A farmland remediation method based on cooperation of biochar and functional microorganisms

Abstract

This invention relates to a farmland remediation method based on the synergistic effect of biochar and functional microorganisms, belonging to the field of farmland soil remediation technology. The method includes: S1, preliminary diagnosis and preparation; S2, preparation of the biochar remediation agent: a. constructing a functional microbial composite flora, wherein the flora includes *Bacillus belye* and *Bacillus subtilis*, with a viable count ≥200 million / g; b. preparing modified biochar, using hemp stalks as raw material for pyrolysis to generate biochar, and then modifying it by acid etching with citric acid solution to increase its specific surface area by 60%; c. loading the composite flora onto the modified biochar using a direct adsorption method to form a biochar composite remediation agent; S3, precise application in the field; S4, post-application monitoring and management. This invention achieves precision and intelligence in farmland remediation through gridded sampling, multi-dimensional soil diagnosis, and intelligent algorithm-generated prescription maps, embodying a green and sustainable environmental protection concept.

Description

Technical Field

[0001] This invention relates to the field of farmland soil remediation technology, and in particular to a farmland remediation method based on the synergistic effect of biochar and functional microorganisms. Background Technology

[0002] Farmland restoration is like diagnosing and treating "sick" farmland, with the aim of restoring it to health and regaining its ability to produce high-yield, stable-yield, and sustainable high-quality crops. There are many reasons why farmland becomes "sick," mainly including physical structure degradation, chemical deterioration, and damage to biological systems.

[0003] Low- and medium-yield farmland in my country generally suffers from thin topsoil, weak microbial function, and frequent soil diseases. Traditional remediation methods rely on chemical agents or single microbial agents, which have technical bottlenecks such as drug residues and unsustainable effects.

[0004] In the prior art, patent CN106238448A provides a farmland remediation device for wastewater irrigation areas, which uses a plow, a crushing wheel and a remediation solution immersion wheel to work together, but the uniformity of the remediation solution mixing with the soil is insufficient; although the Nanjing Institute of Soil Science proposed a biochar-plant rhizosphere co-remediation of PAHs pollution, it has not solved the problem of poor microbial colonization ability.

[0005] Therefore, there is an urgent need for a comprehensive remediation solution that can synergistically improve soil physical structure, chemical fertility, and microbial ecology. Through gridded sampling, multi-dimensional soil diagnosis, and intelligent algorithm-generated prescription maps, precise and intelligent farmland remediation has been achieved. Citric acid modification has created an ideal microbial sanctuary. Biochar not only adsorbs pollutants but also provides physical protection, nutrition, and habitat for microorganisms, significantly improving the survival rate of microbial agents during storage and application. The metabolic activities of microorganisms further promote the restoration of soil ecological functions, demonstrating the outstanding advantages of the synergistic effect of "microorganism-biochar". By combining variable-rate fertilizer spreaders with intelligent deep-tillage machines with built-in deep injection systems, a "three-dimensional remediation" system from the surface to the depths has been constructed. This system integrates precise diagnosis in the early stage, intelligent application in the middle stage, and dynamic monitoring in the later stage, forming a closed-loop management system of "diagnosis-remediation-monitoring". It embodies the environmental protection concept of "green sustainability" and ensures the controllability of product quality and remediation effect. Summary of the Invention

[0006] This invention provides a farmland remediation method based on the synergy of biochar and functional microorganisms, which solves the problems of existing farmland remediation technologies, such as the extensive and imprecise remediation schemes, the single and unsustainable remediation effects, the difficulty in colonizing and low survival rate of functional microorganisms, the difficulty in reaching deep soil layers with remediation agents, and the "black box" nature of the remediation process, making it difficult to quantify and evaluate the effects.

[0007] The present invention provides the following solution to the above-mentioned technical problems: a farmland remediation method based on the synergistic effect of biochar and functional microorganisms, the farmland remediation method comprising the following steps:

[0008] S1, Preliminary diagnosis and preparation: After gridded sampling, multidimensional soil precision diagnosis is carried out through laboratory analysis equipment, and a remediation prescription map is generated based on decision algorithm;

[0009] S2, Preparation of microbial charcoal remediation agent:

[0010] a. Construct a functional microbial complex containing Bacillus velezensis and Bacillus subtilis, with a viable count ≥200 million / g;

[0011] b. Prepare modified biochar by pyrolysis of hemp stalks to generate biochar, and then modify it by acid etching with citric acid solution to increase its specific surface area by 60%;

[0012] c. The composite microbial community is loaded onto modified biochar using a direct adsorption method to form a microbial-char composite remediation agent;

[0013] S3, precise field application, evenly applies the biochar remediation agent to farmland soil, and achieves integrated soil remediation through deep plowing, injection or irrigation;

[0014] S4, post-monitoring and management: Field sensor networks and UAV multispectral imaging systems collect data, and the cloud platform uses change detection algorithms to dynamically monitor and provide feedback on the effects.

[0015] Based on the above technical solution, the present invention can be further improved as follows.

