A soil fertility maintenance system under water and drought rotation system

Abstract

The application belongs to the technical field of agricultural soil management, and is especially a soil fertility maintenance system under water-flood alternate cropping, which comprises a local data acquisition module arranged in a water-flood alternate cropping field and used for acquiring local soil fertility parameters according to a preset time period, wherein the soil fertility parameters include at least one of soil pH value, organic matter content, total nitrogen content, total phosphorus content, total potassium content, alkali-hydrolyzable nitrogen content, available phosphorus content, available potassium content, soil moisture content, soil temperature and soil enzyme activity; the application is specially designed for the water-flood alternate cropping scene, and the cloud database module stores standard soil fertility parameter data specially for the water-flood alternate cropping, which can accurately reflect the unique change rule of soil fertility under the water-flood alternate cropping condition, and overcome the defect that the general system cannot accurately capture the fertility change characteristics of the scene.

Description

Technical Field

[0001] This invention relates to the field of agricultural soil management technology, specifically to a soil fertility maintenance system under crop rotation, and more particularly to a system that enables precise monitoring and maintenance decisions of soil fertility in crop rotation by comparing and analyzing local measured data with a cloud-based standard database. Background Technology

[0002] Crop rotation, or paddy-upland cropping, is a farming system in which aquatic crops such as rice are planted alternately with dryland crops on the same field according to the seasons. It is a widely adopted planting model in the seed production industry. Crop rotation, through alternating wet and dry conditions, allows for alternating oxidation and reduction processes in the soil, which helps improve soil physical and chemical properties and reduce the incidence of soil-borne diseases. However, during crop rotation seed production, the soil is subjected to drastic changes in wet and dry conditions over a long period. This easily disrupts the dynamic balance of soil organic matter decomposition and accumulation, and the transformation, migration, and loss of soil nutrients become complex, placing higher demands on maintaining soil fertility. A significant characteristic of crop rotation systems is the alternating changes in soil hydrothermal conditions, which makes the soil properties, nutrient cycling, and energy flow and conversion significantly different from dryland or wetland ecosystems, forming a unique ecosystem. How to effectively maintain soil fertility while ensuring seed production yield and quality has become a crucial issue that urgently needs to be addressed in the field of crop rotation seed production.

[0003] Currently, several technical solutions exist for maintaining soil fertility under crop rotation systems. For example, some studies have analyzed the changes in soil fertility indicators such as organic matter and total nitrogen content under crop rotation conditions through long-term field experiments; patents have disclosed crop rotation cultivation methods that improve soil physicochemical properties through segmented irrigation and alternating wet-dry irrigation; and other studies have explored the impact of long-term no-till farming on soil physicochemical properties under crop rotation conditions. Regarding soil monitoring and management informatization, existing technologies have begun to apply information technologies such as the Internet of Things (IoT) and cloud computing to the agricultural field. For instance, some proposals suggest building a big data platform for farmland quality protection, comprehensively utilizing advanced technologies such as GIS, IoT, and artificial intelligence, and filling in soil-related indicator data according to a standardized table format; cloud-based soil quality testing systems and internet-based soil fertility monitoring and query information systems have also been reported.

[0004] However, existing technologies still have the following shortcomings: Firstly, existing soil fertility monitoring and management systems are mostly general-purpose agricultural monitoring systems, lacking specific designs for the unique scenario of crop rotation. Under crop rotation, the soil experiences frequent and cyclical wet-dry cycles, and the changes in soil physicochemical properties exhibit unique dynamic patterns. General-purpose systems struggle to accurately capture and assess the characteristics of soil fertility changes under this specific scenario.

[0005] Secondly, existing soil data management methods are mostly one-way data collection and uploading, lacking an effective comparative analysis mechanism between local measured data and cloud-based standard databases. Most systems only store the collected soil data in the cloud or perform simple statistical analysis, failing to fully utilize the massive historical soil fertility data in cloud databases as a reference benchmark, and thus unable to accurately determine the changing trends and deviations of local soil fertility through data comparison.

[0006] Third, existing systems lack decision support functions for maintaining soil fertility based on the comparison results between local and cloud-based data after soil data collection. Maintaining soil fertility requires not only monitoring but also timely implementation of corresponding soil enrichment, improvement, or regulation measures based on monitoring results. However, existing systems mostly remain at the level of data collection and display, failing to effectively link the results of data comparison and analysis with soil fertility maintenance measures, making it difficult to form a closed-loop management system of "monitoring-comparison-diagnosis-regulation".

