Crop growth environment data processing method

Abstract

The invention relates to the technical field of crops, in particular to a crop growth environment data processing method. The method comprises the following steps: acquiring crop planting environment data, acquiring soil sample data, and detecting soil characteristics; predicting a microbial dynamic activity attenuation state based on soil characteristics, calculating a soil structure stability gradient attenuation degree, and evaluating a soil growth potential constraint trend; the method comprises the following steps: collecting crop growth climate data, analyzing the climate data, estimating a growth response situation of crops, detecting a disorder condition of a crop growth cycle according to a soil growth potential constraint trend, identifying disorder of the growth cycle, evaluating an abnormal state of a crop growth environment, and analyzing an abnormal driving factor; the crop growth environment is optimized in combination with the abnormal driving factors and the abnormal state of the growth environment, and crop growth environment optimization data are obtained; according to the invention, the growth environment of crops is optimized, so that the growth environment of crops is more stable.

Description

Technical Field

[0001] The present invention relates to the technical field of crops, and particularly to a method for processing crop growth environment data. Background Art

[0002] The processing of crop growth environment data relies on single meteorological data or soil tests, providing references for certain environmental parameters of crops, but still insufficient for the comprehensive evaluation of multi-variable and complex environments. Existing crop growth environment monitoring technologies mostly rely on fixed-point data collection, which limits the monitoring scope and cannot fully reflect the spatial distribution characteristics of soil and climate conditions. For example, key parameters such as soil humidity, temperature, and salinity are often collected only in local areas, lacking comprehensive and real-time data support. Most existing technologies cannot effectively integrate the complex relationships between multiple environmental parameters (such as temperature, humidity, salinity, pH value, etc.) and crop growth status. This results in limited dynamic monitoring and prediction effects of crop growth environments, making it difficult to achieve personalized and precise farmland management. However, traditional crop growth environment data processing has problems of inaccurate analysis of crop growth environments and inaccurate analysis of crop growth soil conditions. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for processing crop growth environment data to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for processing crop growth environment data includes the following steps:

[0005] Step S1: Obtain crop planting environment data; collect crop soil sample data according to the crop planting environment data; detect crop soil characteristics according to the crop soil sample data;

[0006] Step S2: Predict the dynamic activity decay state of microorganisms based on crop soil characteristics; estimate the gradient decay degree of soil structure stability based on the dynamic activity decay state of microorganisms; evaluate the constraint trend of soil growth potential based on the gradient decay degree of soil structure stability;

[0007] Step S3: Collect crop growth climate data based on the crop planting environment; estimate the crop growth response trend according to the crop growth climate data; detect the disorder situation of the crop growth cycle based on the crop growth response trend and the constraint trend of soil growth potential;

[0008] Step S4: Evaluate the abnormal state of the crop growth environment based on the disorder of the crop growth cycle; analyze the abnormal driving factors of the crops by using the abnormal state of the crop growth environment; optimize the crop growth environment according to the abnormal driving factors of the crops and the abnormal state of the crop growth environment to obtain the optimized data of the crop growth environment.

[0009] By obtaining the crop planting environment data and collecting the soil sample data, the present invention can accurately detect the soil characteristics and understand the basic conditions of the soil. This provides a basis for predicting the dynamic activity attenuation state of microorganisms subsequently, thus helping to estimate the soil structure stability and attenuation degree, and further evaluating the constraint trend of the soil growth potential. This process ensures a comprehensive understanding of the soil ecosystem and provides data support for the healthy growth of crops. Based on the collection and analysis of the crop growth climate data, the growth response trend of the crops is accurately predicted, and the disorder of the growth cycle caused by environmental factors is timely identified, thus providing a basis for early intervention. Through the monitoring and analysis of the growth cycle, the abnormal state of the crop growth environment can be comprehensively evaluated, and by deeply analyzing the abnormal driving factors, a scientific basis for optimizing the crop growth environment can be provided. This series of operations enables the real-time adjustment of the crop growth environment, and the optimized environmental data lays a foundation for subsequent measures such as soil improvement and planting mode optimization, thus promoting the healthy growth of crops, improving production efficiency, and reducing the negative impact of the natural environment on crop growth. Therefore, the present invention is an optimized treatment for the traditional crop growth environment data processing, solving the problems existing in a traditional crop growth environment data processing method, including inaccurate analysis of the crop growth environment and inaccurate analysis of the crop growth soil conditions. It improves the accuracy of analyzing the crop growth environment and the accuracy of analyzing the crop growth soil conditions. Brief Description of the Drawings

[0010] Figure 1 It is a schematic diagram of the step flow of a crop growth environment data processing method;

[0011] Figure 2 It is Figure 1 a detailed implementation step flow schematic diagram of step S3 in

[0012] Figure 3 It is Figure 1 a detailed implementation step flow schematic diagram of step S4 in

[0013] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0014] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0015] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0016] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0017] To achieve the above object, please refer to Figures 1 to 3 , a method for processing crop growth environment data, comprising the following steps:

[0018] Step S1: Obtain crop planting environment data; collect crop soil sample data according to the crop planting environment data; detect crop soil characteristics according to the crop soil sample data;

[0019] Step S2: Predict the dynamic activity decay state of microorganisms based on the crop soil characteristics; estimate the degree of gradient decay of soil structure stability based on the dynamic activity decay state of microorganisms; evaluate the trend of soil growth potential constraint based on the degree of gradient decay of soil structure stability;

[0020] Step S3: Collect crop growth climate data based on the crop planting environment; estimate the crop growth response trend according to the crop growth climate data; detect the crop growth cycle disorder situation based on the crop growth response trend and the soil growth potential constraint trend;

[0021] Step S4: Evaluate the abnormal state of the crop growth environment based on the disorder of the crop growth cycle; analyze the abnormal driving factors of the crop using the abnormal state of the crop growth environment; optimize the crop growth environment according to the abnormal driving factors of the crop and the abnormal state of the crop growth environment to obtain the optimized data of the crop growth environment.

[0022] In the embodiment of the present invention, with reference to Figure 1 As shown, in this example, the method for processing crop growth environment data includes the following steps:

[0023] Step S1: Obtain crop planting environment data; collect crop soil sample data according to the crop planting environment data; detect the crop soil characteristics according to the crop soil sample data;

[0024] In the embodiment of the present invention, obtaining crop planting environment data is the starting step of the entire data processing process. In this embodiment, the crop planting environment data is automatically collected through a farm management system. The system obtains various types of data on the crop growth environment in real time through sensors deployed at various key positions in the farmland. Specifically, the data collected by the sensors includes soil temperature, soil humidity, light intensity, precipitation, air temperature, air humidity, etc. These data will be uploaded to the cloud server in real time to ensure the timeliness and accuracy of the data and facilitate subsequent data processing and analysis. The collected data will be stored in the database and used as the basic data for subsequent operations such as soil characteristic analysis and prediction of microbial dynamic activity. After collecting these environmental data, the system determines the sampling time and location according to the soil sample requirements in the crop planting area. The specific sampling areas are distributed in different geographical locations, such as the north side, south side, middle part, and edge areas of the soil, etc. To ensure the representativeness and reliability of the sampling, the sampling depth is set to 0 - 20 cm, and a standardized soil sampler is used, which can maintain the original state of the soil sample and avoid the influence of external environmental factors such as temperature and humidity on the soil sample. After the soil sample is collected, it is quickly processed in the laboratory, and the sample is sent to an advanced analysis instrument for further analysis. Through high-precision instruments, the system can detect the chemical and physical properties of the soil, such as soil particle size (e.g., whether it is sandy soil, clay, etc.), porosity (i.e., the size of the storage space for gas and water in the soil), pH value, etc. After all these physical and chemical property data are processed by the analysis system, the basic characteristics of the soil can be accurately judged according to these data, and a detailed soil characteristic report is formed. This report will contain the soil characteristic data of each sampling point.

[0025] Step S2: Predict the decay state of microbial dynamic activity based on the crop soil characteristics; estimate the degree of gradient decay of soil structure stability based on the decay state of microbial dynamic activity; evaluate the constraint trend of soil growth potential based on the degree of gradient decay of soil structure stability;

[0026] In the embodiments of the present invention, after the soil characteristics are detected, predicting the dynamic activity decay state of microorganisms based on the soil characteristics of crops is an important step next. In this implementation process, multiple important parameters of the soil are collected, including salt concentration, organic matter content, and pH value, etc. The soil salt concentration is usually an important influencing factor for microbial activity. When the soil salt concentration is too high, it will inhibit the metabolic activities of microorganisms and even cause the death of microorganisms. The soil organic matter content directly affects the living environment of microorganisms, and organic matter is the main energy source for microbial metabolism. In addition, the pH value of the soil also affects the activities of microbial populations and the changes in populations. After obtaining these soil physical and chemical property data, the metabolic activities of microorganisms in the soil are monitored in real time by a soil microbial activity monitor. The monitor can sense the microbial metabolites in the soil, such as the release rate of CO2 and the consumption rate of oxygen, etc. These metabolites are important indicators of microbial activities. The instrument calculates the dynamic activity changes of microorganisms through the real-time detection of these metabolites. Combining with the soil physical and chemical property data, the system predicts the dynamic activity of microorganisms according to the set algorithms and decay models. For example, when the soil salt concentration exceeds 5.2 dS / m, the activities of microorganisms will show a significant decline, and the system will automatically calculate the degree of decline, such as the decay state is 85%. At this time, the system inputs these real-time data into the prediction model and combines the known soil conditions to obtain the specific value of the dynamic activity decay of microorganisms. Next, based on the dynamic activity decay state of microorganisms, the decay model is used to predict the gradient decay of the stability of the soil structure. When the microbial activity decreases, the decomposition rate of organic matter in the soil will also slow down, and the organic matter in the soil cannot be converted into nutrients in time, resulting in a decrease in the stability of the soil structure. The decline in microbial activities will affect the cohesiveness and structural compactness of the soil, further affecting the porosity, air permeability and other characteristics of the soil, and then leading to the gradual destruction of the soil structure. In this process, the system predicts the changes in soil stability according to the degree of dynamic activity decay of microorganisms. For example, when the decay degree reaches 85%, the soil structure stability will decrease by 15%. Then, based on the gradient decay degree of the soil structure stability, the system evaluates the constraint trend of the soil growth potential. The degradation of the soil structure will directly affect the growth potential of crops, especially for the growth of roots and the absorption of water and nutrients. Through this continuous dynamic monitoring and evaluation, the constraint trend of the soil growth potential in the next period of time is predicted.

