Soil ARGs resistance and control intelligent system based on root exudate monitoring and feedback and application

Abstract

The invention discloses a soil ARGs resistance and control intelligent system based on root exudate monitoring and feedback and application, and belongs to the field of environmental engineering and agricultural ecological restoration. The system comprises a sensing layer, a data transmission and processing layer and a decision and execution layer to form a closed-loop feedback control system of'monitoring-analysis-decision-execution-re-monitoring '. The sensing layer is used for collecting root exudates in situ through a micro-dialysis technology and dynamically monitoring key components and target resistance gene abundance of the exudates by utilizing an online sensor and a quasi-real-time molecular detection unit; the data processing layer analyzes the multi-source data by means of a machine learning model and generates a regulation and control strategy; the execution layer actively stimulates plants to secrete specific root exudates by precisely applying a composite inducer or finely adjusting environmental factors, so that diffusion of antibiotic resistance genes in soil is inhibited. According to the invention, the conversion from passive restoration to dynamic intelligent regulation and control is realized, and the method has the advantages of high accuracy, high efficiency, quick response and high system integration degree.

Description

Technical Field

[0001] This invention belongs to the field of environmental engineering and agricultural ecological restoration, specifically relating to a soil ARGs resistance control intelligent system and its application based on root exudate monitoring and feedback. Background Technology

[0002] Antibiotic resistance genes, as a novel environmental pollutant, are accumulating and spreading in soil, posing a serious threat to ecosystem stability, sustainable agricultural development, and public health security. They can enter the human body through the food chain and microbial migration, exacerbating bacterial resistance and reducing the effectiveness of traditional antibiotics, thus becoming a global environmental and health challenge.

[0003] Currently, technologies for controlling soil antibiotic resistance genes mainly fall into two categories: physicochemical remediation and bioremediation. Physicochemical methods, such as leaching and redox treatment, are relatively quick to take effect, but are often costly and can damage soil structure and biological activity. Bioremediation methods, such as microbial remediation and phytoremediation, have better environmental compatibility, but generally suffer from long remediation cycles, unstable effects, and are greatly affected by environmental conditions, making large-scale application difficult.

[0004] In recent years, green remediation strategies based on regulating the rhizosphere microenvironment through plant root exudates to inhibit the transfer of resistance genes have attracted widespread attention. Specific metabolites in root exudates can regulate soil microbial communities and interfere with plasmid conjugation, thereby blocking the spread of resistance genes. However, this strategy still faces significant bottlenecks in practical applications: existing technologies mostly rely on natural plant exudates and lack proactive responses to pollution dynamics; at the same time, due to the lack of in-situ, real-time monitoring methods, a closed-loop regulation of "monitoring-assessment-intervention" cannot be achieved, resulting in lagging remediation measures, low precision, and insufficient systemicity.

[0005] Therefore, there is an urgent need to develop an integrated system that can sense rhizosphere processes in real time, make intelligent decisions, and actively intervene to overcome the passivity, lag, and fragmentation of existing technologies and achieve efficient, precise, and sustainable control of the spread of antagonistic genes. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent system and method for controlling soil antibiotic resistance genes based on dynamic monitoring and feedback of root exudates. This system achieves dynamic intervention in the rhizosphere microenvironment by constructing a closed loop of "in-situ sensing-intelligent decision-making-precise execution," thereby proactively, efficiently, and sustainably inhibiting the migration and diffusion of antibiotic resistance genes in the soil.

[0007] On the one hand, the present invention provides a technical solution using the following method: A smart system for controlling soil antibiotic resistance genes based on root exudate monitoring and feedback, comprising: Sensing layer, used for in-situ collection of biochemical and environmental parameters of rhizosphere soil; The data transmission and processing layer is used to receive and store data from the perception layer and run intelligent decision-making models. The decision-making and execution layer is used to control the execution mechanism to make precise interventions in the plant-soil system based on the instructions output by the intelligent decision-making model; The perception layer, data transmission and processing layer, and decision-making and execution layer together constitute a closed-loop feedback control system, realizing a dynamic control process of "monitoring-analysis-decision-execution-re-monitoring".

[0008] Preferably, the perception layer includes: The root exudate in-situ microdialysis collection module is used for continuous collection of rhizosphere soil solution; An online analysis unit for key exudate components, which is connected online to the microdialysis acquisition module, is used to detect the concentration of at least one root exudate marker in the soil solution in real time or near real time. A multi-parameter sensor array for soil environment is used to monitor the temperature, humidity, pH value and redox potential of rhizosphere soil; ARGs abundance dynamic monitoring unit is used to monitor the abundance of at least one target antibiotic resistance gene in near real-time mode.