[0016] Furthermore, in step S1, the gridding process employs GPS positioning equipment and automated soil sampling drills to collect topsoil samples from the farmland to be restored at a depth of 0-20 cm according to the grid, and records the location information of the samples. Laboratory analysis equipment includes atomic absorption spectrometer or inductively coupled plasma mass spectrometer, gas chromatography-mass spectrometry, soil nutrient rapid analyzer, and high-throughput microbial sequencer, which enables precise capture of soil spatial variability, divides a large area of ​​farmland into multiple uniform management units, lays the data foundation for subsequent "variable precision restoration", and achieves a "full body check" of soil health status, which can comprehensively and accurately diagnose the physical, chemical and biological obstacles of the soil.

[0017] Furthermore, in step S1, the step of generating the repair prescription diagram is as follows:

[0018] a. Input the test data from laboratory analysis equipment into the prediction model, and use the fuzzy comprehensive evaluation algorithm or the prediction model based on machine learning to standardize and normalize each indicator;

[0019] b. Assign different weights to each indicator based on its impact on soil health (e.g., heavy metal pollution has the highest weight).

[0020] c. Using fuzzy mathematics membership functions or trained machine learning models (such as random forests or neural networks), the soil health status of each grid cell is comprehensively scored, and the pollution / degradation level is classified (e.g., mild, moderate, severe).

[0021] d. Calculate the recommended application rate of biochar remediation agent for each grid cell based on different levels;

[0022] f. The GIS workstation generates a variable application prescription map with geographic coordinates, which is then imported into the next step of the deep rendering equipment.

[0023] This process transforms complex multi-source data into intuitive and executable decision-making instructions. Its core advantages lie in its intelligence and quantification. It can handle conflicts among multiple indicators that are difficult for experts to weigh, and objectively calculate the most economical amount of repair agent for each region, thereby achieving optimal resource allocation and maximizing the repair effect.

[0024] Further, in step S2, the preparation of the functional microbial complex includes: screening strains with antibacterial function using *Fusarium graminearum* as a target; optimizing the high-density fermentation process to increase the activity of the inoculant by 40% compared to traditional strains; and the modification treatment of the biochar includes using a concentration of 0.5~1.5%. Biochar was soaked in a mol / L citric acid solution for 12-24 hours. The modified biochar formed a multi-level pore structure, increasing the microbial survival rate from 30% to 71.54%. Targeted screening ensured that the disease resistance function of functional microorganisms was highly targeted and effective, guaranteeing the reliability of the remediation effect from the source. The high-density fermentation process directly improved the efficacy and production efficiency of the microbial agent, making it possible to produce highly active microbial agents on a large scale and at low cost. Citric acid, as an environmentally friendly organic acid, avoids the secondary pollution risk that may be caused by traditional strong acid modification. The multi-level pore structure formed after modification greatly increased the specific surface area of ​​biochar, which not only improved its adsorption capacity for pollutants, but more importantly, provided a better "sanctuary" for functional microorganisms, significantly improving the survival rate of the microbial community during storage and application. This is the key to achieving synergistic effects between microorganisms and biochar.

[0025] Furthermore, in step S2, the preparation of the microbial char remediation agent is completed in a factory or fixed site to ensure the quality of the remediation agent; the construction of the functional microbial complex uses a fully automated stainless steel fermenter, a high-speed centrifuge, and a freeze dryer; the fully automated stainless steel fermenter is equipped with a pH sensor, dissolved oxygen sensor, temperature sensor, and an automatic feeding system to precisely control fermentation conditions; the high-speed centrifuge is used to collect the microbial cells after fermentation; and the freeze dryer is used to freeze-dry the microbial sludge with a preservative (such as trehalose or skim milk) to prepare a highly active microbial powder, which is easy to store and transport. The microbial agent is produced on a large scale using a series of industrial equipment such as a fully automated fermenter equipped with multiple sensors, a high-speed centrifuge, and a freeze dryer. The fully automated control system ensures that the fermentation process conditions are constant and optimal, thereby ensuring the quality stability and high activity of each batch of microbial agent. The freeze-drying technology transforms the liquid microbial agent into a microbial powder that is easy to store, transport, and reactivate, greatly extending the shelf life of the product and facilitating practical applications.

[0026] Furthermore, in step S2, the biochar modification and microbial-char composite process utilizes a biochar pyrolysis furnace, an acid-resistant reactor, a dynamic adsorption tank, and a low-temperature drying oven. The biochar pyrolysis furnace, under anaerobic or limited oxygen conditions, pyrolyzes biomass such as hemp stalks and straw at approximately 500°C to generate raw biochar. The acid-resistant reactor, equipped with a stirrer and temperature control system, is used to hold a citric acid solution and soak the biochar for modification. The dynamic adsorption tank mixes the modified, washed, and dried biochar with the rehydrated composite microbial agent, maintaining a slow rotation to ensure uniform adsorption of the microbial solution into the pores of the biochar. The low-temperature drying oven gently dries the microbial-loaded microbial-char remediation agent below 40°C, ensuring its moisture content meets safe packaging standards. The slow rotation of the dynamic adsorption tank guarantees uniform and gentle mixing of the microbial solution and biochar, avoiding mechanical damage. Low-temperature drying removes excess moisture while maximizing the protection of the activity of the loaded microorganisms, ensuring the quality of the finished product.

