Peanut planting method for relieving continuous cropping obstacles of peanuts

Abstract

The invention relates to the technical field of agricultural information and seed and seedling cultivation, and discloses a peanut planting method for relieving continuous cropping obstacles of peanuts. The method comprises the following steps: constructing initial digital twins of a land parcel, and synchronously constructing a disease-resistant peanut variety screening and seedling pretreatment system; historical continuous cropping information, soil physicochemical and microorganism data, meteorological time sequence data and seedling cultivation parameters are fused, the system is driven to evolve into operation state digital twins, and seedling-soil-environment collaborative simulation is achieved. Evaluating the continuous cropping obstacle risk and the seedling adaptability based on the twinborn body, and generating a comprehensive diagnosis report containing a nutrient imbalance index, pathogenic bacteria abundance prediction, a root exudates cumulative effect and a seedling disease-resistant adaptation coefficient; according to the method, differentiated seed and seedling optimization, soil improvement and cultivation strategies are implemented, and management measures are dynamically optimized through real-time data feedback in a growth cycle. Through cooperative driving of digital twinning and seed and seedling cultivation, source prevention, control and intervention of continuous cropping obstacles are achieved.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of agricultural information technology and seed and seedling cultivation technology, and specifically relates to a peanut planting method to alleviate the obstacles of continuous cropping of peanuts. Background Technology

[0002] With the promotion of intensive and specialized agricultural planting models, peanuts, as an important oilseed and cash crop in my country, have long faced the problem of continuous cropping obstacles in major producing areas. Continuous cropping leads to an imbalance in the soil ecosystem, manifested as nutrient structure disorder, accumulation of autotoxic substances, frequent occurrence of soil-borne diseases, and decline of beneficial microbial communities, which in turn leads to inhibited plant growth, decreased disease resistance, and a continuous decline in yield and quality. Traditional mitigation strategies mostly rely on empirical crop rotation, chemical fumigation, or increased application of organic fertilizers. Although these methods have some effect in the short term, they lack a precise perception and response mechanism to dynamic changes in the soil microecology, and fail to systematically integrate seed and seedling cultivation as a core link, making it difficult to achieve systematic and sustainable management of continuous cropping obstacles.

[0003] Digital twin-based smart agriculture technology offers a new paradigm for solving the problem of continuous cropping. This technology achieves full-element digital representation and dynamic simulation of the crop-soil-environment system by constructing a real-time mapping between physical farmland and a virtual model. However, existing digital twin applications mostly focus on macro-management scenarios such as irrigation, weather, or yield forecasting, and have not yet deeply integrated key agronomic measures such as seed treatment, microbial regulation, and crop rotation system optimization. In particular, it lacks an intelligent decision-making system that establishes a closed-loop linkage between disease-resistant variety breeding, biological agent intervention, seedling cultivation, and soil health status.

[0004] In existing technologies, variety selection still relies on years of field trials, which is time-consuming and costly; seed treatment lacks specificity and is not precisely adapted to soil microecological characteristics; the seedling cultivation process is not well adapted to the continuous cropping soil environment, resulting in limited disease resistance; and pest and disease control is mainly reactive, lacking a proactive intervention mechanism based on risk prediction.

[0005] More importantly, various agricultural operations are fragmented, failing to form a continuous optimization loop of "seedling cultivation—monitoring—simulation—decision-implementation—verification," resulting in fragmented, outdated, and poorly adaptable solutions for mitigating continuous cropping obstacles. Given the constraints of limited land resources and restricted rotation space in major peanut-producing areas, there is an urgent need for an intelligent planting method that deeply integrates digital twin technology, seed and seedling cultivation innovation, and modern agronomic systems to achieve accurate diagnosis, dynamic simulation, and coordinated control of continuous cropping obstacles. Summary of the Invention

[0006] This invention provides a planting method to alleviate peanut continuous cropping obstacles, aiming to solve the problems of soil nutrient imbalance, increased pests and diseases, and reduced yield caused by continuous peanut cropping. This method constructs a high-fidelity digital twin covering the entire life cycle of the land plot, integrating key technologies for seed and seedling cultivation, multi-source heterogeneous farmland sensing data, and crop physiological models. This enables dynamic identification, quantitative assessment, and precise intervention of continuous cropping obstacle factors, thereby effectively restoring soil microecological balance, inhibiting the proliferation of soil-borne pathogens, optimizing nutrient supply structure, and enhancing seedling disease resistance without changing the crop rotation system. Ultimately, this improves peanut yield and quality stability.

[0007] This invention provides a planting method for alleviating peanut continuous cropping obstacles, comprising: Establish an initial digital twin of the target planting plot, and simultaneously carry out the screening of disease-resistant peanut varieties and the construction of a seedling pretreatment system; Collect historical continuous cropping information, current soil physicochemical properties data, microbial community structure data, meteorological environment time series data, and basic parameters for seedling cultivation of the target planting plots; The collected data is input into the initial digital twin, driving it to evolve into a dynamically updated running digital twin, thereby realizing the collaborative simulation of seedling-soil-environment; Based on the aforementioned operational digital twin, a risk level assessment of continuous cropping obstacles and seedling suitability analysis are performed, generating a comprehensive diagnostic report that includes soil nutrient imbalance index, pathogen abundance prediction value, root exudate cumulative effect intensity, and seedling disease resistance suitability coefficient. Based on the comprehensive diagnostic report, develop and implement differentiated seed and seedling optimization programs, soil improvement and cultivation management strategies; Real-time field data and seedling growth status data are continuously collected during the peanut growth cycle and fed back to the operational digital twin to correct model parameters and dynamically adjust subsequent seedling management, soil improvement and field cultivation measures.