[0009] Preferably, the ARGs abundance dynamic monitoring unit includes: The in-situ nucleic acid collection and pretreatment module is used to periodically capture free DNA or microorganisms in the soil and complete nucleic acid extraction and preliminary purification. Portable on-site nucleic acid quantification analyzer interface, used to receive pre-processed nucleic acid samples and perform quantitative PCR analysis.

[0010] Preferably, the data transmission and processing layer includes: Edge computing gateway is used to receive and preprocess data from the perception layer; The cloud platform or local server has a built-in intelligent decision-making model trained based on machine learning algorithms. The input features of the intelligent decision-making model include at least: time-series data of key secretion component concentrations, time-series data of soil environmental parameters, abundance data of target ARGs, and plant growth stage information; The model outputs the root secretion induction regulation strategy.

[0011] Preferably, the decision-making and execution layer includes: The intelligent decision engine is used to parse and execute control strategies and generate control commands; Precision agriculture implementers include at least one of root secretion inducer precision application systems and environmental factor micro-regulation systems.

[0012] Preferably, the root secretion inducer precision application system includes a storage tank, a proportioning pump, a drip / sprinkler irrigation network, and a solenoid valve, for applying a compound inducer to the root zone or leaf surface of plants.

[0013] Preferably, the composite inducer comprises: Plant signaling molecules: at least one of methyl jasmonate and salicylic acid at a concentration of 10–100 μM; Metabolic precursors: phenylalanine or organic acid salts at a concentration of 1–5 mM; Plant nutrient fortifiers: chelated trace elements; Additives: Environmentally friendly surfactants.

[0014] This invention also provides a method for controlling soil antibiotic resistance genes, comprising the following steps: S1: Continuously collect data on the concentration of key exudate components, environmental parameters, and abundance of target ARGs in rhizosphere soil through the sensing layer; S2: Input the data into the intelligent decision-making model, and the model will generate root secretion induction and regulation strategies; S3: The decision-making and execution layer controls the execution mechanism to make precise interventions in plants according to the strategy, so as to enhance the secretion of target root exudates; S4: After intervention, return to step S1 to form a closed-loop feedback regulation until the abundance of target ARGs drops below the preset threshold.

[0015] In summary, the beneficial effects of the present invention are as follows: The beneficial effects of this invention are mainly reflected in its fundamental change to the passive repair mode of existing technologies. By constructing a complete closed loop of "sensing-analysis-decision-execution," the system can intervene in plant physiology in real time and with precision based on the dynamic changes in the rhizosphere microenvironment and the abundance of resistance genes, achieving a leap from "static treatment" to "dynamic response," and significantly improving control efficiency and resource utilization. Simultaneously, the system integrates microdialysis in-situ sampling, online biochemical sensing, and near-real-time molecular detection technologies, effectively overcoming the core bottlenecks of long analysis cycles and delayed feedback in traditional laboratories. For the first time, it achieves quasi-continuous monitoring and evaluation of the key chain of "induction-secretion-gene suppression," providing a reliable data foundation for precise regulation.

[0016] Furthermore, this invention demonstrates a high degree of system integration and intelligent foresight. It is not merely an improvement on a single technology, but rather a deep integration of environmental sensing, plant physiology, precision agriculture equipment, and artificial intelligence algorithms, forming an intelligent ecosystem with self-learning and adaptive capabilities. The system's modular architecture also endows it with excellent scalability; current key monitoring modules can be directly upgraded as new technologies such as real-time biosensors mature in the future, ensuring the system's long-term technological advancement and application viability. Attached Figure Description

[0017] Figure 1 This is a diagram illustrating the overall architecture and workflow of the intelligent system in Example 1. Figure 2 This is a schematic diagram of the in-situ microdialysis collection and online analysis module for root exudates in the sensing layer.

[0018] Figure 3 This is a schematic diagram illustrating the working logic and input-output relationship of an intelligent decision-making model. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the embodiments.

[0020] Example Example 1

[0021] A smart system for controlling soil antibiotic resistance genes based on root exudate monitoring and feedback includes the following subsystems: 1. Sensing layer: Used for in-situ, continuous acquisition of soil environmental and biochemical signals.

[0022] 2. Data transmission and processing layer: Used to receive and store data from the perception layer and run intelligent decision-making models.

[0023] 3. Decision-making and execution level: Based on the model instructions, precisely control the execution mechanism to intervene.

[0024] The perception layer includes: In-situ microdialysis collection module for root exudates: buried in the rhizosphere soil of plants, it collects soil solution at a low flow rate through a semi-permeable membrane and connects it online to the analysis unit.

[0025] Online analysis unit for key exudate components: connected to the flow path of the microdialysis acquisition module, integrating a miniature optical sensor or electrochemical sensor, used for real-time or near-real-time detection of the concentration of at least one key marker of root exudates (such as oxalic acid, malic acid, total phenolic acid, etc.) in the soil solution.