[0027] Furthermore, in step S3, precise application in the field is carried out using variable-rate fertilizer spreaders, intelligent deep plows, intelligent drip irrigation systems, or pointer-type sprinkler irrigation machines, with an application rate of 50-200 kg / mu, adjusted according to the degree of soil pollution. This combined "surface application + deep application" operation mode creates a "three-dimensional remediation" system, which ensures the uniform distribution of the remediation agent throughout the soil profile of the entire crop root zone (from the surface to the deep layer), solving the problem that the remediation agent is difficult to reach the deep soil under traditional tillage, and is especially important for controlling deep soil-borne diseases and activating deep nutrients.

[0028] Furthermore, in step S3, the variable-rate fertilizer spreader, equipped with a GPS receiver and control system, enables precise application of the biochar remediation agent. The intelligent deep-plowing machine turns the topsoil to the lower layers while simultaneously delivering another portion of the remediation agent directly to the bottom of the furrow. The intelligent drip irrigation system or pointer-type sprinkler immediately irrigates after deep plowing, controlling the soil moisture content at 60%-70% of field capacity. This precise irrigation creates an optimal environment for microbial activity, ensuring that the remediation agent can quickly take effect once it enters the soil, rather than remaining dormant or dead. This shortens the remediation activation time and improves the reliability of the remediation process.

[0029] Furthermore, the intelligent deep tillage machine is equipped with a deep injection system for the repair agent. The system includes a silo, a pneumatic conveying pipeline, and a control system. The silo stores the biochar repair agent, and the pneumatic conveying pipeline connects the silo to the furrow opener at the rear of the plow. The control system is synchronized with the fertilizer spreader and controls the injection amount of the repair agent according to the prescription diagram. The pneumatic conveying system ensures the accuracy and smoothness of the repair agent delivery, while the control system ensures the consistency between the deep application amount and the prescription diagram requirements.

[0030] Furthermore, the field sensor network includes soil temperature and humidity sensors, conductivity sensors, and pH sensors deployed in the field, with data wirelessly transmitted to a cloud platform. The UAV multispectral imaging system flies regularly, capturing multispectral images of crops to invert crop growth and chlorophyll content, indirectly assessing the soil remediation effect. A change detection algorithm compares the remediated multispectral images with the baseline images before remediation. The change detection algorithm calculates vegetation indices such as the normalized vegetation index, uses image difference or principal component analysis to quantify changes in vegetation coverage and health, generates a remediation effect change map, and visually displays the remediation effectiveness. Irrigation and fertilization plans are dynamically adjusted based on monitoring results to consolidate the remediation effect and promote crop growth, ultimately achieving... The comprehensive restoration of farmland ecosystems and the sustainable improvement of yields are achieved through this combination of comprehensive, multi-scale monitoring of "points" (sensors) and "areas" (drones), as well as "underground" (soil) and "aboveground" (crops). Ground sensors provide continuous and accurate local data, while drones can quickly and macroscopically reflect the crop response of the entire field. The two complement each other, providing rich and three-dimensional data support for effect evaluation. The advantages of this method lie in its "objectivity" and "visualization." It can transform abstract restoration effects into concrete, quantifiable data and intuitive images, providing indisputable evidence of restoration effectiveness. At the same time, it can also promptly identify areas with poor restoration effects, providing a basis for decision-making for supplementary and targeted management.

[0031] The beneficial effects of this invention are: This invention provides a farmland remediation method based on the synergistic effect of biochar and functional microorganisms, which has the following advantages:

[0032] 1. This invention achieves precision and intelligence in farmland restoration. By generating prescription maps through grid sampling and multi-dimensional soil diagnosis, it elevates restoration from an extensive experience-based model to a precise and scientific decision-making process. It can "allocate" restoration agents "on demand" according to the specific needs of each small area, achieving optimal resource allocation. While ensuring effectiveness, it significantly reduces costs and avoids waste.

[0033] 2. It leverages the outstanding advantages of synergistic effects between microorganisms and biochar. This invention does not simply mix biochar with microorganisms, but rather constructs an ideal microbial "sanctuary" through citric acid modification. Biochar not only adsorbs pollutants but also provides physical protection, nutrition, and habitat for microorganisms, significantly improving the survival rate of the microbial agent during storage and application. At the same time, the metabolic activities of microorganisms further promote the restoration of soil ecological functions, thereby achieving a synergistic effect of 1+1>2.

[0034] 3. A "three-dimensional remediation" system from the surface to the deep layer has been constructed. By combining a variable-rate fertilizer spreader with an intelligent deep-tillage machine with a built-in deep injection system, this invention ensures the uniform distribution of the remediation agent in the entire crop root zone soil profile (0-25 cm). This completely solves the industry problem that the remediation agent is difficult to reach the deep soil layer, and realizes comprehensive treatment of the topsoil layer without dead corners. It is especially significant for eradicating deep soil-borne diseases and improving saline-alkali deep soil.

[0035] 4. A closed-loop management system of "diagnosis-repair-monitoring" has been formed. This invention integrates accurate diagnosis in the early stage, intelligent application in the middle stage and dynamic monitoring in the later stage. Through the monitoring network and change detection algorithm, the repair effect can be objectively and visually quantitatively evaluated, and subsequent agronomic measures can be dynamically adjusted according to the feedback data. This forms a complete, traceable and self-optimizing technical closed loop, ensuring the long-term and stable repair effect.