[0007] Therefore, there is an urgent need for a soil fertility maintenance system specifically designed for crop rotation seed production scenarios, which can effectively compare locally collected measured soil fertility data with standard soil fertility data in a cloud database, thereby accurately assessing the local soil fertility status and providing a scientific basis for soil fertility maintenance decisions. Summary of the Invention

[0008] This invention addresses the shortcomings of existing technologies by providing a soil fertility maintenance system under a crop rotation system, aiming to solve the following technical problems: (1) The existing system lacks a special design for the special scenario of crop rotation between water and dryland, and cannot accurately capture the characteristics of soil fertility changes in this scenario; (2) The existing system lacks an effective comparative analysis mechanism between local measured data and cloud-based standard databases; (3) The existing system lacks decision support function for maintaining soil fertility based on data comparison results, and cannot form closed-loop management.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a soil fertility maintenance system under a crop rotation system, the system comprising: The local data acquisition module is set up in the paddy-dryland rotation seed production field and is used to collect local soil fertility parameters according to a preset time period. The soil fertility parameters include at least one of soil pH value, organic matter content, total nitrogen content, total phosphorus content, total potassium content, available nitrogen content, available phosphorus content, available potassium content, soil moisture content, soil temperature and soil enzyme activity. The cloud database module is used to store standard soil fertility parameter data under the seed production scenario of water-dry rotation. The standard soil fertility parameter data includes the benchmark values, threshold ranges and trend data of soil fertility parameters under different water-dry rotation modes, different rotation years and different soil types. The data comparison and analysis module is communicatively connected to the local data acquisition module and the cloud database module, respectively, and is used to compare and analyze the local soil fertility parameters with the standard soil fertility parameters in the cloud database module, and generate comparison and analysis results. The comparison and analysis results include the deviation value, deviation direction and deviation degree of the local soil fertility parameters and the standard values. The soil fertility assessment module, connected to the data comparison and analysis module, is used to assess the current state and trend of local soil fertility based on the comparison and analysis results, combined with the rotation cycle of water-dryland cropping and the current rotation stage, and generate a soil fertility assessment report. The soil fertility maintenance decision module is connected to the soil fertility assessment module and is used to generate a soil fertility maintenance decision scheme for the current soil fertility status by matching a pre-stored soil fertility maintenance measures library with the soil fertility assessment report. The soil fertility maintenance decision scheme includes at least one of the following: soil fertility improvement scheme, irrigation regulation scheme, crop rotation adjustment scheme, and soil improvement scheme. The output and display module is used to output and display the comparative analysis results, soil fertility assessment report, and soil fertility maintenance decision-making scheme.

[0010] Preferably, the local data acquisition module includes a soil sensor unit, a positioning unit, and a time stamping unit; the soil sensor unit is set at different soil depths in the crop rotation seed production field to collect soil fertility parameters of different soil layers; the positioning unit is used to obtain the geographical location information of the sampling point; and the time stamping unit is used to record the time information of each sampling.

[0011] Preferably, the cloud database module includes a standard soil fertility parameter sub-library and a water-dryland rotation scenario parameter sub-library; the standard soil fertility parameter sub-library stores benchmark data of soil fertility parameters under different regions, different soil types, and different climatic conditions; the water-dryland rotation scenario parameter sub-library stores data on the variation patterns of soil fertility parameters and empirical data on soil fertility maintenance under different water-dryland rotation patterns (including rice-wheat rotation, rice-rapeseed rotation, rice-vegetable rotation, etc.).

[0012] Preferably, the data comparison and analysis module includes a data preprocessing unit, a deviation calculation unit, and a trend analysis unit; the data preprocessing unit is used to clean, normalize, and unify the format of the locally collected soil fertility parameters; the deviation calculation unit is used to calculate the deviation between the local soil fertility parameters and the corresponding standard values ​​in the cloud database; the trend analysis unit is used to analyze the changing trend of local soil fertility based on the local soil fertility parameter data collected multiple times, and compare it with the standard changing trend in the cloud database.

[0013] Preferably, the soil fertility assessment module includes a single-index assessment unit and a comprehensive assessment unit; the single-index assessment unit is used to independently assess each soil fertility parameter and determine whether it is within the normal range; the comprehensive assessment unit is used to give an overall grade evaluation of soil fertility based on the comprehensive score of multiple soil fertility parameters.

