CN-121970661-A - Peanut planting method for relieving continuous cropping obstacle of peanuts

CN121970661ACN 121970661 ACN121970661 ACN 121970661ACN-121970661-A

Abstract

The invention relates to the technical field of agricultural information and seed seedling cultivation, and discloses a peanut planting method for relieving continuous cropping obstacle of peanuts. The method comprises the steps of constructing an initial digital twin body of a land block, synchronously constructing a disease-resistant peanut variety screening and seedling pretreatment system, fusing historical continuous cropping information, soil physicochemical and microorganism data, meteorological time sequence data and seedling cultivation parameters, driving the continuous cropping information, the soil physicochemical and microorganism data, the meteorological time sequence data and the seedling cultivation parameters to evolve into an operational digital twin body, realizing seedling-soil-environment collaborative simulation, evaluating continuous cropping obstacle risks and seedling suitability based on the twin body, generating a comprehensive diagnosis report containing nutrient imbalance indexes, pathogen abundance predictions, root secretion accumulation effects and seedling disease-resistant adaptation coefficients, and implementing differentiated seed seedling optimization, soil improvement and cultivation strategies according to the comprehensive diagnosis report, and dynamically optimizing management measures through real-time data feedback in a growth period. The invention realizes the source prevention, control and intervention of continuous cropping obstacle through the cooperative driving of digital twin and seed seedling cultivation.

Inventors

TAN JIANRONG
TAN XIAOMING
ZHANG XUYAN
Cheng Yuman
He Chengdui

Assignees

阳江市漠阳花农业科技有限公司
广东漠阳花粮油有限公司

Dates

Publication Date: 20260505
Application Date: 20260120

Claims (10)