[0036] 5. It embodies the concept of green and sustainable environmental protection, using environmentally friendly materials and processes throughout the entire process. Biochar is prepared from agricultural waste hemp stalks, realizing the resource utilization of waste; citric acid modification is used to avoid secondary pollution that may be caused by traditional strong acid modification; functional microorganisms are used to replace some chemical pesticides and fertilizers, which meets the requirements of ecological agriculture and sustainable development.

[0037] 6. The controllability of product quality and remediation effect is guaranteed. From the high-density fermentation of the strains and the standardized modification of biochar to the gentle compounding and drying of the biochar, the entire preparation process is completed under industrial equipment and controlled conditions. This ensures that each batch of biochar remediation agent has high and stable activity, laying a solid foundation for the reliability and consistency of field remediation effect. Precise activation irrigation further ensures that the remediation agent can quickly start the remediation function after entering the soil.

[0038] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0039] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0040] Figure 1 The following is a flowchart of a method for farmland remediation based on the synergistic effect of biochar and functional microorganisms, provided in an embodiment of the present invention.

[0041] Figure 2 This is a system architecture diagram of a farmland remediation method based on the synergy of biochar and functional microorganisms, provided in an embodiment of the present invention.

[0042] Figure 3 This is a flowchart illustrating the preparation of a biochar remediation agent based on a synergistic method of biochar and functional microorganisms for farmland remediation, as provided in an embodiment of the present invention. Detailed Implementation

[0043] The following is in conjunction with the appendix Figure 1-3 The principles and features of the present invention are described below. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically in the following paragraphs by way of example with reference to the accompanying drawings. The advantages and features of the invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0045] like Figure 1-3 As shown, this invention provides a farmland remediation method based on the synergistic effect of biochar and functional microorganisms. The specific working principle and usage of this invention are as follows:

[0046] S1, Preliminary diagnosis and preparation stage;

[0047] 1. Multidimensional soil precision diagnosis;

[0048] First, grid sampling is carried out: using GPS positioning equipment and automated soil sampling drills, topsoil samples with a depth of 0-20 cm are collected in the farmland to be restored according to a certain grid (such as 50 meters × 50 meters), and the location information of each sampling point is accurately recorded;

[0049] The collected soil samples were sent to the laboratory for analysis using the following equipment;

[0050] a. Atomic absorption spectrometer or inductively coupled plasma mass spectrometer: used for precise detection of heavy metal (such as cadmium, arsenic, lead) content in soil;

[0051] b. Gas chromatography-mass spectrometry: used for analyzing organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs) and pesticide residues;

[0052] c. Soil nutrient rapid tester: used to measure basic physicochemical indicators of soil such as pH value, organic matter, nitrogen, phosphorus, and potassium;

[0053] d. High-throughput microbial sequencer: used to analyze soil microbial community structure and assess the ratio of pathogenic bacteria to beneficial bacteria;

[0054] 2. Generate a repair prescription map based on a decision algorithm;

[0055] a. Input the test data from laboratory analysis equipment into the prediction model, and use the fuzzy comprehensive evaluation algorithm or the prediction model based on machine learning to standardize and normalize each indicator;

[0056] b. Assign different weights to each indicator based on its impact on soil health (e.g., heavy metal pollution has the highest weight).

[0057] c. Using fuzzy mathematics membership functions or trained machine learning models (such as random forests or neural networks), the soil health status of each grid cell is comprehensively scored, and the pollution / degradation level is classified (e.g., mild, moderate, severe).

[0058] d. Calculate the recommended application rate of biochar remediation agent for each grid cell based on different levels;

[0059] f. The geographic information system workstation generates a variable application prescription map with geographic coordinates, which is then imported into the next step of precision application equipment in the field.

[0060] The following is an example of a soil remediation prescription generation algorithm:

[0061] import numpy as np

[0062] import pandas as pd

[0063] from sklearn.ensemble import RandomForestRegressor

[0064] from sklearn.preprocessing import StandardScaler

[0065] from sklearn.model_selection import train_test_split

[0066] import rasterio

[0067] from rasterio.transform import from_origin

[0068] import joblib

[0069] class SoilRehabilitationPrescription:

[0070] def __init__(self):

[0071] self.model = RandomForestRegressor(

[0072] n_estimators=100,

[0073] max_depth=10,

[0074] min_samples_split=5,

[0075] random_state=42)

[0076] self.scaler = StandardScaler()

[0077] self.feature_names = ['heavy_metal_cd', 'heavy_metal_as', 'organic_matter',

[0078] 'ph_value', 'pathogen_count', 'electrical_conductivity']

[0079] def load_training_data(self, csv_path):

[0080] "Load historical soil testing data and corresponding optimal remediation agent dosages."