[0014] Preferably, the soil fertility maintenance measures library is classified and stored according to different rotation stages of water-dryland cropping, different soil fertility levels, and different deviation types. Each measure entry includes the measure name, implementation conditions, implementation method, expected effect, and precautions.

[0015] Preferably, the system further includes an early warning module, which is connected to the soil fertility assessment module and is used to issue an early warning message when the soil fertility parameters deviate from the standard value by more than a preset threshold.

[0016] Preferably, the system further includes a historical data storage module for storing local soil fertility parameter data collected in previous periods, as well as corresponding comparative analysis results, soil fertility assessment reports, and soil fertility maintenance decision-making schemes, forming a historical archive of local soil fertility changes.

[0017] Compared with the prior art, the beneficial effects of the present invention are: Highly targeted: This invention is specifically designed for the water-dry rotation seed production scenario. The cloud database module stores standard soil fertility parameter data specifically for water-dry rotation seed production, which can accurately reflect the unique variation law of soil fertility under water-dry rotation seed production conditions, overcoming the shortcomings of general systems that cannot accurately capture the soil fertility variation characteristics of this scenario.

[0018] Improved comparative analysis mechanism: By setting up a data comparison and analysis module, this invention enables effective comparison between local measured data and cloud standard database, and can accurately determine the deviation value, direction and degree of deviation between local soil fertility parameters and standard values, overcoming the deficiency of existing systems that lack an effective comparative analysis mechanism.

[0019] Closed-loop management: This invention forms a complete closed loop of "monitoring-comparison-diagnosis-regulation" through the collaborative work of local data acquisition module, cloud database module, data comparison and analysis module, soil fertility assessment module and soil fertility maintenance decision module, realizing full-process automated management from data acquisition to decision output.

[0020] Scientific decision support: Based on the results of soil fertility assessment, this invention generates targeted soil fertility maintenance decision schemes by matching them from a pre-stored database of soil fertility maintenance measures. This makes the selection of soil fertility maintenance measures more scientific and accurate, overcoming the shortcomings of existing systems that remain at the data display level and lack decision support functions.

[0021] High traceability: By setting up a historical data storage module, this invention forms a historical archive of local soil fertility changes, providing a data foundation for long-term soil fertility change trend analysis and soil fertility maintenance effect evaluation. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the overall architecture of the system of the present invention.

[0023] Figure 2 This is a schematic diagram of the data flow of the system of the present invention.

[0024] Figure 3 This is a schematic diagram of the local data acquisition module in this invention.

[0025] Figure 4 This is a schematic diagram of the cloud database module in this invention.

[0026] Figure 5 This is a schematic diagram of the data comparison and analysis module in this invention.

[0027] Figure 6 This is a schematic diagram of the structure of the soil fertility assessment module in this invention.

[0028] Figure 7 This is a schematic diagram of the operation process of the system of the present invention.

[0029] Figure 8 This is a schematic diagram of the decision logic of the geotechnical maintenance decision module in this invention.

[0030] Figure 9 This is a schematic diagram illustrating the application scenario of the system of the present invention.

[0031] Figure 10 This is a schematic diagram of the deployment of the system of the present invention in a crop rotation seed production field. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: like Figure 1 As shown, the soil fertility maintenance system under the crop rotation system provided by the present invention includes a local data acquisition module, a cloud database module, a data comparison and analysis module, a soil fertility assessment module, a soil fertility maintenance decision module, an output and display module, an early warning module, and a historical data storage module.

[0034] like Figure 2 As shown, the system's data flow is as follows: the local data acquisition module sends the collected measured data to the data comparison and analysis module; the cloud database module sends the stored standard data to the data comparison and analysis module; the data comparison and analysis module sends the comparison and analysis results to the soil fertility assessment module; the soil fertility assessment module sends the soil fertility assessment report to the soil fertility maintenance decision module; the soil fertility maintenance decision module sends the generated decision scheme to the output and display module; and the output and display module displays the results to the user terminal. Simultaneously, the early warning module is connected to the soil fertility assessment module and issues an early warning when soil fertility parameters exceed thresholds; the historical data storage module is connected to the local data acquisition module, the soil fertility assessment module, and the soil fertility maintenance decision module respectively, and is used to store historical data.

