Microbial culture scheme customization recommendation method and system based on soil data analysis

Abstract

The invention discloses a microbiological culture scheme customization recommendation method and system based on soil data analysis, and relates to the technical field of microbiological culture plants.The method comprises the steps that soil samples at different positions and depths are collected, soil characteristic data are analyzed, and data processing is carried out; microbial community information is obtained through soil DNA extraction, PCR amplification and high-throughput sequencing, and distribution and functions of microorganisms are evaluated in combination with data preprocessing and diversity analysis; selecting proper microbial strains according to soil characteristics and microbial community analysis results; a microbial culture scheme is combined with agricultural management measures; the conditions of soil and microorganisms are monitored in real time, and a microorganism culture scheme is dynamically adjusted based on a feedback mechanism; through field trials and advanced performance evaluation indexes, the effect of the microbial culture scheme is verified for a long time. The method has high adaptability and sustainability, and customized microorganism management schemes can be provided in different agricultural environments.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial culture programs, and particularly to a method and system for customizing and recommending microbial culture programs based on soil data analysis. Background Art

[0002] With the continuous development of agricultural production methods, soil health problems have become the key factors restricting the sustainable development of agriculture. Soil properties such as organic matter content, nutrient levels, and microbial community diversity in the soil have important impacts on crop growth and agricultural yields. However, traditional soil management methods mainly rely on the application of chemical fertilizers and pesticides. Although they can improve soil fertility and crop yields in the short term, long-term use will lead to soil degradation, environmental pollution, and imbalances in the microbial community. Therefore, finding more sustainable soil management methods has become a hot topic in current agricultural research.

[0003] Microbial culture technology, as a green agricultural technology, has been widely used to improve soil health, promote plant growth, and increase crop yields. Microorganisms can improve soil structure and function, and play a role in enhancing and repairing the soil by means of nitrogen fixation, decomposing organic matter, inhibiting pathogens, etc. However, traditional microbial applications are usually based on experience or fixed programs, and fail to fully combine the characteristics and changes of the soil for precise management. Therefore, how to customize microbial culture programs according to soil types, crop requirements, and environmental changes has become the key to improving the efficiency and effectiveness of microbial applications.

[0004] To address the above problems, the present invention provides a method and system for customizing and recommending microbial culture programs based on soil data analysis. By real-time monitoring of soil and environmental parameters, analyzing the dynamic changes of the microbial community, and combining a data-driven decision support system, it can provide precise microbial management programs to achieve the optimization and sustainable development of agricultural production. Summary of the Invention

[0005] The present invention aims at the above problems and provides a method and system for customizing and recommending microbial culture programs based on soil data analysis to solve the problems of inaccurate analysis of soil characteristics and dynamic changes of the microbial community, and low soil health and crop production efficiency in the prior art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A method for customizing and recommending microbial culture programs based on soil data analysis, comprising the following steps: Step S1, by collecting soil samples at different locations and depths, analyzing soil characteristic data, and performing preprocessing, format conversion, and normalization on the data; Among them, in step S1, the following sub-steps are further included: S1-1. Analyze soil property data by collecting soil samples at different depths and locations in the field. The soil property data includes: soil pH value, soil moisture, soil texture, organic matter content, nutrient level, and microbial diversity. S1-2. Perform data preprocessing on the soil data. The data preprocessing includes: handling missing data, outlier detection and removal, and consistency checking; and perform format conversion to maintain data consistency, as follows: Handling missing data: Use the mean imputation technique to fill in the missing values. For numerical data, estimate the missing values using the mean or median of the observed values. Outlier detection and removal: Use the interquartile range method (IQR) to identify and remove data points that are outside 1.5 times the IQR above the third quartile or below the first quartile. Data detection: Use cross-validation to cross-check the data against external sources. Format conversion: The organic matter content and microbial counts usually span multiple orders of magnitude. Use logarithmic transformation for data normalization to address the skewed distribution problem. S1-3. Create new features from existing soil attributes. The new features include a soil health index and a soil nutrient ratio. Soil health index: Combine multiple soil properties into an index score that reflects the overall soil health status. The soil properties T (pH, Y, YF) include the pH value, organic matter, and nutrient level. Soil nutrient ratio: Calculate the nutrient ratio to capture important aspects of soil balance. The nutrient ratio is the nitrogen-phosphorus ratio of the nutrient level. S1-4. Normalize the data by using min-max normalization, scaling each feature so that it lies within a fixed range, as shown in Equation (1): Equation (1) where, is the normalized data, is the original data, is the minimum value of the original data, is the maximum value of the original data.

[0007] Step S2. Obtain microbial community information through soil DNA extraction, PCR amplification, and high-throughput sequencing, and evaluate the distribution and function of microorganisms in the soil by combining data preprocessing and diversity analysis. Among them, in step S2, the following sub-steps are also included: S2-1. Use a soil DNA extraction kit to extract soil microbial DNA from soil samples. This process includes homogenizing soil samples at different depths and locations, releasing DNA through cell lysis, and purifying and quantifying the extracted DNA. S2-2. Directionally amplify key genetic markers using universal primers, selecting 16S rRNA of bacteria and ITS of fungi; the PCR amplification process includes denaturation, primer annealing, and extension of each marker under optimized conditions, and check the quality and size of the PCR products using gel electrophoresis; S2-3. Perform high-throughput sequencing on the PCR-amplified DNA using a next-generation sequencing platform, prepare a sequencing library by adding adapters to the PCR products for sequencing, and obtain a comprehensive microbial characterization profile of the soil samples to ensure sufficient coverage of common and rare microbial species in the samples; S2-4. Process the raw sequencing data, use QIIME2 to remove low-quality reads, adapters, and primers, classify through a reference database, and align the cleaned sequences with known species; evaluate microbial diversity using α-diversity and β-diversity indices to assess the diversity and differences of the microbial communities in the soil samples; use the weighted diversity index to further evaluate the functional diversity of microorganisms, as shown in Equation (2): Equation (2) where, is the weighted diversity index, n is the index, is the weight of the i-th microorganism, is the abundance of this microorganism in the sample, is the total abundance of all microorganisms; S2-5. Combine the microbial data with the soil property data and environmental factors for time integration, spatial integration, and environmental data integration; Time integration: Soil properties change over time. Integrate time series data analysis of seasonal changes, collect historical soil data or real-time measurements over a period of time, and use time series data to correlate soil changes with the trends of microbial activities over time; Spatial integration: Geospatial information helps analyze the spatial variability of soil properties. Use GIS data to map soil data in different fields, incorporate GPS coordinates into the soil property data, and analyze the spatial trends and variability in the fields; Environmental data integration: Environmental factors such as temperature, precipitation, and crop management practices affect soil properties and microbial activities. Integrate local environmental data from meteorological sources or sensors to enhance the soil property dataset.