1. A planting method for alleviating continuous cropping obstacle of peanuts, which is characterized by comprising the following steps: Establishing an initial digital twin body of a target planting land, and synchronously carrying out screening of disease-resistant peanut varieties and construction of a seedling pretreatment system; Collecting historical continuous cropping information, current soil physicochemical property data, microbial community structure data, meteorological environment time sequence data and seedling cultivation basic parameters of a target planting land block; Inputting the acquired data into the initial digital twin body, and driving the initial digital twin body to evolve into a dynamically updated running state digital twin body so as to realize the collaborative simulation of seedling-soil-environment; performing continuous cropping obstacle risk level evaluation and seedling suitability analysis based on the running state digital twin body to generate a comprehensive diagnosis report containing soil nutrient imbalance index, pathogen abundance predicted value, root secretion cumulative effect strength and seedling disease resistance adaptation coefficient; according to the comprehensive diagnosis report, a differentiated variety and seedling optimization scheme and a soil improvement and cultivation management strategy are formulated and executed; Continuously collecting real-time field data and seedling growth state data in a peanut growth period, feeding back to the running state digital twin body to correct model parameters, and dynamically adjusting subsequent seedling management, soil improvement and field cultivation measures.
2. The peanut planting method for relieving peanut continuous cropping obstacles according to claim 1, wherein the establishing of the initial digital twin body of the target planting land parcels, the synchronous development of disease-resistant peanut variety screening and seedling pretreatment system construction, comprises: Obtaining geographic space boundary information, terrain elevation data, a soil type distribution map and a historical cultivation record of a target planting land block; based on the information, constructing a geometric solid model of the land block in a three-dimensional space modeling engine; A physical-biological-seedling coupling simulation kernel consisting of a soil hydrothermal conduction equation, a nitrogen-phosphorus-potassium migration transformation kinetic model, an organic matter mineralization rate function, a microorganism metabolism network and a seedling growth dynamic model is embedded in the geometric solid model; Configuring initial state variables for the simulation kernel, wherein the initial state variables comprise soil volume weight, porosity, cation exchange capacity, basic organic matter content, background microorganism species list and relative abundance thereof, and a common peanut variety disease resistance parameter library; Screening 3-5 disease-resistant adaptive varieties from a peanut variety resource library based on the main types of continuous cropping obstacles of the target land parcels to form a candidate variety list; Aiming at candidate varieties, a pretreatment scheme library based on biological bacterial agents is established, and bacterial agent types, concentration gradients, treatment time and seed dressing proportion parameters are set.
3. The peanut planting method for alleviating peanut continuous cropping obstacle according to claim 2, wherein the collecting of historical continuous cropping information, current soil physicochemical property data, microbial community structure data, meteorological environment time sequence data and seedling cultivation basic parameters of a target planting land comprises: extracting peanut sowing areas, harvest yields, fertilization types and amounts, irrigation frequency and total amount and pesticide application records of plots in the past 3 years continuously through an agricultural machinery operation log database; Measuring the effective state contents of nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, manganese, zinc, copper and boron in the soil by utilizing an inductively coupled plasma mass spectrometer through multipoint gridding sampling; Sequencing the amplicon of the 16SrRNA gene and ITS region of the soil sample by using a high-throughput sequencing platform to obtain a relative abundance matrix of phylum, class, order, family and genus classification units of bacteria and fungi; Continuously recording data of air temperature, relative humidity, precipitation, solar radiation intensity, wind speed and wind direction through miniature weather stations arranged at the edge of the land block; Collecting basic parameters of candidate variety seedlings, including thousand seed weight, germination rate, germination vigor, seedling height, root length, root fresh weight, leaf chlorophyll content, and seedling disease-resistant related enzyme activity after pretreatment by different biological bactericides.
4. A peanut planting method for alleviating peanut continuous cropping obstacles as claimed in claim 3, wherein said inputting the collected data into said initial digital twin to drive its evolution into dynamically updated running digital twin to realize a seedling-soil-environment co-simulation comprising: mapping the historical continuous cropping information into nutrient consumption accumulation factors in the simulation kernel, correction coefficients of initial inoculum density of pathogenic bacteria and seedling disease resistance demand weights; Taking the current soil physicochemical property data as actual measurement initial values of all state variables in the simulation kernel to replace default initialization parameters; converting the microbial community structure data into a key functional gene abundance vector through a pre-trained community function inference model, inputting the key functional gene abundance vector into a microbial metabolism network module, and simultaneously associating a biological agent pretreatment effect prediction model; Taking the meteorological environment time sequence data as an external driving field to act on the boundary conditions of a soil hydrothermal conduction equation, a crop transpiration model and a seedling growth dynamic model; The seedling cultivation basic parameters are input into a seedling growth dynamic model, and the dynamic changes of germination rate, seedling formation rate and disease resistance of seedlings under different varieties and different pretreatment schemes are simulated by combining soil and meteorological data; and (3) carrying out numerical integration on the whole coupling system through a time stepping solver to generate a soil nutrient dynamic distribution map, a pathogenic bacteria population growth track, a rhizosphere micro-domain pH change cloud map and a seedling growth suitability prediction curve within 90 days in the future.
5. The peanut planting method for relieving peanut continuous cropping obstacle according to claim 4, wherein the step of performing continuous cropping obstacle risk level assessment and seedling suitability analysis based on the running state digital twin body to generate a comprehensive diagnosis report comprising soil nutrient imbalance index, pathogen abundance prediction value, root secretion accumulation effect intensity and seedling disease resistance adaptation coefficient comprises the following steps: Calculating a soil nutrient imbalance index, which is defined as the standard deviation of the ratio of the effective state content of each macroelement to the lower limit of the optimum threshold of the peanut, and is defined as true imbalance when the standard deviation is greater than 0.3; Predicting the abundance of pathogenic bacteria, reading absolute abundance values of fusarium and rhizoctonia at the moment corresponding to the flowering needle-down period through population quantity curves of fusarium and rhizoctonia output by the simulation kernel, and marking the fusarium and rhizoctonia as high risk if the absolute abundance values are more than 10 4 copies per gram of dry soil; Quantifying the accumulated effect intensity of root exudates, and judging the strengthening inhibition effect according to the concentration integral value of the phenolic acids and the long-chain fatty acids in the rhizosphere micro-domain, which are simulated in the simulation kernel, when the concentration integral value is greater than 50% of the contemporaneous mean value of the historical rotation plots; Calculating a seedling disease-resistant adaptation coefficient, and based on simulation results, integrating the matching degree of disease-resistant parameters of varieties, pretreatment effects of biological bacteria and continuous cropping obstacle risk types, wherein the coefficient value range is 0-1, and the suitability is judged to be excellent when the coefficient value range is more than 0.7; and (3) combining the four indexes, and generating a comprehensive score of continuous cropping obstacle risk and seedling adaptation by adopting a weighted summation method, wherein the total score is more than 0.6, and then starting cooperative intervention measures.
6. The peanut planting method for alleviating peanut continuous cropping obstacles of claim 5, wherein the continuously collecting real-time field data and seedling growth state data in a peanut growth period, feeding back to the running state digital twin body to correct model parameters, and dynamically adjusting subsequent seedling management, soil improvement and field cultivation measures comprises: Measuring canopy normalized vegetation indexes in 3 key growth stages of seedling emergence stage, flowering stage and pod bearing stage, synchronously measuring plant growth indexes, and collecting soil samples to monitor nutrient dynamics and pathogen abundance changes; Carrying out residual analysis on the actual measurement value and the digital twin body synchronous simulation value, and triggering a parameter inversion algorithm to carry out online correction on photosynthetic efficiency coefficient, root absorption radius, water utilization efficiency and seedling disease resistance response parameters in the simulation kernel if the absolute value of the residual is larger than a preset threshold value; based on the corrected model, predicting the nutrient demand peak value, the disease occurrence window period and the seedling growth situation in the subsequent growth stage again; and dynamically adjusting topdressing, drainage and biological microbial agent spraying according to the prediction result.
7. The peanut planting method for relieving peanut continuous cropping obstacle according to claim 6, wherein the parameter inversion algorithm adopts a Bayesian optimization framework, the objective function is the mean square error of the simulation value and the actual measurement value, and the optimization variables comprise photosynthetic efficiency coefficient, root absorption radius, water utilization efficiency and seedling disease resistance response parameters, and the optimal parameter combination is searched for through iteration of a Gaussian process proxy model.
8. The peanut planting method for relieving peanut continuous cropping obstacles according to claim 7, wherein the seedling growth dynamic model is based on a thermal accumulating method and a physiological development time model, integrates disease resistance parameters of varieties and biological agent pretreatment effects, and simulates seedling germination, rooting, growth and disease resistance response processes.
9. The peanut planting method for relieving peanut continuous cropping obstacle according to claim 8, wherein the running state digital twin body is provided with an abnormal data processing protocol, when the reported data of the sensor exceeds a historical extremum range or violates physical constraint, the system automatically marks the data point as suspicious, and a cross verification program is started, and correction is carried out by using space-time kriging interpolation after triple verification of adjacent sensor data, weather driving field consistency and crop physiological rationality.
10. The peanut planting method for relieving peanut continuous cropping obstacles according to claim 9, wherein the running state digital twin body is provided with a stability monitoring module, whether each state variable meets conservation law and monotonicity constraint is checked after each time step integration, and if numerical concussion or non-physical understanding occurs, the time step is automatically reduced or the system is switched to an implicit solving format.