[0035] The local data acquisition module is installed within the paddy-dryland rotation seed production field and is used to collect local soil fertility parameters according to a preset time cycle. In the paddy-dryland rotation seed production scenario, the soil is in a drastically changing environment of alternating wet and dry conditions, with alternating oxidation and reduction processes. Therefore, the acquisition cycle of the local data acquisition module is preferably set according to the rotation stage of the paddy-dryland rotation: during the paddy stage (such as the rice planting period), the acquisition interval is 7-15 days; during the dryland stage (such as the dryland crop planting period), the acquisition interval is 10-20 days; and during the rotation transition period (the transition period from paddy field to dryland or from dryland to paddy field), the acquisition interval is 3-7 days. Through this phased, variable-cycle acquisition strategy, the dynamic changes in soil fertility parameters under paddy-dryland rotation seed production conditions can be accurately captured.

[0036] The cloud-based database module stores standard soil fertility parameter data for a crop rotation seed production scenario. A significant characteristic of crop rotation systems is the alternating changes in soil hydrothermal conditions, resulting in soil properties, nutrient cycling, energy flow, and conversion that differ significantly from dryland or wetland ecosystems. Therefore, the standard data stored in the cloud-based database module is specifically designed for crop rotation seed production scenarios, including benchmark values, threshold ranges, and trend data for soil fertility parameters under different crop rotation patterns (such as rice-wheat rotation, rice-rapeseed rotation, rice-vegetable rotation, etc.), different rotation years, and different soil types.

[0037] The data comparison and analysis module is communicatively connected to both the local data acquisition module and the cloud database module. It is used to compare and analyze local soil fertility parameters with standard soil fertility parameters in the cloud database module, and generate comparison and analysis results. The comparison and analysis results include the deviation value of the local soil fertility parameters from the standard value, the direction of the deviation (higher or lower), and the degree of deviation (slight deviation, moderate deviation, or severe deviation).

[0038] The soil fertility assessment module is connected to the data comparison and analysis module. Based on the comparison and analysis results, combined with the rotation cycle of water-dryland cropping and the current rotation stage, it assesses the current state and trend of local soil fertility and generates a soil fertility assessment report.

[0039] The soil fertility maintenance decision module is connected to the soil fertility assessment module and is used to generate a soil fertility maintenance decision scheme based on the soil fertility assessment report, matching it with a pre-stored soil fertility maintenance measures library. The soil fertility maintenance decision scheme includes at least one of the following: soil fertility improvement scheme, irrigation regulation scheme, crop rotation adjustment scheme, and soil improvement scheme.

[0040] The output and display module is used to output and display the comparative analysis results, soil fertility assessment report, and soil fertility maintenance decision-making scheme. Output methods include, but are not limited to: user terminal display, mobile terminal push, and print output.

[0041] The early warning module is connected to the soil fertility assessment module and is used to issue early warning information when soil fertility parameters deviate from the standard value by more than a preset threshold. The early warning information includes the early warning level, early warning indicators, early warning reasons, and recommended handling measures.

[0042] The historical data storage module is used to store local soil fertility parameter data collected in previous periods, as well as corresponding comparative analysis results, soil fertility assessment reports, and soil fertility maintenance decision-making schemes, forming a local historical archive of soil fertility changes, providing a data foundation for long-term soil fertility change trend analysis and soil fertility maintenance effect evaluation.

[0043] Example 2: like Figure 3As shown, the local data acquisition module includes a soil sensor unit, a positioning unit, and a time stamping unit.

[0044] The soil sensor unit is installed at different soil depths (e.g., 0-20cm topsoil, 20-40cm subsoil, and 40-60cm basement) in the paddy-upland rotation seed production field to collect soil fertility parameters at different soil layers. The soil fertility parameters collected by the soil sensor unit include: soil pH, organic matter content, total nitrogen content, total phosphorus content, total potassium content, available nitrogen content, available phosphorus content, available potassium content, soil moisture content, soil temperature, and soil enzyme activity. Under paddy-upland rotation seed production conditions, soil indicators such as pH and organic matter content change systematically with alternating paddy and upland conditions. Studies have shown that paddy-upland rotation can shift soil pH towards neutral, while the contents of organic matter, total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, and available potassium are all higher than in monoculture soils. Therefore, comprehensively collecting these multiple fertility parameters helps to accurately assess the soil fertility status under paddy-upland rotation seed production conditions.