[0027] Step S3: Collect crop growth climate data based on the crop planting environment; estimate the crop growth response trend according to the crop growth climate data; detect the crop growth cycle disorder situation based on the crop growth response trend and the soil growth potential constraint trend;

[0028] In an embodiment of the present invention, when collecting climate data for crop growth and predicting the growth response of crops, climate monitoring equipment such as weather stations are installed in the farm. These weather stations can collect and record climate data such as temperature, humidity, light intensity, wind speed, precipitation, etc. in real time, and upload these data to the database via a wireless network. The latest climate data is analyzed through centralized storage and real-time updating of data. These data are an important basis for predicting crop growth, because the growth state of crops is closely related to climatic conditions, especially factors such as temperature, humidity and light have a direct impact on the photosynthesis, transpiration and root absorption capacity of crops. Next, the system uses a climate model to estimate the growth response of crops based on the collected climate data and historical climate patterns. The climate model will predict the growth status according to the changing trends of current temperature, humidity and light, combined with the physical and chemical properties of the soil (such as porosity, humidity, pH, etc.) and the decay state of microbial activity. During this prediction process, the system will analyze the potential impact of climate change on crop growth. For example, when the temperature is too high, the evaporation of water intensifies, resulting in crop water shortage; and when the humidity is too low, crop growth is restricted. Assume that through the calculation of the climate model, the system estimates that the growth response of crops is "normal growth", indicating that the current climate conditions are suitable for crop growth; "hindered growth", that is, under the existing climate conditions, crop growth is adversely affected. If the system determines that the growth response of crops is "hindered growth", it will enter the next step to detect whether there is a disorder in the crop growth cycle. In this process, the system not only considers the impact of climate change on crop growth, but also conducts a comprehensive analysis in combination with soil characteristics and growth data. For example, when high temperature and insufficient precipitation occur, the system detects that crops are at risk of "insufficient water" or "high temperature stress". These factors limit the water absorption of crop roots, which in turn affects the normal growth of crops and even causes a disorder in the growth cycle. The system monitors the growth cycle of crops in real time, promptly detects changes between climate, soil and crop status, and quickly identifies potential problems that affect crop growth.

[0029] Step S4: Evaluate the abnormal state of the crop growth environment based on the disorder of the crop growth cycle; use the abnormal state of the crop growth environment to analyze the abnormal driving factors of the crops; optimize the crop growth environment according to the abnormal driving factors of the crops and the abnormal state of the crop growth environment to obtain crop growth environment optimization data.

[0030] In the embodiments of the present invention, after identifying the disorder of the crop growth cycle, the abnormal state of the crop growth environment is further evaluated. This process relies on a real-time monitoring system, which consists of multiple sensors and monitoring devices and can continuously collect environmental parameters such as soil and climate, such as soil humidity, air temperature, precipitation, wind speed, light intensity, etc. When large fluctuations or deviations in the monitored environmental parameters are detected, the abnormal state evaluation is automatically started. For example, if the monitoring data shows that the temperature and humidity exceed the normal range and the soil humidity is low, the system will determine that the crop growth environment is abnormal. At this time, the system will comprehensively analyze the causes of the abnormal state and conduct data analysis to identify the changing trends of various factors in the environment. The system will combine climate change and soil characteristics to determine the root cause of the abnormality, such as too high temperature or insufficient precipitation, or too high soil salt concentration, uneven nutrients, etc. The system will quantitatively evaluate these potential problems through data models and empirical rules to confirm the specific driving factors of the abnormality. Suppose the system detects that the high soil salt content is the main factor causing the abnormality, the system will further analyze the reasons for this phenomenon. For example, through the analysis of historical climate data, it is found that the high salt content is related to the lack of proper irrigation or soil salt accumulation, and the system will recommend corresponding soil improvement measures, such as using organic fertilizers or taking irrigation measures to dilute the salt. When the situation of insufficient nutrients is detected, suggestions for fertilization or soil improvement will also be given to ensure that the crops obtain sufficient nutrient support. In addition, the system also improves the growth environment by optimizing the crop planting pattern. For example, according to the current soil and climate conditions, the system recommends adjusting the planting density of the crops, selecting suitable varieties, or recommending rotation methods to avoid excessive consumption of soil nutrients. These optimization suggestions are all generated based on the growth environment optimization data, and the system will further combine all relevant data to provide a scientific planting pattern and environmental adjustment plan for the crops, so as to help the crops grow under the best environmental conditions. All the optimization plans can not only improve the current growth environment.

[0031] Preferably, step S1 includes the following steps:

[0032] Step S11: Obtain crop planting environment data;

[0033] In the embodiments of the present invention, crop planting environment data is obtained. A set of sensor devices will be installed in the farm management system, and these sensors will monitor and collect relevant environmental data in real time in the crop planting area. The collected data includes soil temperature, soil humidity, light intensity, precipitation, air temperature, air humidity, etc. These environmental data are uploaded to the central server through a wireless transmission module. In the specific operation process, through the accuracy of the sensors and real-time data collection, it is ensured that the environmental data has high timeliness and accuracy. Each sensor uses a highly sensitive probe, which can accurately monitor parameters such as temperature changes, humidity fluctuations in the soil, and changes in the environmental climate.

[0034] Step S12: Collect crop soil sample data according to the crop planting environment data;

[0035] In the embodiment of the present invention, according to the environmental data collected from the crop planting environment, the crop soil sample data is further collected. To ensure the representativeness of the soil samples, the sampling work is carried out according to the predetermined distribution area and depth. During the operation, several sampling points will be selected in the crop planting area, such as different positions on the north side, south side, middle and field edge, etc., and a reasonable sampling depth will be selected according to the growth stage of the crop, which is 0-20 cm. A soil sampler is used to extract soil samples from each sampling point according to the depth. The sampler is made of a material that is not easily contaminated to avoid the influence of the external environment on the samples.

[0036] Step S13: Estimate the physical properties of the crop soil according to the crop soil sample data;

[0037] In the embodiment of the present invention, based on the collected crop soil sample data, the physical properties of the soil are estimated. The physical properties include the particle size, porosity, soil structure, air permeability, etc. of the soil. The specific operation process is to conduct laboratory analysis on the collected soil samples, use a particle size distribution instrument to separate and measure the soil particles, and then obtain the particle size distribution curve of the soil. Then, a porosity meter is used to test the pore ratio and water retention capacity of the soil. These physical property data are crucial for understanding the drainage capacity, air circulation, and water holding capacity of the soil.

[0038] Step S14: Estimate the chemical properties of the crop soil according to the crop soil sample data;

[0039] In the embodiment of the present invention, based on the collected soil sample data, the chemical properties of the crop soil are further estimated. The chemical properties include the soil pH value, organic matter content, salt concentration, and the content of nutrients such as nitrogen, phosphorus, and potassium. For this purpose, a series of chemical analyses are carried out in the laboratory. A pH meter is used to measure the soil pH value. Then, the content of main nutrient elements such as nitrogen, phosphorus, and potassium in the soil is determined by chemical titration or using an ion selective electrode. Finally, a salt concentration meter is used to measure the salt concentration in the soil.

[0040] Step S15: Detect the crop soil properties based on the physical properties and chemical properties of the crop soil.

[0041] In the embodiments of the present invention, the physical and chemical property data of crop soil are combined to comprehensively detect the characteristics of crop soil. By analyzing the physical and chemical property data of the soil, the overall quality of the soil is systematically evaluated. For example, the porosity and particle size data in the physical properties reveal the drainage and air permeability of the soil, while the pH value, salt concentration, etc. in the chemical properties determine the nutrient supply capacity and acid-base adaptability of the soil. Combining the data in these two aspects, the system can obtain the comprehensive characteristic evaluation results of the soil, such as the fertility status of the soil, the types of crops suitable for planting, and potential soil improvement measures.

[0042] Preferably, step S13 includes the following steps:

[0043] Step S131: Identify the crop soil particle size data using the crop soil sample data with a depth of 0 - 20 cm.

[0044] In the embodiments of the present invention, the crop soil sample data with a depth of 0 - 20 cm collected is used to identify the soil particle size data. The specific operation is to conduct a physical analysis of the soil sample through the standard soil particle size distribution test method in the laboratory. During the experiment, the sieving method or laser particle size analyzer is used. First, the soil sample is dried to remove moisture and impurities. Then, the soil sample is divided into particles of different particle sizes using a standard sieve mesh, the mass of each level of particles is weighed one by one, and the relative content of each particle level is calculated. The laser particle size analyzer measures the particle size distribution curve of the soil sample through the principle of laser scattering, analyzes the particle size range of the soil particles with a high-precision instrument, and obtains the detailed data of the soil particle size.

[0045] Step S132: Measure the soil porosity according to the crop soil particle size data.

[0046] In the embodiments of the present invention, measuring the porosity of the soil based on the data of the particle size of the crop soil is a key step in understanding the soil water retention capacity, air permeability, and drainage performance. Soil porosity represents the proportion of the void volume in the soil to the total soil volume, usually expressed as a percentage, and has a direct impact on the growth of crops. The measurement of porosity generally adopts the volume method, and the specific operation is as follows: Prepare a dry soil sample with a known mass, weigh the dry weight of the soil using a balance to ensure that the moisture content of the sample is close to zero. Then, conduct a water immersion test, completely immerse the soil sample in water to ensure that the pores in the soil are completely filled with water. During this process, the density difference of the soil enables the calculation of the soil volume through the displacement of water, thereby obtaining the total soil volume data. Next, place the soaked soil sample in a special container for a saturated water penetration test. Through this test, the proportion of water in the pores of the soil sample can be determined, and this proportion directly reflects the size of the soil porosity. Specifically, after the soil sample is saturated with water and after a period of time, measure the water flow rate through the soil pores, and combine the mass and volume data of the sample to calculate the percentage of porosity.

[0047] Step S133: Evaluate the crop soil texture data based on the soil porosity and the data of the particle size of the crop soil;

[0048] In the embodiments of the present invention, during the process of evaluating the texture of soil based on the particle size data and porosity data of the soil, it is understood that soil texture refers to the relative proportions of the three soil particles, namely sand, clay, and silt, in the soil, and these proportions directly affect the water permeability, water retention capacity, fertility, and physical structure of the soil. The basic principle of evaluating soil texture is to comprehensively analyze the particle size and porosity data in combination with the standard soil texture triangle diagram, collect the particle size data of the soil, and measure the specific contents of sand, clay, and silt therein. Sand particles are usually larger and have strong water permeability, while clay particles are smaller and have strong water retention capacity, and silt is in between. By grading the particle size, the proportions of sand, clay, and silt in the soil are obtained. Then, using these proportion data in combination with the measurement results of porosity, the aeration and permeability of the soil are further analyzed. Porosity reflects the proportion of voids in the soil, and a higher porosity usually means stronger aeration and water retention capacity of the soil. Next, by combining the particle size data with the porosity data, the standard soil texture triangle diagram or interpolation method is used to determine the texture type of the soil. In the soil texture triangle diagram, the three corners respectively represent the percentages of sand, clay, and silt, and each point in the diagram represents a specific soil texture. For example, when the proportion of sand is relatively high, the texture of the soil is loose and has strong water permeability, usually sandy soil; if the proportion of clay is relatively high, the soil shows strong water retention and viscosity, usually clay; and the presence of silt determines the water retention characteristics of the soil. During the experiment, according to the data in the soil sample, its position in the texture triangle diagram is calculated by the interpolation method to determine its soil type. For example, if the sand particle content is 60%, the clay is 20%, and the silt is 20%, then according to the triangle diagram, the soil is determined to be sandy loam type. By comprehensively analyzing the particle size and porosity data and combining with the soil texture triangle diagram, the specific texture type of the soil, such as sandy soil, clay soil, loam soil, etc., is evaluated, and the evaluation data of the soil texture are obtained.

