Facility agriculture crop growth synergistic regulation method, device, equipment and medium

Abstract

This application discloses a method, device, equipment, and medium for the synergistic regulation of crop growth in facility agriculture, relating to the field of facility agriculture technology. By combining crop modification attributes with real-time environmental data and rhizosphere microbiome status in a pre-constructed biological function database to match synergistic regulation strategies, it can significantly improve crop growth regulation effects from multiple dimensions. The method includes: acquiring crop type, real-time environmental data, and rhizosphere microbiome status of the target planting area; determining crop modification attributes based on crop type, and matching synergistic regulation strategies suitable for the target planting area in a pre-constructed biological function database, combined with real-time environmental data and rhizosphere microbiome status; generating a strategy execution plan containing multi-factor synergistic regulation parameters based on the synergistic regulation strategy suitable for the target planting area; and distributing the strategy execution plan to the corresponding execution equipment to implement regulation of the target planting area.

Description

Technical Field

[0001] This application relates to the field of facility agriculture technology, and in particular to a method, device, equipment and medium for the coordinated regulation of crop growth in facility agriculture. Background Technology

[0002] With population growth, arable land scarcity, and escalating climate change, developing efficient, controllable, and sustainable agricultural production models has become a core direction for global agricultural transformation. Facility agriculture, by integrating environmental control, fertigation, intelligent equipment, and information management technologies in closed or semi-closed environments such as greenhouses, plant factories, and solar greenhouses, significantly improves output per unit area and resource utilization efficiency, and has become a key pathway for high-quality development in modern agriculture. Related technologies in facility agriculture systems involve crop growth being influenced by a combination of environmental factors and agronomic management measures.

[0003] In facility agriculture systems, related technologies are used to control crop growth by light, temperature, humidity, and other factors. The combined influence of various environmental factors, such as concentration, nutrient supply, and water status, and the complex nonlinear coupling relationships among these factors, makes it difficult to construct a comprehensive growth environment that meets the actual physiological needs of crops. Traditional regulation strategies typically employ a single-factor independent control model, where each regulator operates independently based on its own threshold. For example, fans are started and stopped based on temperature, supplemental lighting is switched on and off based on light intensity, or irrigation pumps are controlled based on time / soil moisture. While such methods achieve basic automation, they lack a multi-factor synergistic mechanism and fail to consider the dynamic coupling relationships between factors, making it difficult to construct a comprehensive growth environment that meets the actual physiological needs of crops. Summary of the Invention

[0004] In view of this, this application provides a method, device, equipment and medium for the coordinated regulation of crop growth in facility agriculture. The main purpose is to solve the problem that existing facility agriculture systems lack multi-factor synergistic mechanisms, fail to consider the dynamic coupling relationship between various factors, and are difficult to construct a comprehensive growth environment that adapts to the actual physiological needs of crops.

[0005] According to the first aspect of this application, a method for synergistic regulation of crop growth in facility agriculture is provided, comprising:

[0006] Acquire crop types, real-time environmental data, and rhizosphere microbiome status in the target planting area;

[0007] Based on the crop type, crop modification attributes are determined, and combined with the real-time environmental data and rhizosphere microbiome status, a synergistic regulation strategy adapted to the target planting area is matched in a pre-constructed biological function database. The biological function database contains multiple synergistic regulation scheme templates, each corresponding to a combination of crop type and modification attributes, and associated with the synergistic application of at least two types of regulation templates: synthetic biology adaptation regulation module, microbiome engineering regulation module, optogenetic regulation module, and biostimulant adaptation module.

[0008] Based on the synergistic regulation strategy adapted to the target planting area, a strategy execution plan containing multi-factor synergistic regulation parameters is generated, which cover the dimensions of microorganisms, light environment, water and fertilizer and soil environment.

[0009] The strategy execution plan is distributed to the corresponding execution device to regulate the target planting area.

[0010] According to a second aspect of this application, a synergistic regulation device for the growth of crops in facility agriculture is provided, comprising:

[0011] The acquisition unit is used to acquire crop types, real-time environmental data, and rhizosphere microbiome status of the target planting area.

[0012] The matching unit is used to determine crop modification attributes based on the crop type, and in combination with the real-time environmental data and rhizosphere microbiome status, match the target planting area with a synergistic regulation strategy in a pre-constructed biological function database. The biological function database contains multiple synergistic regulation scheme templates, each of which corresponds to a combination of crop type and modification attribute, and is associated with the synergistic application of at least two types of regulation templates: synthetic biology adaptation regulation module, microbiome engineering regulation module, optogenetics regulation module, and biostimulant adaptation module.

[0013] The generation unit is used to generate a strategy execution plan containing multi-factor synergistic regulation parameters based on the synergistic regulation strategy adapted to the target planting area. The multi-factor synergistic regulation parameters cover the dimensions of microorganisms, light environment, water and fertilizer and soil environment.

[0014] The control unit is used to distribute the strategy execution plan to the corresponding execution device to control the target planting area.

[0015] According to a third aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the synergistic regulation method for the growth of facility agriculture crops described in the first aspect.

[0016] According to a fourth aspect of this application, a readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the synergistic regulation method for the growth of facility agriculture crops described in the first aspect above.

[0017] By employing the above technical solution, this application provides a method, device, equipment, and medium for the coordinated regulation of crop growth in facility agriculture. Compared with existing methods that use single-factor independent control modes for the coordinated regulation of crop growth in facility agriculture, this application acquires the crop type, real-time environmental data, and rhizosphere microbiome status of the target planting area; determines crop modification attributes based on crop type, and, combined with real-time environmental data and rhizosphere microbiome status, matches a suitable coordinated regulation strategy for the target planting area in a pre-constructed biological function database. The biological function database pre-stores multiple coordinated regulation scheme templates, each corresponding to a combination of crop type and modification attributes, and is associated with the coordinated application of at least two types of regulation templates: synthetic biology-adapted regulation module, microbiome engineering regulation module, optogenetics regulation module, and biostimulant-adapted module; based on the coordinated regulation strategy adapted to the target planting area, a strategy execution plan containing multi-factor coordinated regulation parameters is generated, covering the dimensions of microorganisms, light environment, water and fertilizer, and soil environment; the strategy execution plan is then distributed to the corresponding execution equipment to implement regulation of the target planting area. The entire process achieves comprehensive perception of multi-dimensional information about the planting scenario by simultaneously acquiring crop type, real-time environmental data, and rhizosphere microbiome status in the target planting area. This accurately reflects the actual growth needs of crops and the status of rhizosphere microorganisms. Furthermore, crop modification attributes are combined with real-time environmental data and rhizosphere microbiome status to match synergistic regulation strategies in a pre-set biological function database. This allows for rapid adaptation to different crops, different improvement goals, and different planting conditions, achieving synergistic optimization at both the biological and environmental levels. It can significantly improve crop growth regulation by working together from multiple dimensions, including crop characteristics, rhizosphere microecology, light environment, water and fertilizer, and soil environment. Based on this, an execution plan is generated that includes synergistic regulation parameters for multiple factors such as microorganisms, light environment, water and fertilizer, and soil environment. This constructs a comprehensive growth environment that adapts to the actual physiological needs of crops, achieving closed-loop management of the entire process from data perception and strategy matching to on-site execution, thereby improving the intelligence level and execution efficiency of planting regulation.

[0018] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of this application more easily understood, specific embodiments of this application are given below. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 This is a flowchart illustrating a method for synergistic regulation of crop growth in facility agriculture according to one embodiment of this application;

[0021] Figure 2 This is a flowchart illustrating the implementation process of the synthetic biology adaptation module in one embodiment of this application;

[0022] Figure 3 This is a flowchart illustrating the implementation process of the biostimulant adaptor module in one embodiment of this application;

[0023] Figure 4 This is a schematic diagram of the structure of a synergistic regulation device for the growth of facility agriculture crops in one embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the device structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0025] The invention will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are described merely to enable those skilled in the art to better understand and thus implement the invention, and are not intended to imply any limitation on the scope of the invention.

[0026] As used herein, the term "comprising" and its variations are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "at least partially based on". The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment". The term "another embodiment" is to be interpreted as "at least one other embodiment".

[0027] In facility agriculture systems, related technologies are used to control crop growth by light, temperature, humidity, and other factors. The combined influence of various environmental factors, such as concentration, nutrient supply, and water status, and the complex nonlinear coupling relationships among these factors, makes it difficult to construct a comprehensive growth environment that meets the actual physiological needs of crops. Traditional regulation strategies typically employ a single-factor independent control model, where each regulator operates independently based on its own threshold. For example, fans are started and stopped based on temperature, supplemental lighting is switched on and off based on light intensity, or irrigation pumps are controlled based on time / soil moisture. While such methods achieve basic automation, they lack a multi-factor synergistic mechanism and fail to consider the dynamic coupling relationships between factors, making it difficult to construct a comprehensive growth environment that meets the actual physiological needs of crops.

[0028] To address this problem, this embodiment provides a method for the coordinated regulation of crop growth in facility agriculture, such as... Figure 1 As shown, it includes the following steps:

[0029] 101. Obtain crop types, real-time environmental data, and rhizosphere microbiome status for the target planting area.