[0045] The positioning unit is used to acquire the geographical location information of the sampling point, including longitude, latitude, altitude, etc., to ensure that the data collected each time can be associated with the accurate geographical location, which facilitates subsequent spatial analysis and regional comparison.

[0046] The time stamp unit is used to record the time information of each sampling, including the sampling date, sampling time, crop rotation stage marker (irrigated period / dry period / transition period), etc., to ensure that the collected data has clear time attributes, which is convenient for time series analysis and trend judgment.

[0047] Example 3: like Figure 4 As shown, the cloud database module includes a standard soil fertility parameter sub-library and a water-dry rotation scenario parameter sub-library.

[0048] The standard soil fertility parameter sub-database stores benchmark data on soil fertility parameters for different regions, soil types, and climatic conditions. This benchmark data originates from national farmland quality monitoring data, long-term locational experimental data, and publicly published academic research findings. The data in the standard soil fertility parameter sub-database is categorized, stored, and indexed according to region (e.g., East China, South China, Central China, Southwest China), soil type (e.g., paddy soil, alluvial soil, yellow-brown soil, red soil), and climatic conditions (e.g., subtropical monsoon climate, temperate monsoon climate).

[0049] The sub-database of water-dryland rotation scenario parameters stores data on the variation patterns of soil fertility parameters and empirical data on soil fertility maintenance under different water-dryland rotation patterns. Specifically, it includes: (1) the seasonal variation patterns of soil fertility parameters under different rotation patterns (rice-wheat rotation, rice-rapeseed rotation, rice-vegetable rotation, rice-peanut rotation, rice-broad bean rotation, etc.); (2) the long-term variation trends of soil fertility parameters under different rotation periods (1-3 years, 4-6 years, 7-10 years, and more than 10 years); and (3) empirical data on soil fertility maintenance and best practice cases under different rotation systems. The results of long-term locational experiments in water-dryland rotation areas show that the organic matter and total nitrogen content of long-term fallow soils are significantly higher than those of soils with continuous cropping; continuous application of organic fertilizers such as pig manure and crop straw to cultivated soils can maintain soil fertility. These empirical data are all stored in the sub-database of water-dryland rotation scenario parameters, providing a reference for soil fertility assessment and decision-making.

[0050] Example 4: like Figure 5 As shown, the data comparison and analysis module includes a data preprocessing unit, a deviation calculation unit, and a trend analysis unit.

[0051] The data preprocessing unit is used to clean, normalize, and standardize the locally collected soil fertility parameters. Data cleaning includes removing outliers, filling in missing values, and correcting systematic errors; normalization converts soil fertility parameters of different dimensions into a unified scale to facilitate subsequent comprehensive comparative analysis; and format standardization converts data collected from different sensors and batches into a unified format standard.

[0052] The deviation calculation unit is used to calculate the deviation between local soil fertility parameters and corresponding standard values ​​in the cloud database. The specific calculation method is as follows: For each soil fertility parameter, firstly, a standard value matching the local geographical location, soil type, crop rotation pattern, crop rotation years, and current crop rotation stage is retrieved from the cloud database. Then, the difference between the local measured value and the standard value, and the percentage deviation, are calculated. The deviation calculation unit also classifies each indicator into four levels based on the degree of deviation: normal (deviation ≤ ±10%), slightly abnormal (deviation between ±10% and ±30%), moderately abnormal (deviation between ±30% and ±50%), and severely abnormal (deviation ≥ ±50%).

[0053] The trend analysis unit is used to analyze the changing trend of local soil fertility based on multiple collections of local soil fertility parameter data, and compare it with the standard changing trend in the cloud database. The trend analysis unit uses time series analysis to calculate the rate and direction of change of various soil fertility parameters, determine whether the local soil fertility is in an improving, maintaining, or declining state, and compare it with the standard changing trend under the same crop rotation pattern to identify abnormal changes.

[0054] Example 5: like Figure 6 As shown, the soil fertility assessment module includes a single-index assessment unit and a comprehensive assessment unit.

[0055] The single-index assessment unit is used to independently assess each soil fertility parameter and determine whether it is within the normal range. For example, for organic matter content, the single-index assessment unit determines whether the locally measured organic matter content is normal based on the standard value and normal fluctuation range of organic matter content matched with local conditions in the cloud database; for soil pH value, it determines whether it is within the pH range suitable for water-dryland rotation seed production (usually 6.0-7.5).