[0049] Step S134: Estimate the flow capacity of the crop soil based on when the soil porosity is greater than 48.2% and the crop soil texture data;

[0050] In an embodiment of the present invention, in the process of estimating the soil flow capacity of crops based on the soil porosity being greater than 48.2% and soil texture data, the concept of soil flow capacity is understood, that is, the penetration and flow rate of water in the soil. The soil porosity directly affects the flow capacity of water in the soil. Generally, the higher the soil porosity, the more water and gas the soil can hold, which will affect the water penetration rate and the water fluidity of the soil. Assuming that the soil porosity is greater than 48.2%, this condition usually indicates that the soil particles are smaller, the water content is larger, and there is better water carrying capacity. Then, according to the soil texture data, analyze the proportions of sand, clay, and loam in the soil, which further affect the water penetration rate. For example, the penetration rate of sandy soil is faster, while that of clayey soil is slower. Next, use a penetration experiment to measure the soil penetration rate, and test the soil sample with a permeameter. During the experiment, by controlling different hydraulic gradients, record the soil penetration speed, which is usually expressed in centimeters of penetration per hour (such as 2.8 - 4.2 cm / h) to represent the penetration rate. Based on this data, calculate the soil flow capacity. If the soil penetration rate is low, it means that the soil drainage is poor, waterlogging is likely to occur, and water cannot quickly penetrate into deeper soil layers. This kind of soil is likely to cause water to accumulate on the surface layer, resulting in insufficient oxygen supply to the roots and affecting the growth of crops. On the contrary, when the penetration rate is high, it indicates that the soil drainage is good, water can quickly flow away, resulting in poor soil water holding capacity, and crops face drought problems. By measuring and analyzing the penetration rate, predict the soil flow capacity.

[0051] Step S135: Calculate the soil water retention capacity of crops when the penetration rate of the soil flow capacity of crops is 2.8 - 4.2 cm / h;

[0052] In the embodiments of the present invention, calculating the water retention capacity of the soil based on the infiltration rate data of the soil flow capacity of crops involves a comprehensive analysis of the soil water retention characteristics, and the infiltration rate data of the soil is obtained through infiltration experiments. Assume that in the experiment, the infiltration rate is measured to be 2.8 - 4.2 cm / h, which reflects the speed of water infiltrating downward in the soil. Then, in combination with the initial water content of the soil sample, further calculation of the water retention capacity is carried out. The initial water content of the soil refers to the water content in the soil at the time of sampling, which is measured by the drying method or an electronic moisture meter. At this time, the infiltration rate is combined with the initial water content, and the calculation is carried out through the water flow model. The specific method is to adopt a soil water transfer model, such as the Richards equation or the Kostiakov model, to simulate the infiltration process of water in the soil, and calculate the maximum amount of water that the soil can retain based on the infiltration rate. For example, if the infiltration rate is low, it means that the water loss rate of the soil is slow, and the soil can retain water for a longer time, thereby improving its water retention capacity. On the contrary, a higher infiltration rate indicates better drainage performance of the soil and weaker water retention capacity. Through this calculation, the water retention capacity data of the soil can be obtained.

[0053] Step S136: Detect the oxygen content of the crop soil based on the soil flow capacity of the crop;

[0054] In the embodiments of the present invention, by measuring the soil flow capacity, the infiltration rate and water retention capacity data of the soil are obtained, and these factors directly affect the gas exchange and oxygen concentration in the soil. Specifically, a gas exchange tester is used to detect the oxygen content in the soil. By inserting an oxygen sensor into the soil sample, the oxygen sensor will continuously monitor the oxygen concentration in the soil. The soil sample is usually collected in the root distribution area of the crop to ensure obtaining real rhizosphere environment data. The sensor will continuously monitor the change in oxygen content and transmit the data to the control system for recording and analysis. When the oxygen content is lower than 10%, it indicates that there is an oxygen deficiency in the soil. This oxygen deficiency phenomenon causes a decrease in the microbial activity in the soil, and the decomposition and metabolism processes of microorganisms are inhibited, thereby affecting the soil fertility and nutrient cycling. For crops, the low-oxygen environment will hinder the normal respiration of the roots, and further affect the absorption of water and nutrients, resulting in plant growth retardation. The system will evaluate the soil aeration and water drainage capacity based on the detected oxygen content data. If the oxygen content is continuously lower than 10%, measures such as improving the soil structure, increasing organic matter, or taking aeration improvement measures, such as turning the soil or using breathable materials, are taken to improve the oxygen supply in the soil to ensure that the crops can grow in a suitable environment.

[0055] Step S137: Estimate the physical properties of the crop soil when the oxygen content of the crop soil is lower than 10% and the water retention capacity of the crop soil.

[0056] In the embodiments of the present invention, when the oxygen content in the crop soil is lower than 10%, the physical properties of the soil, especially its air permeability and water retention capacity, can be effectively estimated by combining the data of the soil water retention capacity. In the measurement of the soil oxygen content, if the result is lower than 10%, it indicates that gas exchange in the soil is relatively difficult, resulting in an oxygen-deficient state of the soil. At this time, it is particularly important to analyze by combining the data of the soil water retention capacity. Soils with strong water retention capacity usually mean that the soil has a high ability to retain water, which indicates that the connectivity of the soil pores is poor, and it is more difficult for water to drain out, and water is likely to accumulate in the soil. When there is too much water accumulated in the soil, the oxygen-deficient situation will be further aggravated, because water-saturated soil cannot provide enough oxygen for the roots and microorganisms, causing root asphyxiation or attenuation of microbial activity, and then affecting the healthy growth of crops. In addition, when there is too much water, it will also increase the viscosity of the soil, reduce the air permeability of the soil, and form a wet environment. When in such an environment for a long time, the microbial community and soil health of the soil will be inhibited, and the beneficial functions of microorganisms (such as organic matter decomposition, nitrogen fixation, etc.) will not be able to function properly, resulting in a decline in soil fertility. At the same time, the wet environment also causes root rot, and crops cannot effectively absorb nutrients and water, and their growth is significantly affected. By comprehensively analyzing the oxygen content and water retention capacity, the drainage performance, air permeability, and water control ability of the soil can be accurately evaluated, so as to provide guidance for soil improvement and optimization of the crop planting environment.

[0057] Preferably, step S14 includes the following steps:

[0058] Step S141: Detect the elemental composition characteristics of the soil based on the crop soil sample data;

[0059] In the embodiments of the present invention, the sampled crop soil sample is dried and crushed. The drying process is usually carried out in a temperature-controlled environment of 60-80 degrees Celsius to ensure that the moisture in the soil sample is completely removed, avoiding the interference of moisture on the elemental analysis results. The dried soil sample is crushed to a certain particle size, usually 0.1-0.2 mm, to ensure uniform soil particles. The main elemental components in the soil, such as nitrogen, phosphorus, potassium, calcium, magnesium, iron, etc., are detected by using a soil chemical analysis instrument (such as an atomic absorption spectrometer or an X-ray fluorescence spectrometer). During the operation, the soil sample is first dried and crushed, and then dissolved or digested to convert the elements in the soil sample into a detectable state. Through this process, the content data of various elements in the soil sample are obtained.

[0060] Step S142: Detect the acid-base properties of the soil according to the crop soil sample data;

[0061] In the embodiments of the present invention, a pH meter is used to detect the acidity and alkalinity of the soil. During the specific operation process, the soil sample that has been dried and crushed is mixed with deionized water in a certain proportion to form a soil suspension. By inserting the pH meter into the suspension, the pH value is measured in real time and this value is recorded. This data can reflect the acidity and alkalinity of the soil. Acidic soil usually has a pH value lower than 7, while alkaline soil has a pH value higher than 7.

[0062] Step S143: Statistically analyze the data of the nutrient content in the crop soil according to the characteristics of the soil element composition;

[0063] In the embodiments of the present invention, after obtaining the soil element composition data, the measurement results of each element are statistically analyzed. Generally, by using a nutrient analysis model to calculate the content of the main nutrients in the soil (such as nitrogen, phosphorus, potassium and trace elements, etc.), the specific operation steps include data standardization and concentration conversion. The measured value of each element is compared with the weight of the sample to calculate the specific content of each element in the soil, usually in units of mg / kg or g / kg. The key to this calculation process is to obtain the element concentration value according to the measurement instrument (for example, the concentration value measured by an atomic absorption spectrometer or an X-ray fluorescence spectrometer), and then according to the mass or volume of the soil sample collected during the experiment, the actual content of the element per kilogram of soil is obtained through conversion. To perform the conversion of nutrient concentration, a standardization formula or a nutrient analysis model is usually used to obtain accurate concentration data. Taking nitrogen as an example, after measuring the nitrogen content, according to its nitrogen element concentration value, its corresponding nutrient concentration (such as mg / kg) is calculated. Similar operations are applicable to elements such as phosphorus, potassium and trace elements. After the concentration values of these elements are converted, they are transformed into the specific content of various nutrients in the soil, which is convenient for subsequent analysis. For each nutrient, according to its proportion and existence state in the soil, it is further subdivided into different types of nutrient data such as available nutrients and total nutrients.

[0064] Step S144: Determine the characteristics of the soil nutrient dissolution ability based on the soil acid-base properties for the data of the nutrient content in the crop soil;

[0065] In the embodiments of the present invention, according to the soil acidity and alkalinity data measured in step S142 and combined with the soil nutrient content data, the evaluation of the nutrient dissolution ability of the soil is carried out by laboratory solution chemistry methods. In this process, by measuring the soil acidity and alkalinity (pH value) and combining with the content of the main nutrients in the soil, the solubility and absorbability of these nutrients in different pH environments are evaluated. The experiment usually starts with simulating the changes in the acid-base environment in the soil. For this purpose, appropriate experimental methods are selected to simulate the changes in the pH value under natural soil conditions. In the experimental operation, the soil sample is placed in a solution with controlled pH, and usually, the soil acidity and alkalinity are adjusted by adding acids (such as sulfuric acid) or alkalis (such as sodium hydroxide) with known concentrations. The experimental steps include measuring and recording the change in pH value, taking out a certain amount of the solution or dissolution solution at the same time, and analyzing the nutrient concentration. Chemical analysis instruments (such as ion-selective electrodes or atomic absorption spectrometers) are used to measure the nutrient content in the solution, and the change in the solubility of nutrients under different pH conditions is recorded. By comparing the differences in the solubility of various elements (such as phosphorus, calcium, magnesium, etc.) in the soil under different pH values, the dissolution ability of soil nutrients in different acid-base environments is obtained. Usually, acidic soil will reduce the solubility of phosphorus because at lower pH, phosphorus is easily combined with iron and aluminum ions to form insoluble compounds and is difficult to be absorbed by plants. In alkaline soil, the solubility of elements such as calcium and magnesium will also be affected. Especially when the pH value is too high, some calcium and magnesium will form insoluble salts, resulting in ineffective dissolution and absorption by crops. Through these test data, it is judged that under specific pH conditions, the solubility of certain nutrients will increase or decrease.