[0030] In this embodiment, the growth requirements and modification directions of different crop types differ significantly, which is a prerequisite for crop modification attributes and matching corresponding regulatory strategy templates. If the crop type is not accurately obtained, it will directly cause the regulation process to deviate from the actual needs. Specific scenario examples are as follows: If the target planting area is a greenhouse planting area in the north, after on-site investigation, variety verification, and retrieval of planting records, it is determined that the crop type in this area is greenhouse tomato, and it is in the fruit setting stage. At the same time, it is determined that this variety of tomato is a high fertilizer requirement and moderately tolerant to low light, and the inherent modification requirement is to improve fruit sweetness and resistance to gray mold. If the target planting area is a paddy field planting area in the south, it is determined that the crop type is hybrid rice, and it is in the tillering stage. This variety of rice has a large water requirement and strict requirements for the soil nitrogen, phosphorus, and potassium ratio. The modification attribute focuses on improving lodging resistance and grain fullness. If the target planting area is a field planting area in the arid northwest region, it is determined that the crop type is drought-resistant wheat, and it is in the jointing stage. The core modification attribute is to further improve drought resistance and reduce water and fertilizer consumption.

[0031] Specifically, automated monitoring equipment, such as IoT sensors and environmental monitoring terminals, can be used to collect various environmental parameters directly related to crop growth in the target planting area in real time, ensuring the real-time nature, continuity, and accuracy of the data. The real-time environmental data covers parameters closely related to crops, including light, humidity, gaseous conditions, and soil conditions. A specific scenario example is as follows: For the aforementioned facility-grown tomato area, light sensors, temperature and humidity sensors, and soil sensors deployed at different locations within the greenhouse collect real-time environmental parameters such as: light intensity of 35,000 lux, light duration of 8 hours / day, air temperature of 26°C, soil temperature of 22°C, relative humidity of 70%, soil moisture content of 28%, carbon dioxide concentration of 800 ppm, and soil pH of 6.5.

[0032] Specifically, soil samples can be collected from around the crop roots within the target planting area. Molecular biological monitoring techniques can be used to analyze parameters such as the rhizosphere microbial community structure, species diversity, functional microbial composition, and activity. The rhizosphere microbiome serves as a hidden nutrient pool and stress barrier for crop growth, and its state directly affects nutrient absorption, resistance to adverse conditions, and overall growth. A specific scenario is as follows: In the aforementioned greenhouse tomato planting area, soil samples were collected from 5-10 cm around the tomato roots. Sequencing analysis revealed that the relative abundances of beneficial bacteria in the rhizosphere microbiome were 8%, 6%, and 5%, respectively, while the relative abundance of harmful bacteria was 2%. The microbial community diversity index was 3.2. This indicates that the activity of beneficial bacteria in the tomato rhizosphere in this area is relatively strong, but the abundance of nitrogen-fixing bacteria is low, which cannot fully meet the nitrogen requirements of tomatoes during the fruit-setting period.

[0033] It should be noted that the collection of the above three types of data can be carried out simultaneously. By combining IoT technology, molecular detection technology and data transmission technology, centralized collection, real-time transmission and unified storage of data can be achieved, ensuring the correlation and timeliness of the three types of data. This lays a solid foundation for subsequent determination of modification attributes and matching of synergistic regulation strategies based on crop type, avoiding the problem of subsequent regulation strategies failing or poor regulation effects due to data disconnection, data lag or data missing. It is applicable to various large-scale and refined planting scenarios, including facility planting, field planting and cash crop planting, and has wide applicability and feasibility.

[0034] 102. Based on the crop type, determine the crop modification attributes, and in conjunction with the real-time environmental data and rhizosphere microbiome status, match the target planting area with a synergistic regulation strategy in a pre-constructed biological function database.

[0035] Determining crop modification attributes based on crop type refers to identifying the traits and functional indicators that need to be targeted for enhancement, improvement, or regulation, based on the type, variety characteristics, growth stage, and pre-set production goals of the crops planted in the target planting area. Specific modification attributes are the key link between the crop's own needs and subsequent regulatory measures; their determination process is clearly targeted and specific, rather than a generalized growth regulation. For example, when the crop type is greenhouse-grown tomatoes in the fruit-setting and expansion stage, considering the fruit development needs and common production challenges of this variety, the crop modification attributes can be determined as increasing the soluble solids content of the fruit, enhancing root nutrient absorption capacity, and improving the plant's resistance to soil-borne diseases such as gray mold. When the crop type is field-grown rice in the tillering stage, the crop modification attributes can be determined as promoting effective tillering, improving lodging resistance, and enhancing nitrogen use efficiency. When the crop type is wheat grown in arid areas in the jointing stage, the crop modification attributes can be determined as enhancing drought resistance, maintaining photosynthetic system stability, and reducing water transpiration loss.

[0036] In practical applications, the control method cannot be determined solely based on the modification attributes. Instead, it requires a comprehensive assessment of the current environmental conditions, the presence of weaknesses in the rhizosphere microecology, and the main limiting factors and control entry points for crop growth. For example, for greenhouse tomatoes in the fruit-setting stage, after determining the modification attributes to increase fruit sugar content, enhance root vitality, and improve resistance to gray mold, further analysis is needed using real-time environmental data: moderate light intensity and suitable soil moisture, but high soil nitrogen content and relatively insufficient phosphorus and potassium supply, and high humidity inside the greenhouse, which easily induces disease. Simultaneously, the rhizosphere microbiome status is considered: low abundance of phosphorus and potassium-solubilizing beneficial bacteria, insufficient proportion of antagonistic bacteria such as biocontrol Bacillus, and a high risk of pathogen accumulation. Through this multi-dimensional information analysis, the current crop status can be clearly identified as: unbalanced nutrient absorption structure, insufficient support from beneficial rhizosphere microorganisms, and environmental conditions prone to disease.

[0037] The aforementioned biological function database is a pre-established standardized database containing multiple synergistic regulation scheme templates. Each template corresponds to a combination of crop type and modification attributes, and is associated with the synergistic application of at least two regulatory templates: synthetic biology adaptation regulation modules, microbiome engineering regulation modules, optogenetic regulation modules, and biostimulant adaptation modules. The specific database matching process can be achieved through feature comparison, fit scoring, or intelligent algorithms to select the strategy that best suits the current planting scenario from the candidate templates. Taking the aforementioned greenhouse tomato during the fruit-setting period as an example, the biological function database matches and outputs synergistic regulatory strategies based on the comprehensive characteristics of the crop type (tomato), the modification attribute (to improve quality and disease resistance), the environmental nutrient imbalance, and the lack of beneficial rhizosphere bacteria. These strategies either match and output synergistic regulatory strategies of the associated synthetic biology adaptation and regulation module and the microbiome engineering regulation module, or match synergistic regulatory strategies of the associated microbiome engineering regulation module and the biostimulant adaptation module. This allows the regulatory strategies to work together from multiple levels, such as crop metabolism regulation, rhizosphere microecological reconstruction, and environmental adaptation optimization. This overcomes the shortcomings of single regulatory methods having limited effects and difficulty in taking multiple improvement goals into account, ultimately obtaining the synergistic regulatory strategy with the highest adaptability and the strongest feasibility.

[0038] 103. Based on the synergistic regulation strategy adapted to the target planting area, generate a strategy execution plan that includes multi-factor synergistic regulation parameters.

[0039] The aforementioned multi-factor synergistic regulation parameters cover the dimensions of microorganisms, light environment, water and fertilizer, and soil environment. Specifically, after clarifying the combination of regulation modules associated with the synergistic regulation strategy, for each type of regulation module, based on the crop type, real-time environmental data, and rhizosphere microbiome status of the target planting area, specific regulation parameters for the corresponding dimension are decomposed. The parameters of all dimensions are synergistically calibrated to ensure that the regulation parameters of different dimensions do not conflict with each other and cooperate with each other, forming a complete execution plan with multi-factor linkage and comprehensive adaptation.

[0040] In this embodiment, the strategy execution scheme, in addition to including core multi-factor collaborative regulation parameters, can also simultaneously specify the execution sequence, execution frequency, adjustment threshold, and anomaly handling rules of the parameters, ensuring that the execution equipment can implement regulation according to a standardized process. The four dimensions covered by the specific multi-factor collaborative regulation parameters are not isolated but are deeply bound to the adapted regulation module based on the collaborative regulation strategy. Furthermore, the setting of each type of parameter is combined with the actual needs of the specific planting scenario. Based on the planting scenarios of greenhouse tomatoes and hybrid rice mentioned earlier, the regulation module is explained as follows:

[0041] Firstly, the microbial-level regulatory parameters mainly correspond to the microbiome engineering regulation module and the synthetic biology adaptation regulation module associated in the synergistic regulation strategy. Based on the rhizosphere microbiome status of the target planting area, the inoculation types, concentrations, timing, and methods of beneficial microorganisms can be set. Simultaneously, auxiliary parameters for maintaining microbial activity are set to ensure that microorganisms can colonize and reproduce in the rhizosphere, fulfilling their functions of promoting growth, disease resistance, and nutrient conversion. Taking the fruit-setting stage of greenhouse tomatoes as an example, the aforementioned matched synergistic regulation strategy combines the microbiome engineering regulation module with the biostimulant adaptation module. Considering the insufficient abundance of nitrogen-fixing bacteria and phosphate-solubilizing bacteria and the low proportion of biocontrol bacteria in the rhizosphere microbiome, the microbial-level regulatory parameters include: the concentration of inoculated nitrogen-fixing bacteria is... The concentration of phosphate-solubilizing bacteria was The concentration of biocontrol Bacillus was The inoculation method was root irrigation, with 200 mL applied to each tomato plant. The inoculation time was selected from 18:00 to 19:00. At the same time, microbial activity maintenance parameters were set, namely, maintaining soil moisture content at 25%-28% and soil temperature at 20-23℃ for 3 days after inoculation to avoid drastic changes in the soil environment that would affect microbial colonization.