[0008] Preferably, the establishment of an initial digital twin of the target planting plot, and the simultaneous screening of disease-resistant peanut varieties and construction of a seedling pretreatment system, includes: Obtain geospatial boundary information, topographic elevation data, soil type distribution map, and historical farming records of the target planting area; Based on the above information, construct the geometric entity model of the land parcel in the 3D spatial modeling engine; Within the geometric solid model, a physical-biological-seedling coupled simulation kernel is embedded, consisting of soil hydrothermal conduction equations, nitrogen, phosphorus and potassium migration and transformation kinetic models, organic matter mineralization rate functions, microbial metabolic networks and seedling growth dynamic models. Configure initial state variables for the simulation kernel, including soil bulk density, porosity, cation exchange capacity, basic organic matter content, background microbial species list and their relative abundance, and a database of disease resistance parameters for common peanut varieties; Based on the main types of continuous cropping obstacles in the target plots, 3-5 disease-resistant and suitable varieties were selected from the peanut variety resource bank to form a candidate variety list; For candidate varieties, a pretreatment scheme library based on biological agents was established, and parameters such as agent type, concentration gradient, treatment time, and seed dressing ratio were set.

[0009] Preferably, the collection of historical continuous cropping information, current soil physicochemical property data, microbial community structure data, meteorological environmental time series data, and basic parameters for seedling cultivation of the target planting plot includes: Extract peanut planting area, harvest yield, fertilizer type and amount, irrigation frequency and total amount, and pesticide application records for the past three consecutive years from the agricultural machinery operation log database. The available contents of nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, manganese, zinc, copper and boron in the soil were determined by inductively coupled plasma mass spectrometry through multi-point grid sampling. The 16S rRNA gene and ITS region amplicon sequencing of soil samples were performed using a high-throughput sequencing platform to obtain the relative abundance matrix of taxonomic units of bacteria and fungi at the phylum, class, order, family, and genus. By deploying miniature weather stations at the edge of the plot, data on air temperature, relative humidity, precipitation, solar radiation intensity, wind speed and wind direction are continuously recorded. Basic parameters of candidate variety seedlings were collected, including thousand-seed weight, germination rate, germination potential, seedling height, root length, root fresh weight, leaf chlorophyll content, and disease resistance-related enzyme activity of seedlings after pretreatment with different biological agents.

[0010] Preferably, the step of inputting the collected data into the initial digital twin and driving it to evolve into a dynamically updated running digital twin to achieve collaborative simulation of seedling-soil-environment includes: Historical continuous cropping information is mapped to the nutrient consumption accumulation factor, the correction coefficient of the initial inoculum density of pathogens, and the seedling disease resistance requirement weight in the simulation kernel. The current soil physicochemical properties data are used as the measured initial values ​​of each state variable in the simulation kernel, replacing the default initialization parameters; Microbial community structure data is converted into key functional gene abundance vectors through a pre-trained community function inference model and input into the microbial metabolic network module, while also being associated with a biological agent pretreatment effect prediction model. Meteorological environmental time series data are used as an external driving field to apply the boundary conditions of the soil water and heat conduction equation, crop transpiration model and seedling growth dynamic model. The basic parameters of seedling cultivation are input into the seedling growth dynamic model. Combined with soil and meteorological data, the dynamic changes of germination rate, seedling survival rate and disease resistance of seedlings under different varieties and different pretreatment schemes are simulated. The entire coupled system is numerically integrated using a time-stepping solver to generate a dynamic distribution map of soil nutrients, a pathogen population growth trajectory, a rhizosphere micro-domain pH change cloud map, and a seedling growth adaptability prediction curve for the next 90 days.

[0011] Preferably, the step of assessing the risk level of continuous cropping obstacles and analyzing seedling suitability based on the operational digital twin, generating a comprehensive diagnostic report including soil nutrient imbalance index, predicted pathogen abundance, intensity of root exudate cumulative effect, and seedling disease resistance suitability coefficient, includes: The soil nutrient imbalance index is calculated and defined as the standard deviation of the ratio of the available content of each macro-element to the lower limit of its optimum threshold for peanuts. When the standard deviation is greater than 0.3, it is defined as a true imbalance. To predict pathogen abundance, the population abundance curves of Fusarium and Rhizoctonia species output by the simulation kernel are used to read their absolute abundance values ​​at the time corresponding to the flowering and pegging stage. If the absolute abundance value is greater than 10 per gram of dry soil, the abundance is predicted. 4 The copy number is marked as high risk; The intensity of the cumulative effect of root exudates was quantified based on the concentration integral values ​​of phenolic acids and long-chain fatty acids in the rhizosphere microdomain simulated in the simulation kernel. When the concentration integral value was greater than 50% of the average value of the same period in historical rotation plots, it was determined to be an enhanced inhibition effect. The disease resistance adaptation coefficient of seedlings was calculated. Based on the simulation results, the matching degree of the variety disease resistance parameters, the effect of biological agent pretreatment and the risk type of continuous cropping obstacles was considered. The coefficient ranged from 0 to 1, and a value greater than 0.7 was considered to be of excellent adaptability. Based on the above four indicators, a weighted summation method is used to generate a comprehensive score for continuous cropping obstacle risk and seedling suitability. If the total score is greater than 0.6, collaborative intervention measures will be initiated.

[0012] Preferably, the continuous collection of real-time field data and seedling growth status data during the peanut growth cycle, feeding this data back to the operational digital twin to correct model parameters, and dynamically adjusting subsequent seedling management, soil improvement, and field cultivation measures, includes: During the three key growth stages of seedling emergence, flowering, and pod formation, the normalized vegetation index of the canopy was measured, plant growth indicators were determined simultaneously, and soil samples were collected to monitor nutrient dynamics and pathogen abundance changes. Residual analysis is performed between the measured values ​​and the simulated values ​​of the digital twin. If the absolute value of the residual is greater than the preset threshold, the parameter inversion algorithm is triggered to correct the photosynthetic efficiency coefficient, root absorption radius, water use efficiency and seedling disease resistance response parameters in the simulation kernel online. Based on the revised model, the peak nutrient demand, disease occurrence window, and seedling growth status of subsequent growth stages are re-predicted. The application of topdressing, drainage, and biological agents will be dynamically adjusted based on the forecast results.

[0013] Preferably, the parameter inversion algorithm adopts a Bayesian optimization framework, the objective function is the mean square error between the simulated value and the measured value, and the optimization variables include photosynthetic efficiency coefficient, root absorption radius, water use efficiency and seedling disease resistance response parameters. The optimal parameter combination is searched iteratively through a Gaussian process surrogate model.