[0066] Step S145: Evaluate the soil fertility status of crops according to the characteristics of soil nutrient dissolution ability and the crop soil nutrient content data;

[0067] In the embodiments of the present invention, the acidic soil affects the solubility of phosphorus mainly because in an environment with a lower pH value, iron and aluminum ions in the soil will combine with phosphate ions to form insoluble iron phosphate and aluminum phosphate compounds. These compounds are difficult to be absorbed by plant roots in the soil, resulting in a decrease in the availability of phosphorus. In an acidic environment, the solubility of phosphorus is often low, and plants are difficult to obtain sufficient phosphorus, which in turn affects the growth and yield of crops. At the same time, in acidic soil, the solubility of certain trace elements such as copper and manganese is relatively high, leading to excessive absorption of these elements by plants, resulting in toxic effects and affecting the health of crops. Under acidic soil conditions, special attention should be paid to the application rate and application method of phosphate fertilizer during fertilization to avoid reducing the availability of phosphorus due to too low soil pH value. Alkaline soil also has a significant impact on the solubility of elements such as calcium and magnesium. In an alkaline environment, the pH value of the soil is relatively high, which usually causes the calcium and magnesium elements in the soil to react with anions in the soil to form insoluble salts. For example, calcium combines with excessive carbonate ions to form calcium carbonate, or combines with sulfate ions to form calcium sulfate. These salts are difficult to dissolve in water, and the available amounts of calcium and magnesium in the soil are significantly reduced. Plant roots cannot effectively absorb these insoluble salts, resulting in a lack of calcium and magnesium, which in turn affects plant growth. Especially in the development of fruits and the construction of cell walls, the deficiency of calcium and magnesium leads to physiological diseases of crops, such as blossom-end rot or chlorosis. Through the experimental data of the soil solution, it is possible to evaluate how the solubility of specific nutrients changes under different pH conditions. For example, if the pH value of the soil is low, observe whether the solubility of phosphate decreases and whether the solubility of trace elements increases. Similarly, if the soil is alkaline, whether the solubility of nutrients such as calcium and magnesium is inhibited, affecting the availability of crops. Through these data, evaluate the soil fertility status of crops.

[0068] Step S146: Estimate the chemical properties of the crop soil according to the soil fertility status of the crop and the soil acid-base properties.

[0069] In the embodiments of the present invention, the fertility status and acid-base characteristics of the soil are comprehensively considered. The fertility status generally includes the concentrations of the main nutrients in the soil, such as nitrogen, phosphorus, potassium, calcium, magnesium, etc., while the acid-base characteristics are reflected by the pH value, which indicates the acidity or alkalinity of the soil. The pH value of the soil directly affects the availability of nutrients because the solubility and absorbability of nutrients vary in different pH value environments. In addition, the Cation Exchange Capacity (CEC) and Base Saturation are key indicators for judging the chemical properties of the soil, which can reflect the ion exchange capacity of the soil at different pH values, and thus affect the stability and availability of nutrients in the soil. In this process, the soil chemical model plays a core role. Based on data such as nutrient content, pH value, CEC, and Base Saturation in the soil, through multivariate analysis methods and combined with existing soil chemical theories, the model comprehensively evaluates the chemical properties of the soil. Specifically, when operating, the nutrient data of the soil sample are collected, and indicators such as its pH value and CEC are measured. By establishing the relationship between soil nutrients and these chemical parameters, the model can calculate the exchange capacity and retention capacity of the soil for different nutrients under specific acid-base conditions. During the model calculation process, the CEC is used as a key indicator, which reflects the cation exchange capacity of the soil and thus affects the stability of nutrients in the soil. For example, soils with a higher CEC have a stronger nutrient retention capacity, but if the soil pH value is too low, the solubility of some nutrients, such as calcium, magnesium, and phosphorus, is low, which limits the absorption by crops. On the contrary, when the alkalinity of the soil is too high, the availability of some trace elements, such as iron and manganese, decreases, resulting in a lack of these essential trace elements in plants. Through the comprehensive analysis of the model, the overall chemical properties of the soil are obtained, including the acid-base balance, nutrient exchange capacity, and nutrient stability of the soil.

[0070] Preferably, the prediction of the microbial dynamic activity decay state in step S2 includes:

[0071] Measuring the over-large data of the crop soil salt concentration when the crop soil property exceeds 5.2 dS / m;

[0072] In the embodiments of the present invention, when obtaining crop soil characteristic data, especially when the soil salt concentration is relatively high, the electrical conductivity (EC) of the soil is measured. Electrical conductivity is an important indicator for measuring the ion concentration in the soil solution and directly reflects the salt concentration in the soil. To obtain accurate soil salt concentration data, soil samples should be collected from the soil surface to a certain depth (e.g., 0 - 20 cm) during operation. Ensure that the soil samples are highly representative during sampling to avoid sample deviation. Next, mix the collected soil samples with distilled water in a certain proportion. Usually, a soil - to - water ratio of 1:1 or 1:2 is used to ensure uniform mixing. The mixed soil solution needs to stand for a period of time to allow the soluble salts in the soil to completely dissolve. Then, use a soil conductivity meter to measure the conductivity of the mixed solution. The conductivity meter estimates the salt concentration in the solution by measuring the conductance ability of the solution, usually with the unit dS / m (the conductivity in deionized water is 0). When the measurement result shows that the soil conductivity exceeds 5.2 dS / m, it indicates that the salt concentration of the soil is relatively high, meeting the data requirement for excessive soil salt concentration. Obtain the data of excessive crop soil salt concentration.

[0073] Predict the growth of soil osmotic pressure based on the data of excessive crop soil salt concentration;

[0074] In the embodiments of the present invention, based on the data of excessive soil salt concentration, the next step is to calculate the growth of soil osmotic pressure. Osmotic pressure refers to the attraction exerted by solute molecules on solvent molecules in a solution and is usually used to reflect the concentration state of the soil solution, especially the salt concentration. The level of osmotic pressure directly affects the movement of soil water and the water absorption of plant roots. Therefore, it is an important parameter for evaluating the availability of soil water. In this process, an osmometer is used to measure the osmotic pressure of the soil solution. During operation, prepare the processed soil sample solution, that is, the soil solution measured by the conductivity meter in the previous step. Then, transfer the mixed soil solution to the measurement cell of the osmometer. The working principle of the osmometer is to determine the osmotic pressure value of the solution by measuring the change in osmotic pressure caused by the solute in the solution. Ensure that the solution volume of the sample is sufficient and uniform during operation to avoid interference from bubbles or impurities on the measurement results. The osmometer senses the change in osmotic pressure of the soil solution through a sensor and gives an accurate osmotic pressure value. Based on this data, the change in osmotic pressure caused by the increase in soil salt concentration can be evaluated. Further, according to the measured osmotic pressure data, calculate the change trend of soil osmotic pressure at different salt concentrations.

[0075] Identify the soil water - binding state when the growth of soil osmotic pressure exceeds 1.25 bar;

[0076] In the embodiments of the present invention, according to the osmotic pressure growth situation, if the soil osmotic pressure exceeds 1.25 bar, the bound state of soil moisture is further identified. The moisture bound state refers to the degree of restriction of the availability of moisture in the soil. Especially under high salt concentration conditions, moisture is more easily adsorbed by soil particles and is difficult to be effectively absorbed by plant roots. To analyze the bound state of soil moisture, moisture binding tests are usually used, and the most common method is the pressure membrane method. The pressure membrane method measures the moisture content in the soil under different pressures, and then evaluates the degree of moisture binding. The specific operation process is as follows: take a soil sample and place it in a specific soil pressure membrane device. This device applies different pressures (such as 0.33 bar, 1.5 bar, and 3 bar, etc.) on the soil sample to simulate the moisture state of the soil under different humidity conditions. As the pressure increases, the free moisture in the soil is gradually removed, and only the moisture tightly adsorbed by the soil particles remains. By using this method, the moisture content of the soil under different pressures is accurately measured, and a moisture binding curve is drawn. According to the data obtained by the pressure membrane method, the proportion of moisture in different bound states is calculated. If the test results show that the bound state of soil moisture exceeds 65%, it means that most of the moisture in the soil has been tightly bound and is not easily absorbed by plant roots, indicating that the water use efficiency of the soil is greatly reduced, which has a negative impact on the growth of crops.

[0077] Detect the degree of water shortage of soil microbial cells when the bound state of soil moisture exceeds 65%;

[0078] In the embodiments of the present invention, according to the osmotic pressure growth situation, if the soil osmotic pressure exceeds 1.25 bar, the bound state of soil moisture is further identified. The analysis of the moisture bound state is completed through a moisture binding test (such as the pressure membrane method). The test shows that when the soil moisture bound state reaches 65%, it means that most of the moisture in the soil has been tightly adsorbed by soil particles and is not easily absorbed by plant roots. The moisture state has a negative impact on the microbial community in the soil. In the case where the soil moisture bound state reaches 65%, it is necessary to detect the degree of water shortage in microbial cells. This detection process is usually completed through a microbial activity test. The specific operation steps involve the analysis of the metabolic activity of the soil microbial community. This analysis is usually carried out using a biochemical kit. By testing the changes in microbial metabolites in the soil sample, the activity of the microorganisms and the degree of water shortage in the cells are evaluated. During the test, samples are taken and the soil samples are exposed to standard conditions, and the metabolites produced by the microorganisms in the soil, such as enzyme activity and CO2 release, are detected using the kit. The changes in these metabolic activities reflect the overall activity level of the microorganisms. If the test results show a significant decrease in microbial activity, especially a decrease in metabolites, it indicates that the cells of the microorganisms are in an inactive state due to water shortage. Usually, the loss of intracellular water in microorganisms in arid or high-salt environments will lead to the inhibition of metabolic processes, thus affecting their growth and reproduction abilities. The degree of water shortage in microbial cells is judged by detecting the reduction in microbial metabolites. Specifically, if the metabolic activity of soil microorganisms drops to a predetermined threshold (for example, the enzyme activity decreases by more than 50%), it is considered that the degree of water shortage in the cells of soil microorganisms is high, and this phenomenon directly affects the growth of microorganisms and the overall ecological function of the soil.