[0008] Step S3. Select appropriate microbial strains based on the analysis results of soil properties and microbial communities, and optimize the microbial culture environment and inoculation strategy; Among them, in step S3, the following sub-steps are also included: S3-1. Select suitable microbial strains based on the comprehensive analysis of soil properties and the existing microbial community, specifically as follows: Derive the optimal microbial culture plan from soil data and microbial community data through multi-level analysis and optimization algorithms. The soil data includes nutrient levels, pH values, and microbial diversity. Use machine learning algorithms to train a large amount of soil data and microbial activity data to establish a relationship model between microbial growth and soil characteristics, predict the growth status of microorganisms and soil health indicators under different soil conditions, and output a series of possible microbial inoculation plans. Use multi-objective optimization algorithms to optimize the plan when considering multiple objectives. Each candidate plan will be evaluated through the following process: Microbial efficacy assessment: Based on the soil characteristics and the needs of the microbial community, evaluate the nutrient fixation ability and disease suppression ability of the microorganisms; Environmental adaptability assessment: Analyze the growth stability and effectiveness of microorganisms in different environments; Soil health impact assessment: Evaluate the effect of the microbial culture plan on soil improvement through the soil health index; Identify the defects or imbalances in the soil based on the analysis results, select strains that can make up for these gaps, and consider the compatibility of the selected strains with the existing microbial community to avoid competition and ensure synergy; S3-2, Determine the optimal growth conditions according to the needs of the selected strains, including temperature, humidity, oxygen availability, and pH value; Through the standardized microbial culture condition method, combine real-time monitoring data for dynamic adjustment, and use sensors to monitor soil humidity, temperature, and nutrient levels in real time to achieve environmental adaptability adjustment and ensure that the microbial growth conditions are always in the optimal state; S3-3, Determine the appropriate inoculation strategy, including inoculation density, inoculation time, and application frequency, to ensure that the microorganisms are applied at a fixed rate and interval; Monitor the microbial population density and health status in real time to determine whether additional inoculation or adjustment of the inoculation dose is required to ensure that the microbial population maintains balance throughout the culture process; S3-4, Combine the microbial culture plan with other agricultural management measures. The management measures include fertilization, irrigation, and crop management; Adjust the fertilization plan according to the real-time performance of the microbial inoculant to ensure that the nutrient supply is synchronized with the microbial needs; Adjust the irrigation strategy according to the soil moisture and microbial activity levels to maintain the optimal moisture conditions required for microbial growth; The microbial plan complements crop rotation or cover crop agricultural practices, and adjusts the microbial culture effect according to seasonal changes and crop growth cycles; S3-5, Adjust the microbial culture plan based on the real-time changes in soil and microbial data. The feedback mechanism makes necessary adjustments to the microbial plan during the process. Use machine learning algorithms to analyze real-time data and predict future soil and microbial states, and actively adjust the culture strategy to improve the efficacy of the microbial inoculant. Use reinforcement learning algorithms, and the reward function is shown in Equation (3): Equation (3) Among them, is the reward at the t-th moment, is the weighting coefficient, reflecting the impact of various soil and microbial indicators on overall health, is the status of the i-th soil or microbial indicator at the t-th moment.

[0009] Step S4, combine the microbial cultivation plan with agricultural management measures such as fertilization, irrigation, and crop rotation to dynamically adjust soil health and crop productivity; Among them, in step S4, the following sub-steps are also included: S4-1, combine the microbial inoculation plan with the fertilization plan. By monitoring the microbial population and nutrient availability, automatically adjust the fertilizer input amount, optimize the microbial efficiency, improve the nutrient cycle, and reduce the demand for excessive chemical fertilizers. Optimize through the comprehensive goal of microbial efficiency and fertilizer usage, as shown in Equation (4): Equation (4) Among them, is the objective function, represents the efficiency of the i-th microorganism, is the weight of this microorganism under specific soil and crop management conditions, is the input amount of the i-th fertilizer; S4-2, adapt the irrigation plan to the microbial growth requirements. Adjust the irrigation amount according to real-time soil moisture data and microbial needs. Combine microbial growth data with soil moisture levels to customize the irrigation plan to ensure that the moisture conditions are suitable for microbial growth and avoid the negative impact of over-saturation or drought stress on microorganisms; S4-3, integrate the microbial inoculation plan with crop rotation and cover cropping practices. Dynamically adjust the microbial inoculation plan according to the type of rotation crop and the presence of cover crops, as follows: Nitrogen-fixing microorganisms are suitable for leguminous crops, while other microorganisms are suitable for non-leguminous crops. This integration method improves soil health by providing appropriate microbial support at the appropriate time.

[0010] Step S5, dynamically adjust the microbial cultivation plan based on the feedback mechanism by real-time monitoring of soil and microbial conditions, and adjust the growth conditions and cultivation effects of microorganisms; Among them, in step S5, the following sub-steps are also included: S5-1, continuously monitor the soil and microbial conditions through a sensor network and a real-time data collection system. Real-time monitor key soil properties and microbial activities through an integrated Internet of Things-based sensor, and immediately detect changes in soil or microbial conditions to ensure timely response to dynamic environmental factors; S5-2 adopts an adaptive feedback system to adjust the microbial culture plan according to real-time data, continuously analyze the real-time data of soil and microorganisms, and automatically adjust the inoculation rate, nutrient delivery, and environmental conditions; When the soil humidity decreases or the microbial population reaches a set threshold, automatically adjust the irrigation or inoculation rate to maintain optimal growth conditions; S5-3 integrates data from soil sensors, microbial activity monitoring, and environmental factors to establish a prediction model. Use machine learning algorithms to analyze the comprehensive data and predict future conditions. Actively adjust the plan according to upcoming weather changes or seasonal changes. Use long short-term memory networks for time series prediction, as shown in Equation (5): Equation (5) where, represents the predicted value of soil or microorganisms at the t-th moment, is the input data at historical moments, is the model parameter, is the LSTM model; S5-4 uses long-term data collection to improve the microbial management strategy. Track the soil and microbial health status over multiple growing seasons, adjust the plan according to long-term trends, continuously learn from historical data, and regularly retrain the machine learning model to reflect changes in soil and microbial conditions, ensuring long-term effectiveness.