[0056] The comprehensive assessment unit is used to provide an overall evaluation of soil fertility based on a comprehensive score of multiple soil fertility parameters. The comprehensive assessment adopts a weighted scoring method, assigning different weights to different soil fertility parameters according to their impact on the yield and quality of seed production in water-upland rotation, calculating a comprehensive score, and then classifying soil fertility into four levels according to the comprehensive score: excellent (comprehensive score ≥ 90), good (80 ≤ comprehensive score < 90), medium (60 ≤ comprehensive score < 80), and poor (comprehensive score < 60).

[0057] The final soil fertility assessment report generated by the soil fertility assessment module includes: the current values ​​of various soil fertility parameters, deviations from standard values, single-index assessment results, comprehensive assessment level, analysis of soil fertility change trends, and major existing problems.

[0058] Example 6: like Figure 8 As shown, the soil fertility maintenance decision module includes a measure matching unit and a soil fertility maintenance measure library.

[0059] The soil fertility maintenance measures database is classified and stored according to different rotation stages (paddy cropping period, dry cropping period, and transition period), different soil fertility levels (excellent, good, medium, and poor), and different deviation types (low organic matter, nutrient imbalance, abnormal pH, salt accumulation, etc.). Each measure entry includes the measure name, implementation conditions, implementation method, expected effects, and precautions.

[0060] Under crop rotation conditions, if soil fertility is not emphasized, soil organic matter tends to decrease, and the available phosphorus content in the soil decreases. Therefore, the soil fertility maintenance measures database stores various measures to address low organic matter levels, including organic fertilizer application schemes (such as the application amount and methods for pig manure and crop straw), green manure planting schemes (such as the planting and management techniques for broad beans as green manure in rice-broad bean rotation), and straw return schemes (such as leaving rice straw in high stubble and returning it to the field as compost). For nutrient imbalances, it stores precision fertilization schemes (nitrogen, phosphorus, and potassium ratio schemes based on different rotation stages and crop requirements). For abnormal soil pH, it stores soil acidity / alkalinity adjustment schemes (such as applying lime to adjust acidic soil and applying sulfur to adjust alkaline soil). For the potential accumulation of soil salinity in crop rotation, it stores irrigation and leaching schemes and drainage improvement schemes.

[0061] The measure matching unit, based on the soil fertility assessment report output by the soil fertility assessment module, matches the measure items that best match the current crop rotation stage, soil fertility level, and deviation type from the soil fertility maintenance measure library, generates a targeted soil fertility maintenance decision-making scheme, and provides suggestions on the implementation sequence of each measure.

[0062] Example 7: like Figure 7 As shown, the operation flow of the system of the present invention is as follows: Step S1: The local data acquisition module collects soil fertility parameters at different soil depths in the paddy-dryland rotation seed production field according to the preset time period. At the same time, it obtains the geographical location information of the sampling point through the positioning unit and records the sampling time information and rotation stage information through the time stamping unit.

[0063] Step S2: Upload the collected local soil fertility parameters and corresponding location and time information to the data comparison and analysis module.

[0064] Step S3: The data preprocessing unit of the data comparison and analysis module cleans, normalizes, and unifies the format of the received data.

[0065] Step S4: The data comparison and analysis module retrieves standard soil fertility parameter data from the cloud database module that matches the local conditions (geographical location, soil type, crop rotation pattern, crop rotation years, and current crop rotation stage).

[0066] Step S5: The deviation calculation unit calculates the deviation value, direction, and degree of deviation between the local geoforce parameters and the standard value; the trend analysis unit analyzes the changing trend of the local geoforce parameters and compares it with the standard changing trend.

[0067] Step S6: Based on the comparative analysis results, the soil fertility assessment module performs single-index assessment and comprehensive assessment of various soil fertility parameters, and generates a soil fertility assessment report.

[0068] Step S7: Determine if any ground force parameters deviate from the standard value and exceed the preset threshold. If so, the early warning module will issue an early warning message.

[0069] Step S8: The soil fertility maintenance decision module generates a targeted soil fertility maintenance decision scheme by matching the soil fertility assessment report with the soil fertility maintenance measures library.

[0070] Step S9: The output and display module outputs and displays the comparative analysis results, the soil fertility assessment report, and the soil fertility maintenance decision-making scheme.

[0071] Step S10: The historical data storage module stores the data collected this time, the comparative analysis results, the evaluation report and the decision-making plan, and updates the local soil fertility change historical archive.

