleaning, formatting and range checking;

[0014] Among them, data cleaning is to remove or correct errors, duplications or outliers in the data; formatting is to convert the data into a unified format or structure; range checking is to verify whether the data is within the expected value or category range.

[0015] Furthermore, the processed data is used to train the maximum photosynthesis efficiency prediction model, including:

[0016] Environmental parameters, crop growth and development stages, and corresponding photosynthesis rates are used as feature data; the feature data are input into a machine learning model for model training;

[0017] The growth and development stage of the crop is obtained according to the following method:

[0018] Use cameras to take pictures of crops regularly, and then use image recognition technology to analyze the growth stages of crops, or train deep learning models with large amounts of labeled data to identify the different growth stages of crops.

[0019] Furthermore, the growth and development stage of the crop can also be obtained according to the following method:

[0020] Sensors are used to monitor crop growth parameters, and the growth stage of crops is determined by combining light, temperature and humidity; wherein the growth parameters include plant height, leaf area and biomass.

[0021] Furthermore, the relationship between the maximum photosynthesis efficiency and CO2 concentration under the corresponding environment is determined according to the following method:

[0022] According to the characteristics of CO2 and photosynthesis, a lookup table or a mapping function is created through statistical analysis or machine learning methods to map the maximum photosynthesis efficiency under different environments to the optimal CO2 concentration.

[0023] Furthermore, the characteristics of CO2 and photosynthesis include:

[0024] Within a certain range, increasing CO2 concentration can increase the photosynthesis rate. When CO2 concentration reaches a certain level, the photosynthesis rate no longer increases. At this time, photosynthesis is limited by other factors, including light intensity, water, temperature, and the activity of enzymes in chloroplasts.

[0025] Photosynthesis is divided into two stages: light reaction and dark reaction. The light reaction occurs on the thylakoids of chloroplasts, producing ATP and NADPH. The dark reaction uses this energy and reducing power to convert CO2 into organic matter.

[0026] Under high oxygen and low CO2 conditions, photorespiration consumes part of the carbon fixed in photosynthesis, affecting photosynthetic efficiency; under high CO2 concentrations, photorespiration is inhibited, thereby increasing photosynthetic efficiency;

[0027] The increase in CO2 concentration can reduce the opening of the stoma, reduce transpiration, and thus improve water use efficiency.

[0028] Furthermore, the CO2 concentration in the environment where the crops are located is adjusted to the optimal CO2 concentration, specifically including:

[0029] Set up a bottled liquid CO2 device, use the bottled liquid CO2 device to carry out intelligent and autonomous control of the CO2 concentration, and achieve precise regulation of the CO2 concentration; and dynamically adjust the CO2 release amount based on real-time CO2 concentration feedback to ensure that the CO2 concentration is stable within the optimal range.

[0030] The beneficial effect of the present invention is: the present invention discloses a method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform, which utilizes the edge computing capability of the agricultural intelligent connection platform to monitor the CO2 concentration around the leaves in real time, and combines other environmental parameters (such as light, temperature and humidity) to achieve intelligent regulation and control of the CO2 concentration around the crop leaves, so as to optimize the photosynthesis efficiency of the crops and ensure the stable growth of the crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0032] Figure 1 It is a flow chart of the regulation and control method of the present invention;

[0033] Figure 2 This is a diagram showing the relationship between CO2 concentration and photosynthesis according to the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described below in conjunction with the accompanying drawings, as shown in the drawings:

[0035] This embodiment discloses a method for intelligently adjusting and controlling CO2 concentration based on an agricultural intelligent connection platform, comprising the following steps:

[0036] Collect environmental parameters of the environment where the crops are located; perform data preprocessing on the environmental parameters to obtain processed data;

[0037] The processed data is used to train the maximum photosynthesis efficiency prediction model to obtain a trained prediction model;

[0038] Input the current environmental parameters into the trained prediction model to obtain the predicted maximum photosynthesis efficiency;

[0039] Find the relationship between the maximum photosynthesis efficiency and CO2 concentration under the corresponding environment, and obtain the optimal CO2 concentration corresponding to the predicted maximum photosynthesis efficiency;

[0040] Adjust the CO2 concentration in the environment where the crops are located to the optimal CO2 concentration.