[0011] Step S6 conducts long-term verification of the effectiveness of the microbial culture plan through field trials and advanced performance evaluation indicators, and continuously adjusts the plan according to the feedback data.

[0012] Among them, in step S6, the following sub-steps are also included: S6-1 conducts field trials to verify the effectiveness of the customized microbial culture plan under actual agricultural conditions. Implement multi-plot field trials under different soil types and environmental conditions to evaluate the growth effect of microorganisms and the long-term impact on soil health, microbial diversity, and crop yield in different growing seasons. Evaluate the adaptability of the microbial plan in diverse environments through these data to provide comprehensive verification; S6-2 uses advanced performance indicators to evaluate the impact of the microbial plan. Incorporate functional microbial activity and soil resilience into key performance indicators, as shown in Equation (6): Equation (6) where, is the total soil health index, is the i-th soil or microbial characteristic, is the weight coefficient, reflecting the contribution of each characteristic to soil health; Functional microbial activity includes nutrient cycling efficiency and disease suppression, and soil resilience includes water retention capacity and erosion resistance; S6-3, continuously adjust the microbial cultivation program based on field trials and real-time monitoring data, adopt a continuous learning mechanism, provide real-time feedback from field trials and monitoring data, and fine-tune the microbial protocol for future growing seasons accordingly; the machine learning model retrains the system based on past performance to ensure that the protocol continues to adapt to changing soil health, microbial behavior and climate conditions.

[0013] A microbial cultivation program customization recommendation system based on soil data analysis, including: Data collection module, data processing and analysis module, microbial culture program recommendation module, monitoring and feedback module; The data collection module collects soil property data, microbial data and environmental data; the data sources mainly include two parts: sensor data and microbial diversity analysis; The sensor data includes environmental data sensor data and soil sensor data, the soil sensor data includes pH, moisture, temperature, texture and nutrient soil properties, and the environmental data sensor data includes environmental parameters acquired by weather stations and crop growth monitoring instruments; The microbial diversity analysis analyzes the microbial community in the soil by soil DNA extraction, PCR amplification and high-throughput sequencing technology; The data processing and analysis module cleans, preprocesses, analyzes and extracts features from the collected soil data, microbial data and environmental data; it uses machine learning algorithms to automatically analyze soil and microbial data, and mines potential rules to provide a basis for recommendations for microbial cultivation programs; The microbial cultivation program recommendation module provides customized microbial cultivation programs based on soil analysis results, combined with historical data and real-time data; automatically recommends suitable microbial strains by analyzing soil defects or imbalances, taking into account soil microbial diversity, avoiding the selection of strains that conflict with existing flora, and ensuring synergy between strains; The monitoring and feedback module monitors the soil and microbial status in real time, and optimizes the microbial cultivation program through an adaptive feedback mechanism; collects soil and microbial data through sensors, and uploads the data to the cloud platform for real-time processing; and automatically adjusts the microbial inoculation strategy, fertilizer use and irrigation strategy based on real-time data to ensure that the microorganisms are in the best growth environment.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention combines real-time soil data collection with microbial community analysis, customizes personalized microbial culture programs according to soil characteristics and microbial diversity, analyzes information such as the pH value, humidity, and nutrient level of the soil, automatically selects suitable microbial strains and adjusts the culture conditions, can accurately match the specific needs of the soil, and significantly improves the effect of microbial application.

[0015] The present invention continuously collects real-time data on soil humidity, temperature, and nutrients through Internet of Things sensors and a real-time monitoring system, combines the changes in the microbial population, and constructs an adaptive feedback mechanism. This mechanism can dynamically adjust the microbial inoculation density, inoculation time, fertilization, and irrigation strategies according to the real-time data, so as to ensure that the growth environment of the microorganisms always remains in the best state.

[0016] The present invention integrates multiple data sources and uses advanced data analysis and machine learning technologies to establish a comprehensive evaluation model, which can deeply analyze multi-dimensional data, generate personalized microbial culture programs. Through machine learning algorithms and prediction models, not only can it predict the soil change trend based on historical data, but also can actively adjust the microbial management strategy to cope with future environmental changes, thereby realizing the intelligentization and precision of agricultural management.

[0017] The present invention reduces the dependence on chemical fertilizers and pesticides through precise microbial inoculation strategies and dynamic adjustment measures. The microbial inoculation program is optimized according to the specific needs of the soil, avoiding the problems of excessive use of fertilizers and pesticides in traditional agriculture, thereby reducing the risk of environmental pollution and soil degradation; at the same time, by optimizing irrigation, fertilization, and the amount of microbial inoculation, the utilization efficiency of resources is improved, not only reducing the input costs of farmers, but also enhancing the sustainability of agricultural production.

[0018] The present invention can promote the growth and yield of crops while optimizing soil health by introducing microorganisms with specific functions. The microorganisms not only improve the soil structure, but also enhance the nutrient cycling efficiency of the soil, and strengthen the drought resistance and disease resistance of the soil. Through long-term microbial management and soil health monitoring, it helps the sustainable use of the soil, thereby improving the long-term yield and quality of crops.

[0019] The present invention can be customized according to the soil and crop needs in different regions, can adapt to different soil types, climate conditions, and crop planting patterns, supports applications in large-scale agricultural production, and can continuously optimize the microbial management program and maintain high production efficiency through continuous data accumulation and the update of machine learning models. Description of the Drawings

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following accompanying drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related accompanying drawings can also be obtained based on these drawings.