[0072] Example 8: like Figure 9 As shown, the system of this invention can be applied to various water-dryland rotation seed production scenarios, including but not limited to: Scenario 1: Rice-Wheat Rotation Crop Field. In this scenario, the system sets different data collection periods and evaluation standards based on the different characteristics of the rice and wheat seasons. During the rice season, the system focuses on indicators such as soil reducing agent content, pH changes, and organic matter decomposition rate; during the wheat season, the system focuses on indicators such as soil aeration, nutrient availability, and moisture status.

[0073] Scenario 2: Rice-Rapeseed Rotation Field. In this scenario, the system focuses on the effects of rice-upland rotation on soil structure and nutrient status. Studies have shown that rice-upland rotation can effectively lower the groundwater level, improve soil aeration, and enhance soil nutrient availability. Through comparative analysis, the system evaluates the actual improvement effect of rotation on soil fertility and provides subsequent fertilization recommendations accordingly.

[0074] Scenario 3: Rice-Vegetable Rotation Seed Production Fields. For rice-vegetable rotation seed production scenarios within facilities such as solar greenhouses, the system focuses on mitigating continuous cropping obstacles and improving soil physicochemical conditions. Through segmented irrigation and alternating wet-dry irrigation, the oxidation and reduction processes in the continuously cropped soil are alternated, improving soil physicochemical conditions. The system continuously monitors and compares data to evaluate the mitigation effect of rice-vegetable rotation on continuous cropping obstacles.

[0075] Example 9: like Figure 10 As shown, the deployment method of the system of the present invention in a crop rotation seed production field is as follows: In the seed production fields of the paddy-upland rotation system, a sampling point is set up at every certain area (e.g., every 1000 square meters) according to the grid-based layout principle. At each sampling point, soil sensors are buried at different soil depths (0-20cm, 20-40cm, 40-60cm). All soil sensors are connected to a local data acquisition terminal via wired or wireless means. The local data acquisition terminal is connected to a cloud server via 4G / 5G or Wi-Fi communication methods to achieve real-time data upload and real-time access to the cloud database.

[0076] During system deployment, the system first downloads corresponding standard soil fertility parameter benchmark data from the cloud database module based on the field's geographical location, soil type, and the adopted paddy-upland rotation pattern, serving as a reference benchmark for local assessment. Then, the local data acquisition module is activated for the initial comprehensive data collection, establishing an initial profile of local soil fertility. Subsequently, the system operates automatically according to a preset data collection cycle, enabling continuous monitoring, comparative evaluation, and maintenance decisions regarding soil fertility throughout the entire paddy-upland rotation seed production process.

[0077] Example 10: The system of this invention was deployed and validated at a rice-wheat rotation seed production base. The rotation pattern at this base is a double cropping system of rice and wheat per year, the soil type is paddy soil, and the rotation period is 5 years. After deployment, the system runs continuously for one complete rotation cycle (rice season + wheat season).

[0078] The verification results show that: The system can accurately identify the seasonal variations in soil fertility parameters under rice-dryland rotation seed production conditions. During the rice season, soil pH is generally higher than in the dryland season, and the rate of organic matter decomposition is faster; during the wheat season, soil aeration improves, and the content of available nutrients increases. The data collected by the system accurately reflects these variations.

[0079] By comparing local data with standard cloud data, the system successfully identified that the soil organic matter content of the base was about 15% lower than the standard value of the same type of paddy-dryland rotation field in the same region, and the alkaline nitrogen content was about 12% lower, and issued an early warning based on this.

[0080] Based on the soil fertility assessment results, the soil fertility maintenance decision module matched and generated a soil improvement plan of "increasing the application of organic fertilizer (pig manure) combined with straw return to the field" and a precision fertilization plan of "adjusting the ratio of nitrogen, phosphorus and potassium fertilizer" from the measure library.

[0081] After implementing a crop rotation cycle according to the decision scheme generated by the system, the soil organic matter content increased by 8.7%, the available nitrogen content increased by 6.3%, and the overall soil fertility level improved from "medium" to "good", verifying the effectiveness and practicality of the system of the present invention.