[0081] data = pd.read_csv(csv_path)

[0082] X = data[self.feature_names]

[0083] y = data['optimal_dosage'] # Historical best dosage (kg / acre)

[0084] # Data Standardization

[0085] X_scaled = self.scaler.fit_transform(X)

[0086] return X_scaled, y

[0087] def train_prescription_model(self, X, y):

[0088] """Training Prescription Model"""

[0089] X_train, X_test, y_train, y_test = train_test_split(

[0090] X, y, test_size=0.2, random_state=42)

[0091] self.model.fit(X_train, y_train)

[0092] # Model Evaluation

[0093] train_score = self.model.score(X_train, y_train)

[0094] test_score = self.model.score(X_test, y_test)

[0095] print(f"Model training complete - Training set R²: {train_score:.3f}, Test set R²:{test_score:.3f}")

[0096] return self.model

[0097] def generate_prescription_map(self, soil_data_grid, output_path):

[0098] """Prescription Diagram for Generative Variables"""

[0099] # soil_data_grid: A three-dimensional array [features, rows, cols]

[0100] original_shape = soil_data_grid.shape[1:]

[0101] n_features = soil_data_grid.shape[0]

[0102] # Reshape into a two-dimensional array for prediction

[0103] grid_flat = soil_data_grid.reshape(n_features, -1).T

[0104] grid_scaled = self.scaler.transform(grid_flat)

[0105] # Predict the amount of repair agent needed for each grid

[0106] dosage_predictions = self.model.predict(grid_scaled)

[0107] # The dosage should be limited to a reasonable range [50, 200] kg / acre

[0108] dosage_predictions = np.clip(dosage_predictions, 50, 200)

[0109] # Reshape back to grid shape

[0110] prescription_grid = dosage_predictions.reshape(original_shape)

[0111] # Save as a GeoTIFF format prescription image

[0112] self._save_geotiff(prescription_grid, output_path)

[0113] return prescription_grid

[0114] def _save_geotiff(self, data, output_path, resolution=10):

[0115] Save as a GeoTIFF file readable by Geographic Information Systems.

[0116] transform = from_origin(0, 0, resolution, resolution) # 10-meter resolution

[0117] with rasterio.open(

[0118] output_path,

[0119] 'w',

[0120] driver='GTiff',

[0121] height = data.shape[0],

[0122] width = data.shape[1],

[0123] count=1,

[0124] dtype=data.dtype,

[0125] crs='EPSG:4326',

[0126] transform = transform,

[0127] asdst:

[0128] dst.write(data,1)

[0129] # Usage Example

[0130] if __name__ == "__main__":

[0131] prescription_system = SoilRehabilitationPrescription()

[0132] # Load historical data to train the model

[0133] X,y=prescription_system.load_training_data('historical_soil_data.csv')

[0134] prescription_system.train_prescription_model(X, y)

[0135] # Save the trained model

[0136] joblib.dump(prescription_system, 'soil_prescription_model.pkl');

[0137] S2, the preparation stage of the biochar remediation agent, is completed in a factory or fixed site to ensure the quality of the remediation agent;

[0138] 1. High-density fermentation by functional microorganisms;

[0139] The biomass raw material is hemp stalks crushed to 3-5 cm (agricultural waste such as straw and sawdust can also be used). The modifier is analytical grade citric acid, prepared as an aqueous solution of 0.5~1.5 mol / L. The functional microbial strains are Bacillus belye and Bacillus subtilis. The fermentation medium consists of peptone, yeast extract, glucose, and inorganic salts (such as magnesium sulfate and potassium dihydrogen phosphate). The pH value is natural. The microbial agent protectant is a mixed solution of trehalose and skim milk.

[0140] First, the strains were activated and expanded. The frozen Bacillus belye and Bacillus subtilis strains were inoculated into Erlenmeyer flasks containing liquid culture medium on a sterile operating table. The flasks were placed in a constant temperature shaker and cultured at 30°C and 180 rpm for 24 hours to complete the activation of the strains. The activated bacterial solution was then transferred to a larger volume of fermentation medium at an inoculation rate of 1% to 2% for scale-up culture.

[0141] High-density fermentation is then carried out. The two expanded bacterial cultures are co-inoculated into the fermenter at a 1:1 volume ratio, with a total inoculation of 5%~10%. Fermentation conditions are strictly controlled: temperature: 30 ± 0.5°C; pH: maintained at 7.0 ± 0.2 by automatic addition of acid or alkali; dissolved oxygen: maintained at dissolved oxygen saturation above 30% by automatically adjusting the stirring speed (100-500 rpm) and aeration rate. The fermentation cycle is approximately 36-48 hours. Fermentation is carried out when the total viable cell count reaches ≥ 2×10⁻⁶. 9 Fermentation was terminated when CFU / mL was reached;

[0142] Bacterial cell collection and formulation: Bacterial cells were collected by centrifugation at 4°C and 8000 rpm using a high-speed centrifuge to obtain a high-concentration bacterial slurry. The bacterial slurry was then uniformly mixed with a pre-sterilized preservative (trehalose and skim milk solution) at a 1:1 (mass ratio). The mixture was then placed in a freeze dryer for vacuum freeze-drying under the following conditions: pre-freezing to -40°C, followed by primary drying at a gradient temperature from -20°C to 25°C for approximately 24 hours, ultimately yielding a viable cell count ≥ 2 billion / g (2 × 10⁻⁶). 9 Dry bacterial powder (CFU / g) should be sealed and refrigerated for later use.