[0041] The present invention aims to realize intelligent regulation and control of CO2 concentration around crop leaves through the agricultural intelligent connection platform, so as to optimize the photosynthesis efficiency of crops and ensure the stable growth of crops. Through real-time monitoring and data analysis, CO2 concentration is automatically adjusted to achieve refined management. Among them, the agricultural production service platform includes a data collection layer, a data analysis layer and an intelligent control layer.

[0042] The data collection layer uses the edge computing capabilities of the Agricultural Intelligence Platform to collect data such as CO2 concentration, light intensity, temperature and humidity in the environment in real time through a sensor network deployed in greenhouses or farmland. The data analysis layer is used to store and analyze the collected data, use machine learning and data mining techniques to analyze the relationship between crop photosynthesis and environmental factors, and determine the optimal CO2 concentration range to optimize photosynthesis efficiency. Intelligent control layer: Based on the analysis results, the Agricultural Intelligence Platform automatically adjusts the CO2 supplement system in the greenhouse, such as CO2 injection equipment, through an integrated intelligent control module to maintain an appropriate CO2 concentration and promote crop growth.

[0043] In this embodiment, sensors such as CO2 sensors, light intensity sensors, temperature sensors and humidity sensors are installed in greenhouses or farmlands to ensure that the sensor network covers all key crop growing areas; the environmental parameters include CO2 concentration, light intensity, temperature and humidity.

[0044] By selecting environmental parameters such as CO2 concentration, light intensity, temperature, and humidity, the main factors affecting crop photosynthesis can be fully reflected, thereby more accurately predicting the maximum photosynthesis efficiency that crops can achieve under given environmental conditions.

[0045] In this embodiment, the edge computing capability of the Agricultural Intelligence Platform is used to collect sensor data in real time; the collected data is processed to ensure data quality. Specifically, you can choose a device with edge computing capabilities, such as an industrial gateway, a smart router, or a dedicated edge computing box, and install the necessary software on the edge device, including applications for data collection, preprocessing, and preliminary analysis. Set the frequency of sensor data collection according to monitoring needs, such as collecting once every minute or every hour, and set data preprocessing rules on the edge device, such as data cleaning, formatting, range verification, etc. After starting the sensor, start real-time monitoring of environmental parameters; the sensor data is transmitted to the edge device in real time, and the edge device processes and analyzes it according to preset rules.

[0046] Data cleaning is to remove or correct errors, duplications or outliers in the data on edge devices to ensure data quality. Formatting is to convert data into a unified format or structure for subsequent processing and analysis. Range checking is to verify whether the data is within the expected range of values ​​or categories to ensure the rationality and accuracy of the data. These preprocessing rules are implemented on edge devices to effectively improve data processing efficiency and the reliability of subsequent analysis.

[0047] In this embodiment, a large amount of data including environmental parameters (such as CO2 concentration, light intensity, temperature, humidity, etc.) and corresponding photosynthesis rates are collected, and a model is trained using these data, which can predict the maximum photosynthesis efficiency that crops can achieve under given environmental conditions. Among them, the photosynthesis rate usually refers to the amount of organic matter (such as glucose) produced by plants through photosynthesis per unit time, or the amount of inorganic matter (such as water, carbon dioxide) consumed, which reflects the speed of photosynthesis of plants under certain environmental conditions. The maximum photosynthesis efficiency refers to the maximum value of the photosynthesis rate that plants can achieve under optimal or specific environmental conditions.

[0048] In practical applications, considering that crops have different requirements for environmental conditions at different growth stages, the photosynthesis efficiency will also change. Therefore, in addition to environmental parameters, the growth and development stage of crops can also be considered as a feature to be added to the model. For example, collect multi-dimensional data on environmental parameters (such as CO2 concentration, light intensity, temperature, humidity, etc.) and crop growth and development stages to ensure that the data covers all stages of crops from seed germination to maturity (such as seedling stage, growth stage, maturity stage, etc.); select machine learning models that can handle multi-feature inputs, such as random forests, gradient boosting machines, neural networks, etc.; use data sets including environmental parameters and growth and development stages to train machine learning models to predict photosynthesis efficiency.