[0026] Soil environment multi-parameter sensor array: deployed in the rhizosphere region to monitor soil temperature, humidity, pH value, and redox potential in real time.

[0027] ARGs Abundance Dynamic Monitoring Unit: As a near real-time monitoring module, it includes the following two modules: a) In-situ nucleic acid collection and pretreatment module: periodically adsorbs or captures free DNA / microorganisms in soil solution, and automatically completes cell lysis and preliminary nucleic acid purification, pretreatment of samples into a form that can be analyzed on-site.

[0028] b) Portable on-site qPCR analyzer interface: Receives pre-processed nucleic acid samples and automatically performs quantitative PCR analysis on 1-3 pre-defined, high-risk, highly mobile key ARGs (such as sul 1, intI 1, tetM), outputting abundance data within a few hours.

[0029] The data transmission and processing layer includes: Edge computing gateway: Receives and temporarily stores all perception layer data, and performs preliminary filtering and formatting.

[0030] Cloud platform or local server: Receives data from the gateway and has a built-in intelligent decision-making model. This model is built based on machine learning algorithms (such as random forest and long short-term memory networks), and its input features include at least: time-series data of key exudate marker concentrations, time-series data of soil environmental parameters, abundance data of target ARGs, and plant growth stage information; the model output is a root exudate induction regulation strategy.

[0031] The decision-making and execution level includes: Intelligent Decision Engine: Analyzes and executes the control strategies output by the intelligent decision model, generating specific control commands.

[0032] Precision agriculture implementers: receive and execute control commands, including: a) Root secretion inducer precision application system: consisting of a storage tank, proportioning pump, drip / sprinkler irrigation network and solenoid valve, used to apply a specific formula of inducer to the root zone soil or plant leaves.

[0033] b) Environmental factor micro-regulation system: including adjustable light source, soil heating / cooling device, and precision irrigation valve, used to apply mild abiotic stress to plants.

[0034] The root exudate inducer employs a compound formulation, the main components of which include: plant signaling molecules, at least one of methyl jasmonate or salicylic acid at a concentration of 10–100 μM; metabolic precursors, specific amino acids (such as phenylalanine) or organic acid salts at a concentration of 1–5 mM; plant nutrient fortifiers, mainly chelated trace elements (such as iron and zinc); and adjuvants, environmentally friendly surfactants, used to enhance foliar absorption efficiency. This formulation, through the synergistic effect of multiple components, aims to safely and efficiently induce the synthesis and release of target root exudates. Figure 1The diagram shown illustrates the overall architecture and workflow of the intelligent system.

[0035] Example 2

[0036] The specific steps for applying this system to control ARGs in vegetable garden soil contaminated with livestock and poultry manure are as follows: S1, System Deployment Within the spinach cultivation experimental area, sensing layer equipment was systematically deployed using a grid-like layout. A microdialysis probe and a multi-parameter soil sensor were embedded in the rhizosphere of each spinach plant. An integrated field monitoring station was established at the center of the experimental area, equipped with an online phenolic acid analysis unit, a nucleic acid pretreatment unit, and a commercial portable qPCR instrument. The accompanying drip irrigation network and foliar spray system were connected to two independent storage tanks, one containing water and the other a compound inducer working solution (containing 50 μM methyl jasmonate, 2 mM phenylalanine, and appropriate trace elements). All monitoring and control equipment achieved real-time data interaction and command transmission via a wireless network and a cloud-based intelligent decision-making platform.

[0037] S2. System Initialization and Model Execution: After the system starts up, the sensing layer immediately enters continuous operation. The micro-dialysis module collects soil solution samples every 2 hours and analyzes the total phenolic acid concentration online; the soil multi-parameter sensor array collects and uploads environmental data every 10 minutes; and the monitoring unit targeting the resistance gene automatically starts every 72 hours to quantitatively analyze the intI 1 gene in the soil. Figure 2 The diagram shown illustrates the overall architecture and workflow of the intelligent system.

[0038] The system spent the initial two weeks in a data accumulation phase, comprehensively recording baseline data for all indicators. Afterward, the pre-trained intelligent decision-making model began formal operation. The intervention trigger condition set by the model was: when the abundance of the intI 1 gene showed an upward trend for two consecutive monitoring cycles, and the total phenolic acid concentration in the rhizosphere did not increase synchronously during the same period, the system would automatically determine that the first-level intervention condition was met.