[0042] Secondly, the light environment dimension regulation parameters mainly correspond to the optogenetic regulation module associated in the synergistic regulation strategy. They can be combined with the light demand characteristics of crop types and real-time light data to set light intensity, light duration, light spectrum distribution and light sequence, so as to ensure that the light environment can adapt to the needs of crop growth and modification attributes, while helping to improve microbial activity and promote water and fertilizer absorption. Taking the greenhouse tomato setting stage as an example, tomatoes need sufficient light to promote fruit sugar accumulation. Based on the current real-time environmental data of 35,000 lux light intensity and 8 hours / day light duration, the following light environment control parameters are set: LED supplemental lighting is used for 4 hours / day; the supplemental lighting intensity is controlled at 20,000-25,000 lux, so that the total light intensity is maintained at 55,000-60,000 lux; the light spectrum is adjusted to a red light to blue light ratio of 7:3, with red light promoting fruit sugar accumulation and blue light enhancing plant disease resistance; at the same time, a light control threshold is set: when the natural light intensity in the greenhouse exceeds 70,000 lux, the shading curtain is activated to prevent strong light from scorching the fruit; when the natural light intensity is below 20,000 lux, the supplemental lighting is automatically turned on to ensure light stability.

[0043] Third, the water and fertilizer dimension regulation parameters mainly correspond to the biostimulant adaptation module and synthetic biology adaptation regulation module associated in the synergistic regulation strategy. They can combine the water and fertilizer requirements of crop types, real-time soil water and fertilizer data, and the nutrient transformation capacity of rhizosphere microbiome to set the type, concentration, application amount, application time and application method of water and fertilizer, so as to achieve precise water and fertilizer supply, avoid water and fertilizer waste or insufficient supply, and at the same time take into account the balance of rhizosphere microecology. Taking the fruit-setting stage of greenhouse tomatoes as an example, and considering the high nitrogen content and low phosphorus and potassium content in the soil, as well as the insufficient nutrient conversion capacity of rhizosphere microorganisms in the real-time environmental data, the following water and fertilizer control parameters were set: the water and fertilizer types were selected as high phosphorus and potassium water-soluble fertilizer and biostimulants; the application method was drip irrigation; the application amount was 20 mL of water-soluble fertilizer and 10 mL of biostimulant per tomato plant per application, once every 7 days; the application time was selected as 9:00-10:00 am; at the same time, a water and fertilizer control linkage rule was set, that water and fertilizer should not be applied within 1 day after microbial inoculation to avoid excessive water and fertilizer concentration inhibiting microbial activity; according to the soil moisture content data, when the soil moisture content is lower than 25%, water should be added to the appropriate range before water and fertilizer are applied.

[0044] Fourth, the soil environment dimension regulation parameters mainly correspond to the microbiome engineering regulation module and biostimulant adaptation module associated in the synergistic regulation strategy. By combining real-time soil environment data and rhizosphere microbiome status, the type, amount, and method of soil conditioner can be set, as well as the regulation range of soil pH and water content, to optimize soil physicochemical properties and provide a suitable environment for crop growth and rhizosphere microbial colonization. Taking the fruit-setting stage of greenhouse tomatoes as an example, based on the current soil pH of 6.5 and soil electrical conductivity of 1.2 mS / cm in real-time environmental data, the following soil environmental control parameters were set: apply biochar at a rate of 1.5 kg per square meter, applied evenly and then lightly tilled to a depth of 5-10 cm; adjust the soil pH to 6.5-7.0 and control the soil electrical conductivity at 1.0-1.5 mS / cm; simultaneously set the soil environmental monitoring frequency, testing soil pH, moisture content, and electrical conductivity every 3 days; when the soil pH is below 6.5, apply a small amount of lime to adjust it; when the soil electrical conductivity is above 1.5 mS / cm, dilute it by drip irrigation to ensure that the soil environment always meets the needs of crop growth and microbial colonization.

[0045] It should be noted that the multi-factor synergistic regulation parameters in the above four dimensions are not set independently, but rather form a unified whole after synergistic calibration. All parameters revolve around crop modification attributes and are adapted to the real-time environment and rhizosphere microbiome state. For example, microbial inoculation parameters are linked with water, fertilizer, and soil parameters to avoid excessive water and fertilizer concentrations inhibiting microbial activity; light environment parameters are linked with water and fertilizer parameters to promote crop absorption and utilization of water and fertilizer, thereby improving the regulation effect. Furthermore, parameter settings vary significantly for different planting scenarios. For instance, in the case of hybrid rice during the tillering stage, if the synergistic regulation strategy combines a microbiome engineering regulation module with an optogenetic regulation module, then the microbial dimension parameters focus on inoculating methanogenic bacteria and photosynthetic bacteria; the light environment parameters focus on regulating light duration and spectrum to match the tillering needs of rice; the water and fertilizer parameters focus on balanced nitrogen, phosphorus, and potassium supply and controlling water content; and the soil environment parameters focus on regulating soil permeability and reducing denitrification, ensuring that the parameter settings are highly adapted to crop type, modification attributes, environment, and microbial state.

[0046] 104. The strategy execution plan is sent to the corresponding execution device to regulate the target planting area.

[0047] In this embodiment, a digital execution plan, including multi-dimensional control parameters, execution timing, control thresholds, and operating modes related to microorganisms, light environment, water and fertilizer, and soil environment, can be transmitted in real time to the corresponding field execution terminal via wired or wireless communication. The corresponding execution equipment here refers to intelligent agricultural equipment that matches the various control dimensions in the plan and possesses independent or collaborative execution capabilities. This includes, but is not limited to: automatic application devices for microbial agents and biostimulants, LED light environment control devices and supplemental lighting control systems, integrated water and fertilizer drip / sprinkler irrigation equipment, soil environment conditioning devices, environmental sensors, and automatic control terminals. These execution devices can be pre-deployed in the target planting area and establish a communication connection with the central control system. Upon receiving the execution plan, they can automatically start, run, and stop according to preset parameters without manual intervention, thereby ensuring the uniformity, timeliness, and accuracy of the control actions.

[0048] Taking the greenhouse tomato planting scenario as an example, after generating an execution plan that includes microbial application, supplemental lighting, drip irrigation, and soil environment regulation, the central control system will uniformly issue the quantified control instructions. Regarding microbial application parameters, the system sends instructions such as inoculation concentration, application amount, application time, and root irrigation method to the root precision application equipment. Correspondingly, the equipment automatically starts at the designated time period, applying 200mL of bacterial solution to the tomato roots. Regarding light environment parameters, the system sends light intensity, red-blue light ratio, supplemental lighting time, and start / stop thresholds to the greenhouse LED supplemental lighting and shading control device. When natural light is insufficient, the supplemental lights are automatically turned on; when the light is too strong, the shading net is automatically closed, achieving real-time adaptive control of the light environment. Regarding water, fertilizer, and soil environment parameters, the system sends nitrogen, phosphorus, and potassium ratios, irrigation volume, application frequency, and soil pH and conductivity control targets to the integrated water and fertilizer equipment and soil amendment device. Based on real-time soil monitoring data and the planned thresholds, the equipment automatically completes drip irrigation fertilization, water replenishment, and fine-tuning of soil physicochemical properties, without requiring manual on-site operation throughout the entire process.

[0049] By distributing the strategy execution plan to the corresponding execution equipment, a seamless connection can be achieved from top-level decision-making to bottom-level execution. This not only significantly reduces labor costs and operational errors, but also ensures the complete implementation of the collaborative regulation strategy, allowing the technological advantages of multi-factor collaborative regulation of microorganisms, light, water and fertilizer, and soil to be fully utilized.

[0050] For example, after performing microbial root irrigation, the system can automatically control the water and fertilizer equipment to stop working for a period of time to avoid the high concentration of fertilizer solution affecting the colonization of beneficial microorganisms; the light environment control system can simultaneously adjust the light intensity and duration according to the crop's photosynthetic needs and the suitable survival conditions for microorganisms, so as to create the optimal environment for crop growth and microbial activity.