Description

Peanut planting method for relieving continuous cropping obstacle of peanuts Technical Field The invention belongs to the field of intersection of agricultural information technology and seed seedling cultivation technology, and particularly relates to a peanut planting method for relieving continuous cropping obstacle of peanuts. Background Along with popularization of agricultural intensive and specialized planting modes, peanuts are used as important oil and economic crops in China, and long-term continuous cropping obstacles are faced in main production areas. The continuous cropping causes unbalance of the soil ecosystem and is characterized by nutrient structural disturbance, accumulation of autotoxic substances, frequent occurrence of soil-borne diseases and decay of beneficial microbial communities, so that plant growth is restrained, disease resistance is reduced and yield and quality are continuously reduced. The traditional relief strategy relies on empirical rotation, chemical fumigation or organic fertilizer application and other means, and has a certain effect in a short period, but lacks accurate sensing and response mechanisms for dynamic change of soil microecology, and does not take seed seedling cultivation as a core link for system integration, so that systematic and sustainable treatment of continuous cropping obstacles is difficult to realize. The intelligent agricultural technology based on digital twinning provides a new paradigm for solving continuous difficulties. The digital twin intelligent agricultural technology realizes full-element digital characterization and dynamic simulation of a crop-soil-environment system by constructing real-time mapping between a physical farmland and a virtual model. However, the existing digital twin application focuses on macroscopic management scenes such as irrigation, weather or yield prediction, key agronomic measures such as seed treatment, microorganism regulation and control, rotation system optimization and the like are not deeply integrated, and an intelligent decision system for performing closed-loop linkage on disease-resistant variety breeding, biological microbial inoculum intervention, seedling cultivation and soil health state is especially lacking. In the prior art, variety selection still depends on field trial planting for many years, the period is long, the cost is high, seed treatment is lack of pertinence, accurate adaptation is not carried out by combining with the microecological characteristics of soil, the adaptability of the seedling cultivation process and the continuous cropping soil environment is insufficient, the disease resistance effect is limited, the pest and disease prevention and control are mainly carried out in a post response mode, and a pre-intervention mechanism based on risk prediction is lacked. More importantly, various farm operations are mutually split, and a continuous optimization closed loop of seedling cultivation, monitoring, simulation, decision making, execution and verification is not formed, so that a continuous cropping obstacle relief scheme is fragmented, high in hysteresis and poor in adaptability. Under the realistic constraints of shortage of land resources and limitation of rotation space in a main peanut producing area, a deep fusion digital twin technology, seed seedling cultivation innovation and an intelligent planting method of a modern agronomic system are needed, and accurate diagnosis, dynamic simulation and collaborative regulation and control of continuous cropping obstacles are realized. Disclosure of Invention The invention provides a planting method for relieving continuous cropping obstacle of peanuts, and aims to solve the problems of unbalanced soil nutrients, aggravation of plant diseases and insect pests and yield reduction caused by continuous cropping of the peanuts. According to the method, by constructing a high-fidelity digital twin body covering the whole life cycle of a land, combining seed seedling cultivation key technology, multisource heterogeneous farmland sensing data and a crop physiological model, dynamic identification, quantitative evaluation and accurate intervention of continuous cropping obstacle factors are realized, so that on the premise of not changing a rotation system, the micro-ecological balance of soil is effectively recovered, the proliferation of soil-borne pathogenic bacteria is inhibited, the nutrient supply structure is optimized, the disease resistance of seedlings is improved, and the single yield level and the quality stability of peanuts are finally improved. The invention provides a planting method for relieving continuous cropping obstacle of peanuts, which comprises the following steps: Establishing an initial digital twin body of a target planting land, and synchronously carrying out screening of disease-resistant peanut varieties and construction of a seedling pretreatment system; Collecting historical continuous cropping in