[0014] Preferably, the seedling growth dynamic model is based on the accumulated temperature method and the physiological development time model, integrating the variety disease resistance parameters and the effect of biological agent pretreatment to simulate the seedling germination, rooting, growth and disease resistance response process.

[0015] Preferably, the operating digital twin is equipped with an abnormal data processing protocol. When the data reported by the sensor exceeds the historical extreme value range or violates physical constraints, the system automatically marks the data point as suspicious and starts a cross-validation procedure. After triple verification using neighboring sensor data, consistency of meteorological driving field and rationality of crop physiology, spatiotemporal kriging interpolation is used for correction.

[0016] Preferably, the running digital twin is equipped with a stability monitoring module, which checks whether each state variable satisfies the conservation law and monotonicity constraint after each time step integration. If numerical oscillation or non-physical solution occurs, the time step is automatically reduced or the solution is switched to an implicit solution format.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Innovatively, seed and seedling cultivation is integrated into the digital twin system as the core link to construct a collaborative simulation model of "seedling-soil-environment", thereby improving the disease resistance of peanuts from the source and overcoming the problem of seed and seedling cultivation being disconnected from the continuous cropping environment in traditional methods; 2. By using digital twins, we can achieve multi-dimensional, quantitative, and forward-looking analysis of the causes of continuous cropping obstacles and seedling suitability. Combined with disease-resistant variety screening and biological agent pretreatment, we can form a dual guarantee of "source control + precision intervention", which overcomes the one-sidedness and lag of traditional experience-based judgment. 3. The proposed compound conditioner formula, combination of biological fumigant and seedling pretreatment scheme work synergistically to target and repair nutrient deficiencies, inhibit pathogen accumulation, and enhance seedling disease resistance, while avoiding secondary damage to the soil ecology caused by broad-spectrum chemical agents. 4. Based on the closed-loop control mechanism of dynamic feedback from digital twins, the timing and dosage of seedling management, soil improvement, and cultivation measures are precisely matched with the real-time status of the plot and the seedling growth stage, which significantly improves resource utilization efficiency and obstacle mitigation effect. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the running digital twin driving and multi-source data fusion in this invention; Figure 3 This is a logical flowchart of the risk level assessment of continuous cropping obstacles and seedling suitability analysis in this invention. Figure 4 This is a logical flowchart of the formulation and implementation of differentiated seed and seedling optimization, soil improvement and cultivation management strategies in this invention; Figure 5 This is a closed-loop control framework diagram of real-time data feedback and dynamic correction of digital twins throughout the entire peanut growth period in this invention; Figure 6 This is a schematic diagram of the multi-level interaction relationship and data flow between the terminal sensing device, digital twin platform, seedling cultivation unit and agricultural execution unit in this invention. Detailed Implementation

[0019] refer to Figures 1 to 6 This invention provides a peanut planting method to alleviate continuous cropping obstacles. The method constructs a high-fidelity digital twin covering the entire lifecycle of the target planting plot, integrating key seed and seedling cultivation technologies, multi-source heterogeneous farmland sensing data, and crop physiological models. This enables dynamic identification, quantitative assessment, and precise intervention of continuous cropping obstacle factors. Thus, without altering the crop rotation system, it effectively restores soil microecological balance, inhibits the proliferation of soil-borne pathogens, optimizes nutrient supply structure, enhances seedling disease resistance, and ultimately improves peanut yield and quality stability.

[0020] The peanut planting method for alleviating peanut continuous cropping obstacles includes the following steps: Establish an initial digital twin of the target planting plot, and simultaneously carry out the screening of disease-resistant peanut varieties and the construction of a seedling pretreatment system; Collect historical continuous cropping information, current soil physicochemical properties data, microbial community structure data, meteorological environment time series data, and basic parameters for seedling cultivation of the target planting plots; The collected data is input into the initial digital twin, driving it to evolve into a dynamically updated running digital twin, thereby realizing the collaborative simulation of seedling-soil-environment; Based on the aforementioned operational digital twin, a risk level assessment of continuous cropping obstacles and seedling suitability analysis are performed, generating a comprehensive diagnostic report that includes soil nutrient imbalance index, pathogen abundance prediction value, root exudate cumulative effect intensity, and seedling disease resistance suitability coefficient. Based on the comprehensive diagnostic report, develop and implement differentiated seed and seedling optimization programs, soil improvement and cultivation management strategies; Real-time field data and seedling growth status data are continuously collected during the peanut growth cycle and fed back to the operational digital twin to correct model parameters and dynamically adjust subsequent seedling management, soil improvement and field cultivation measures.

[0021] The process of establishing an initial digital twin of the target planting plot and simultaneously carrying out the screening of disease-resistant peanut varieties and the construction of a seedling pretreatment system includes obtaining the geospatial boundary information, topographic elevation data, soil type distribution map, and historical cultivation records of the target planting plot.

[0022] Geospatial boundary information was obtained by matching high-precision remote sensing imagery provided by the National Bureau of Surveying and Mapping with a land parcel vector boundary database, ensuring that the area error of the land parcels was less than 0.5%. Topographic elevation data was acquired by centimeter-level scanning using a UAV equipped with a lidar system, generating a digital elevation model with a resolution of 10 centimeters.

[0023] The soil type distribution map was revised based on the results of the Second National Soil Census and combined with provincial soil detailed survey data from the past five years, clearly defining the parent material type, texture classification, and profile configuration of each sub-region. Historical cultivation records were retrieved from the provincial agricultural big data platform, including agricultural machinery operation logs, agricultural input ledgers, and yield reporting data, with a time span of no less than three consecutive years.

[0024] In the 3D spatial modeling engine, a geometric solid model of the land parcel is constructed based on the above information. This geometric solid model uses a non-uniform rational B-spline surface to describe the surface undulation, and its interior is divided into cubic grid cells with a side length of 1 meter. Each grid cell independently stores soil physical properties, biological state variables, and seedling growth adaptation parameters. Within the geometric solid model, a physical-biological-seedling coupled simulation kernel is embedded, consisting of soil hydrothermal conduction equations, nitrogen, phosphorus, and potassium migration and transformation kinetic models, organic matter mineralization rate functions, microbial metabolic networks, and seedling growth dynamic models.

