Planting management system and method based on soil suitability

Abstract

The invention discloses a planting management system and method based on soil suitability, relates to the technical field of planting management, and solves the problems that in the prior art, the suitability between a planted plant and soil is evaluated only before planting, but the suitability between the planted plant and the soil in the growth stage process of the planted plant is ignored, and the planting efficiency is low. And the evaluation result is not accurate. According to the method, by analyzing the planting matching index between the planting information of the to-be-planted area and the standard growth information of the to-be-planted plant, the suitability between the to-be-planted area and the to-be-planted plant is determined; analyzing the relative change value of the weather information in the prediction time period, and analyzing the change condition of the associated soil information; acquiring required nutrition information based on the soil information with abnormal change conditions; the problem of adaptability between the to-be-planted area and the to-be-planted plant is effectively solved, and adverse conditions such as slow crop growth or yield reduction possibly caused by an inappropriate planting environment are avoided.

Description

Technical Field

[0001] The invention belongs to the field of planting management, and in particular relates to a planting management system and method based on soil suitability. Background Art

[0002] The invention patent application with publication number CN115375163A discloses a method for evaluating tobacco-growing soil suitability based on indicator weights and membership functions, including: chemically testing tobacco-growing soil samples to obtain evaluation indicators; analyzing the evaluation indicators based on principal component analysis to obtain weights of the evaluation indicators; determining the membership score of each evaluation indicator through a membership function; calculating a tobacco-growing soil suitability index based on the weights and the membership scores; and evaluating tobacco-growing soil suitability based on the tobacco-growing soil suitability index.

[0003] The above-mentioned prior art evaluates the compatibility of soil and tobacco growth by analyzing the various components of the soil before planting. However, during the tobacco growth stage, tobacco has different requirements for soil components, and the soil components will change with the weather, which will destroy the compatibility between tobacco and soil. Therefore, the evaluation method has limitations, resulting in inaccurate evaluation results. Therefore, the present invention provides a planting management system and method based on soil suitability. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a planting management system and method based on soil suitability, which is used to solve the technical problem that the prior art only evaluates the compatibility between the planted plants and the soil before planting, but ignores the compatibility between the planted plants and the soil during the growth stage, resulting in inaccurate evaluation results.

[0005] To achieve the above-mentioned object, a first aspect of the present invention provides a planting management system based on soil suitability, comprising a data analysis module, a soil information prediction module and a maintenance implementation module;

[0006] Data analysis module: used to extract the planting information of the area to be planted and the standard growth information of the plants to be planted, and analyze whether the area to be planted is suitable for planting the plants to be planted; if yes, the area to be planted is suitable for planting the plants to be planted; if not, reselect the plants to be planted or reselect the planting area for the plants to be planted;

[0007] Among them, the planting information includes soil information and weather information; the standard growth information includes the growth cycle and the standard growth value; the standard growth value includes the standard value of weather information and the standard value of soil information at each growth stage;

[0008] Soil information prediction module: used to analyze the correlation between each soil information and each weather information, and obtain the weather information associated with each soil information;

[0009] Extracting weather information within the forecast period, analyzing the relative change value of the weather information within the forecast period relative to the corresponding standard weather information; analyzing the change status of the associated soil information based on the relative change value of the weather information within the forecast period; wherein the change status includes normal and abnormal;

[0010] Maintenance implementation module: used to analyze the nutritional information required for abnormal soil changes; the nutritional information includes nutritional fertilizers and corresponding dosages.

[0011] Preferably, the extracting of the planting information of the area to be planted and the standard growth information of the plants to be planted includes:

[0012] Extract standard growth information of several plants to be planted from historical data; based on the growth cycle of the plants to be planted, extract planting information of the areas to be planted within several growth cycles.

[0013] Preferably, the analyzing whether the area to be planted is suitable for planting the plants to be planted includes:

[0014] The growth cycle of the plant to be planted is divided into growth stages, and the values ​​of each planting information in the same growth stage are respectively averaged to obtain the actual value of each planting information; wherein the actual value of each planting information includes the actual value corresponding to the soil information and the actual value corresponding to each weather information;

[0015] Based on the actual value of the planting information and the standard growth value, the planting matching index between the area to be planted and the plant to be planted is calculated; whether the matching index is greater than the index threshold is determined; if yes, the area to be planted is suitable for planting the plant to be planted; if no, the area to be planted is not suitable for planting the plant to be planted; wherein the growth stage includes the seedling stage, the growth stage and the fruiting stage.

[0016] Preferably, the calculating of the planting matching index between the to-be-planted area and the to-be-planted plant based on the actual value and the standard growth value of the planting information includes:

[0017] Performing absolute difference processing between the actual value of each planting information at each growth stage and the corresponding standard growth value to obtain the absolute value of the difference, and then dividing it by the corresponding standard growth value to obtain the relative difference value between the actual value of each planting information and the corresponding standard growth value; wherein the relative difference value includes the relative difference value of weather information and the relative difference value of soil;

[0018] The relative difference values ​​of weather information and the relative difference values ​​of soil are accumulated respectively to obtain the total relative difference value of weather and the total relative difference value of soil;

[0019] The total relative difference values ​​of weather and soil at each growth stage are weighted and summed to obtain the difference index between the planting information at each growth stage and the standard growth information; wherein the weight coefficients are all greater than 0 and the sum is 1;

[0020] The difference index is multiplied by the proportional coefficient and the sum is calculated to obtain the planting matching index between the to-be-planted area and the to-be-planted plant. The proportional coefficient is greater than 0 and the sum is 1.