[0021] Figure 1 is the flowchart of the method of the present invention; Figure 2 is the system architecture diagram of the present invention. Specific Embodiments

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but is merely for the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0023] Please refer to Figure 1 - Figure 2 which is a schematic diagram of a method and system for customizing and recommending a microbial culture plan based on soil data analysis provided by an embodiment of the present invention, and includes the following steps: Step S1, collect soil samples at different locations and depths, analyze soil characteristic data, and perform data preprocessing, format conversion, and normalization on the data; Among them, in step S1, the following sub-steps are further included: S1-1, analyze soil characteristic data by collecting soil samples at different depths and locations in the field. The soil characteristic data includes: soil pH value, soil moisture, soil texture, organic matter content, nutrient level, and microbial diversity; S1-2, perform data preprocessing on the soil data. The data preprocessing includes: handling missing data, outlier detection and deletion, and consistency check; and perform format conversion to maintain data consistency, specifically as follows: Handling missing data: Use the mean imputation technique to fill in the missing values, and estimate the missing values using the average or median of the observed values for numerical data; Outlier detection and removal: Using the interquartile range method (IQR), identify and remove data points that are outside 1.5 times the IQR above the third quartile or below the first quartile; Data detection: Use cross-validation to cross-check the data against external sources; Format conversion: Organic matter content and microbial counts typically span multiple orders of magnitude. Use logarithmic transformation for data normalization to address skewed distributions; S1-3, Create new features from existing soil properties, where the new features include a soil health index and a soil nutrient ratio; Soil health index: Combine multiple soil properties into an index score that reflects the overall soil health status, where the soil properties T (pH, Y, YF) include pH value, organic matter, and nutrient levels; Soil nutrient ratio: Calculate the nutrient ratio to capture important aspects of soil balance, where the nutrient ratio is the nitrogen-phosphorus ratio of nutrient levels; S1-4, Normalize the data by using min-max normalization to scale each feature so that it lies within a fixed range, as shown in Equation (1): Equation (1) where, is the normalized data, is the original data, is the minimum value of the original data, is the maximum value of the original data.

[0024] It should be noted that soil pH value is used to determine the acidity or alkalinity of the soil, which affects microbial activity and nutrient availability, and microorganisms have different pH preferences; soil moisture content affects the growth of microorganisms, and both overwatering and underwatering can stress the microbial community; the relative amounts of sand, silt, and clay in the soil determine the water retention, drainage, and aeration of the soil; high organic matter content provides nutrients for the microbial population and is an energy source for microbial growth; nitrogen, phosphorus, and potassium are essential for the growth of plants and microorganisms and help identify nutrient deficiencies; microbial diversity is a basic analysis of the existing microbial community structure that can reveal the types of microorganisms present and their relative abundances.

[0025] Crop management practices belong to environmental factors because they directly affect the soil environment and microbial activities in the soil, as follows: Tillage, crop rotation, or the use of cover crops can change soil structure, nutrient availability, and organic matter content, and these changes in turn affect the microbial community in the soil; fertilization methods affect the nutrient balance in the soil, and different microorganisms require different nutrient conditions to survive and grow, so they also affect the prosperity of different microbial species.

[0026] Step S2: Obtain microbial community information through soil DNA extraction, PCR amplification, and high-throughput sequencing. Combine data preprocessing and diversity analysis to evaluate the distribution and function of microorganisms in the soil. In step S2, the following sub-steps are also included: S2-1: Use a soil DNA extraction kit to extract soil microbial DNA from soil samples. This process includes homogenizing soil samples at different depths and locations, releasing DNA through cell lysis, and purifying and quantifying the extracted DNA. S2-2: Use universal primers to perform directional amplification of key genetic markers. Select 16S rRNA of bacteria and ITS of fungi. The PCR amplification process includes denaturation, primer annealing, and extension of each marker under optimized conditions. Check the quality and size of the PCR products using gel electrophoresis. S2-3: Use a next-generation sequencing platform to perform high-throughput sequencing on the PCR-amplified DNA. Prepare a sequencing library by adding adapters to the PCR products for sequencing to obtain a comprehensive microbial profile of the soil samples, ensuring sufficient coverage of common and rare microbial species in the samples. S2-4: Process the raw sequencing data. Use QIIME2 to remove low-quality reads, adapters, and primers, classify through a reference database, and align the cleaned sequences with known species. Microbial diversity assessment uses α-diversity and β-diversity indices to evaluate the diversity and differences of the microbial communities in soil samples. Use a weighted diversity index to further evaluate the functional diversity of microorganisms, as shown in Equation (2): Equation (2) Where, is the weighted diversity index, n is the index, is the weight of the i-th microorganism, is the abundance of this microorganism in the sample, is the total abundance of all microorganisms; S2-5: Combine microbial data with soil property data and environmental factors for time integration, spatial integration, and environmental data integration. Time integration: Soil properties change over time. Integrate time-series data analysis of seasonal changes, collect historical soil data or real-time measurements over a period of time, and use time-series data to correlate soil changes with the trends of microbial activities over time. Spatial integration: Geospatial information helps analyze the spatial variability of soil properties. Use GIS data to map soil data in different fields, incorporate GPS coordinates into soil property data, and analyze spatial trends and variability in the fields. Environmental data integration: Temperature, precipitation, and crop management practices. Environmental factors affect soil properties and microbial activity. Integrating local environmental data from meteorological sources or sensors enhances the soil property dataset.

[0027] It should be noted that by integrating soil property, microbial diversity data, and environmental data, machine learning algorithms are used for data analysis to automatically generate microbial cultivation plans suitable for different soil and climate conditions. The system can identify the most suitable microbial strains based on historical and real-time data and provide users with personalized fertilization, irrigation, and inoculation suggestions.