[0143] 2. Biochar modification and microbial-char composite;

[0144] To prepare biochar, the hemp stalk raw material is evenly spread in a crucible, placed in a pyrolysis furnace, and nitrogen gas is introduced into the furnace (flow rate ~0.5 L / min) to remove air and create an oxygen-limited or anaerobic environment. The temperature is programmed to rise to 500°C at a heating rate of 10°C / min, and pyrolysis is maintained at this final temperature for 2 hours. After pyrolysis, the biochar is naturally cooled to room temperature under continuous nitrogen gas supply. The raw biochar is then removed, pulverized, and passed through a 60-mesh sieve.

[0145] Citric acid modification of biochar: A 1.0 mol / L citric acid solution (modifier to biochar mass ratio of 10:1) was added to a reactor, and the sieved raw biochar was slowly added. Stirring was started (50 rpm) to ensure that the biochar was completely submerged. The modification was carried out for 16 hours at room temperature of 25°C. After the modification was completed, the biochar was repeatedly washed with deionized water until the filtrate was neutral (pH≈7.0). The washed wet biochar was transferred to an oven and dried at 105°C to constant weight, finally obtaining modified biochar with a specific surface area increased by about 60% and rich hierarchical pores.

[0146] The preparation of the bacterial charcoal composite remediation agent involves mixing the two bacterial powders prepared in Unit 1 in a sterile mixing tank according to a certain ratio, rehydrating with sterile water or a mild nutrient solution, and preparing a high-concentration composite bacterial suspension (live bacteria count ≥ 5 × 10⁻⁶). 9 The bacterial suspension and the modified biochar prepared in Unit 2 were prepared at a mass ratio of 1:5 (dry weight of bacterial suspension: biochar).

[0147] Adsorption and colonization: Modified biochar is put into a dynamic adsorption tank. The composite bacterial suspension is added evenly by spraying or slowly dripping while rotating at a slow speed (10-20 rpm). The mixture is kept rotating slowly for 2-4 hours to ensure that the bacterial solution is fully and evenly adsorbed by the biochar. Under these conditions, the survival rate of microorganisms in the pores of the biochar can reach more than 71.54%.

[0148] Mild drying and packaging: The wet microbial charcoal repair agent loaded with microorganisms is spread in a thin layer on a drying tray and placed in a low-temperature drying oven. It is dried under circulating air conditions below 40°C (preferably 35-38°C) until the moisture content of the material is less than 10%. The dried microbial charcoal repair agent is immediately vacuum or nitrogen-filled sealed packaging and stored in a cool and dry place.

[0149] S3, Precision application stage in the field;

[0150] The variable-rate fertilizer spreader is equipped with a GPS receiver and control system, which can read the prescription map generated in the first stage. When the machine moves to different grids, the control system automatically adjusts the opening of the feeding valve to achieve variable and precise application of the biochar remediation agent.

[0151] The variable-rate fertilizer spreader first applies the top dressing, spreading most of the remediation agent evenly on the field surface. The intelligent deep tillage machine then follows up with its operation. Its plow blades turn the top soil (including the newly applied remediation agent) to the lower layer, while the deep injection system delivers another part of the remediation agent directly to the bottom of the furrow (about 20-25 cm deep).

[0152] This forms a three-dimensional remediation system of "surface-middle-deep layers", ensuring that the remediation agent is evenly mixed with the soil throughout the entire topsoil profile, and in particular, solving the problem of remediation of deep soil.

[0153] Irrigation should be carried out immediately after deep plowing. Through an intelligent irrigation system, the soil moisture content should be controlled at 60%-70% of field capacity, creating the best water conditions for the revival and colonization of functional microorganisms. This step is the key to "activating" the repair agent.

[0154] S4, the later monitoring and management stage, is used to evaluate the effects and guide subsequent agricultural operations;

[0155] The field sensor network includes soil temperature and humidity sensors, conductivity sensors, and pH sensors deployed in the field. Data is wirelessly transmitted to a cloud platform. A drone multispectral imaging system flies regularly to capture multispectral images of crops, inverting crop growth and chlorophyll content to indirectly assess the soil remediation effect. A change detection algorithm compares the remediated multispectral images with the baseline images before remediation, calculates vegetation indices such as the normalized vegetation index, and uses image difference or principal component analysis to quantify changes in vegetation coverage and health, generating a remediation effect change map to visually demonstrate the remediation results.

[0156] The change detection algorithm is as follows:

[0157] import numpy as np

[0158] import cv2

[0159] from osgeo import gdal

[0160] from sklearn.cluster import KMeans

[0161] from sklearn.decomposition import PCA

[0162] class MultiTemporalChangeDetection:

[0163] def __init__(self):

[0164] self.baseline_ndvi = None

[0165] self.baseline_date = None

[0166] def calculate_ndvi(self, nir_band, red_band):

[0167] Calculating the Normalized Difference Vegetation Index

[0168] nir = nir_band.astype(np.float32)

[0169] red = red_band.astype(np.float32)

[0170] # Avoid division by zero

[0171] mask = (nir + red) == 0

[0172] ndvi = (nir - red) / (nir + red + 1e-8)

[0173] ndvi[mask] = 0

[0174] return np.clip(ndvi, -1, 1)

[0175] def set_baseline(self, baseline_multispectral):

[0176] """Set the baseline image before repair"""