[0025] The soil water and heat conduction equation adopts a coupled form of the Richards equation and the Fourier law of heat conduction to describe the interaction between water movement and heat transfer; the nitrogen, phosphorus and potassium migration and transformation kinetic model is based on first-order reaction kinetics and adsorption-desorption isotherms to characterize the dynamic distribution of nutrients among the solid, liquid and gas phases; the organic matter mineralization rate function adopts a dual-library first-order decomposition model to distinguish the degradation pathways of active organic matter and inert organic matter; the microbial metabolic network is based on a genome-scale metabolic model, integrating the carbon, nitrogen and phosphorus metabolic pathways of key functional bacterial groups to simulate the response relationship to root exudates and biological agents; the seedling growth dynamic model is based on the accumulated temperature method and the physiological development time model, integrating varietal disease resistance parameters and the effect of biological agent pretreatment to simulate the seedling germination, rooting, growth and disease resistance response process.

[0026] Configure initial state variables for the simulation kernel, including but not limited to soil bulk density, porosity, cation exchange capacity, basic organic matter content, background microbial species list and their relative abundance, and a database of disease resistance parameters for common peanut varieties (indicators such as drought resistance, resistance to soil-borne diseases, and tolerance to nutrient imbalance).

[0027] Soil bulk density was measured in-situ using the ring cutter method and then interpolated to fill each grid cell; porosity was calculated from bulk density and particle density; cation exchange capacity was estimated based on empirical formulas for soil colloid type and pH value; basic organic matter content was determined using the average of historical samples by the dry burning method as the initialization benchmark; the background microbial species list was downloaded from the National Microbial Resource Platform from the metagenomic reference database of typical peanut continuous cropping soils in the same region, and the relative abundance was weighted and averaged according to phylum, class, order, family, and genus levels and then distributed to each grid; the peanut variety disease resistance parameter database was constructed based on the National Crop Variety Resource Database and related research literature data, containing disease resistance index data for more than 100 peanut varieties.

[0028] The screening of disease-resistant peanut varieties involves the following steps: Based on the main types of historical continuous cropping obstacles in the target plot (such as nutrient imbalance-dominated, pathogen-dominated, and autotoxic substance-dominated), and combined with initial parameters from the digital twin database, 3-5 disease-resistant and suitable varieties are selected from the peanut variety resource bank. Screening indicators include field continuous cropping survival rate (weight 0.2), resistance to Fusarium / Rhizoctonia (weight 0.3), nutrient uptake efficiency (weight 0.2), tolerance to phenolic acid stress coefficient (weight 0.2), and yield stability (weight 0.1), forming a candidate variety list. For example, in pathogen-dominated continuous cropping plots, varieties with strong resistance to Fusarium and Rhizoctonia species, such as Huayu 25 and Jihua 12, are prioritized.

[0029] The seedling pretreatment system is constructed as follows: For candidate varieties, a pretreatment scheme library based on biological agents is established. These biological agents include phosphorus- and potassium-solubilizing agents (a compound of Bacillus subtilis and Bacillus megaterium), pathogen-resistant compound agents (a compound of Bacillus subtilis and Trichoderma), and growth-promoting agents (a gelatinous Bacillus). Each agent is configured with three concentration gradients (low concentration: 1×10⁻⁶). 8 CFU / mL, medium concentration: 5×10 8 CFU / mL, High concentration: 1×10 9 The parameters include CFU / mL, and preset seed soaking time (6 hours, 12 hours, 24 hours) and seed mixing ratio (inoculant to seed mass ratio 1:50, 1:100, 1:200).

[0030] The data collected includes historical continuous cropping information, current soil physicochemical properties, microbial community structure data, meteorological environmental time-series data, and basic parameters for seedling cultivation for the target planting plot. Specifically, this includes extracting peanut planting area, harvest yield, fertilizer type and amount, irrigation frequency and total amount, and pesticide application records for the target planting plot over the past three consecutive years from an agricultural machinery operation log database. Planting area and harvest yield are used to invert nutrient efflux per unit area; fertilizer type and amount are used to calculate cumulative nutrient input load; irrigation frequency and total amount are used to calibrate the initial soil moisture field; and pesticide application records are used to infer the evolution trend of pathogen resistance.

[0031] The bioavailable contents of nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, manganese, zinc, copper, and boron in soil were determined using inductively coupled plasma mass spectrometry (ICP-MS) through multi-point grid sampling. Sampling points were arranged in a 5m × 5m grid, with no fewer than 40 points per hectare, at a depth of 0 to 20 cm in the topsoil layer. After air-drying, grinding, and sieving through a 2mm sieve, bioavailable elements were extracted using Mehlich No. 3 extractant. Before analysis, a rhodium internal standard was added for signal correction to ensure a detection limit of less than 0.01 mg / kg.

[0032] High-throughput sequencing was used to sequence 16S rRNA genes and ITS region amplicon sequences from soil samples to obtain relative abundance matrices of phylum, class, order, family, and genus taxonomic units for bacteria and fungi. DNA extraction was performed using the PowerSoil kit. PCR amplification primers were 340F and 806R for bacterial V3-V4 regions, and ITS1F and ITS2R for fungal ITS1 regions. The amplified products were purified, quantified, and mixed before pairwise 250-base sequencing on the Illumina MiSeq platform. The raw sequences were quality filtered, dechimeric, and clustered into OTUs. The sequences were then compared with the SILVA and UNITE databases to generate taxonomic annotation results. The final output was a relative abundance matrix with rows representing samples and columns representing taxonomic units.

[0033] Miniature weather stations deployed at the edge of the site continuously record air temperature, relative humidity, precipitation, solar radiation intensity, wind speed, and wind direction data, with a sampling frequency of more than once every 10 minutes. The weather stations are equipped with platinum resistance temperature sensors, capacitive humidity sensors, tipping bucket rain gauges, silicon photodiode radiation sensors, and ultrasonic anemometers. All sensors are calibrated annually, and data is uploaded to a cloud platform in real time via a LoRa wireless module.