[0051] The method for synergistic regulation of facility agriculture crop growth provided in this application, compared with the existing method of synergistic regulation of facility agriculture crop growth using a single-factor independent control mode, obtains the crop type, real-time environmental data, and rhizosphere microbiome status of the target planting area; determines the crop modification attributes based on the crop type, and, combined with the real-time environmental data and rhizosphere microbiome status, matches a synergistic regulation strategy suitable for the target planting area in a pre-constructed biological function database. The biological function database pre-stores multiple synergistic regulation scheme templates, each corresponding to a combination of crop type and modification attributes, and is associated with the synergistic application of at least two types of regulation templates: synthetic biology adaptation regulation module, microbiome engineering regulation module, optogenetics regulation module, and biostimulant adaptation module; based on the synergistic regulation strategy suitable for the target planting area, a strategy execution plan containing multi-factor synergistic regulation parameters is generated, covering the dimensions of microorganisms, light environment, water and fertilizer, and soil environment; the strategy execution plan is then distributed to the corresponding execution device to implement regulation of the target planting area. The entire process achieves comprehensive perception of multi-dimensional information about the planting scenario by simultaneously acquiring crop type, real-time environmental data, and rhizosphere microbiome status in the target planting area. This accurately reflects the actual growth needs of crops and the status of rhizosphere microorganisms. Furthermore, crop modification attributes are combined with real-time environmental data and rhizosphere microbiome status to match synergistic regulation strategies in a pre-set biological function database. This allows for rapid adaptation to different crops, different improvement goals, and different planting conditions, achieving synergistic optimization at both the biological and environmental levels. It can significantly improve crop growth regulation by working together from multiple dimensions, including crop characteristics, rhizosphere microecology, light environment, water and fertilizer, and soil environment. Based on this, an execution plan is generated that includes synergistic regulation parameters for multiple factors such as microorganisms, light environment, water and fertilizer, and soil environment. This constructs a comprehensive growth environment that adapts to the actual physiological needs of crops, achieving closed-loop management of the entire process from data perception and strategy matching to on-site execution, thereby improving the intelligence level and execution efficiency of planting regulation.

[0052] In practical applications, the synthetic biology adaptation module is used to match functional engineered bacteria and generate environmental adaptation strategies. It functions as the biological computing engine of the entire collaborative regulatory system, its core task being to transform abstract biological characteristics into executable engineering instructions. Specifically, for example... Figure 2 As shown, the implementation process of the above-mentioned synthetic biology adaptation module includes the following steps:

[0053] 201. Call the parameters of the functional engineered bacteria in the biological function database.

[0054] 202. Calculate the expected metabolic activity of engineered bacteria based on real-time environmental parameters.

[0055] 203. Based on the expected metabolic activity of the engineered bacteria, generate an application strategy and environmental adaptation control instruction for the engineered bacteria.

[0056] In this embodiment, the synthetic biology adaptation module first calls the pre-stored functional engineered bacteria parameters in the biological function database. Based on these parameters, the synthetic biology adaptation module no longer relies on rules of thumb, but instead calculates the expected metabolic activity of the engineered bacteria at the current moment by substituting real-time environmental sensor data into a preset metabolic kinetic model.

[0057] The aforementioned biological function database is a pre-constructed standardized parameter database covering various functional engineered bacteria, such as crop growth-promoting bacteria, disease-controlling bacteria, soil-remediating bacteria, and pollutant-degrading bacteria. When accessing the database, users can quickly match the corresponding functional engineered bacteria by inputting keywords such as application scenario, core requirements, and crop variety, and retrieve their complete parameters. The specific parameters of functional engineered bacteria include at least the applicable environmental range, target sites, metabolic cycle, and crop-specific metabolic activity model.

[0058] The above applicable environmental range refers to the environmental conditions thresholds that enable the functional engineered bacteria to survive, multiply, and perform their preset functions. All parameters have been calibrated through repeated laboratory tests and field / in-situ tests, covering key environmental factors such as temperature, humidity, pH value, salinity, dissolved oxygen concentration, and light intensity. For each environmental factor, the minimum threshold, suitable range, and maximum threshold are clearly marked. When the threshold range is exceeded, the metabolism of the engineered bacteria will decrease significantly, or even lose its function or die.

[0059] The aforementioned targets are the substances, cells, or metabolic pathways that engineered bacteria directly act upon when performing their pre-defined functions. Identifying these targets ensures the functional specificity of the engineered bacteria, avoids ineffective metabolism, and provides a basis for subsequent metabolic activity calculations. Different functional types of engineered bacteria exhibit significant differences in their target targets. The database clearly labels the target type, target sequence, and mechanism of action.

[0060] The metabolic cycle described above refers to the time it takes for a functional engineered bacterium to complete one full metabolic cycle after inoculation. It is divided into a basal metabolic cycle and an environmental adaptation metabolic cycle. The length of the metabolic cycle directly affects the efficiency of the engineered bacteria and the frequency of application. The database will indicate the metabolic cycle parameters under different environmental conditions and provide correlation formulas between the metabolic cycle and environmental factors, supporting dynamic calculations.

[0061] The aforementioned crop-specific metabolic model is a correlation model between the metabolic activity of engineered bacteria and crop growth status and metabolites, constructed for specific crop varieties. The model is built upon crop genome, transcriptome, and metabolic data, and incorporates the metabolic characteristics of the engineered bacteria to inversely optimize their metabolic activity parameters. This model differs from general metabolic activity models in that its parameters are customized and calibrated to meet the specific growth needs and metabolic characteristics of the crop, ensuring that the metabolic activity of the engineered bacteria is synchronized with crop growth and enhancing the targeted application of the engineered bacteria.

[0062] The specific logic for calculating metabolic activity is based on the basal metabolic activity of the engineered bacteria, combined with the deviation between real-time environmental parameters and the applicable environmental range of the engineered bacteria, introducing the influence coefficient of environmental factors, and calculating the expected metabolic activity of the engineered bacteria through an iterative method.

[0063] Correspondingly, if the expected metabolic activity is greater than or equal to a preset threshold, an instruction for the engineered bacteria application strategy and environmental adaptation regulation is generated.

[0064] If the expected metabolic activity is less than the preset threshold, the measured environmental parameters are adjusted in reverse to calculate the expected metabolic activity of the engineered bacteria under the adjusted environment until the expected metabolic activity is greater than or equal to the preset threshold, and then the engineered bacteria application strategy and environmental adaptation control instructions are generated.

[0065] The expected metabolic activity of a specific engineered microorganism is expressed using the following formula:

[0066]

[0067] in, The expected metabolic activity of the engineered bacteria; To maximize metabolic activity against the target crop; It is the Michaelis constant; for The impact coefficient is set according to crop preference; This is the temperature influence coefficient, set according to the suitable temperature range for crop growth.

[0068] For example, when the expected metabolic activity is below 30%, it is determined that the engineered bacteria cannot perform its preset function, and environmental regulation or a more suitable engineered bacteria needs to be activated; when the expected metabolic activity is between 30% and 70%, it is determined that the metabolic activity of the engineered bacteria is insufficient, and the application strategy needs to be adjusted or the environment slightly regulated; when the expected metabolic activity is greater than or equal to 70%, it is determined that the engineered bacteria can perform its preset function normally, and it should be applied according to the conventional strategy. Simultaneously, parameters such as cell metabolic flux adjustment factors and comprehensive fluctuation factors are introduced during the calculation process. These are combined with real-time environmental factor fluctuations to dynamically correct the calculated results of the expected metabolic activity, improving the calculation accuracy. Specifically, the cell metabolic flux adjustment factor is calculated based on the ratio of matrix consumption rate to target product generation rate; the comprehensive fluctuation factor is constructed by combining parameters such as pH fluctuations and temperature fluctuations to further ensure the accuracy of the calculation results.

[0069] In practical applications, there are fundamental differences in crop genetic background, modification methods, metabolic characteristics, and target functions. A single regulatory model cannot adapt to the diverse modified structures and functional requirements. For crops that have only undergone conventional breeding or mild gene editing, their metabolic basis is still mainly based on natural pathways, and synthetic biology adaptation modules only need to provide basic environmental adaptation. However, for synthetic biology crops carrying artificially designed metabolic pathways, targeted induction and precise regulation are necessary to activate exogenous pathways and improve product synthesis efficiency. Ordinary water and fertilizer regulation cannot meet the induction requirements of artificial pathways.

[0070] Specifically, when the crop type is a crop modified through synthetic biology and has a synthetic pathway for artificially designed high-value-added products, the synthetic biology adaptation module activates the artificial synthetic pathway during the critical expression period of the target metabolic pathway through light environment regulation, temperature difference regulation, and water and fertilizer composition regulation to improve the accumulation efficiency of the target product. The critical expression period is determined according to the crop type and the synthetic kinetics of the target product. Light environment regulation includes setting the proportion of specific wavelengths of light in the supplemental light spectrum, temperature difference regulation includes setting the diurnal temperature difference, and water and fertilizer composition regulation includes adding precursor substances of the target metabolic pathway.