[0177] nir = baseline_multispectral[:, :, 3] # Assuming the 4th band is near-infrared

[0178] red = baseline_multispectral[:, :, 2] # Assume the 3rd band is the red band

[0179] self.baseline_ndvi = self.calculate_ndvi(nir, red)

[0180] self.baseline_date = 'baseline_date'

[0181] def detect_rehabilitation_effect(self, current_multispectral):

[0182] "Changes in the effect of detection and repair"

[0183] nir = current_multispectral[:, :, 3]

[0184] red = current_multispectral[:, :, 2]

[0185] current_ndvi = self.calculate_ndvi(nir, red)

[0186] # Calculate NDVI difference

[0187] ndvi_diff = current_ndvi - self.baseline_ndvi

[0188] # Automatically determine the threshold of change using the Otsu thresholding method

[0189] diff_normalized = cv2.normalize(ndvi_diff, None, 0, 255,cv2.NORM_MINMAX).astype(np.uint8)

[0190] _, binary_change = cv2.threshold(diff_normalized, 0, 255,cv2.THRESH_BINARY + cv2.THRESH_OTSU)

[0191] # Calculate the proportion of improved area

[0192] improvement_ratio = np.sum(binary_change == 255) / binary_change.size

[0193] # Cluster analysis of regions of change

[0194] change_regions = self._cluster_change_regions(ndvi_diff)

[0195] results = {

[0196] 'improvement_ratio': improvement_ratio,

[0197] 'mean_ndvi_improvement': np.mean(ndvi_diff[ndvi_diff >0]),

[0198] 'change_regions': change_regions,

[0199] 'ndvi_difference_map': ndvi_diff,

[0200] 'change_binary_map': binary_change

[0201] }

[0202] return results

[0203] def _cluster_change_regions(self, ndvi_diff):

[0204] """Perform cluster analysis on the regions of change"""

[0205] # Extract significantly improved pixels

[0206] improved_pixels = ndvi_diff[ndvi_diff > 0.1] # Threshold is adjustable

[0207] if len(improved_pixels) == 0:

[0208] return {'clusters': [], 'centers': []}

[0209] # Use K-means clustering to identify regions with different levels of improvement

[0210] X = improved_pixels.reshape(-1, 1)

[0211] kmeans = KMeans(n_clusters=3, random_state=42)

[0212] clusters = kmeans.fit_predict(X)

[0213] return {

[0214] 'clusters': clusters,

[0215] 'centers': kmeans.cluster_centers_.flatten(),

[0216] 'cluster_sizes': np.bincount(clusters)}

[0217] def generate_effect_report(self, baseline_date, current_date):

[0218] Generate a repair effect report.

[0219] # This allows for the integration of multiple metrics for comprehensive evaluation.

[0220] pass

[0221] # Usage Example

[0222] detector = MultiTemporalChangeDetection()

[0223] # Load baseline image (before restoration)

[0224] baseline_img = load_multispectral_image('baseline_20240101.tif')

[0225] detector.set_baseline(baseline_img)

[0226] # Load monitoring images (after restoration)

[0227] current_img = load_multispectral_image('current_20240701.tif')

[0228] results = detector.detect_rehabilitation_effect(current_img)

[0229] print(f"Percentage of area with improved vegetation: {results['improvement_ratio']:.2%}");

[0230] Irrigation and fertilization plans are dynamically adjusted based on monitoring results to consolidate the restoration effect, promote crop growth, and ultimately achieve the full restoration of the farmland ecosystem and the sustainable increase in yield.

[0231] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Content not described in detail in this specification is prior art known to those skilled in the art.

[0232] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A farmland remediation method based on the synergy of biochar and functional microorganisms, characterized in that, Farmland restoration methods include the following steps: S1, Preliminary diagnosis and preparation: After gridded sampling, multidimensional soil precision diagnosis is carried out through laboratory analysis equipment, and a remediation prescription map is generated based on decision algorithm; S2, Preparation of microbial charcoal remediation agent: a. Construct a functional microbial complex, wherein the complex contains Bacillus belye and Bacillus subtilis, with a viable count ≥200 million / g; b. Prepare modified biochar by pyrolysis of hemp stalks to generate biochar, and then perform acid etching modification with citric acid solution to increase its specific surface area by 60%; the modification treatment of the biochar includes: soaking the biochar in citric acid solution with a concentration of 0.5~1.5 mol / L for 12~24 hours; c. The composite microbial community is loaded onto modified biochar using a direct adsorption method to form a microbial-char composite remediation agent; S3, precise field application, involves uniformly applying the biochar remediation agent to farmland soil and achieving integrated soil remediation through deep plowing, injection, or irrigation; the precise field application utilizes a variable-rate fertilizer spreader, intelligent deep plowing machine, intelligent drip irrigation system, or pointer-type sprinkler; the variable-rate fertilizer spreader is equipped with a GPS receiver and control system to achieve variable and precise application of the biochar remediation agent, and the intelligent deep plowing machine turns the topsoil to the lower layers while simultaneously delivering another portion of the remediation agent directly to the bottom of the furrow; S4, Post-monitoring and Management: Field sensor networks and UAV multispectral imaging systems collect data, and the cloud platform uses change detection algorithms to dynamically monitor and provide feedback on the effects; The intelligent deep tillage machine is equipped with a deep injection system for repair agents. The system includes a silo, a pneumatic conveying pipeline, and a control system. The silo stores the microbial charcoal repair agent. The pneumatic conveying pipeline connects the silo to the furrow opener at the rear of the plow. The control system is synchronized with the fertilizer spreader and controls the injection amount of the repair agent according to the prescription diagram.