[0034] Basic parameters of candidate variety seedlings were collected, including thousand-seed weight, germination rate, germination potential, seedling height, root length, root fresh weight, leaf chlorophyll content, and the activities of disease-resistant enzymes (peroxidase POD, superoxide dismutase SOD, and polyphenol oxidase PPO) in seedlings pretreated with different biological agents. Seed germination rate and germination potential were determined according to GB / T3543.4-1995 "Specifications for Seed Inspection of Crops - Germination Test"; enzyme activities were determined by spectrophotometry. For POD, a change in absorbance at OD470nm per minute of 0.01 was defined as the enzyme activity unit; for SOD, an inhibition of 50% photochemical reduction of nitroblue tetrazolium (NBT) was defined as the enzyme activity unit; and for PPO, a change in absorbance at OD410nm per minute of 0.01 was defined as the enzyme activity unit.

[0035] The process involves inputting the collected data into the initial digital twin, driving its evolution into a dynamically updated running digital twin to achieve collaborative simulation of seedlings, soil, and environment. Specifically, this includes mapping historical continuous cropping information to the simulation kernel, which includes a nutrient consumption accumulation factor, a correction coefficient for the initial pathogen inoculum density, and a seedling disease resistance requirement weight. The nutrient consumption accumulation factor is defined as the cumulative difference between the amount of nitrogen, phosphorus, and potassium removed per unit area and the amount of nitrogen and phosphorus removed over three consecutive years, used to adjust the initial nutrient inventory. The initial pathogen inoculum density is calculated based on the frequency and type of pesticide application, using a resistance decay function to infer the current survival rate. The seedling disease resistance requirement weight is set according to the type of continuous cropping obstacle: 0.4 for pathogen-dominant types, 0.3 for nutrient imbalance-dominant types, and 0.3 for autotoxic substance-dominant types.

[0036] The current soil physicochemical properties data are used as the measured initial values ​​of each state variable in the simulation kernel, replacing the default initialization parameters. For example, the measured available potassium content is directly assigned to the liquid phase potassium concentration variable of each grid cell; the measured pH value is used to correct the proton saturation of cation exchange sites.

[0037] Microbial community structure data was converted into key functional gene abundance vectors using a pre-trained community function inference model, and then input into a microbial metabolic network module. This was simultaneously linked to a bio-agent pretreatment effect prediction model. The community function inference model, based on a random forest algorithm, takes genus-level relative abundance as input and outputs the abundance of the KEGGOrthology gene family involved in nitrification, denitrification, phosphorus solubilization, and iron transport. The training set is derived from a globally publicly available soil metagenomics project. The bio-agent pretreatment effect prediction model, based on a machine learning algorithm, uses agent type, concentration, treatment time, and soil microbial community structure as input to predict the increase in seedling disease-resistant enzyme activity.

[0038] Meteorological environmental time-series data are used as external driving fields to influence the boundary conditions of soil water and heat conduction equations, crop transpiration models, and seedling growth dynamic models. Air temperature and radiation intensity drive the surface energy balance equation to calculate net radiation and latent heat flux; precipitation serves as the upper boundary water flux input; wind speed affects the canopy turbulence exchange coefficient, thereby regulating the transpiration rate; accumulated temperature data drives the seedling growth and development process.

[0039] Basic parameters for seedling cultivation are input into a seedling growth dynamic model. Combined with soil and meteorological data, the model simulates the dynamic changes in germination rate, seedling survival rate, and disease resistance of seedlings under different varieties and pretreatment schemes. For example, seedling emergence time is predicted based on accumulated temperature data, root absorption efficiency is simulated by combining soil nutrient data, and seedling disease risk is predicted by combining pathogen abundance data.

[0040] Numerical integration of the entire coupled system using a time-stepping solver generates a dynamic distribution map of soil nutrients, pathogen population growth trajectory, rhizosphere microdomain pH change cloud map, and seedling growth adaptability prediction curves for the next 90 days. The solver employs the fourth-order Runge-Kutta method with a time step of 6 hours, and the spatial discretization uses the finite volume method to ensure mass conservation. Simulation outputs include daily concentrations of nitrate nitrogen, ammonium nitrogen, available phosphorus, and available potassium in each grid cell; biomass density of Fusarium and Rhizoctonia species; rhizosphere microdomain pH value; and predicted values ​​for germination rate, plant height, root length, and disease-resistant enzyme activity of different seedling varieties.

[0041] The process involves assessing the risk level of continuous cropping obstacles and analyzing seedling suitability based on the operational digital twin, generating a comprehensive diagnostic report that includes soil nutrient imbalance index, predicted pathogen abundance, intensity of root exudate cumulative effect, and seedling disease resistance coefficient. Specifically, this includes calculating the soil nutrient imbalance index. , defined as the standard deviation of the ratio of the available content of each macronutrient to its lower optimum threshold for peanuts. The lower optimum threshold for peanuts is set as follows: available nitrogen 120 mg / kg, available phosphorus 30 mg / kg, and available potassium 150 mg / kg. The calculation formula is: ; For the first Measured concentrations of the elements This is the lower limit of the optimal threshold. When A value greater than 0.3 is defined as a true imbalance.

[0042] To predict pathogen abundance, the population abundance curves of *Fusarium* and *Rhizoctonia* genera output by the simulation kernel are used to read their absolute abundance values ​​at the time corresponding to the flowering and pegging stage. The flowering and pegging stage is defined as 60 to 75 days after sowing, and the peak pathogen biomass within this period is taken as the predicted value. If it is greater than 10 per gram of dry soil... 4 Copy number is marked as high risk. The conversion between copy number and biomass is based on a standard curve, with approximately 10 copies per nanogram of DNA. 6 copy.