[0028] Step S3: Select appropriate microbial strains according to the soil properties and the results of microbial community analysis, and optimize the microbial cultivation environment and inoculation strategy. Among them, in step S3, the following sub-steps are also included: S3-1: Select suitable microbial strains based on the comprehensive analysis of soil properties and the existing microbial community, specifically as follows: Derive the optimal microbial cultivation plan from soil data and microbial community data through multi-level analysis and optimization algorithms. The soil data includes nutrient levels, pH values, and microbial diversity. Use machine learning algorithms to train a large amount of soil data and microbial activity data, establish a relationship model between microbial growth and soil properties, predict the growth status of microorganisms and soil health indicators under different soil conditions, and output a series of possible microbial inoculation plans; use a multi-objective optimization algorithm to optimize the plan when considering multiple objectives. Each candidate plan will be evaluated through the following process: Microbial efficacy assessment: Evaluate the nutrient fixation ability and disease suppression ability of microorganisms based on soil properties and the needs of the microbial community. Environmental adaptability assessment: Analyze the growth stability and effectiveness of microorganisms in different environments. Soil health impact assessment: Evaluate the effect of the microbial cultivation plan on soil improvement through the soil health index. Identify the defects or imbalances in the soil based on the analysis results, select strains that can make up for these gaps, consider the compatibility of the selected strains with the existing microbial community, avoid competition, and ensure synergy. S3-2: Determine the optimal growth conditions according to the requirements of the selected strains, including temperature, humidity, oxygen availability, and pH value; through a standardized method of microbial cultivation conditions, dynamically adjust in combination with real-time monitoring data. Real-time monitor soil humidity, temperature, and nutrient levels through sensors to achieve environmental adaptability adjustment and ensure that the microbial growth conditions are always in the optimal state. S3-3. Determine an appropriate inoculation strategy, including inoculation density, inoculation time, and application frequency, to ensure that microorganisms are applied at a fixed rate and interval; monitor the microbial population density and health status in real time, and determine whether additional inoculation or adjustment of the inoculation dose is required to ensure that the microbial population remains balanced throughout the cultivation process; S3-4. Combine the microbial cultivation plan with other agricultural management measures, including fertilization, irrigation, and crop management; adjust the fertilization plan according to the real-time performance of the microbial inoculant to ensure that the nutrient supply is synchronized with the microbial demand; adjust the irrigation strategy according to the soil moisture and microbial activity levels to maintain the optimal moisture conditions required for microbial growth; the microbial plan complements crop rotation or cover crop agricultural practices, and adjusts the microbial cultivation effect according to seasonal changes and crop growth cycles; S3-5. Adjust the microbial cultivation plan based on the real-time changes in soil and microbial data. The feedback mechanism makes necessary adjustments to the microbial plan during the process. Apply machine learning algorithms to analyze real-time data and predict future soil and microbial states, and proactively adjust the cultivation strategy to improve the efficacy of the microbial inoculant. Use reinforcement learning algorithms, and the reward function is shown in Equation (3): Equation (3) where is the reward at the t-th moment, is the weighting coefficient, reflecting the impact of various soil and microbial indicators on overall health, is the state of the i-th soil or microbial indicator at the t-th moment.

[0029] It should be noted that during the process of selecting microbial strains, real-time soil data and changes in the microbial community are used to dynamically adjust strain recommendations. For example, it can automatically recommend appropriate microbial strains, such as nitrogen-fixing bacteria and phosphorus-solubilizing bacteria, according to certain specific deficiencies in the soil, such as nitrogen deficiency and phosphorus deficiency, so as to automatically optimize the microbial inoculation plan under different environmental conditions.

[0030] Step S4. Combine the microbial cultivation plan with agricultural management measures such as fertilization, irrigation, and crop rotation to dynamically adjust soil health and crop productivity; Among them, in step S4, the following sub-steps are also included: S4-1. Combine the microbial inoculation plan with the fertilization plan. By monitoring the microbial population and nutrient availability, automatically adjust the fertilizer input amount, optimize the microbial efficacy, improve nutrient cycling, and reduce the need for excessive chemical fertilizers. Optimize through the comprehensive goal of microbial efficacy and fertilizer usage, as shown in Equation (4): Equation (4) where is the objective function, represents the efficiency of the i-th microorganism, is the weight of the microorganism under specific soil and crop management conditions, is the input amount of the i-th fertilizer; S4-2. The irrigation plan is adapted to the growth requirements of microorganisms. The irrigation amount is adjusted according to real-time soil moisture data and microbial needs. Combining microbial growth data with soil moisture levels, a customized irrigation plan is developed to ensure that the moisture conditions are suitable for microbial growth and to avoid the negative impacts of over-saturation or drought stress on microorganisms; S4-3. The microorganism inoculation plan is integrated with crop rotation and cover cropping practices. The microorganism inoculation plan is dynamically adjusted according to the type of rotation crops and the presence of cover crops, as follows: Nitrogen-fixing microorganisms are suitable for leguminous crops, while other microorganisms are suitable for non-leguminous crops. This integration method improves soil health by providing appropriate microbial support at the appropriate time.

[0031] It should be noted that through the synergistic effect of microorganisms and fertilizers, the optimization of fertilizer usage is achieved. The fertilizer application rate is automatically adjusted according to the activity level of microorganisms. When the microbial community can effectively fix nitrogen, the system will reduce the use of nitrogen fertilizers to avoid over-fertilization; through this synergistic effect, not only is chemical fertilizer waste reduced, but also soil health and crop yields are improved.

[0032] Step S5. By continuously monitoring the soil and microbial conditions in real time, the microbial culture plan is dynamically adjusted based on a feedback mechanism to adjust the growth conditions and culture effects of microorganisms; Among them, in step S5, the following sub-steps are also included: S5-1. Through a sensor network and a real-time data collection system, continuously monitor the soil and microbial conditions. By integrating Internet of Things-based sensors, key soil properties and microbial activities are monitored in real time, and changes in soil or microbial conditions are detected immediately to ensure timely response to dynamic environmental factors; S5-2. An adaptive feedback system is adopted to adjust the microbial culture plan according to real-time data. Continuously analyze the real-time data of the soil and microorganisms, and automatically adjust the inoculation rate, nutrient delivery, and environmental conditions; When the soil humidity decreases or the microbial population reaches a set threshold, the irrigation or inoculation rate is automatically adjusted to maintain optimal growth conditions; S5-3. Integrate data from soil sensors, microbial activity monitoring, and environmental factors to establish a prediction model. Use machine learning algorithms to analyze the comprehensive data and predict future conditions. Actively adjust the plan according to upcoming weather changes or seasonal changes. Use long short-term memory networks for time series prediction, as shown in Equation (5): Equation (5) where, represents the predicted value of the soil or microorganism at the t-th moment, Input data for historical moments Model parameters An LSTM model S5-4. Use long-term data collection to improve the microbial management strategy. Track soil and microbial health conditions over multiple growing seasons, adjust the plan according to long-term trends, and regularly retrain the machine learning model by continuously learning from historical data to reflect changes in soil and microbial conditions and ensure long-term effectiveness.