[0071] The aforementioned light environment regulation is not ordinary supplemental lighting, but rather precise spectral regulation targeting light-responsive initiators, light-dependent rate-limiting enzymes, and the distribution direction of photosynthetic products in artificial pathways. Specifically, it involves setting the proportion of specific wavelengths in the supplemental lighting spectrum. In synthetic biology design, artificial pathways often use photoinducible promoters; only when a specific wavelength reaches a threshold can the pathway gene be transcribed efficiently. For example, in a medicinal lettuce that synthesizes high-purity rosmarinic acid, the artificial pathway uses a blue light-responsive promoter. During the critical expression period, the synthetic biology adaptation module sets the supplemental lighting spectrum to: 40% blue light, 50% red light, and 10% far-red light, and controls the light exposure duration to 16 hours / day, which can increase the transcription level of the rosmarinic acid synthesis gene by 8 to 12 times.

[0072] The aforementioned temperature difference regulation enhances the metabolic flux and product stability of artificial pathways by setting a diurnal temperature range. For high-value-added secondary metabolites, recombinant proteins, and other artificial products, the accumulation efficiency is typically highest under conditions where high daytime temperatures promote enzymatic reactions and low nighttime temperatures reduce decomposition and respiration. The synthetic biology adaptation module automatically sets the diurnal temperature range based on the optimal enzyme temperature and product thermal stability of the target pathway, typically controlling it between 8℃ and 15℃. For example, for grape plants synthesizing resveratrol dimers, setting a daytime temperature of 28℃ and a nighttime temperature of 15℃ during the critical expression period, with a diurnal temperature range of 13℃, increases the accumulation of the target product by more than 60% compared to a constant temperature of 25℃. For tobacco plants synthesizing recombinant collagen, controlling the diurnal temperature range at 10℃ significantly reduces protease activity, decreases target protein degradation, and increases the yield of the final product.

[0073] The aforementioned water and fertilizer regulation involves the targeted addition of precursors to target metabolic pathways, rather than conventional nitrogen, phosphorus, and potassium compound fertilizers. Artificially synthesized pathways often face bottlenecks due to insufficient natural substrates. Synthetic biology adaptation modules precisely supply precursors, inducers, or cofactors during critical expression periods, based on pathway design. For example, in flowering crops synthesizing high-value phenylpropane products, targeted application of phenylalanine and tyrosine maintains the concentration of artificial pathway substrates at a saturated level.

[0074] Specifically, when the crop is a crop jointly modified by gene editing and synthetic biology, the synthetic biology adaptation module adopts a dual-path synergistic mode of gene expression enhancement and engineered bacteria functional complementarity. Gene expression enhancement is achieved by regulating soil temperature, mineral element ratio or water stress, while engineered bacteria functional complementarity is achieved by applying engineered bacteria with the same or synergistic stress resistance functions.

[0075] Gene-edited and synthetic biology combined modifiers refer to crops that undergo dual modification using both gene-editing and synthetic biology techniques. This differs from single-gene-edited crops or single-synthetic-biological modifiers. Their characteristics include: using gene-editing technology to precisely knock out, knock in, or modify the crop's inherent genes, optimizing its inherent stress resistance and metabolic regulation; and using synthetic biology technology to introduce human-designed functional pathways into the crop, endowing it with new functions not naturally present. Because these crops simultaneously carry both their own edited genes and artificially introduced pathways, a single regulatory approach is insufficient to ensure the optimal functioning of both. Therefore, the synthetic biology adaptation module must adopt a dual-pathway synergistic model to maximize the advantages of combined modification.

[0076] The enhanced gene expression described above is an intrinsic regulatory pathway, achieved by regulating soil temperature, mineral element ratios, or water stress. This regulatory approach can precisely induce efficient expression of crop-edited genes while simultaneously activating the transcriptional activity of exogenous artificial pathways, tapping into the inherent potential for dual modification of the crop itself. The regulatory parameters can be calibrated according to the crop modification type and target trait to ensure efficient regulation without affecting normal crop growth.

[0077] The aforementioned engineered bacteria, with their complementary functions, form an external auxiliary pathway, achieved through the application of engineered bacteria possessing the same or synergistic stress-resistance functions. The engineered bacteria used are well-matched and compatible with the crop's stress-resistance function, and can colonize and proliferate in the crop's growth environment. This not only assists the crop in exerting its stress-resistance function but also compensates for the limitations of the crop's own regulation, synergizing with gene expression enhancement pathways to ensure the stability of traits and the achievement of functional targets in the jointly modified crops.

[0078] It should be noted that the above-mentioned dual-path synergistic mode is not a simple superposition of two paths, but rather a precise regulation by the synthetic biology adaptation module: in terms of timing, the gene expression enhancement pathway and the engineered bacteria functional complementarity pathway are activated simultaneously, ensuring that the activation of the crop's own gene expression and the assistance of the engineered bacteria occur at the same time; in terms of regulation precision, the regulation parameters of both pathways are determined in combination with the crop's joint modification characteristics, gene expression kinetics, and the metabolic cycle of the engineered bacteria, and can be automatically regulated through the synthetic biology adaptation module without human intervention, making it suitable for large-scale production scenarios such as facility agriculture and plant factories.

[0079] In practical applications, the microbiome engineering regulation module is used to optimize the rhizosphere microbial community structure or suppress soil-borne diseases. It includes at least one of rhizosphere microbial community optimization regulation, microbial community antagonism regulation, and microbial-water-fertilizer synergistic regulation. It can be executed individually or in combination according to the actual rhizosphere environment to improve rhizosphere health and nutrient utilization efficiency.

[0080] The aforementioned rhizosphere microbiome optimization and regulation includes: generating a compound probiotic application plan and corresponding environmental adaptation strategies when the abundance of beneficial rhizosphere bacteria is detected to be below a preset threshold; specifically, the optimization and regulation of the rhizosphere microbiome is used to maintain a stable proportion of beneficial microorganisms in the rhizosphere. When the system detects that the abundance of beneficial rhizosphere bacteria is below a preset threshold, it automatically generates a compound probiotic application plan and outputs corresponding environmental adaptation strategies such as temperature, humidity, and ventilation to promote the colonization and proliferation of exogenous probiotics in the rhizosphere and quickly restore the benign balance of the rhizosphere microbiome structure.

[0081] The aforementioned microbial community antagonistic regulation includes: when the abundance of soil-borne disease pathogens exceeds the warning threshold, optimizing the application parameters of antagonistic bacteria and ecological inhibition conditions based on the microbial community antagonistic efficiency formula to generate a synergistic regulation scheme; specific microbial community antagonistic regulation is used to address the risk of soil-borne diseases. When the abundance of soil-borne disease pathogens exceeds the warning threshold, the module optimizes parameters such as the application dosage, application time, and application location of antagonistic bacteria based on the preset microbial community antagonistic efficiency formula, and matches ecological inhibition conditions such as light, soil pH, and nutrients to form a synergistic regulation scheme that combines biological antagonism and environmental regulation, achieving targeted inhibition of pathogens.

[0082] The aforementioned microbial-water-fertilizer synergistic regulation includes: when the abundance of rhizosphere nitrogen-fixing bacteria reaches a preset threshold, dynamically reducing the amount of chemical nitrogen fertilizer applied based on a nitrogen fertilizer reduction formula, and simultaneously adding a microbial growth promoter; specifically, this microbial-water-fertilizer synergistic regulation is used to couple fertilizer reduction and efficiency enhancement with microbial function. When the abundance of rhizosphere nitrogen-fixing bacteria reaches a preset threshold and can stably exert nitrogen-fixing effects, the system dynamically reduces the intensity of chemical nitrogen fertilizer application based on a nitrogen fertilizer reduction formula, and simultaneously adds a growth promoter for the corresponding microbial community, thereby reducing fertilizer input and strengthening the rhizosphere's self-generated nitrogen fixation function while ensuring crop nitrogen supply.

[0083] The specific antagonistic efficiency of bacterial communities is expressed using the following formula:

[0084]

[0085] in, For the efficiency of bacterial antagonism, The initial abundance of pathogens, To regulate the abundance of pathogens, For the fit coefficient, The humidity adaptation coefficient is set according to the actual preferences of the target crop.

[0086] The specific reduction in nitrogen fertilizer use is expressed using the following formula:

[0087]

[0088] in, The amount of nitrogen fertilizer that can be reduced; This represents the standard nitrogen fertilizer application rate for this crop under conventional planting conditions. This is a crop-specific nitrogen fixation synergistic coefficient; This represents the current measured abundance of rhizosphere nitrogen-fixing bacteria. The threshold abundance at which nitrogen-fixing bacteria can exert their effective function.