[0043] The intensity of the cumulative effect of root exudates was quantified based on the integrated concentration values ​​of phenolic acids and long-chain fatty acids in the rhizosphere microdomain, simulated in the simulation kernel. The rhizosphere microdomain was defined as the soil area within 0.5 mm of the root surface, and the integrated concentration was the daily concentration over time during the first 30 days after sowing. When this integrated value was greater than 50% of the historical average for the same period in crop rotation plots, it was considered to have a stronger inhibitory effect. The historical average for crop rotation plots was derived from control experiments of the maize-peanut rotation pattern within the same ecological zone over the past three years.

[0044] Calculate the disease resistance compatibility coefficient of seedlings Based on simulation results, the formula for matching the variety's disease resistance parameters, the effect of biological agent pretreatment, and the risk type of continuous cropping obstacles is as follows: , The disease resistance matching score is calculated based on the degree of fit between the disease resistance index of the variety and the type of obstacle in the plot, with a value of 0-1. The score for the adaptation of biological agent pretreatment (calculated based on the inhibitory effect of the agent on the barrier factors and the increase in the activity of seedling disease-resistant enzymes, with a value of 0-1). The seedling and soil-meteorological environment compatibility score is calculated based on the matching degree between seedling growth indicators and environmental conditions, with a value of 0-1. The coefficient ranges from 0 to 1, and a value greater than 0.7 is considered to be of excellent compatibility.

[0045] Based on the above four indicators, a weighted summation method was used to generate a comprehensive score for continuous cropping obstacle risk and seedling suitability, with weights of 0.3, 0.3, 0.1, and 0.3, respectively. The total score was... , The score is given for nutrient imbalance (1 for imbalance, 0 otherwise). Pathogen risk score (1 for high risk, 0 otherwise). The secretion effect is scored (1 for enhanced inhibition, 0 otherwise). This represents the disease resistance fitness coefficient of the seedlings. A total score greater than 0.6 indicates that collaborative intervention measures should be initiated.

[0046] Based on the comprehensive diagnostic report, differentiated seed and seedling optimization programs, soil improvement, and cultivation management strategies are formulated and implemented. Specifically, the seed and seedling optimization program includes: selecting varieties with the highest disease resistance compatibility coefficients as the primary varieties; determining the optimal inoculant type, concentration, and treatment method (seed soaking / seed dressing) based on the predicted effects of biological inoculant pretreatment; and prioritizing the use of pathogen-resistant compound inoculants in high-risk pathogen-prone areas at medium to high concentrations (5×10⁻⁶). 8 CFU / mL - 1×10 9 Soak seeds for 12-24 hours (CFU / mL); for high-risk plots of nutrient imbalance, prioritize the use of phosphorus- and potassium-solubilizing bacterial agents at a medium concentration (5×10⁻⁶ CFU / mL). 8 CFU / mL), seed dressing ratio 1:100; standardized cultivation of selected seedlings, seedbed temperature controlled at 25-28℃, humidity controlled at 60%-70%, and micronutrient solution (containing zinc, boron, iron, etc.) sprayed once a week after emergence to ensure a seedling survival rate of more than 90%.

[0047] Soil improvement strategy: When the soil nutrient imbalance index exceeds the standard, apply a compound conditioner seven days before sowing. This compound conditioner is composed of potassium humate, silicon-calcium-magnesium fertilizer, and phosphorus- and potassium-solubilizing bacteria in a mass ratio of 5:3:2. The effective content of potassium humate is greater than 55%, the total content of silicon oxide, calcium oxide, and magnesium oxide in the silicon-calcium-magnesium fertilizer is greater than 80%, and the viable count of the phosphorus- and potassium-solubilizing bacteria is greater than 2 × 10⁻⁶. 8 per gram.

[0048] The application rate is 40 kg per acre, precisely applied using a variable-rate fertilizer applicator according to the grid prescription map. When the predicted pathogen abundance is in the high-risk range, a bio-fumigation agent is simultaneously applied during land preparation. This bio-fumigation agent is a mixture of mustard seed powder and horseradish peroxidase. The mustard seed powder has a particle size of less than 200 micrometers to ensure rapid hydrolysis and release of isothiocyanates; the horseradish peroxidase activity is greater than 250 units per milligram, used to catalyze the polymerization of phenolic substances to form an antibacterial film. The mixing ratio is 100:1, and the application rate is 15 kg per acre. Immediately after application, it is rotary tilled to incorporate into the soil layer to a depth of 10 to 15 cm, and then covered and sealed for 72 hours to enhance the fumigation effect.

[0049] Cultivation and management strategies: When the intensity of the root exudate accumulation effect is determined to be a strong inhibitory effect, the sowing density is adjusted to 18,000 holes per mu (667 square meters), which is 15% lower than the conventional density, in order to reduce root competition and local accumulation of autotoxic substances; and exogenous proline solution is sprayed during the seedling stage at a concentration of 50 mmol / L and a spraying amount of 30 liters per mu.

[0050] Based on the seedling nutrient and water requirements predicted by digital twins, a precise fertilization and irrigation plan was developed. One week after transplanting, slow-release nitrogen fertilizer (urea) was applied at a rate of 5 kg per acre. Before flowering, phosphorus and potassium fertilizer (potassium dihydrogen phosphate) was applied at a rate of 3 kg per acre. Foliar spraying of micronutrient fertilizer (0.2%-0.3% concentration) was also performed every two weeks. Irrigation was carried out using drip irrigation, with the irrigation amount dynamically adjusted according to predicted soil moisture levels to maintain soil moisture content at 60%-80% of field capacity.

[0051] Real-time field data and seedling growth status data were continuously collected throughout the peanut growth cycle and fed back to the operational digital twin to correct model parameters and dynamically adjust subsequent seedling management, soil improvement, and field cultivation measures. Specifically, during the three key growth stages of emergence, flowering, and pod formation, a portable spectrometer was used to measure the normalized vegetation index of the canopy. The spectrometer band covered 400 to 1000 nanometers, and the sampling height was 1.5 meters. The average value of 5 points was taken for each measurement. At the same time, plant height, stem diameter, number of branches, number of root nodules, and number of pods were measured. Soil samples were collected to monitor nutrient dynamics and pathogen abundance changes. Sampling was carried out every 20 days, and the detection method was the same as described above.