[0079] Estimating the toxic effect of soil microorganisms using the large data of the salt concentration in crop soil;

[0080] In the embodiments of the present invention, based on the data of excessive soil salt concentration and combined with the composition of salts in the solution, the process of predicting the microbial toxicity effect in the soil is carefully determined through a laboratory model. Salts in the soil can have a toxic effect on the microbial community. Especially in an environment with a high salt concentration, the growth of microorganisms will be inhibited. Detailed data on soil salt concentration and salt composition are obtained through laboratory tests. By measuring the electrical conductivity (EC) of the soil and analyzing the composition of salts therein, the type and concentration range of soil salts can be initially judged. This data provides a basis for subsequent analysis of microbial toxicity effects. Next, in the laboratory, standard soil microorganism culture methods are used, and petri dishes and standard culture media are used to simulate the growth status of microorganisms in a high-salt environment. Usually, different salt concentration control groups and experimental groups are set up in the laboratory. By gradually increasing the salts in the solution (such as common salts like sodium chloride and sodium sulfate), and observing the growth status of microorganisms. By measuring parameters such as the growth rate of microorganisms, the number of cell proliferations, and metabolic activity, the inhibitory effect of different salt concentrations on microorganisms can be intuitively seen. When the salinity increases, the metabolic rate of microorganisms will decrease significantly, the cell proliferation rate slows down or even stops, and the reduction of metabolites (such as CO2 release, enzyme activity, etc.) is also used as an indirect sign of the toxic effect. Through experimental data, the growth inhibition of microorganisms in a high-salt environment can be obtained, and further predict the toxic effect of soil salts on the microbial community.

[0081] Detect the ion imbalance status of soil microorganisms based on the microbial toxicity effect in the soil;

[0082] In the embodiments of the present invention, based on the data of microbial toxicity effect, the ion imbalance status of soil microorganisms is further detected. The ion imbalance test is carried out by the ion selective electrode method, analyzing the content changes of key elements (such as sodium, calcium, magnesium, etc.) in the soil to determine whether the ion balance is disturbed. This process helps to reveal the impact of excessive salts on soil microorganisms. The specific operation is to measure the ion concentration in the soil solution through an electrode to obtain the impact of ion imbalance on microorganisms.

[0083] Predict the degree of inhibition of the microbial metabolic process based on the ion imbalance status of microorganisms and the microbial toxicity effect in the soil;

[0084] In the embodiments of the present invention, according to the microbial ion imbalance condition and toxicity effect data, it is further predicted whether the metabolic process of microorganisms is inhibited. The key to this process is to evaluate the degree of inhibition of their metabolic activities by measuring the respiration rate or metabolic indicators of microorganisms. As a direct indicator of microbial metabolic activities, the respiration rate is monitored by a gas exchange analysis device. During the decomposition of organic matter by microorganisms, carbon dioxide (CO2) is released, and this process is called respiration. Using a microbial respiration analyzer (such as a respiration analyzer based on infrared absorption method or electrochemical principle), the amount of CO2 released in the microbial sample is monitored in real time, and this value is an important reference for evaluating its metabolic level. When microorganisms face ion imbalance or salt toxicity, the integrity of the cell membrane, the normal operation of metabolic pathways, and the generation of energy are all disturbed, resulting in a significant decrease in the respiration rate. During the experiment, the microbial community is cultured under certain conditions to ensure that it is in a stable metabolic state. Then, the amount of CO2 released is measured and recorded regularly by the gas exchange analysis device. If the respiration rate in the experimental group is lower than that in the control group, or gradually decreases with the increase of salt concentration, it indicates that the metabolic process of microorganisms is inhibited. By comparing the changes in the respiration rate under different salt concentrations and different toxicity conditions, the degree of inhibition of microbial metabolism is quantified. In addition, in addition to the respiration rate, metabolic-related indicators such as enzyme activity, ATP content, and the generation of metabolites are measured. The combination of these data helps to comprehensively evaluate the decline of the metabolic function of microorganisms in a high-salt environment.

[0085] Detect the growth inhibition of soil microorganisms according to the toxicity effect of soil microorganisms and the degree of water shortage in soil microbial cells;

[0086] In the embodiments of the present invention, based on the test results of the toxic effects of soil microorganisms and the degree of cell water deficiency, the growth inhibition of soil microorganisms is further analyzed. In specific operations, the inhibition degree is determined by comparing the microbial growth curve and measuring its metabolic activity. When the growth inhibition degree of microorganisms exceeds 60%, it indicates that the activity of microorganisms in the soil has decayed severely. To achieve this analysis, microorganisms are cultivated in different soil samples through standard cultivation methods, and their growth conditions are measured regularly. The growth curve is obtained by recording the change in the number of microbial colonies or cell density, and techniques such as the optical density method, plate counting method, and fluorescence microscopy counting method are usually used. Over time, the growth curve of microorganisms is plotted to analyze their growth trends under different salt concentrations and water deficiency conditions. If under certain conditions, the growth curve of microorganisms is significantly lower than that of the control group, it indicates that their growth is inhibited. On this basis, combined with the data on the degree of cell water deficiency, the cause of the inhibition is further confirmed. Cell water deficiency is usually evaluated by measuring the osmotic pressure and water content of microbial cells, and detection tools for microbial cell hydration, such as fluorescent dyes or mass spectrometry techniques, are used to quantitatively analyze the hydration of microbial cells. Through these tests, the impact of water deficiency on microbial growth is accurately identified, and then its inhibition degree is evaluated. If the test results show that the growth of microorganisms is significantly affected, that is, the inhibition degree exceeds 60%, it can be judged that the activity of microorganisms in the soil has significantly decayed.

[0087] When the growth inhibition of soil microorganisms exceeds 60% and the inhibition degree of the microbial metabolic process predict the dynamic activity decay state of microorganisms.

[0088] In the embodiments of the present invention, when the degree of soil microorganism growth inhibition exceeds 60%, in combination with the degree of inhibition of the metabolic process, the dynamic activity decay state of soil microorganisms is further predicted, and the data on soil microorganism activities and environmental change data are integratively analyzed, including key environmental factors such as salt concentration, osmotic pressure, and water binding. These environmental factors directly affect the growth, metabolism, and ecological functions of microorganisms in the soil, and are of great significance for predicting the dynamic activity decay of microorganisms. In specific operations, through the long-term monitoring of the activities of soil microorganisms and environmental changes, a multi-parameter model is used to analyze the changing trends of microorganism activities. By measuring the salt concentration of the soil, the conductivity data of the soil is obtained through a conductivity measuring instrument, and then the salt concentration in the soil is deduced. In combination with the osmotic pressure data, the soil solution is measured by an osmotic pressure instrument to further understand the concentration level of the soil solution. These data help to confirm the availability of water in the soil and the growth environment of microorganisms. In addition, the degree of water binding is measured by using the pressure membrane method or the water binding test method. Combining these environmental change data, the restricted conditions of microorganism growth and metabolism in the soil are more accurately evaluated. After obtaining the microorganism growth inhibition data, microorganism metabolic activity analysis techniques, such as measuring the respiration rate of microorganisms or metabolic indicators, are further used to evaluate whether the metabolic process of microorganisms is inhibited. By using a gas exchange analyzer or biochemical methods, the changes in the respiration rate or metabolic activities of microorganisms are monitored. A decrease in the metabolic rate is usually an indication of the decay of microorganism activity. Combining the degree of growth inhibition and metabolic inhibition data, and in combination with the changes in environmental data, a comprehensive prediction of the dynamic activity decay of soil microorganisms is carried out. The dynamic activity decay state of microorganisms is obtained.

[0089] Preferably, the estimation of the degree of gradient decay of soil structure stability in step S2 includes:

[0090] Detecting the imbalance state of the soil microorganism community according to the dynamic activity decay state of soil microorganisms;

[0091] In the embodiments of the present invention, according to the dynamic activity decay state of soil microorganisms, the imbalance state of the soil microorganism community is detected. The imbalance of the soil microorganism community usually manifests as a decrease or increase in a specific microorganism community, which is caused by soil environmental factors (such as salt concentration, osmotic pressure, etc.). The method for detecting the imbalance of the microorganism community can be through molecular biology techniques, such as the 16S rRNA gene amplification technique, to analyze the composition of the microorganism community in the soil sample. Soil samples are collected, the microorganism DNA in the soil is obtained through a DNA extraction method, and then the 16S rRNA gene is amplified by PCR, and the composition of the microorganism community is analyzed in combination with high-throughput sequencing technology. By comparing the changes in the microorganism community under different environmental conditions, it is judged whether the soil microorganism community is imbalanced. If it is found that the diversity of the microorganism community has significantly decreased, it indicates that there is an imbalance in the soil microorganism community.

[0092] Estimating the growth trend of soil pathogenic bacteria based on the imbalance state of the soil microbial community;

[0093] In the embodiments of the present invention, based on the detected imbalance state of the soil microbial community, the growth trend of pathogenic bacteria in the soil is estimated. The growth of pathogenic bacteria in the soil is usually closely related to the environmental conditions in the soil. In particular, changes in salt concentration can accelerate the growth of certain pathogenic bacteria. By adopting the pathogenic bacteria culture technology, representative pathogenic bacteria are selected, and the growth experiments of pathogenic bacteria are carried out using soil solution or soil samples. In the experiment, the soil samples are divided into different treatment groups (such as different salt concentrations, different pH values, etc.), and the growth rate is monitored by regularly sampling and counting the colony-forming units (CFUs) of pathogenic bacteria. Using these data, the growth trend of pathogenic bacteria reproduction is established, and the growth law of pathogenic bacteria under different soil conditions is analyzed.

[0094] Detecting the trend of soil disease aggravation based on the growth trend of soil pathogenic bacteria;

[0095] In the embodiments of the present invention, based on the growth trend of soil pathogenic bacteria, the detection of the trend of soil disease aggravation is further carried out. The increase in the reproduction of pathogenic bacteria usually leads to the aggravation of diseases in the soil. Especially under high-salt or adverse conditions, the incidence of diseases will increase rapidly. By setting up disease monitoring devices, the occurrence of diseases is monitored in real time. The soil disease detection method is used, such as checking the lesion conditions of plant roots by taking soil samples, and evaluating the aggravation of diseases through the observation method of plant disease symptoms (such as leaf yellowing, withering, etc.). In addition, a disease model is used to predict the diffusion and spread of pathogenic bacteria, and based on the existing trend of diseases, the development status of soil diseases is speculated, and the aggravation degree of diseases in the soil is further determined.