[0089] In practical applications, the optogenetic regulation module is used to activate the expression of light-sensitive genes or to execute the timing control of light signals. The module's regulatory initiation has specific triggering conditions, and it only operates under specific scenarios, ensuring targeted and efficient regulation. Specifically, when environmental stress events are detected or crops are in a specific growth stage, light regulation instructions are generated based on a preset light response model. The light response model is used to predict the expression level of target genes driven by photosensitive promoters and configures wavelength-specific response coefficients according to crop type. The light regulation instructions include light wavelength, light intensity, and irradiation duration. The light regulation instructions are sent to programmable light source devices to activate the expression of target genes controlled by photosensitive promoters, or to achieve precise regulation of crop plant type, stress resistance, or secondary metabolites through the temporal combination of multi-band light signals. Among them, environmental stress events include adverse environmental changes that affect the normal growth of crops, such as drought, high temperature, low temperature, salinity, and pests and diseases. The specific growth stage is determined according to crop type and regulation target, such as the seedling stage, tillering stage, and grain-filling stage of crops. The triggering conditions can be dynamically adjusted by the system preset parameters in combination with crop growth status and environmental monitoring data.

[0090] The light response model generates light regulation instructions containing the core parameters required for precise actual regulation, covering at least light wavelength, light intensity, and irradiation duration. These three parameters work together synergistically to determine the expression effect of photosensitive genes. Specifically, light wavelength determines the specificity of regulation, requiring matching the response wavelength of the crop's photosensitive promoter to ensure specific activation of the target gene without affecting the expression of other unrelated genes. Light intensity determines the efficiency of gene expression, requiring setting the optimal intensity based on the prediction results of the light response model; too low an intensity will not effectively activate the gene, while too high an intensity may damage crop cells. Irradiation duration determines the duration of gene expression, requiring setting based on the crop's growth stage requirements and the duration of stress events to ensure that the expression level of the target gene meets the crop's growth and regulatory needs.

[0091] Correspondingly, the light regulation command is directly sent to the programmable light source device, which then precisely executes the light parameters. On the one hand, by accurately matching the light parameters, the target gene is quantitatively expressed at a specific time and in a specific location, thereby achieving localized regulation at the gene transcription level. On the other hand, by combining the time sequence of multi-band signals, the directional regulation of crop phenotype and function can be achieved without changing the crop gene structure.

[0092] The specific optical response model is expressed using the following formula:

[0093]

[0094] in, This represents the expected expression level of the target gene under specific light conditions. This represents the basal expression level of the target gene under conditions without induced light. The wavelength-specific response coefficient reflects the sensitivity of a specific photosensitizing system in a crop to a particular wavelength of light. Light intensity, This represents the cumulative exposure time per day.

[0095] In practical applications, the biostimulant adaptation module is used to match stimulant types and coordinate with environmental or microbial application. Its core purpose is to achieve precise selection of biostimulants and to synergize with environmental control and microbial application strategies to maximize the role of biostimulants in enhancing crop stress resistance and promoting growth. Specifically, such as... Figure 3 As shown, the implementation process of the above-mentioned biostimulant adaptor module includes the following steps:

[0096] 301. Call the biostimulants in the biological function database.

[0097] 302. Based on the identified crop species, current growth stage, and environmental stress level, calculate the expected physiological response value of biostimulants using a stress resistance effect prediction model.

[0098] In this embodiment, the biostimulant is selected from one or more of seaweed extract, humic acid, amino acids, or plant-derived oligosaccharides. These four types of substances are recognized as highly effective biostimulants in the agricultural field, each possessing unique physiological functions: seaweed extract can regulate crop hormone balance, humic acid can optimize soil structure and enhance root vitality, amino acids can provide direct nutrition to crops and strengthen metabolism, and plant-derived oligosaccharides can induce the crop's own disease resistance and stress resistance system. The specific biostimulant adaptation module can retrieve a single type or a combination of types according to actual needs.

[0099] As an output of the stress resistance effect prediction model, the stress resistance effect index is used to comprehensively evaluate the expected application effect of biostimulants; the higher the value, the more significant the stress resistance enhancement effect. The biostimulant type response coefficient is a core characteristic parameter, which can be assigned a value based on the inherent characteristics of different types of stimulants such as seaweed extract and humic acid, reflecting the basic compatibility of different stimulants with the target crop. The biostimulant application concentration is a core regulatory parameter, directly determining the intensity of the stimulant's effect. The growth stage regulation coefficient is set according to the physiological characteristics of the crop's current growth stage, used to correct for differences in the response to stimulants at different growth stages. The environmental stress intensity coefficient is determined based on the real-time monitored stress level, reflecting the influence of stress degree on the stimulant's effect. Through the calculation of the stress resistance effect prediction model, the above-mentioned biostimulant compatibility module can quickly screen out the optimal biostimulant type and the best application concentration, and simultaneously, combined with the crop growth stage and environmental stress status, formulate a comprehensive plan that coordinates environmental regulation and microbial application.

[0100] For example, when crops encounter mild drought stress during the grain-filling stage, the stress resistance prediction model will use parameter calculations to prioritize the combination of seaweed extract and amino acids, calculating the optimal application concentration. Simultaneously, it will match appropriate water regulation and synergistic application of nitrogen-fixing microorganisms, allowing the stress resistance effect of biostimulants to synergize with the effects of the environment and microorganisms. This quantitative prediction and synergistic regulation model achieves intelligent and precise application of biostimulants, effectively improving crop growth performance under stress conditions and adapting to various large-scale production scenarios such as facility agriculture and field planting.

[0101] The specific model for predicting the resilience effect is expressed by the following formula:

[0102]

[0103] in, The stress resistance index; The response coefficient is the type of biostimulant. The concentration at which biostimulants are administered; This is the reproductive period adjustment coefficient; This represents the environmental stress intensity coefficient.

[0104] Furthermore, in order to achieve intelligent management of the entire cycle from growth regulation and quality formation to harvesting and marketing, after real-time and precise regulation of the target planting area, we can continue to enter the harvest prediction stage, planning execution and data closure stage.

[0105] In the specific harvest prediction stage, a built-in harvest prediction model continuously collects and integrates crop growth cycle data, environmental cumulative effect data, real-time quality monitoring indicators, and market demand data. This multi-source data is then uniformly input into the harvest prediction model for processing. Specifically, growth cycle data determines the current developmental stage of the crop; environmental cumulative effect data reflects the comprehensive impact of light, temperature, water, and fertilizer conditions on crop growth throughout the entire growth period; quality monitoring indicators objectively characterize fruit maturity and marketability; and market demand data, combined with economic benefits, determines the optimal harvest window. Based on this multi-dimensional information, the harvest prediction model simultaneously outputs the optimal harvest time, expected yield, and expected quality grade, providing a quantitative basis for harvest decisions.

[0106] After completing the harvest forecast, the system enters the planning and execution phase, automatically generating and outputting a standardized harvest forecast report. The report includes, but is not limited to, harvest time windows, suggested harvesting order based on regional maturity, recommendations for suitable post-harvest storage conditions, and market sales suggestions. This standardized harvest forecast report can be directly used to guide harvesting operations and post-harvest processing, ensuring a high degree of alignment between harvesting, storage, and distribution processes and field growth conditions. While ensuring quality and improving economic efficiency, it also allows users to flexibly adjust harvesting plans based on actual production conditions, market changes, or operational arrangements. After users modify key parameters such as harvest time, the system can automatically recalculate and synchronously update the expected yield and quality assessment results, ensuring the practicality and operability of the plan.

[0107] After harvesting is completed, the system enters the post-harvest data closed-loop phase. Users input real production data such as actual harvest time, actual yield, quality grade distribution, and loss rate into the system. The system compares and analyzes the actual results with the previously predicted data to generate a deviation evaluation and effect summary. Based on the comparison results, the system automatically extracts the advantageous aspects and areas for optimization of the control measures in this planting cycle, updates the planting management database synchronously, and iteratively corrects the relevant algorithm model parameters to continuously improve the model's prediction accuracy and the rationality of the control strategy.

[0108] Furthermore, as a specific implementation of the above method, embodiments of this application provide a synergistic regulation device for the growth of facility agriculture crops, such as... Figure 4 As shown, the device includes: an acquisition unit 41, a matching unit 42, a generation unit 43, and a control unit 44.

[0109] Acquisition unit 41 is used to acquire crop type, real-time environmental data and rhizosphere microbiome status of the target planting area;

[0110] The matching unit 42 is used to determine crop modification attributes based on the crop type, and in combination with the real-time environmental data and rhizosphere microbiome status, match the target planting area with a synergistic regulation strategy in a pre-constructed biological function database. The biological function database contains multiple synergistic regulation scheme templates, each of which corresponds to a combination of crop type and modification attributes, and is associated with the synergistic application of at least two types of regulation templates: synthetic biology adaptation regulation module, microbiome engineering regulation module, optogenetics regulation module, and biostimulant adaptation module.

[0111] The generation unit 43 is used to generate a strategy execution plan containing multi-factor synergistic regulation parameters based on the synergistic regulation strategy adapted to the target planting area. The multi-factor synergistic regulation parameters cover the dimensions of microorganisms, light environment, water and fertilizer and soil environment.

[0112] The control unit 44 is used to send the strategy execution plan to the corresponding execution device to control the target planting area.