[0033] It should be noted that the adaptive adjustment of the microbial inoculation plan is achieved through the reinforcement learning algorithm and the real-time feedback mechanism. According to changes in soil conditions and microbial communities, continuously optimize the density, time, and frequency of microbial inoculation. When the soil humidity is too low or the microbial activity is low, the system will automatically increase the inoculation frequency or adjust the inoculation amount to ensure that the microorganisms can play their roles under the most suitable conditions.

[0034] Step S6. Long-term verify the effectiveness of the microbial culture plan through field trials and advanced performance evaluation indicators, and continuously adjust the plan according to the feedback data.

[0035] Among them, in step S6, the following sub-steps are also included: S6-1. Conduct field trials to verify the effectiveness of the customized microbial culture plan under actual agricultural conditions. Implement multi-zone field trials under different soil types and environmental conditions, evaluate the growth effect of microorganisms, and the long-term impact on soil health, microbial diversity, and crop yield in different growing seasons. Evaluate the adaptability of the microbial plan in diverse environments through these data and provide comprehensive verification. S6-2. Use advanced performance indicators to evaluate the impact of the microbial plan. Incorporate functional microbial activity and soil resilience into the key performance indicators, as shown in Equation (6): Equation (6) Where Is the total soil health index Is the i-th soil or microbial characteristic Is the weight coefficient, reflecting the contribution of each characteristic to soil health Functional microbial activity includes nutrient cycling efficiency and disease suppression, and soil resilience includes water retention capacity and erosion resistance S6-3. Continuously adjust the microbial culture plan according to on-site test and real-time monitoring data. Adopt a continuous learning mechanism, provide real-time feedback from field trials and monitoring data, and accordingly fine-tune the microbial protocol for future growing seasons; the machine learning model retrains the system based on past performance to ensure that the protocol continuously adapts to changing soil health, microbial behavior, and climate conditions.

[0036] It should be noted that microorganisms not only improve the nutrient cycle of the soil, but also enhance the soil's resistance to pests and diseases through biological control. It is recommended to use microbial strains with antibacterial and insecticidal effects to help reduce pesticide use in farmland. For example, microorganisms such as Trichoderma and Bacillus can inhibit pathogenic bacteria and pests in the soil, reduce the frequency of pesticide use, and further improve soil health and the natural resistance of crops.

[0037] A customized recommendation system for microbial culture schemes based on soil data analysis, comprising: A data collection module, a data processing and analysis module, a microbial culture scheme recommendation module, and a monitoring and feedback module; The data collection module collects soil property data, microbial data, and environmental data; the data sources mainly include two parts: sensor data and microbial diversity analysis; The sensor data includes environmental data sensor data and soil sensor data. The soil sensor data includes soil properties such as pH, humidity, temperature, texture, and nutrients, and the environmental data sensor data includes environmental parameters obtained from weather stations and crop growth monitoring instruments; The microbial diversity analysis analyzes the microbial community in the soil through soil DNA extraction, PCR amplification, and high-throughput sequencing technology; The data processing and analysis module cleans, preprocesses, analyzes, and extracts features from the collected soil data, microbial data, and environmental data; automatically analyzes the soil and microbial data through machine learning algorithms to mine potential laws and provide a recommendation basis for microbial culture schemes; The microbial culture scheme recommendation module provides a customized microbial culture scheme based on the soil analysis results, combined with historical data and real-time data; automatically recommends appropriate microbial strains by analyzing the defects or imbalances of the soil, considering soil microbial diversity, avoiding selecting strains that conflict with the existing microbial community, and ensuring the synergistic effect between strains; The monitoring and feedback module monitors the soil and microbial status in real time and optimizes the microbial culture scheme through an adaptive feedback mechanism; collects soil and microbial data through sensors, uploads the data to the cloud platform for real-time processing; automatically adjusts the microbial inoculation strategy, fertilizer use, and irrigation strategy according to real-time data to ensure that the microorganisms are in the best growth environment.

[0038] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, there are various changes and modifications to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for customizing and recommending microbial cultivation schemes based on soil data analysis, characterized in that: The following steps are involved: Step S1, analyzing soil characteristic data by collecting soil samples at different locations and depths, and preprocessing, format conversion and normalization of the data; Step S2, obtaining microbial community information through soil DNA extraction, PCR amplification and high-throughput sequencing, and combining data preprocessing and diversity analysis to evaluate the distribution and function of microorganisms in the soil; Step S3, selecting appropriate microbial strains according to soil characteristics and microbial community analysis results, and optimizing the microbial culture environment and inoculation strategy; Step S4, combining the microbial cultivation program with agricultural management measures such as fertilization, irrigation, and crop rotation to dynamically adjust soil health and crop productivity; Step S5, by real-time monitoring of soil and microbial conditions, dynamically adjusting the microbial cultivation scheme based on a feedback mechanism, and adjusting the growth conditions and cultivation effects of the microorganisms; Step S6, through field trials and advanced performance evaluation indicators, the effectiveness of the microbial cultivation program is verified over a long period of time, and the program is continuously adjusted based on feedback data.