[0096] Estimating the attenuation of the soil microbial community according to the trend of soil disease aggravation and the growth trend of soil pathogenic bacteria;

[0097] In the embodiments of the present invention, according to the aggravation trend of soil diseases and the growth trend of pathogenic bacteria, the attenuation of the soil microbial community is predicted. The rapid reproduction of pathogenic bacteria and the aggravation of diseases often inhibit the growth of beneficial microorganisms in the soil. By analyzing the change in the number of beneficial microorganisms in soil samples, combined with the culture medium culture method or fluorescence microscopy technology for microbial counting, the change of the microbial community is analyzed. If it is found that the number of beneficial microorganisms decreases significantly, it indicates that the soil microbial community is undergoing attenuation. Especially under the conditions of soil pollution or rapid reproduction of pathogenic bacteria, the decline of the microbial community will be more obvious.

[0098] Evaluating the slowdown of the soil organic matter decomposition ability by using the attenuation of the soil microbial community;

[0099] In the embodiments of the present invention, by evaluating the attenuation of the soil microbial community, the slowdown of the soil organic matter decomposition ability is further evaluated. The activities of microorganisms in the soil directly affect the decomposition process of organic matter, and the decline of the microbial community usually leads to a decrease in the rate of organic matter degradation. Using the soil organic matter decomposition experiment, the decomposition ability is evaluated by measuring the organic matter content and the microbial respiration rate of the soil. The specific operation is to mix the soil sample with a certain amount of organic matter, measure the respiration rate of the microorganisms in the soil sample through a closed system, and regularly take samples to analyze the degradation of the organic matter. A lower respiration rate and a decrease in the organic matter content indicate a slowdown in the soil organic matter decomposition ability.

[0100] Estimate the limitation of soil nutrient cycling according to the slowdown of soil organic matter decomposition ability;

[0101] In the embodiments of the present invention, according to the slowdown of the soil organic matter decomposition ability, the limitation of the soil nutrient cycling is estimated. Nutrient cycling is an important process in the soil ecosystem and depends on the organic matter decomposition activities of soil microorganisms. If the decomposition ability slows down, the release and cycling rate of nutrients will also be affected. By measuring the concentration changes of the main nutrients (such as nitrogen, phosphorus, potassium, etc.) in the soil, the degree of limitation of soil nutrient cycling is inferred. The specific operations include using soil nutrient analysis instruments to conduct elemental analysis on soil samples, comparing the nutrient concentrations at different time points and under different soil conditions, and analyzing the changes in nutrient release and absorption.

[0102] Determine the degradation of crop soil according to the attenuation of the soil microbial community and the limitation of soil nutrient cycling;

[0103] In the embodiments of the present invention, through the comprehensive analysis of the decline of the soil microbial community and the limitation of soil nutrient cycling, the degradation of crop soil is further determined. Soil degradation usually manifests as the deterioration of soil physical, chemical and biological properties, including the decline of soil fertility, the destruction of soil structure, etc. By conducting systematic physical and chemical analysis on soil samples, such as soil pH value, bulk density, total organic carbon content, etc., combined with microbial community analysis and nutrient cycling assessment, the degree of soil degradation is judged. Indicators such as lower organic carbon content, high salt concentration, and low pH value indicate the severity of soil degradation.

[0104] Detect the degree of damage to soil aggregate structure based on the degradation of crop soil and the attenuation of the soil microbial community;

[0105] In the embodiments of the present invention, in combination with the degradation of crop soil and the decline of soil microbial communities, the degree of damage to soil aggregate structure is further detected. Soil aggregate structure is an important indicator of soil health, and the damage to the structure will affect the air permeability and water retention capacity of the soil. The damage to soil aggregate structure is analyzed by measuring the particle size distribution of soil aggregates. The particle size distribution of soil samples is measured using the soil sieving method or through soil image analysis technology. An increase in the proportion of smaller soil particles indicates that the soil aggregate structure is damaged.

[0106] Based on the degree of damage to soil aggregate structure and the degradation of crop soil, the degree of gradient attenuation of soil structure stability is estimated.

[0107] In the embodiments of the present invention, based on the degree of damage to soil aggregate structure and the degradation of crop soil, the degree of gradient attenuation of soil structure stability is estimated. Soil structure stability is usually a key indicator to measure whether the soil can maintain a good growth environment. Soils with poor stability are easily eroded or compacted, affecting the root development of crops. Through the physical properties of the soil (such as soil bulk density, porosity, etc.) and the soil stability test data, combined with the data of soil degradation and aggregate structure damage, a comprehensive analysis is carried out to estimate the degree of attenuation of soil structure.

[0108] Preferably, the evaluation of the constraint trend of soil growth potential in step S2 includes:

[0109] Estimating the degree of increased soil erosion according to the degree of gradient attenuation of soil structure stability;

[0110] In the embodiments of the present invention, according to the degree of gradient attenuation of soil structure stability, the degree of increased soil erosion is estimated. Soil structure stability is an important factor affecting soil erosion. Soils with poor structure stability are easily eroded by external factors such as water flow and wind. The attenuation of soil structure stability is evaluated by measuring the soil particle size distribution, aggregate structure and soil water content. The specific operation steps are to collect soil samples at different depths, use soil particle analysis instruments to measure their particle size distribution, and evaluate the distribution and damage degree of aggregates. At the same time, use soil water content test instruments (such as the drying method or time domain reflectometry) to measure the soil water content, and combine data such as the bulk density of the soil to calculate the degree of stability attenuation of the soil. By analyzing the looseness of soil particles and the damage degree of aggregate structure, the trend of increased soil erosion is predicted. If the soil particles are relatively loose and the water content is high, the risk of soil erosion will increase significantly.

[0111] Detecting the state of surface soil loss based on the degree of increased soil erosion;

[0112] In the embodiments of the present invention, based on the degree of intensification of soil erosion, the loss state of the soil surface layer is further detected. The loss of the soil surface layer is caused by soil erosion and is usually manifested as an obvious loss of the soil surface layer. By setting up a soil loss monitoring system and using soil and water conservation test techniques, the loss situation of the soil surface layer is simulated under different precipitation intensities. The soil sample is placed in a water flow simulation device to simulate the scouring effect of precipitation or irrigation on the soil surface layer. By measuring the mass or volume of the lost soil, the degree of soil surface layer loss is evaluated. Particle analysis technology and particle size distribution analysis of the lost soil samples are adopted, and combined with the increasing trend of the loss amount, the degree of soil surface layer loss is accurately judged. If the loss amount increases significantly, it indicates that the stability of the soil surface layer is poor and the loss problem is more serious.

[0113] Determine the degree of intensification of soil nutrient leaching according to the loss state of the soil surface layer;

[0114] In the embodiments of the present invention, according to the loss state of the soil surface layer, the degree of intensification of soil nutrient leaching is further determined. The loss of the surface soil directly leads to the loss of nutrients, especially the loss of important nutrients such as nitrogen, phosphorus, and potassium. Through soil nutrient monitoring technology, soil surface layer and deep layer samples are collected, and a soil nutrient analyzer is used for chemical analysis to determine the main nutrient content in the soil. The nutrient concentration in the lost soil sample is compared with that in the non-lost sample, and the nutrient loss rate is calculated. If the nutrient loss amount is large, it indicates that the nutrient loss in the soil is intensified, which usually means that the soil fertility has seriously declined, further affecting the growth of crops.

[0115] Estimate the soil compaction state by using the degree of gradient attenuation of soil structure stability;

[0116] In the embodiments of the present invention, by using the degree of gradient attenuation of soil structure stability, the soil compaction state is further estimated. Soil compaction refers to the tight combination of soil particles, resulting in a decrease in soil porosity and affecting the water permeability and gas exchange of the soil. The soil compaction state is usually evaluated by a pressure test or a soil hardness test. The specific operation is to use a soil hardness meter to measure the compaction degree of the soil and test the water permeability of the soil by the pressure membrane method. By analyzing the deformation characteristics of the soil sample under different pressures, it is judged whether there is soil compaction. If the soil hardness is high and the water permeability is poor, it indicates that the soil compaction situation is more serious.

[0117] Determine the degree of deterioration of the soil physical shape according to the soil compaction state;

[0118] In the embodiments of the present invention, according to the soil compaction state, the degree of deterioration of the soil physical shape is further measured. The deterioration of the soil physical shape is usually accompanied by the compaction phenomenon, manifested as a decrease in soil porosity and the loss of soil structure looseness. By analyzing indicators such as soil porosity, permeability, and bulk density, the change in the soil physical shape is evaluated. The specific method is to use a soil permeameter to measure the water permeability of the soil and a soil bulk density meter to test the compactness of the soil. At the same time, soil samples are collected and the morphological changes of soil particles are observed through a scanning electron microscope (SEM). If the pore structure of the soil is significantly compact and the water permeability is poor, it indicates that the physical shape of the soil has deteriorated.

[0119] Based on the degree of intensification of soil nutrient leaching and the degree of deterioration of the soil physical shape, the constraint trend of soil growth potential is evaluated.

[0120] In the embodiments of the present invention, based on the degree of intensification of soil nutrient leaching and the degree of deterioration of the soil physical shape, the constraint trend of soil growth potential is evaluated. The constraint of soil growth potential is usually closely related to factors such as soil fertility, water permeability, and gas exchange ability. By comprehensively analyzing the loss of soil nutrients, the compaction phenomenon, and the changes in the physical form, it is predicted whether the growth potential of the soil is restricted. The evaluation steps include nutrient analysis, hardness test, permeability test, etc. of soil samples, and based on these data, it is judged whether the potential of the soil is effectively utilized. If the nutrient loss is serious, the soil is compacted, and the water permeability is poor, it indicates that the growth potential of the soil is significantly restricted, and the growth of crops will also be significantly affected.

[0121] Preferably, step S3 includes the following steps:

[0122] Step S31: Collect crop growth climate data based on the crop planting environment;

[0123] Step S32: Estimate the crop growth response trend according to the crop growth climate data;

[0124] Step S33: Estimate the crop growth negative feedback effect based on the crop growth response trend and the soil growth potential constraint trend;

[0125] Step S34: Detect the disorder of the crop growth cycle based on the crop growth negative feedback effect.

[0126] As an example of the present invention, referring to Figure 2 As shown, in this example, step S3 includes:

[0127] Step S31: Collect crop growth climate data based on the crop planting environment;

[0128] In the embodiments of the present invention, climate data of crop growth is collected. These climate data include temperature, humidity, precipitation, light intensity, wind speed, etc. These data can be monitored in real time through an automatic weather station, meteorological sensors, and a data acquisition system. The specific operation method is to deploy a meteorological sensor array in the farmland, place these sensors in different crop planting areas, collect real-time meteorological data, and transmit the data to the central data processing platform through a wireless data transmission system. The meteorological sensors should be able to record key climate factors such as the average temperature and humidity, precipitation, light intensity, etc. for each hour, day, and month. The basic climate parameters of the crop growth environment are obtained through these data.