[0113] The synergistic regulation device for facility agriculture crop growth provided in this application, compared with the existing method of synergistic regulation of facility agriculture crop growth using a single-factor independent control mode, acquires the crop type, real-time environmental data, and rhizosphere microbiome status of the target planting area; determines the crop modification attributes based on the crop type, and, combined with the real-time environmental data and rhizosphere microbiome status, matches a synergistic regulation strategy suitable for the target planting area in a pre-constructed biological function database. The biological function database pre-stores multiple synergistic regulation scheme templates, each corresponding to a combination of crop type and modification attributes, and is associated with the synergistic application of at least two types of regulation templates: synthetic biology adaptation regulation module, microbiome engineering regulation module, optogenetics regulation module, and biostimulant adaptation module; based on the synergistic regulation strategy suitable for the target planting area, a strategy execution plan containing multi-factor synergistic regulation parameters is generated, covering the dimensions of microorganisms, light environment, water and fertilizer, and soil environment; the strategy execution plan is then distributed to the corresponding execution device to implement regulation of the target planting area. The entire process achieves comprehensive perception of multi-dimensional information about the planting scenario by simultaneously acquiring crop type, real-time environmental data, and rhizosphere microbiome status in the target planting area. This accurately reflects the actual growth needs of crops and the status of rhizosphere microorganisms. Furthermore, crop modification attributes are combined with real-time environmental data and rhizosphere microbiome status to match synergistic regulation strategies in a pre-set biological function database. This allows for rapid adaptation to different crops, different improvement goals, and different planting conditions, achieving synergistic optimization at both the biological and environmental levels. It can significantly improve crop growth regulation by working together from multiple dimensions, including crop characteristics, rhizosphere microecology, light environment, water and fertilizer, and soil environment. Based on this, an execution plan is generated that includes synergistic regulation parameters for multiple factors such as microorganisms, light environment, water and fertilizer, and soil environment. This constructs a comprehensive growth environment that adapts to the actual physiological needs of crops, achieving closed-loop management of the entire process from data perception and strategy matching to on-site execution, thereby improving the intelligence level and execution efficiency of planting regulation.

[0114] In practical applications, the synthetic biology adaptation module is used to match functional engineered bacteria and generate environmental adaptation strategies, including:

[0115] The parameters of the functional engineered bacteria in the biological function database are called up. The parameters of the functional engineered bacteria include at least the applicable environmental range, target site, metabolic cycle and crop-specific metabolic activity model.

[0116] The expected metabolic activity of the engineered bacteria was calculated based on real-time environmental parameters.

[0117] Based on the expected metabolic activity of the engineered bacteria, generate application strategies and environmental adaptation control instructions for the engineered bacteria.

[0118] Correspondingly, if the expected metabolic activity is greater than or equal to a preset threshold, an engineered bacteria application strategy and environmental adaptation regulation instruction is generated.

[0119] If the expected metabolic activity is less than the preset threshold, the measured environmental parameters are adjusted in reverse to calculate the expected metabolic activity of the engineered bacteria under the adjusted environment until the expected metabolic activity is greater than or equal to the preset threshold, and an engineered bacteria application strategy and environmental adaptation control instruction are generated.

[0120] The expected metabolic activity of the engineered bacteria is expressed using the following formula:

[0121]

[0122] in, The expected metabolic activity of the engineered bacteria; To maximize metabolic activity against the target crop; It is the Michaelis constant; for The impact coefficient is set according to crop preference; This is the temperature influence coefficient, set according to the suitable temperature range for crop growth.

[0123] In practical applications, when the crop type is a crop modified through synthetic biology and possesses a synthetic pathway for artificially designed high-value-added products, the synthetic biology adaptation module activates the artificial synthetic pathway during the critical expression period of the target metabolic pathway through light environment regulation, temperature difference regulation, and water and fertilizer composition regulation to improve the accumulation efficiency of the target product. The critical expression period is determined based on the crop type and the synthetic kinetics of the target product. The light environment regulation includes setting the proportion of specific wavelengths of light in the supplemental light spectrum, the temperature difference regulation includes setting the diurnal temperature difference, and the water and fertilizer composition regulation includes adding precursor substances of the target metabolic pathway.

[0124] In practical applications, when the crop is a crop jointly modified by gene editing and synthetic biology, the synthetic biology adaptation module adopts a dual-path synergistic mode of gene expression enhancement and engineered bacteria functional complementarity. The gene expression enhancement is achieved by regulating soil temperature, mineral element ratio or water stress, and the engineered bacteria functional complementarity is achieved by applying engineered bacteria with the same or synergistic stress resistance functions.

[0125] In practical applications, the microbiome engineering regulation module is used to optimize the rhizosphere microbial community structure or suppress soil-borne diseases, including at least one of rhizosphere microbial community optimization regulation, microbial community antagonism regulation, and microbial-water-fertilizer synergistic regulation.

[0126] The optimization and regulation of rhizosphere microbiota includes: when the abundance of beneficial rhizosphere bacteria is detected to be lower than a preset threshold, generating a compound probiotic agent application plan and a corresponding environmental adaptation strategy.

[0127] The microbial community antagonistic regulation includes: when the abundance of soil-borne disease pathogens exceeds the warning threshold, optimizing the application parameters of antagonistic bacteria and ecological inhibition conditions based on the microbial community antagonistic efficiency formula to generate a synergistic regulation scheme.

[0128] The microbial-water-fertilizer synergistic regulation includes: when the abundance of rhizosphere nitrogen-fixing functional bacteria reaches a preset threshold, dynamically reducing the amount of chemical nitrogen fertilizer applied based on the nitrogen fertilizer reduction formula, and simultaneously adding a microbial growth promoter.

[0129] The antagonistic efficiency of the bacterial community is expressed by the following formula:

[0130]

[0131] in, For the efficiency of bacterial antagonism, The initial abundance of pathogens, To regulate the abundance of pathogens, For the fit coefficient, The humidity adaptation coefficient is set according to the actual preferences of the target crop.

[0132] The reduction in nitrogen fertilizer use is expressed using the following formula:

[0133]

[0134] in, The amount of nitrogen fertilizer that can be reduced; This represents the standard nitrogen fertilizer application rate for this crop under conventional planting conditions. This is a crop-specific nitrogen fixation synergistic coefficient; This represents the current measured abundance of rhizosphere nitrogen-fixing bacteria. The threshold abundance at which nitrogen-fixing bacteria can exert their effective function.

[0135] In practical applications, the optogenetic regulation module is used to activate the expression of photosensitive genes or perform temporal control of light signals, including:

[0136] When environmental stress events are detected or crops are in a specific growth stage, light regulation instructions are generated based on a preset light response model. The light response model is used to predict the expression level of target genes driven by photosensitive promoters and configures wavelength-specific response coefficients according to crop type. The light regulation instructions include light wavelength, light intensity and irradiation duration. The light regulation instructions are sent to programmable light source devices to activate the expression of target genes controlled by photosensitive promoters, or to achieve precise regulation of crop plant type, stress resistance or secondary metabolites through the temporal combination of multi-band light signals.

[0137] The optical response model is expressed using the following formula:

[0138]

[0139] in, This represents the expected expression level of the target gene under specific light conditions. This represents the basal expression level of the target gene under conditions without induced light. The wavelength-specific response coefficient reflects the sensitivity of a specific photosensitizing system in a crop to a particular wavelength of light. Light intensity, This represents the cumulative exposure time per day.

[0140] In practical applications, the biostimulant adaptation module is used to match stimulant types and coordinate with environmental or microbial application, including:

[0141] The biostimulants in the biological function database are called up, and the biostimulants are selected from one or more of seaweed extracts, humic acid, amino acids or plant-derived oligosaccharides.

[0142] Based on the identified crop species, current growth stage, and environmental stress level, the expected physiological response value of biostimulants is calculated using a stress resistance effect prediction model.

[0143] The resilience effect prediction model is expressed using the following formula:

[0144]

[0145] in, The stress resistance index; The response coefficient is the type of biostimulant. The concentration at which biostimulants are administered; This is the reproductive period adjustment coefficient; This represents the environmental stress intensity coefficient.

[0146] It should be noted that other corresponding descriptions of the functional units involved in the synergistic regulation device for the growth of facility agriculture crops provided in this embodiment can be found in the corresponding descriptions in the above-mentioned synergistic regulation method for the growth of facility agriculture crops, and will not be repeated here.

[0147] Based on the above method, the present application also provides a storage medium storing a computer program that, when executed by a processor, implements the above-mentioned method for coordinated regulation of crop growth in facility agriculture.

[0148] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.

[0149] Based on the above method and the virtual device embodiment, in order to achieve the above objectives, this application embodiment also provides a physical device for the coordinated regulation method of facility agriculture crop growth, which can be a computer, smartphone, tablet computer, smartwatch, server, or network device, etc. The physical device includes a storage medium and a processor; the storage medium is used to store computer programs; the processor is used to execute the computer programs to implement the above-mentioned coordinated regulation method of facility agriculture crop growth.

[0150] Optionally, the physical device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0151] In an exemplary embodiment, see Figure 5 The aforementioned physical device includes a communication bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device. The various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the coordinated regulation method for the growth of facility agriculture crops described in the above embodiments.