[0039] S3, a complete regulatory cycle: On day N, the intelligent decision-making model determined that the conditions for a first-level intervention were met, and the decision engine immediately generated and issued control instructions. The following morning, the precision irrigation system sprayed a low dose of a compound inducer onto the spinach leaves in the target area according to the instructions. After application, the system continuously monitored the dynamic changes in total phenolic acid concentration in the rhizosphere at 24, 48, and 72 hours. Data showed that the phenolic acid concentration increased significantly after 48 hours, approximately 150% higher than the baseline level before intervention. By day 6 after intervention (i.e., the next round of gene testing), quantitative analysis of the intI 1 gene showed that its abundance growth trend was effectively suppressed, decreasing by approximately 40% compared to the model's prediction of no intervention. The complete data of this "intervention implementation - secretion response - resistance gene suppression" process was fully recorded by the system and used to iteratively optimize the predictive accuracy and control strategies of the intelligent decision-making model. Figure 3 The diagram shown illustrates the working logic and input-output relationship of the intelligent decision-making model.

[0040] Over a full growing season (90 days), the final abundance of the mobile genetic element intI 1 in the soil of the test area using this system was reduced by 1.8 orders of magnitude compared to the control area using only conventional irrigation, with no significant difference in spinach biomass. This indicates that the system of the present invention effectively controls ARGs while ensuring normal crop growth.

[0041] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A smart system for controlling soil antibiotic resistance genes based on root exudate monitoring and feedback, characterized in that, include: Sensing layer, used for in-situ collection of biochemical and environmental parameters of rhizosphere soil; The data transmission and processing layer is used to receive and store data from the perception layer and run intelligent decision-making models. The decision-making and execution layer is used to control the execution mechanism to make precise interventions in the plant-soil system according to the instructions output by the intelligent decision-making model.

2. The intelligent system for controlling soil antibiotic resistance genes based on root exudate monitoring and feedback as described in claim 1, characterized in that, The sensing layer includes: The root exudate in-situ microdialysis collection module is used for continuous collection of rhizosphere soil solution; An online analysis unit for key exudate components, which is connected online to the microdialysis acquisition module, is used to detect the concentration of at least one root exudate marker in the soil solution in real time or near real time. A multi-parameter sensor array for soil environment is used to monitor the temperature, humidity, pH value and redox potential of rhizosphere soil; ARGs abundance dynamic monitoring unit is used to monitor the abundance of at least one target antibiotic resistance gene in near real-time mode.

3. The intelligent system for controlling soil antibiotic resistance genes based on root exudate monitoring and feedback as described in claim 2, characterized in that, The ARGs abundance dynamic monitoring unit includes: The in-situ nucleic acid collection and pretreatment module is used to periodically capture free DNA or microorganisms in the soil and complete nucleic acid extraction and preliminary purification. Portable on-site nucleic acid quantification analyzer interface, used to receive pre-processed nucleic acid samples and perform quantitative PCR analysis.

4. The intelligent system for controlling soil antibiotic resistance genes based on root exudate monitoring and feedback as described in claim 1, characterized in that, The data transmission and processing layer includes: Edge computing gateway is used to receive and preprocess data from the perception layer; The cloud platform or local server has a built-in intelligent decision-making model trained based on machine learning algorithms. The input features of the intelligent decision-making model include at least: time-series data of key secretion component concentrations, time-series data of soil environmental parameters, abundance data of target ARGs, and plant growth stage information; The output of the model is a root secretion induction regulation strategy.

5. The system according to claim 1, characterized in that, The decision-making and execution layer includes: The intelligent decision engine is used to parse and execute control strategies and generate control commands; Precision agriculture implementers include at least one of root secretion inducer precision application systems and environmental factor micro-regulation systems.

6. The system according to claim 5, characterized in that, The root secretion inducer precision application system includes a storage tank, a proportioning pump, a drip / sprinkler irrigation network, and a solenoid valve, used to apply a compound inducer to the root zone or leaf surface of plants.

7. The system according to claim 6, characterized in that, The composite inducer comprises: Plant signaling molecules: at least one of methyl jasmonate and salicylic acid at a concentration of 10–100 μM; Metabolic precursors: phenylalanine or organic acid salts at a concentration of 1–5 mM; Plant nutrient fortifiers: chelated trace elements; Additives: Environmentally friendly surfactants.

8. A method for controlling soil antibiotic resistance genes using the system described in any one of claims 1-7, characterized in that, Includes the following steps: S1: Continuously collect data on the concentration of key exudate components, environmental parameters, and abundance of target ARGs in rhizosphere soil through the sensing layer; S2: Input the data into the intelligent decision-making model, and the model generates a root secretion induction and regulation strategy; S3: The decision-making and execution layer controls the execution mechanism to precisely intervene in the plant according to the strategy to enhance the secretion of target root exudates; S4: After intervention, return to step S1 to form a closed-loop feedback regulation until the abundance of target ARGs drops below the preset threshold.

Images