2. The method for customizing and recommending microbial cultivation schemes based on soil data analysis according to claim 1, characterized in that: Wherein step S1 also includes the following sub-steps: S1-1, soil characteristic data are analyzed by collecting soil samples at different depths and locations in the field. The soil characteristic data include: soil pH, soil moisture, soil texture, organic matter content, nutrient level, and microbial diversity; S1-2, preprocess the soil data. Data preprocessing includes: processing missing data, detecting and deleting outliers, and checking consistency; and performing format conversion to maintain data consistency. The details are as follows: Handling missing data: Use mean interpolation techniques to fill missing values. For numerical data, use the mean or median of the observed values ​​to estimate missing values. Outlier detection and removal: Using the interquartile range (IQR) method, data points that are above the third quartile or below the first quartile by 1.5 times the IQR are identified and removed; Data checking: Use cross-validation to cross-check data against external sources; Format conversion: Organic matter content and microbial counts usually span multiple orders of magnitude. Logarithmic transformation is used to normalize the data to solve the skewed distribution problem; S1-3, creating new features through existing soil properties, wherein the new features include soil health index and soil nutrient ratio; Soil Health Index: combines multiple soil properties T (pH, Y, YF) including pH, organic matter and nutrient levels into one index score that reflects overall soil health; Soil nutrient ratios: Calculating nutrient ratios captures important soil balance aspects, nutrient ratios are nitrogen and phosphorus to nutrient levels; S1-4, by using the minimum-maximum normalization to normalize the data, each feature is scaled so that it is within a fixed range, as shown in formula (1): Formula (1) in, is the normalized data, is the original data, is the minimum value of the original data, is the maximum value of the original data.

3. The method for customizing and recommending microbial cultivation schemes based on soil data analysis according to claim 1, characterized in that: Wherein step S2 also includes the following sub-steps: S2-1, soil microbial DNA was extracted from soil samples using a soil DNA extraction kit. This process included homogenizing soil samples from different depths and locations, releasing DNA by cell lysis, and purifying and quantifying the extracted DNA; S2-2, directed amplification of key genetic markers using universal primers, selecting 16S rRNA of bacteria and ITS of fungi; The PCR amplification process includes denaturation, primer annealing, and extension of each marker under optimized conditions, and the quality and size of the PCR products are checked by gel electrophoresis; S2-3, high-throughput sequencing of PCR-amplified DNA was performed using a next-generation sequencing platform. By adding adapters to the PCR products for sequencing, a comprehensive microbial profile of the soil samples was obtained to prepare sequencing libraries, ensuring adequate coverage of common and rare microbial species in the samples; S2-4, the raw sequencing data were processed, low-quality reads, adapters and primers were removed using QIIME2, the cleaned sequences were classified using the reference database, and the cleaned sequences were compared with known species; the microbial diversity was assessed using α diversity and β diversity indicators to evaluate the diversity and variability of the microbial communities in soil samples; the weighted diversity index was used to further evaluate the functional diversity of microorganisms, as shown in formula (2): Formula (2) in, is the weighted diversity index, n is the index, is the weight of the i-th microorganism, is the abundance of the microorganism in the sample, is the total abundance of all microorganisms; S2-5, combining microbial data with soil property data and environmental factors to perform temporal integration, spatial integration, and environmental data integration; Temporal integration: Soil properties change over time. Integrate temporal data to analyze seasonal changes. Collect historical soil data or real-time measurements over a period of time. Use time series data to correlate soil changes with changes in microbial activity trends over time. Spatial integration: Geospatial information helps analyze the spatial variability of soil properties. Use GIS data to map soil data for different fields, incorporate GPS coordinates into soil property data, and analyze spatial trends and variability between fields. Environmental data integration: Environmental factors such as temperature, precipitation, and crop management practices can affect soil properties and microbial activity. Integrating local environmental data from meteorological sources or sensors can enhance soil property datasets.

4. The method for customizing and recommending microbial cultivation schemes based on soil data analysis according to claim 1, characterized in that: Wherein step S3 also includes the following sub-steps: S3-1, based on a comprehensive analysis of soil characteristics and existing microbial communities, select appropriate microbial strains as follows: The soil data and microbial community data are analyzed and optimized at multiple levels to obtain the optimal microbial cultivation scheme. The soil data includes nutrient levels, pH values ​​and microbial diversity. A machine learning algorithm is used to train a large amount of soil data and microbial activity data, establish a relationship model between microbial growth and soil properties, predict the growth status of microorganisms and soil health indicators under different soil conditions, and output a series of possible microbial inoculation schemes. A multi-objective optimization algorithm is used to optimize the scheme when considering multiple objectives. Each candidate scheme will be evaluated through the following process: Microbial efficacy assessment, which evaluates the ability of microorganisms to fix nutrients and inhibit diseases based on soil characteristics and the needs of the microbial community; Environmental adaptability assessment, analyzing the growth stability and effects of microorganisms in different environments; Soil health impact assessment: Evaluate the effect of microbial cultivation program on soil improvement through soil health index; Based on the analysis results, identify deficiencies or imbalances in the soil and select strains that can fill these gaps, taking into account the compatibility of the selected strains with the existing microbial community, avoiding competition and ensuring synergy; S3-2, determine the optimal growth conditions according to the needs of the selected strain, including temperature, humidity, oxygen availability and pH value; through standardized microbial culture condition methods, combined with real-time monitoring data for dynamic adjustment, and through sensors to monitor soil moisture, temperature and nutrient levels in real time, to achieve environmental adaptability adjustment and ensure that microbial growth conditions are always in the optimal state; S3-3, determine the appropriate inoculation strategy, including inoculation density, inoculation time and application frequency, to ensure that the microorganisms are applied at a fixed rate and interval; monitor the density and health of the microbial population in real time to determine whether additional inoculation or adjustment of the inoculation dose is needed to ensure that the microbial population maintains a balance throughout the cultivation process; S3-4, the microbial cultivation program is combined with other agricultural management measures, including fertilization, irrigation and crop management; the fertilization plan is adjusted according to the real-time performance of the microbial inoculant to ensure that the nutrient supply is synchronized with the microbial demand; the irrigation strategy is adjusted according to the soil moisture and microbial activity level to maintain the optimal moisture conditions required for microbial growth; the microbial program complements the agricultural practice of crop rotation or cover crops, and the microbial cultivation effect is adjusted according to seasonal changes and crop growth cycles; S3-5, adjust the microbial cultivation scheme based on the real-time changes of soil and microbial data. The feedback mechanism makes necessary adjustments to the microbial scheme during the process. Apply machine learning algorithms to analyze real-time data and predict future soil and microbial states. Actively adjust the cultivation strategy to improve the effectiveness of the microbial inoculant. Use a reinforcement learning algorithm. The reward function is shown in formula (3): Formula (3) in, is the reward at time t, is a weighted coefficient that reflects the impact of various soil and microbial indicators on overall health. is the state of the i-th soil or microbial indicator at the t-th moment.