[0152] Those skilled in the art will understand that the physical device structure for the coordinated regulation of crop growth in facility agriculture provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0153] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device for the coordinated regulation of crop growth in the aforementioned facility agriculture, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. By applying the technical solution of this application, compared with the existing methods, this application achieves comprehensive perception of multi-dimensional information of the planting scenario by simultaneously acquiring crop type, real-time environmental data and rhizosphere microbiome status of the target planting area, accurately reflecting the actual growth needs of crops and rhizosphere microbiome status, and further matching synergistic regulation strategies in a preset biological function database by combining crop modification attributes with real-time environmental data and rhizosphere microbiome status. It can quickly adapt to different crops, different improvement targets and different planting conditions, and achieve synergistic optimization at the biological and environmental levels. It can significantly improve the crop growth regulation effect by working together from multiple dimensions such as crop characteristics, rhizosphere microecology, light environment, water and fertilizer and soil environment. On this basis, it generates an execution plan that includes multi-factor synergistic regulation parameters such as microorganisms, light environment, water and fertilizer and soil environment to build a comprehensive growth environment that adapts to the actual physiological needs of crops, realizes closed-loop management of the whole process from data perception, strategy matching to on-site execution, and improves the intelligence level and execution efficiency of planting regulation.

[0155] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0156] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for synergistic regulation of crop growth in facility agriculture, characterized in that, include: Acquire crop types, real-time environmental data, and rhizosphere microbiome status in the target planting area; Based on the crop type, crop modification attributes are determined, and combined with the real-time environmental data and rhizosphere microbiome status, a synergistic regulation strategy adapted to the target planting area is matched in a pre-constructed biological function database. The biological function database contains multiple synergistic regulation scheme templates, each corresponding to a combination of crop type and modification attributes, and associated with the synergistic application of at least two types of regulation templates: synthetic biology adaptation regulation module, microbiome engineering regulation module, optogenetic regulation module, and biostimulant adaptation module. Based on the synergistic regulation strategy adapted to the target planting area, a strategy execution plan containing multi-factor synergistic regulation parameters is generated, which cover the dimensions of microorganisms, light environment, water and fertilizer and soil environment. The strategy execution plan is distributed to the corresponding execution device to regulate the target planting area.

2. The method according to claim 1, characterized in that, The synthetic biology adaptation module is used to match functional engineered bacteria and generate environmental adaptation strategies, including: The parameters of the functional engineered bacteria in the biological function database are called up. The parameters of the functional engineered bacteria include at least the applicable environmental range, target site, metabolic cycle and crop-specific metabolic activity model. The expected metabolic activity of the engineered bacteria was calculated based on real-time environmental parameters. Based on the expected metabolic activity of the engineered bacteria, generate engineered bacteria application strategies and environmental adaptation control instructions; Correspondingly, if the expected metabolic activity is greater than or equal to a preset threshold, an engineered bacteria application strategy and environmental adaptation regulation instruction is generated. If the expected metabolic activity is less than the preset threshold, the measured environmental parameters are adjusted in reverse to calculate the expected metabolic activity of the engineered bacteria under the adjusted environment until the expected metabolic activity is greater than or equal to the preset threshold, and an engineered bacteria application strategy and environmental adaptation control instruction are generated. The expected metabolic activity of the engineered bacteria is expressed using the following formula: in, The expected metabolic activity of the engineered bacteria; To maximize metabolic activity against the target crop; It is the Michaelis constant; for The influence coefficient is set according to crop preference; This is the temperature influence coefficient, set according to the suitable temperature range for crop growth.

3. The method according to claim 2, characterized in that, When the crop type is a crop modified through synthetic biology and possesses a synthetic pathway for the synthesis of artificially designed high-value-added products, the synthetic biology adaptation module activates the artificial synthetic pathway during the critical expression period of the target metabolic pathway through light environment regulation, temperature difference regulation, and water and fertilizer composition regulation to improve the accumulation efficiency of the target product. The critical expression period is determined according to the crop type and the synthesis kinetics of the target product. The light environment regulation includes setting the proportion of specific wavelengths of light in the supplemental light spectrum, the temperature difference regulation includes setting the diurnal temperature difference, and the water and fertilizer composition regulation includes adding precursor substances of the target metabolic pathway.

4. The method according to claim 2, characterized in that, When the crop is a crop jointly modified by gene editing and synthetic biology, the synthetic biology adaptation module adopts a dual-path synergistic mode of gene expression enhancement and engineered bacteria functional complementarity. The gene expression enhancement is achieved by regulating soil temperature, mineral element ratio or water stress, and the engineered bacteria functional complementarity is achieved by applying engineered bacteria with the same or synergistic stress resistance function.

5. The method according to any one of claims 1-4, characterized in that, The microbiome engineering regulation module is used to optimize the rhizosphere microbial community structure or inhibit soil-borne diseases, including at least one of rhizosphere microbial community optimization regulation, microbial community antagonism regulation and microbial-water-fertilizer synergistic regulation. The optimization and regulation of rhizosphere microbiota includes: when the abundance of beneficial rhizosphere bacteria is detected to be lower than a preset threshold, generating a compound probiotic agent application plan and a corresponding environmental adaptation strategy. The microbial community antagonistic regulation includes: when the abundance of soil-borne disease pathogens exceeds the warning threshold, optimizing the application parameters of antagonistic bacteria and ecological inhibition conditions based on the microbial community antagonistic efficiency formula to generate a synergistic regulation scheme. The microbial-water-fertilizer synergistic regulation includes: when the abundance of rhizosphere nitrogen-fixing functional bacteria reaches a preset threshold, dynamically reducing the amount of chemical nitrogen fertilizer applied based on the nitrogen fertilizer reduction formula, and simultaneously adding a microbial growth promoter. The antagonistic efficiency of the bacterial community is expressed by the following formula: in, For the efficiency of bacterial antagonism, The initial abundance of pathogens, To regulate the abundance of pathogens, For the fit coefficient, The humidity adaptation coefficient is set according to the actual preferences of the target crop. The reduction in nitrogen fertilizer use is expressed using the following formula: in, The amount of nitrogen fertilizer that can be reduced; This represents the standard nitrogen fertilizer application rate for this crop under conventional planting conditions. This is a crop-specific nitrogen fixation synergistic coefficient; This represents the current measured abundance of rhizosphere nitrogen-fixing bacteria. The threshold abundance at which nitrogen-fixing bacteria can exert their effective function.

6. The method according to any one of claims 1-4, characterized in that, The optogenetic regulation module is used to activate the expression of light-sensitive genes or to execute the timing control of light signals, including: When environmental stress events are detected or crops are in a specific growth stage, light regulation instructions are generated based on a preset light response model. The light response model is used to predict the expression level of target genes driven by photosensitive promoters and configures wavelength-specific response coefficients according to crop type. The light regulation instructions include light wavelength, light intensity and irradiation duration. The light regulation instructions are sent to programmable light source devices to activate the expression of target genes controlled by photosensitive promoters, or to achieve precise regulation of crop plant type, stress resistance or secondary metabolites through the temporal combination of multi-band light signals. The optical response model is expressed using the following formula: in, This represents the expected expression level of the target gene under specific light conditions. This represents the basal expression level of the target gene under conditions without induced light. The wavelength-specific response coefficient reflects the sensitivity of a specific photosensitizing system in a crop to a particular wavelength of light. Light intensity, This represents the cumulative exposure time per day.

7. The method according to any one of claims 1-4, characterized in that, The biostimulant adaptation module, used to match stimulant types and coordinate with environmental or microbial application, includes: The biostimulants in the biological function database are called up, and the biostimulants are selected from one or more of seaweed extracts, humic acid, amino acids or plant-derived oligosaccharides. Based on the identified crop species, current growth stage, and environmental stress level, the expected physiological response value of biostimulants is calculated using a stress resistance effect prediction model. The resilience effect prediction model is expressed using the following formula: in, The stress resistance index; The response coefficient is the type of biostimulant. The concentration at which biostimulants are administered; This is the reproductive period adjustment coefficient; This represents the environmental stress intensity coefficient.

8. A synergistic regulation device for the growth of crops in facility agriculture, characterized in that, include: The acquisition unit is used to acquire crop types, real-time environmental data, and rhizosphere microbiome status of the target planting area. The matching unit is used to determine crop modification attributes based on the crop type, and in combination with the real-time environmental data and rhizosphere microbiome status, match the target planting area with a synergistic regulation strategy in a pre-constructed biological function database. The biological function database contains multiple synergistic regulation scheme templates, each of which corresponds to a combination of crop type and modification attribute, and is associated with the synergistic application of at least two types of regulation templates: synthetic biology adaptation regulation module, microbiome engineering regulation module, optogenetics regulation module, and biostimulant adaptation module. The generation unit is used to generate a strategy execution plan containing multi-factor synergistic regulation parameters based on the synergistic regulation strategy adapted to the target planting area. The multi-factor synergistic regulation parameters cover the dimensions of microorganisms, light environment, water and fertilizer and soil environment. The control unit is used to distribute the strategy execution plan to the corresponding execution device to control the target planting area.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for coordinated regulation of crop growth in facility agriculture as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for coordinated regulation of crop growth in facility agriculture as described in any one of claims 1 to 7.

Images