[0049] Among them, the growth and development stage of crops can be obtained by various methods, such as using image analysis to obtain the growth and development stage of crops:

[0050] Use cameras to take pictures of crops regularly, and then use image recognition technology to analyze the growth stages of crops, or train deep learning models with large amounts of labeled data to identify the different growth stages of crops.

[0051] In addition, sensor monitoring can also be used to obtain the growth and development stages of crops:

[0052] Various sensors such as laser rangefinders, strain gauges, photoelectric sensors, etc. are used to monitor crop growth parameters, and light, temperature, and humidity are combined to determine the growth stage of crops; wherein the growth parameters include plant height, leaf area, and biomass.

[0053] In this embodiment, the relationship between the maximum photosynthesis efficiency and the CO2 concentration under the corresponding environment is determined according to the following method:

[0054] According to the characteristics of CO2 and photosynthesis, a lookup table or a mapping function is created through statistical analysis or machine learning methods to map the maximum photosynthesis efficiency under different environments to the optimal CO2 concentration, where the optimal CO2 concentration can be a CO2 concentration value or a CO2 concentration range.

[0055] The characteristics of CO2 and photosynthesis include:

[0056] CO2 concentration and photosynthesis rate: within a certain range, increasing CO2 concentration can increase photosynthesis rate. When CO2 concentration reaches a certain level, photosynthesis rate no longer increases. At this time, photosynthesis is limited by other factors, including light intensity, water, temperature and enzyme activity in chloroplasts.

[0057] Two stages of photosynthesis: Photosynthesis is divided into two stages: light reaction and dark reaction; the light reaction occurs on the thylakoids of chloroplasts, producing ATP and NADPH; the dark reaction (or Calvin cycle) uses this energy and reducing power to convert CO2 into organic matter;

[0058] Photorespiration and CO2: Under high oxygen and low CO2 conditions, photorespiration consumes part of the carbon fixed in photosynthesis, affecting photosynthetic efficiency; under high CO2 concentrations, photorespiration is inhibited, thereby improving photosynthetic efficiency;

[0059] Effect of CO2 concentration on stomata: An increase in CO2 concentration can reduce the opening of the stomata, reduce transpiration, and thus improve water use efficiency.

[0060] In this embodiment, the intelligent control layer automatically adjusts the CO2 supplement system in the greenhouse, such as the CO2 injection equipment, through an integrated intelligent control module to maintain an appropriate CO2 concentration and promote crop growth. Specifically, a canned liquid CO2 device with an intelligent control module can be arranged and installed on site. The intelligent control module is customized based on the technology of the Agricultural Intelligence Platform. It can control the regulating valve, monitor the flow meter, pressure gauge, etc., and combine with intelligent analysis to intelligently and autonomously control the CO2 concentration, thereby achieving precise regulation of the CO2 concentration. The intelligent control module has a feedback regulation mechanism, which dynamically adjusts the release of CO2 according to the real-time CO2 concentration feedback to ensure that the CO2 concentration is stable within the optimal range.

[0061] Among them, the average CO2 concentration in the atmosphere can generally reach about 330ml / L, that is, 0.65g / m3, which is far lower than the ideal value required by crops. CO2 intelligent regulation and controlled fertilization have become important measures that are indispensable for efficient crop production. Figure 2 The relationship between CO2 concentration and photosynthesis shown in the figure shows that the saturation point of CO2 is generally 800-1800ml / L or higher. The stronger the light, the higher the saturation point. However, the higher the concentration of CO2 applied, the higher the cost. Therefore, comprehensive optimization control generally chooses a more economical concentration, such as 800-1000ml / L.