[0052] Residual analysis is performed between the measured values ​​and the simulated values ​​from the digital twin. If the absolute value of the residual exceeds a preset threshold of 0.05, the parameter inversion algorithm is triggered. This parameter inversion algorithm employs a Bayesian optimization framework, with the objective function being the mean square error between the simulated and measured values. Optimization variables include photosynthetic efficiency coefficient, root absorption radius, water use efficiency, and seedling disease resistance response parameters (such as the disease resistance enzyme activity response coefficient and nutrient absorption efficiency adjustment coefficient). The optimal parameter combination is iteratively searched using a Gaussian process surrogate model, allowing for a maximum of 20 simulation evaluations.

[0053] Based on the revised model, the peak nutrient requirements, disease outbreak windows, and seedling growth status for subsequent growth stages are re-predicted. If the prediction shows that the available potassium content in the soil will be less than 80 mg / kg during the pod-setting stage, potassium sulfate will be applied at the end of the full bloom period at a rate of 10 kg / mu, using a drone for variable-rate application, with the prescription map generated by a digital twin. If the prediction shows that rainfall will be greater than 100 mm during the fruit-filling stage, drainage ditch dredging will be initiated in advance, ensuring a ditch depth of more than 30 cm and a slope of no less than 3 / 1000. A permeable rainproof film will be covered on the ridges, with a porosity controlled at 15%-20%, which can both prevent rainwater erosion and allow gas exchange to prevent root hypoxia. If seedling growth is observed to be weakened (plant height growth rate less than 0.5 cm / day), based on the model prediction, supplementary application of biostimulants (such as humic acid or amino acids) or adjustment of the foliar spraying scheme for microbial agents (concentration of 1×10⁻⁶) will be performed. 8 CFU / mL, spray once every 10 days).

[0054] The maintenance mechanism of the operational digital twin also includes an anomaly data processing protocol. When sensor-reported data exceeds historical extreme values ​​or violates physical constraints such as soil moisture content exceeding porosity, the system automatically marks the data point as suspicious and initiates a cross-validation procedure. Cross-validation utilizes triple verification based on neighboring sensor data, consistency with meteorological driving fields, and crop physiological rationality. After confirming anomalies, spatiotemporal kriging interpolation is used for correction to ensure the reliability of the input data.

[0055] Furthermore, the simulation kernel of the digital twin includes a stability monitoring module. After each time step integration, it checks whether each state variable satisfies conservation laws and monotonicity constraints. If numerical oscillations or non-physical solutions occur, it automatically reduces the time step or switches to an implicit solution format to ensure the robustness of long-term simulations.

[0056] This embodiment fully describes the entire technical solution from plot modeling, seed and seedling cultivation, data acquisition, twin-driven approach, risk assessment and seedling adaptation analysis, strategy formulation to dynamic feedback. All steps are operable and repeatable, meeting the requirements of full disclosure under patent law.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A planting method for alleviating peanut continuous cropping obstacles, characterized in that, include: Establish an initial digital twin of the target planting plot, and simultaneously carry out the screening of disease-resistant peanut varieties and the construction of a seedling pretreatment system; Collect historical continuous cropping information, current soil physicochemical properties data, microbial community structure data, meteorological environment time series data, and basic parameters for seedling cultivation of the target planting plots; The collected data is input into the initial digital twin, driving it to evolve into a dynamically updated running digital twin, thereby realizing the collaborative simulation of seedling-soil-environment; Based on the aforementioned operational digital twin, a risk level assessment of continuous cropping obstacles and seedling suitability analysis are performed, generating a comprehensive diagnostic report that includes soil nutrient imbalance index, pathogen abundance prediction value, root exudate cumulative effect intensity, and seedling disease resistance suitability coefficient. Based on the comprehensive diagnostic report, develop and implement differentiated variety and seedling optimization programs, soil improvement and cultivation management strategies; Real-time field data and seedling growth status data are continuously collected during the peanut growth cycle and fed back to the operational digital twin to correct model parameters and dynamically adjust subsequent seedling management, soil improvement and field cultivation measures.

2. The peanut planting method for alleviating continuous cropping obstacles according to claim 1, characterized in that, The process of establishing an initial digital twin of the target planting area, and simultaneously conducting screening of disease-resistant peanut varieties and constructing a seedling pretreatment system, includes: Obtain geospatial boundary information, topographic elevation data, soil type distribution map, and historical farming records of the target planting area; Based on the above information, construct the geometric entity model of the land parcel in the 3D spatial modeling engine; Within the geometric solid model, a physical-biological-seedling coupled simulation kernel is embedded, consisting of soil hydrothermal conduction equations, nitrogen, phosphorus and potassium migration and transformation kinetic models, organic matter mineralization rate functions, microbial metabolic networks and seedling growth dynamic models. Configure initial state variables for the simulation kernel, including soil bulk density, porosity, cation exchange capacity, basic organic matter content, background microbial species list and their relative abundance, and a database of disease resistance parameters for common peanut varieties; Based on the main types of continuous cropping obstacles in the target plots, 3-5 disease-resistant and suitable varieties were selected from the peanut variety resource bank to form a candidate variety list; For candidate varieties, a pretreatment scheme library based on biological agents was established, and parameters such as agent type, concentration gradient, treatment time, and seed dressing ratio were set.

3. The peanut planting method for alleviating peanut continuous cropping obstacles according to claim 2, characterized in that, The data collected include historical continuous cropping information, current soil physicochemical properties, microbial community structure data, meteorological environmental time-series data, and basic parameters for seedling cultivation for the target planting plots. Extract peanut planting area, harvest yield, fertilizer type and amount, irrigation frequency and total amount, and pesticide application records for the past three consecutive years from the agricultural machinery operation log database. The available contents of nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, manganese, zinc, copper and boron in the soil were determined by inductively coupled plasma mass spectrometry through multi-point grid sampling. The 16S rRNA gene and ITS region amplicon sequencing of soil samples were performed using a high-throughput sequencing platform to obtain the relative abundance matrix of taxonomic units of bacteria and fungi at the phylum, class, order, family, and genus. By deploying miniature weather stations at the edge of the plot, data on air temperature, relative humidity, precipitation, solar radiation intensity, wind speed and wind direction are continuously recorded. Basic parameters of candidate variety seedlings were collected, including thousand-seed weight, germination rate, germination potential, seedling height, root length, root fresh weight, leaf chlorophyll content, and disease resistance-related enzyme activity of seedlings after pretreatment with different biological agents.