5. The method for customizing and recommending microbial cultivation schemes based on soil data analysis according to claim 1, characterized in that: Wherein step S4 also includes the following sub-steps: S4-1, the microbial inoculation program is combined with the fertilization plan. By monitoring the microbial population and nutrient availability, the fertilizer input is automatically adjusted to optimize the microbial efficacy, improve nutrient cycling, and reduce the demand for excessive fertilizers. The optimization is carried out through the comprehensive goals of microbial efficacy and fertilizer usage, as shown in formula (4): Formula (4) in, is the objective function, represents the efficiency of the i-th microorganism, is the weight of the microorganism under specific soil and crop management conditions, is the input amount of the i-th fertilizer; S4-2, the irrigation scheme is adapted to the growth needs of microorganisms. The irrigation amount is adjusted according to the real-time soil moisture data and microbial needs. The irrigation scheme is customized based on the microbial growth data and soil moisture level to ensure that the moisture conditions are suitable for microbial growth and avoid the negative impact of oversaturation or drought stress on microorganisms; S4-3, integrate the microbial inoculation program with crop rotation and cover cropping practices, and dynamically adjust the microbial inoculation program according to the type of rotation crops and the presence of cover crops, as follows: The nitrogen-fixing microbes are suitable for leguminous crops, while the other microbes are suitable for non-leguminous crops. This integrated approach improves soil health by providing the right microbial support at the right time.

6. The method for customizing and recommending microbial cultivation schemes based on soil data analysis according to claim 1, characterized in that: Wherein, in step S5, the following sub-steps are also included: S5-1, continuous monitoring of soil and microbial conditions through sensor networks and real-time data collection systems, real-time monitoring of key soil properties and microbial activity by integrating IoT-based sensors, instant detection of changes in soil or microbial conditions, and ensuring timely response to dynamic environmental factors; S5-2, uses an adaptive feedback system to adjust the microbial cultivation plan based on real-time data, continuously analyzes real-time data of soil and microorganisms, and automatically adjusts the inoculation rate, nutrient delivery and environmental conditions; Automatically adjust irrigation or inoculation rates to maintain optimal growing conditions when soil moisture decreases or microbial populations reach set thresholds; S5-3 integrates data from soil sensors, microbial activity monitoring, and environmental factors, builds a prediction model, uses machine learning algorithms to analyze the integrated data and predict future conditions, actively adjusts the plan according to upcoming weather changes or seasonal changes, and uses long short-term memory networks for time series prediction, as shown in formula (5): Formula (5) in, represents the predicted value of soil or microorganism at time t, is the input data at the historical moment, are model parameters, It is an LSTM model; S5-4, use long-term data collection to improve microbial management strategies, track soil and microbial health over multiple growing seasons, adjust plans based on long-term trends, and ensure long-term effectiveness by continuously learning from historical data and regularly retraining machine learning models to reflect changes in soil and microbial conditions.

7. The method for customizing and recommending microbial cultivation schemes based on soil data analysis according to claim 1, characterized in that: Wherein, in step S6, the following sub-steps are also included: S6-1, conduct field trials to verify the effectiveness of customized microbial cultivation programs under actual agricultural conditions, implement multi-region field trials under different soil types and environmental conditions, evaluate the growth effects of microorganisms, and the long-term effects of different growing seasons on soil health, microbial diversity and crop yields. Through these data, the adaptability of the microbial program in diverse environments is evaluated to provide comprehensive verification; S6-2, use advanced performance indicators to evaluate the impact of microbial programs, and incorporate functional microbial activity and soil resilience into key performance indicators, as shown in formula (6): Formula (6) in, is the total soil health index, is the i-th soil or microbial characteristic, is the weight coefficient, reflecting the contribution of each characteristic to soil health; Functional microbial activity includes nutrient cycling efficiency and disease suppression, and soil resilience includes water retention capacity and erosion resistance; S6-3, continuously adjust the microbial cultivation program based on field trials and real-time monitoring data, adopt a continuous learning mechanism, provide real-time feedback from field trials and monitoring data, and fine-tune the microbial protocol for future growing seasons accordingly; the machine learning model retrains the system based on past performance to ensure that the protocol continues to adapt to changing soil health, microbial behavior and climate conditions.

8. A microbial cultivation scheme customization recommendation system based on soil data analysis, applied to a microbial cultivation scheme customization recommendation method based on soil data analysis according to any one of claims 1 to 7, characterized in that: Data collection module, data processing and analysis module, microbial culture program recommendation module, monitoring and feedback module; The data collection module collects soil property data, microbial data and environmental data; the data sources mainly include two parts: sensor data and microbial diversity analysis; The sensor data includes environmental data sensor data and soil sensor data, the soil sensor data includes pH, moisture, temperature, texture and nutrient soil properties, and the environmental data sensor data includes environmental parameters acquired by weather stations and crop growth monitoring instruments; The microbial diversity analysis analyzes the microbial community in the soil by soil DNA extraction, PCR amplification and high-throughput sequencing technology; The data processing and analysis module cleans, preprocesses, analyzes and extracts features from the collected soil data, microbial data and environmental data; Automatically analyze soil and microbial data through machine learning algorithms to mine potential patterns and provide recommendations for microbial cultivation programs; The microbial cultivation program recommendation module provides customized microbial cultivation programs based on soil analysis results, combined with historical data and real-time data; automatically recommends suitable microbial strains by analyzing soil defects or imbalances, taking into account soil microbial diversity, avoiding the selection of strains that conflict with existing flora, and ensuring synergy between strains; The monitoring and feedback module monitors the soil and microbial status in real time, and optimizes the microbial cultivation program through an adaptive feedback mechanism; collects soil and microbial data through sensors, and uploads the data to the cloud platform for real-time processing; and automatically adjusts the microbial inoculation strategy, fertilizer use and irrigation strategy based on real-time data to ensure that the microorganisms are in the best growth environment.

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