[0062] In addition, in order to achieve refined management, the Agricultural Intelligence Platform can also dynamically adjust CO2 concentration and other environmental factors, such as adjusting lighting and irrigation systems, by real-time monitoring of crop growth status and environmental parameters. The Agricultural Intelligence Platform can also provide an intuitive user interface that enables operators to view data, system status and historical trends in real time, manually adjust control parameters or intervene in intelligent control modules. The CO2 regulation system can also be integrated with other agricultural management systems (such as irrigation systems and nutrient solution supply systems) to achieve automation and intelligence of the entire agricultural production process. In order to ensure the high availability and fault tolerance of the system, redundant design and fault detection mechanisms are adopted to ensure that the system can continue to operate in the event of equipment failure or network interruption.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform, characterized in that: include: Collect environmental parameters of the environment in which the crops are located; Perform data preprocessing on environmental parameters to obtain processed data; The processed data is used to train the maximum photosynthesis efficiency prediction model to obtain a trained prediction model; Input the current environmental parameters into the trained prediction model to obtain the predicted maximum photosynthesis efficiency; Find the relationship between the maximum photosynthesis efficiency and CO2 concentration under the corresponding environment, and obtain the optimal CO2 concentration corresponding to the predicted maximum photosynthesis efficiency; Adjust the CO2 concentration in the environment where the crops are located to the optimal CO2 concentration.

2. The method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform according to claim 1 is characterized in that: The environmental parameters include CO2 concentration, light intensity, temperature and humidity.

3. The method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform according to claim 1 is characterized in that: The data processing includes data cleaning, formatting and range checking; Among them, data cleaning is to remove or correct errors, duplications or outliers in the data; formatting is to convert the data into a unified format or structure; range checking is to verify whether the data is within the expected value or category range.

4. The method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform according to claim 1 is characterized in that: The processed data is used to train the maximum photosynthesis efficiency prediction model, including: Environmental parameters, crop growth and development stages, and corresponding photosynthesis rates are used as feature data; the feature data are input into a machine learning model for model training; The growth and development stage of the crop is obtained according to the following method: Use cameras to take pictures of crops regularly, and then use image recognition technology to analyze the growth stage of crops, or train deep learning models with large amounts of labeled data to identify the different growth stages of crops.

5. The method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform according to claim 4 is characterized in that: The growth and development stage of crops can also be obtained according to the following methods: Sensors are used to monitor crop growth parameters, and the growth stage of crops is determined by combining light, temperature and humidity; wherein the growth parameters include plant height, leaf area and biomass.

6. The method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform according to claim 1 is characterized in that: Determine the relationship between the maximum photosynthesis efficiency and CO2 concentration under the corresponding environment according to the following method: According to the characteristics of CO2 and photosynthesis, a lookup table or a mapping function is created through statistical analysis or machine learning methods to map the maximum photosynthesis efficiency under different environments to the optimal CO2 concentration.

7. The method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform according to claim 6 is characterized in that: The characteristics of CO2 and photosynthesis include: Within a certain range, increasing CO2 concentration can increase the photosynthesis rate. When CO2 concentration reaches a certain level, the photosynthesis rate no longer increases. At this time, photosynthesis is limited by other factors, including light intensity, water, temperature, and the activity of enzymes in chloroplasts. Photosynthesis is divided into two stages: light reaction and dark reaction. The light reaction occurs on the thylakoids of chloroplasts, producing ATP and NADPH. The dark reaction uses this energy and reducing power to convert CO2 into organic matter. Under high oxygen and low CO2 conditions, photorespiration consumes part of the carbon fixed in photosynthesis, affecting photosynthetic efficiency; under high CO2 concentrations, photorespiration is inhibited, thereby increasing photosynthetic efficiency; The increase in CO2 concentration can reduce the opening of the stoma, reduce transpiration, and thus improve water use efficiency.

8. The method for intelligent regulation and control of CO2 concentration based on the agricultural intelligent connection platform according to claim 1 is characterized in that: Adjust the CO2 concentration of the crop environment to the optimal CO2 concentration, including: Set up a bottled liquid CO2 device, use the bottled liquid CO2 device to carry out intelligent and autonomous control of the CO2 concentration, and achieve precise regulation of the CO2 concentration; and dynamically adjust the CO2 release amount based on real-time CO2 concentration feedback to ensure that the CO2 concentration is stable within the optimal range.

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