4. The peanut planting method for alleviating peanut continuous cropping obstacles according to claim 3, characterized in that, The process of inputting the collected data into the initial digital twin and driving it to evolve into a dynamically updated running digital twin to achieve collaborative simulation of seedling-soil-environment includes: Historical continuous cropping information is mapped to the nutrient consumption accumulation factor, the correction coefficient of the initial inoculum density of pathogens, and the seedling disease resistance requirement weight in the simulation kernel. The current soil physicochemical properties data are used as the measured initial values ​​of each state variable in the simulation kernel, replacing the default initialization parameters; Microbial community structure data is converted into key functional gene abundance vectors through a pre-trained community function inference model and input into the microbial metabolic network module, while also being associated with a biological agent pretreatment effect prediction model. Meteorological environmental time series data are used as an external driving field to apply the boundary conditions of the soil water and heat conduction equation, crop transpiration model and seedling growth dynamic model. The basic parameters of seedling cultivation are input into the seedling growth dynamic model. Combined with soil and meteorological data, the dynamic changes of germination rate, seedling survival rate and disease resistance of seedlings under different varieties and different pretreatment schemes are simulated. The entire coupled system is numerically integrated using a time-stepping solver to generate a dynamic distribution map of soil nutrients, a pathogen population growth trajectory, a rhizosphere micro-domain pH change cloud map, and a seedling growth adaptability prediction curve for the next 90 days.

5. A peanut planting method for alleviating continuous cropping obstacles according to claim 4, characterized in that, The continuous cropping obstacle risk level assessment and seedling suitability analysis based on the operational digital twin generate a comprehensive diagnostic report including soil nutrient imbalance index, pathogen abundance prediction value, root exudate cumulative effect intensity, and seedling disease resistance suitability coefficient, including: The soil nutrient imbalance index is calculated and defined as the standard deviation of the ratio of the available content of each macro-element to the lower limit of its optimum threshold for peanuts. When the standard deviation is greater than 0.3, it is defined as a true imbalance. To predict pathogen abundance, the population abundance curves of Fusarium and Rhizoctonia species output by the simulation kernel are used to read their absolute abundance values ​​at the time corresponding to the flowering and pegging stage. If the absolute abundance value is greater than 10 per gram of dry soil, the abundance is predicted. 4 The copy number is marked as high risk; The intensity of the cumulative effect of root exudates was quantified based on the concentration integral values ​​of phenolic acids and long-chain fatty acids in the rhizosphere microdomain simulated in the simulation kernel. When the concentration integral value was greater than 50% of the average value of the same period in historical rotation plots, it was determined to be an enhanced inhibition effect. The disease resistance adaptation coefficient of seedlings was calculated. Based on the simulation results, the matching degree of the variety disease resistance parameters, the effect of biological agent pretreatment and the risk type of continuous cropping obstacles was considered. The coefficient ranged from 0 to 1, and a value greater than 0.7 was considered to be of excellent adaptability. Based on the above four indicators, a weighted summation method is used to generate a comprehensive score for continuous cropping obstacle risk and seedling suitability. If the total score is greater than 0.6, collaborative intervention measures will be initiated.

6. A peanut planting method for alleviating peanut continuous cropping obstacles according to claim 5, characterized in that, The process of continuously collecting real-time field data and seedling growth status data throughout the peanut growth cycle, feeding this data back to the operational digital twin to correct model parameters, and dynamically adjusting subsequent seedling management, soil improvement, and field cultivation measures includes: During the three key growth stages of seedling emergence, flowering, and pod formation, the normalized vegetation index of the canopy was measured, plant growth indicators were determined simultaneously, and soil samples were collected to monitor nutrient dynamics and pathogen abundance changes. Residual analysis is performed between the measured values ​​and the simulated values ​​of the digital twin. If the absolute value of the residual is greater than the preset threshold, the parameter inversion algorithm is triggered to correct the photosynthetic efficiency coefficient, root absorption radius, water use efficiency and seedling disease resistance response parameters in the simulation kernel online. Based on the revised model, the peak nutrient demand, disease occurrence window, and seedling growth status of subsequent growth stages are re-predicted. The application of topdressing, drainage, and biological agents will be dynamically adjusted based on the forecast results.

7. A peanut planting method for alleviating continuous cropping obstacles according to claim 6, characterized in that, The parameter inversion algorithm adopts a Bayesian optimization framework. The objective function is the mean square error between the simulated and measured values. The optimization variables include photosynthetic efficiency coefficient, root absorption radius, water use efficiency, and seedling disease resistance response parameters. The optimal parameter combination is searched iteratively through a Gaussian process surrogate model.

8. A peanut planting method for alleviating continuous cropping obstacles according to claim 7, characterized in that, The seedling growth dynamic model is based on the accumulated temperature method and the physiological development time model, integrating the varietal disease resistance parameters and the effect of biological agent pretreatment to simulate the seedling germination, rooting, growth and disease resistance response process.

9. A peanut planting method for alleviating continuous cropping obstacles according to claim 8, characterized in that, The operational digital twin is equipped with an abnormal data processing protocol. When the data reported by the sensor exceeds the historical extreme value range or violates physical constraints, the system automatically marks the data point as suspicious and starts a cross-validation procedure. After triple verification using neighboring sensor data, consistency of meteorological driving field and rationality of crop physiology, spatiotemporal kriging interpolation is used for correction.

10. A peanut planting method for alleviating peanut continuous cropping obstacles according to claim 9, characterized in that, The running digital twin is equipped with a stability monitoring module, which checks whether each state variable satisfies the conservation law and monotonicity constraint after each time step integration. If numerical oscillation or non-physical solution occurs, the time step is automatically reduced or the solution format is switched to implicit solution.

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