[0129] Step S32: Estimate the growth response trend of the crop according to the climate data of crop growth;

[0130] In the embodiments of the present invention, after collecting the climate data of the crop growth environment, the next step is to estimate the growth response trend of the crop under the current climate conditions through data analysis and statistical models. Specifically, according to historical data and empirical formulas, combined with key climate factors such as temperature, humidity, and light, the growth process and growth rate of the crop under different climate conditions are predicted. In the operation, an agricultural meteorological model, such as a crop growth model (such as FAO-56, APSIM model, etc.), is used to simulate crop growth in combination with meteorological data. The climate data is converted into key indicators of crop growth, such as biomass accumulation, root development, leaf area index, etc., through the model to evaluate the growth dynamics of the crop in the current environment. The input data of this model includes real-time air temperature, precipitation, humidity, etc., and the output result is the prediction of the crop growth state, evaluating the adaptability of the crop to climate change.

[0131] Step S33: Estimate the negative feedback effect of crop growth based on the growth response trend of the crop and the constraint trend of soil growth potential;

[0132] In the embodiments of the present invention, by analyzing the association between the growth response trend of crops and the constraint trend of soil growth potential, the negative feedback effect that occurs during the growth process of crops is estimated. The negative feedback effect usually manifests as when external environmental factors (such as climate change) or soil conditions (such as excessive salt content, nutrient deficiency, etc.) affect crop growth, the crop growth will be inhibited or growth stagnation will occur. In this step, the crop growth prediction data obtained in step S32 is combined with the constraint trend of soil growth potential evaluated in step S2 to comprehensively judge the influence of climate and soil factors on crop growth. By quantifying the interaction of these factors, for example, when the salt concentration in the soil is too high, the water absorption capacity of the crop roots will be limited, thereby affecting the growth rate of the crop. Through model calculation and historical data analysis, combined with climate data and soil constraint conditions, it is estimated whether the crop will exhibit a negative feedback effect on growth. If the soil is nutrient-deficient or the water supply is insufficient, and the temperature and precipitation instability caused by climate change occur, then it will further exacerbate the negative feedback effect of crop growth.

[0133] Step S34: Detect the disorder of the crop growth cycle based on the negative feedback effect of crop growth.

[0134] In the embodiments of the present invention, after detecting the negative feedback effect of crop growth, the growth cycle of the crop is further analyzed for disorder. The growth cycle of the crop includes various growth stages from germination to maturity, such as the germination stage, the branching stage, the heading stage, etc. By real-time monitoring the growth stages of the crop, especially using a crop growth monitoring system, the changes of the crop at different growth stages are detected. For example, the crop growth monitoring system evaluates whether the growth of the crop is in a normal growth cycle by collecting growth parameters such as the leaf area index, plant height, and stem thickness of the crop, combined with climate data and soil data. If there are abnormal growth rates or the growth stages are advanced or delayed, it is determined that the crop growth cycle is disordered due to the influence of the climate and soil environment. The specific operation steps include using remote sensing technology and unmanned aerial vehicle imaging technology to regularly collect the growth image data of the crop and analyze it in combination with plant physiological indicators. By analyzing these data, the abnormality of the crop growth cycle is identified in advance.

[0135] Preferably, step S32 includes the following steps:

[0136] Step S321: Collect the temperature parameters of the crop growth environment according to the climate data of the crop growth environment;

[0137] In the embodiments of the present invention, temperature sensors are deployed in farmland. These sensors can use high-precision digital thermometers or weather stations for data collection to ensure that temperature information can be obtained at different heights and different regions (such as crop roots, ground, plant tops, etc.). The temperature sensors should have high sensitivity and be able to record the changes in temperature during the day and at night. The data collection frequency can be set to once per hour, and the collected temperature data is uploaded to the central processing platform in real time through a wireless communication system. These temperature data will reflect the air temperature state of the environment where the current crops are located.

[0138] Step S322: Collect the crop light intensity data according to the climate data of the crop growth environment;

[0139] In the embodiments of the present invention, while collecting temperature data, the collection of light intensity data is equally crucial. Light intensity directly affects the photosynthesis of crops. Light sensors need to be installed in the farmland. These sensors should be able to accurately measure the light intensity throughout the day, especially the changes in light intensity under sunny and cloudy conditions. The placement of the light sensors should take into account the height and growth direction of the crops to ensure the representativeness of the data. Each sensor should collect data at least once per hour and send the data to the central control system through a wireless network for subsequent analysis. This light intensity data will help analyze the growth environment of the crops, especially the photosynthesis intensity of the crops under different light conditions.

[0140] Step S323: Estimate the excessive transpiration of the crops according to the temperature parameters of the crop growth environment and the crop light intensity data;

[0141] In the embodiments of the present invention, by combining the temperature parameters and the light intensity data, the excessive transpiration of the crops can be estimated through a transpiration rate model. Transpiration is the main way of water loss during crop growth and is affected by environmental temperature and light. When the temperature is too high or the light intensity is too large, the transpiration of the crops will intensify, resulting in increased water loss. The specific operation is to use the transpiration calculation formula, input the temperature and light intensity data into the model, adjust the parameters according to historical data, and estimate the transpiration rate of the crops under the current climate conditions. By analyzing the transpiration rate, it is judged whether there is excessive transpiration in the crops.

[0142] Step S324: Detect the degree of water loss of the crops based on the excessive transpiration of the crops;

[0143] In the embodiments of the present invention, according to the over-transpiration condition estimated in step S323, the degree of crop water loss is further calculated. The calculation of the water loss degree is based on the relationship between the transpiration rate and the soil water content. When transpiration is excessive, the demand of the crop for water absorption from the soil increases, and if the soil water supply is insufficient, the water loss will be exacerbated. By setting up soil moisture sensors to monitor the changes in soil moisture, combining with the calculation results of transpiration, and using a water loss assessment model, the severity of water loss can be accurately predicted. If the degree of water loss is too high, it means that the crop is experiencing relatively severe drought conditions.

[0144] Step S325: Estimate the drought stress state of the crop based on the degree of crop water loss;

[0145] In the embodiments of the present invention, according to the degree of water loss measured in step S324, combined with the soil water content and climate change data, it is further evaluated whether the crop is under drought stress. Drought stress is a physiological stress caused by long-term water shortage, which affects the growth and yield of the crop. By establishing a drought stress model and setting thresholds according to different degrees of water loss, it is judged whether the crop enters the drought stress state. If the water loss exceeds a certain critical value, the crop will face relatively serious drought problems. This step will rely on soil moisture data and climate data, combined with the transpiration rate, to comprehensively evaluate the degree of drought stress of the crop.

[0146] Step S326: Analyze the crop yield decline trend based on the drought stress state of the crop and the degree of crop water loss;

[0147] In the embodiments of the present invention, in the case of confirming that the crop is under drought stress, the impact of drought on the crop yield is further evaluated by analyzing the degree of water loss of the crop. By comparing historical climate data and yield data, combined with the growth conditions under drought stress, and using a yield prediction model, the growth process and yield change trend of the crop under drought conditions are simulated. If the water loss is severe and the degree of drought stress is high, the growth of the crop will be greatly inhibited and the yield will show a decline trend. Through this analysis, the specific impact of drought stress on the crop yield is estimated, and the crop yield decline trend is obtained.

[0148] Step S327: Analyze the degree of damage to the accumulation of crop nutrients based on the drought stress state of the crop and the degree of crop water loss;

[0149] In the embodiments of the present invention, drought stress not only affects water supply, but also inhibits the absorption and accumulation of nutrients by crops. According to the drought stress state determined in step S325 and the water loss data in step S324, the nutrient accumulation of crops under drought conditions is further analyzed. The specific operation is to use the crop nutrient absorption model to predict the nutrient absorption rate of crops according to the severity of drought stress. If the water loss of the crop is excessive, resulting in weakened root function, the situation of insufficient nutrient absorption will occur, thus affecting the nutrient accumulation of the crop. This step evaluates the impact of drought on the accumulation of crop nutrients by analyzing the relationship between water loss and nutrient absorption.

[0150] Step S328: Estimate the growth response trend of crops based on the degree of damage to the accumulation of crop nutrients and the trend of crop yield decline.

[0151] In the embodiments of the present invention, according to the degree of damage to the accumulation of crop nutrients and the yield decline trend obtained in step S326, combined with the growth state of the crop, the growth response trend of the crop is predicted. Under the combined action of drought stress and water loss, the nutrient accumulation and yield decline trend of the crop will affect its growth state. By comprehensively analyzing the nutrient accumulation and yield changes, it is predicted whether the growth trend of the crop is affected by long-term environmental factors such as drought, and the growth response trend of the crop is obtained.

[0152] Preferably, step S4 includes the following steps:

[0153] Step S41: Evaluate the abnormal state of the crop growth environment by using the disorder of the crop growth cycle;

[0154] Step S42: Analyze the abnormal driving factors of the crop by using the abnormal state of the crop growth environment;

[0155] Step S43: Perform soil improvement treatment on the abnormal driving factors of the crop and the abnormal state of the crop growth environment to obtain crop soil improvement data;

[0156] Step S44: Optimize the planting pattern according to the abnormal driving factors of the crop and the abnormal state of the crop growth environment to obtain crop planting pattern optimization data;

[0157] Step S45: Optimize the crop growth environment by using the crop planting pattern optimization data and the crop soil improvement data to obtain crop growth environment optimization data.

[0158] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes:

[0159] Step S41: Evaluate the abnormal state of the crop growth environment by using the disorder of the crop growth cycle;

[0160] In the embodiments of the present invention, according to the normal mode of the crop growth cycle, by long-term monitoring of the growth status of crops (such as germination, tillering, flowering, fruiting, etc.), collecting their growth curve data, and comparing it with environmental variables such as meteorological data, soil humidity, nutrient concentration, etc. If there are obvious deviations in the growth rate and development status of a certain stage from the historical data, it indicates that the crops are affected by abnormal environment. These deviations are collected by a data acquisition system (such as a wireless sensor network), and the data processing platform will perform real-time analysis on these data. By identifying the disorder of the growth cycle, further warning of the abnormal state of the growth environment of the crops, such as extreme temperature, excessive precipitation, drought or soil salinization, etc. The key to this step lies in high-frequency data acquisition and comparative analysis. Through the correlation model between the planting environment and crop growth, accurately evaluate whether the crops are affected by environmental anomalies.

[0161] Step S42: Analyze the abnormal driving factors of the crops by using the abnormal state of the crop growth environment;

[0162] In the embodiments of the present invention, after confirming that the crop growth environment is abnormal, by analyzing meteorological data, soil data and the growth data of the crops, find out the driving factors leading to the abnormality. These driving factors usually include climate change (such as abnormal precipitation, drastic temperature fluctuations), soil fertility problems (such as excessive fertilization or lack of specific nutrient elements), or ecological factors such as pests and diseases. Use the long-term climate data collected by the meteorological prediction system and the real-time data obtained from the soil sensor network, and conduct a detailed analysis in combination with the response mode of the crops. With the help of multi-factor analysis tools, input these data into regression analysis or causal analysis models, and identify the environmental factors causing abnormal growth through data cross-validation. For example, if the soil pH is too high and the water content is too low, it is the combined effect of drought and soil acidification that affects the growth of the crops and their growth cycle, and clarify the abnormal driving factors.