[0082] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A soil fertility maintenance system under a crop rotation system, characterized in that, The system includes: The local data acquisition module is set up in the paddy-dryland rotation seed production field and is used to collect local soil fertility parameters according to a preset time period. The soil fertility parameters include at least one of soil pH value, organic matter content, total nitrogen content, total phosphorus content, total potassium content, available nitrogen content, available phosphorus content, available potassium content, soil moisture content, soil temperature and soil enzyme activity. The cloud database module is used to store standard soil fertility parameter data under the seed production scenario of water-dry rotation. The standard soil fertility parameter data includes the benchmark values, threshold ranges and trend data of soil fertility parameters under different water-dry rotation modes, different rotation years and different soil types. The data comparison and analysis module is communicatively connected to the local data acquisition module and the cloud database module, respectively, and is used to compare and analyze the local soil fertility parameters with the standard soil fertility parameters in the cloud database module, and generate comparison and analysis results. The comparison and analysis results include the deviation value, deviation direction and deviation degree of the local soil fertility parameters and the standard values. The soil fertility assessment module, connected to the data comparison and analysis module, is used to assess the current state and trend of local soil fertility based on the comparison and analysis results, combined with the rotation cycle of water-dryland cropping and the current rotation stage, and generate a soil fertility assessment report. The soil fertility maintenance decision module is connected to the soil fertility assessment module and is used to generate a soil fertility maintenance decision scheme for the current soil fertility status by matching a pre-stored soil fertility maintenance measures library with the soil fertility assessment report. The soil fertility maintenance decision scheme includes at least one of the following: soil fertility improvement scheme, irrigation regulation scheme, crop rotation adjustment scheme, and soil improvement scheme. The output and display module is used to output and display the comparative analysis results, soil fertility assessment report, and soil fertility maintenance decision-making scheme.

2. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The local data acquisition module includes a soil sensor unit, a positioning unit, and a time stamping unit; The soil sensor unit is installed at different soil depths in the crop rotation seed production field to collect soil fertility parameters of different soil layers. The positioning unit is used to acquire the geographical location information of the sampling point; The time stamp unit is used to record the time information of each sampling.

3. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The cloud database module includes a standard soil fertility parameter sub-library and a water-dry rotation scenario parameter sub-library; The standard geofertility parameter sub-database stores benchmark data of soil geofertility parameters for different regions, soil types, and climatic conditions. The sub-database of parameters for the rice-dryland rotation scenario stores data on the variation patterns of soil fertility parameters and empirical data on soil fertility maintenance under different rice-dryland rotation modes. The rice-dryland rotation modes include rice-wheat rotation, rice-rapeseed rotation, and rice-vegetable rotation.

4. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The data comparison and analysis module includes a data preprocessing unit, a deviation calculation unit, and a trend analysis unit; The data preprocessing unit is used to clean, normalize, and unify the format of locally collected soil fertility parameters. The deviation calculation unit is used to calculate the deviation between the local soil force parameters and the corresponding standard values ​​in the cloud database; The trend analysis unit is used to analyze the changing trend of local soil fertility based on the local fertility parameter data collected multiple times, and compare it with the standard changing trend in the cloud database.

5. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The soil fertility assessment module includes a single-index assessment unit and a comprehensive assessment unit; The single-index evaluation unit is used to independently evaluate each soil fertility parameter and determine whether it is within the normal range. The comprehensive evaluation unit is used to provide an overall evaluation of soil fertility level based on the comprehensive score of multiple soil fertility parameters.

6. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The soil fertility maintenance measures database is classified and stored according to different rotation stages of water-dryland cropping, different soil fertility levels, and different deviation types. Each measure entry includes the measure name, implementation conditions, implementation method, expected effect, and precautions.

7. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The system also includes an early warning module, which is connected to the soil fertility assessment module and is used to issue an early warning message when soil fertility parameters deviate from the standard value by more than a preset threshold.

8. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The system also includes a historical data storage module, which stores local soil fertility parameter data collected in previous periods, as well as corresponding comparative analysis results, soil fertility assessment reports, and soil fertility maintenance decision-making schemes, forming a historical archive of local soil fertility changes.

9. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The data collection cycle of the local data acquisition module is set according to the rotation stage of paddy and dryland crops: during the paddy crop stage, the collection interval is 7-15 days; during the dryland crop stage, the collection interval is 10-20 days; and during the rotation transition period, the collection interval is 3-7 days.

10. The soil fertility maintenance system under crop rotation according to claim 1, characterized in that, The soil fertility assessment report generated by the soil fertility assessment module includes: the current values ​​of various soil fertility parameters, deviations from standard values, single-index assessment results, comprehensive assessment level, soil fertility change trend analysis, and major existing problems.

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