2. The farmland remediation method based on the synergistic effect of biochar and functional microorganisms according to claim 1, characterized in that, In step S1, the gridding is carried out using GPS positioning equipment and automated soil sampling drills. Soil samples from the topsoil layer at a depth of 0-20 cm are collected in the farmland to be restored according to the grid, and the location information of the samples is recorded. Laboratory analysis equipment includes atomic absorption spectrometer or inductively coupled plasma mass spectrometer, gas chromatography-mass spectrometry, soil nutrient rapid analyzer, and high-throughput microbial sequencer.

3. The farmland remediation method based on the synergistic effect of biochar and functional microorganisms according to claim 1, characterized in that, In step S1, the step of generating the repair prescription diagram is as follows: a. Input the test data from laboratory analysis equipment into the prediction model, and use the fuzzy comprehensive evaluation algorithm or the prediction model based on machine learning to standardize and normalize each indicator; b. Assign different weights to each indicator based on its impact on soil health; c. Using fuzzy mathematics membership functions or trained machine learning models, comprehensively score the soil health status of each grid cell and classify it into pollution / degradation levels; d. Calculate the recommended application rate of biochar remediation agent for each grid cell based on different levels; e. The GIS workstation generates a variable application prescription map with geographic coordinates, which is then imported into the next step of the deep processing equipment.

4. The farmland remediation method based on the synergistic effect of biochar and functional microorganisms according to claim 1, characterized in that, In step S2, the preparation of the functional microbial complex includes: using Fusarium graminearum as a target, screening strains with antibacterial function; optimizing the high-density fermentation process to increase the activity of the microbial agent by 40% compared with traditional strains; and forming multi-level channels in the pore structure of the modified biochar, increasing the survival rate of the microbial community from 30% to 71.54%.

5. A method for farmland remediation based on the synergistic effect of biochar and functional microorganisms according to claim 1 or 4, characterized in that, In step S2, the preparation of the biochar remediation agent is completed in a fixed location to ensure the quality of the remediation agent; the construction of the functional microbial complex uses a fully automatic stainless steel fermenter, a high-speed centrifuge, and a freeze dryer; the fully automatic stainless steel fermenter is equipped with a pH sensor, a dissolved oxygen sensor, a temperature sensor, and an automatic feeding system to precisely control the fermentation conditions; the high-speed centrifuge is used to collect the microbial cells after fermentation; and the freeze dryer is used to mix the microbial sludge with the preservative and freeze-dry it to prepare a highly active microbial powder for easy storage and transportation.

6. A method for farmland remediation based on the synergistic effect of biochar and functional microorganisms according to claim 1 or 5, characterized in that, In step S2, the biochar modification and microbial-char composite process utilizes a biochar pyrolysis furnace, an acid-resistant reactor, a dynamic adsorption tank, and a low-temperature drying oven. The biochar pyrolysis furnace, under anaerobic or limited oxygen conditions, pyrolyzes biomass such as hemp stalks and straw at approximately 500°C to generate raw biochar. The acid-resistant reactor, equipped with a stirrer and temperature control system, is used to hold a citric acid solution and soak the biochar for modification. The dynamic adsorption tank mixes the modified, washed, and dried biochar with the rehydrated composite microbial agent; the tank rotates slowly to ensure the microbial solution is evenly adsorbed into the pores of the biochar. The low-temperature drying oven gently dries the microbial-loaded microbial-char remediation agent below 40°C, ensuring its moisture content meets safe packaging standards.

7. The farmland remediation method based on the synergistic effect of biochar and functional microorganisms according to claim 1, characterized in that, In step S3, the application rate is 50~200 kg / mu, and adjusted according to the degree of soil pollution; the intelligent drip irrigation system or pointer sprinkler is used for irrigation immediately after deep plowing, and the intelligent drip irrigation system or pointer sprinkler controls the soil moisture content at 60%-70% of field capacity.

8. The farmland remediation method based on the synergistic effect of biochar and functional microorganisms according to claim 1, characterized in that, The field sensor network includes soil temperature and humidity sensors, conductivity sensors, and pH sensors deployed in the field, with data wirelessly transmitted to a cloud platform. The UAV multispectral imaging system flies regularly, capturing multispectral images of crops to infer crop growth and chlorophyll content, indirectly assessing the soil remediation effect. The change detection algorithm compares the remediated multispectral images with the baseline images before remediation. The change detection algorithm calculates vegetation indices such as the normalized vegetation index, uses image differencing or principal component analysis to quantify changes in vegetation coverage and health, generates a remediation effect change map, and visually displays the remediation results. Irrigation and fertilization plans are dynamically adjusted based on monitoring results to consolidate the remediation effect, promote crop growth, and ultimately achieve comprehensive restoration of the farmland ecosystem and sustainable yield improvement.

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