[0163] Step S43: Perform soil improvement treatment on the abnormal driving factors of the crops and the abnormal state of the crop growth environment to obtain crop soil improvement data;

[0164] In the embodiments of the present invention, after identifying abnormal driving factors such as soil and climate, specific soil improvement measures are taken. According to the specific problems of the soil (such as acid-base imbalance, salinization, lack of trace elements, etc.), corresponding soil conditioners are selected. For example, lime is used to adjust the soil pH, and organic fertilizers and trace element fertilizers are used to improve soil fertility. At this time, based on the monitoring data of soil sensors, important indicators such as the soil pH, salt content, temperature, and moisture content are determined, and precise fertilization is carried out according to these data. By adding conditioners to the soil and combining farming techniques such as deep plowing the soil or covering with green manure, the soil structure can be improved, and the soil's water retention capacity and nutrient supply capacity can be enhanced. After implementing these measures, the physical and chemical properties of the soil are re-measured by soil analysis equipment to verify the improvement effect and obtain crop soil improvement data.

[0165] Step S44: Optimize the planting pattern according to the abnormal driving factors of the crops and the abnormal state of the crop growth environment to obtain optimized crop planting pattern data;

[0166] In the embodiments of the present invention, the planting pattern of the crops is optimized according to the specific conditions of soil and environmental anomalies. For example, if it is found that the growth of the crops is affected by high temperature or drought, crop varieties that are heat-resistant and drought-tolerant are selected; if the soil is too saline-alkali, crop varieties that are adapted to saline-alkali soil are selected. The optimization of the planting pattern also includes reasonably arranging crop rotation, selecting appropriate sowing densities and cultivation methods, etc. Through the comprehensive analysis of meteorological data, the data of the effect after soil improvement, and crop growth data, combined with the ecological habits of the crops, the planting cycle, sowing depth, and fertilization frequency of the crops are reasonably planned. During the optimization process, intelligent planting management systems are also applied. These systems monitor the environmental data in real time and make automatic adjustments to ensure that the environmental conditions required by the crops at different growth stages are met. Through these means, optimized crop planting pattern data are obtained.

[0167] Step S45: Optimize the crop growth environment with the optimized crop planting pattern data and the crop soil improvement data to obtain optimized crop growth environment data.

[0168] In the embodiment of the present invention, after the optimized planting pattern data obtained in step S44 is combined with the soil improvement data in step S43, a comprehensive optimization of the crop growth environment is carried out. The specific operation is to adjust the farm management plan according to the optimized planting pattern and soil improvement measures. For example, the soil moisture is adjusted through an automatic irrigation system, and the timing and amount of fertilization and pesticide use are adjusted. With the help of environmental monitoring equipment, the growth conditions of the soil and crops are continuously tracked, and the fertilization and irrigation strategies are adjusted in a timely manner to ensure that the crops grow in an ideal growth environment. Through this process, the comprehensively optimized growth environment data is obtained, including the optimized soil properties, crop planting patterns, irrigation strategies, etc. These data will be further used to improve farm management and increase the growth efficiency and yield of crops.

[0169] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for processing crop growth environment data, characterized in that, It includes the following steps: Step S1: Obtain crop planting environment data; collect crop soil sample data according to the crop planting environment data; detect crop soil characteristics according to the crop soil sample data; Step S2: Predict the dynamic activity decay state of microorganisms based on the crop soil characteristics; estimate the gradient decay degree of soil structure stability based on the dynamic activity decay state of microorganisms; evaluate the constraint trend of soil growth potential based on the gradient decay degree of soil structure stability; Step S3: Collect crop growth climate data based on the crop planting environment; estimate the crop growth response trend according to the crop growth climate data; detect the disorder of the crop growth cycle based on the crop growth response trend and the constraint trend of soil growth potential; Step S4: Evaluate the abnormal state of the crop growth environment based on the disorder of the crop growth cycle; analyze the abnormal driving factors of the crop using the abnormal state of the crop growth environment; optimize the crop growth environment according to the abnormal driving factors of the crop and the abnormal state of the crop growth environment to obtain the optimized data of the crop growth environment.

2. The method for processing crop growth environment data according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain crop planting environment data; Step S12: Collect crop soil sample data according to the crop planting environment data; Step S13: Estimate the physical properties of the crop soil according to the crop soil sample data; Step S14: Estimate the chemical properties of the crop soil according to the crop soil sample data; Step S15: Detect the crop soil characteristics based on the physical properties and chemical properties of the crop soil.

3. The method for processing crop growth environment data according to claim 2, wherein Step S13 includes the following steps: Step S131: Use the crop soil sample data with a depth of 0 - 20 cm to identify the data of the crop soil particle size; Step S132: Measure the soil porosity according to the data of the crop soil particle size; Step S133: Evaluate the data of the crop soil texture according to the soil porosity and the data of the crop soil particle size; Step S134: Estimate the crop soil flow capacity based on when the soil porosity is greater than 48.2% and the data of the crop soil texture; Step S135: Calculate the crop soil water retention capacity when the infiltration rate of the crop soil flow capacity is 2.8 - 4.2 cm / h; Step S136: Detect the oxygen content of the crop soil based on the crop soil flow capacity; Step S137: Estimate the physical properties of the crop soil when the crop soil oxygen content is lower than 10% and the crop soil water retention capacity.

4. The method for processing crop growth environment data according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Detect the characteristics of the soil element composition based on the crop soil sample data; Step S142: Detect the soil acid-base property according to the crop soil sample data; Step S143: Statistically analyze the data of the crop soil nutrient content according to the characteristics of the soil element composition; Step S144: Determine the characteristics of the soil nutrient dissolution capacity for the data of the crop soil nutrient content based on the soil acid-base property; Step S145: Evaluate the fertility status of the crop soil according to the characteristics of the soil nutrient dissolution capacity and the data of the crop soil nutrient content. Step S146: Estimate the chemical properties of the crop soil based on the soil fertility status and soil acid-base properties of the crops.

5. The method for processing crop growth environment data according to claim 1, characterized in that, The prediction of the microbial dynamic activity decay state described in step S2 includes: Measure the excessive data of the crop soil salt concentration when the crop soil property exceeds 5.2 dS / m; Predict the growth of the soil osmotic pressure based on the excessive data of the crop soil salt concentration; Identify the soil water binding state when the growth of the soil osmotic pressure exceeds 1.25 bar; Detect the degree of water shortage in soil microbial cells when the soil water binding state exceeds 65%; Estimate the toxic effect of soil microorganisms using the excessive data of the crop soil salt concentration; Detect the ion imbalance state of soil microorganisms based on the toxic effect of soil microorganisms; Predict the degree of inhibition of the microbial metabolic process based on the ion imbalance state of microorganisms and the toxic effect of soil microorganisms; Detect the growth inhibition of soil microorganisms based on the toxic effect of soil microorganisms and the degree of water shortage in soil microbial cells; Predict the microbial dynamic activity decay state when the growth inhibition of soil microorganisms exceeds 60% and the degree of inhibition of the microbial metabolic process.

6. The method for processing crop growth environment data according to claim 1, wherein The estimation of the degree of attenuation of the soil structure stability gradient described in step S2 includes: Detect the imbalance state of the soil microbial community based on the microbial dynamic activity decay state; Estimate the growth trend of soil pathogens based on the imbalance state of the soil microbial community; Detect the trend of aggravated soil diseases based on the growth trend of soil pathogens; Estimate the attenuation of the soil microbial community based on the trend of aggravated soil diseases and the growth trend of soil pathogens; Evaluate the slowdown of the soil organic matter decomposition ability using the attenuation of the soil microbial community; Estimate the limited soil nutrient cycling based on the slowdown of the soil organic matter decomposition ability; Determine the degradation of the crop soil based on the attenuation of the soil microbial community and the limited soil nutrient cycling; Detect the degree of damage to the soil aggregate structure based on the degradation of the crop soil and the attenuation of the soil microbial community; Estimate the degree of attenuation of the soil structure stability gradient based on the degree of damage to the soil aggregate structure and the degradation of the crop soil.

7. The method for processing crop growth environment data according to claim 1, wherein The evaluation of the soil growth potential constraint trend described in step S2 includes: Estimate the degree of aggravated soil erosion based on the degree of attenuation of the soil structure stability gradient; Detect the state of surface soil loss based on the degree of aggravated soil erosion; Determine the degree of aggravated soil nutrient leaching based on the state of surface soil loss; Estimate the soil compaction state using the degree of attenuation of the soil structure stability gradient; Determine the degree of deterioration of the soil physical shape based on the soil compaction state; Evaluate the soil growth potential constraint trend based on the degree of aggravated soil nutrient leaching and the degree of deterioration of the soil physical shape.

8. The method for processing crop growth environment data according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Collect crop growth climate data based on the crop planting environment; Step S32: Estimate the crop growth response trend based on the crop growth climate data; Step S33: Estimate the crop growth negative feedback effect based on the crop growth response trend and the soil growth potential constraint trend; Step S34: Detect the disorder of the crop growth cycle based on the crop growth negative feedback effect.

9. The method for processing crop growth environment data according to claim 8, wherein, Step S32 includes the following steps: Step S321: Collect the temperature parameters of the crop growth environment based on the climate data of the crop growth environment; Step S322: Collect the light intensity data of the crops based on the climate data of the crop growth environment; Step S323: Estimate the excessive transpiration situation of the crops according to the temperature parameters of the crop growth environment and the light intensity data of the crops; Step S324: Detect the degree of water loss of the crops based on the excessive transpiration situation of the crops; Step S325: Estimate the drought stress state of the crops based on the degree of water loss of the crops; Step S326: Analyze the crop yield decline trend based on the drought stress state of the crops and the degree of water loss of the crops; Step S327: Analyze the degree of damage to the accumulation of crop nutrients based on the drought stress state of the crops and the degree of water loss of the crops; Step S328: Estimate the growth response trend of the crops based on the degree of damage to the accumulation of crop nutrients and the crop yield decline trend.

10. The method for processing crop growth environment data according to claim 1, wherein Step S4 includes the following steps: Step S41: Evaluate the abnormal state of the crop growth environment by using the disorder situation of the crop growth cycle; Step S42: Analyze the abnormal driving factors of the crops by using the abnormal state of the crop growth environment; Step S43: Carry out soil improvement treatment on the abnormal driving factors of the crops and the abnormal state of the crop growth environment to obtain the crop soil improvement data; Step S44: Optimize the planting pattern according to the abnormal driving factors of the crops and the abnormal state of the crop growth environment to obtain the optimized crop planting pattern data; Step S45: Optimize the crop growth environment with the optimized crop planting pattern data and the crop soil improvement data to obtain the optimized crop growth environment data.

Images