[0021] The present invention extracts the planting information of the area to be planted within the growth cycle of the plant to be planted, which is beneficial to analyzing the difference between the planting information of the area to be planted and the growth information required by the plant to be planted at different growth stages of the plant to be planted, and comprehensively evaluates the difference between the two at each growth stage to evaluate whether the area to be planted is suitable for planting the plant to be planted; by comprehensively evaluating the difference between the planting information and the plant requirements at each growth stage, the overall suitability of the area to be planted can be judged more accurately, avoiding the limitations of a single time point or static data, and providing a more comprehensive and reliable basis for planting decisions.

[0022] Preferably, the analyzing the correlation between each soil information and each weather information includes:

[0023] Extract some weather information and corresponding soil information from historical data, and use Pearson correlation coefficient method to calculate the absolute value of correlation coefficient between each weather information and each soil information; judge whether the absolute value of correlation coefficient is greater than a preset threshold; if yes, the weather information and soil information are correlated; if no, they are not correlated.

[0024] The present invention is based on weather information and soil information in historical data, and uses the Pearson correlation coefficient method to analyze the potential connection between them, which helps to reveal the impact mechanism of weather changes on soil status, and obtain which weather information the changes in various soil information are related to, laying the foundation for subsequent analysis and prediction of the impact of weather changes on soil within the time period; secondly, the Pearson correlation coefficient method, as a classic statistical analysis method, is simple to calculate and has reliable results. It can quantify the degree of linear correlation between weather information and soil information, and intuitively determine whether there is a significant correlation between them.

[0025] Preferably, extracting weather information of the area to be planted within the forecast period and analyzing the relative change value of the weather information within the forecast period relative to the standard weather information includes:

[0026] Subtract the standard value of the corresponding weather information from the predicted average value of each weather information in the planting area within the predicted time period, and then divide it by the standard value of the corresponding weather information to obtain the relative change value of each weather information within the predicted time period relative to the standard value of the corresponding weather information; among which, the predicted average value of each weather information is the mean of the corresponding values ​​of each weather information within the predicted time period.

[0027] Preferably, the analyzing the change status of the associated soil information based on the relative change value of the weather information within the forecast period includes:

[0028] Extracting a number of weather information and corresponding relative change values, as well as associated soil information and corresponding change values ​​from historical data; combining the number of weather information and corresponding relative change values ​​with the associated soil information and corresponding change values ​​to obtain a number of training data groups of a CNN neural network model; training the CNN neural network model through the several training groups to obtain a soil information prediction model; wherein the number of weather information and corresponding relative change values ​​are input data of the CNN neural network model, and the number of associated soil information and corresponding change values ​​are output data of the CNN neural network model;

[0029] The weather information and the corresponding relative change value within the forecast period are input into the soil information estimation model, and the associated soil information and the corresponding change value are output; if the change value of the soil information is greater than or less than the corresponding change threshold, the soil information is abnormal; otherwise, the soil information is normal.

[0030] The present invention constructs several groups of training data groups of CNN neural network models based on historical data of various weather information and related soil information, repeatedly trains the CNN neural network model, obtains a soil information evaluation model, evaluates the impact of changes in various weather information on related soil information, and lays a foundation for subsequent analysis and prediction of the impact of weather changes on soil within a time period, thereby taking measures in advance to avoid severe weather changes that lead to the loss of soil-related nutrients and affect plant growth; secondly, the CNN neural network model has powerful feature extraction and pattern recognition capabilities. Through repeated training, the model can automatically extract useful features from input data and construct an effective evaluation model, which enables the model to quickly and accurately give evaluation results when facing new weather and soil data, thereby improving the efficiency and accuracy of the evaluation.

[0031] Preferably, based on the soil information and the corresponding change value output by the soil information estimation model, the change values ​​of each soil information are accumulated respectively to obtain a comprehensive change value of each soil information.

[0032] Preferably, based on the comprehensive change value of each soil information, the nutritional information required by each soil information is analyzed, including:

[0033] An electronic lookup table of comprehensive change values ​​of soil information and corresponding nutritional information is constructed based on historical data; the required nutritional information is found by searching the soil information with keywords.

[0034] By constructing an electronic lookup table, the present invention allows users to easily search based on the key words of soil information and quickly locate the required nutritional information, which greatly saves time and energy; secondly, the electronic lookup table ensures the accuracy and consistency of the information. Since the data is derived from historical records and has been scientifically processed and verified, the correspondence between the presented nutritional information and soil information has a high degree of credibility, avoiding misjudgment and losses caused by inaccurate information.

[0035] Preferably, the second aspect of the present invention provides a planting management method based on soil suitability, comprising the following steps:

[0036] Step 1: Analyze the compatibility between the area to be planted and the plants to be planted;

[0037] Step 2: Analyze the correlation between each soil information and each weather information;

[0038] Step 3: Extract weather information within the forecast period, and analyze the relative change value of the weather information within the forecast period relative to the corresponding standard weather information;

[0039] Step 4: Analyze the change status of the associated soil information based on the relative change value of the weather information within the forecast period;

[0040] Step 5: Analyze the nutritional information required for abnormal soil information; the nutritional information includes nutritional fertilizers and corresponding dosages.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention extracts some planting information of the planting area during the entire growth cycle of the plant to be planted, which is helpful to analyze the specific differences between the planting conditions of the planting area and the growth conditions required by the plant to be planted. By comprehensively considering the differences between the two at each growth stage, the suitability of the planting area for specific plants can be accurately evaluated; avoiding the limitations of a single time point or static data, and providing a more comprehensive and reliable basis for planting decisions by comprehensively evaluating the information differences throughout the growth cycle;

[0043] 2. The present invention determines the compatibility between the area to be planted and the plants to be planted by analyzing the planting matching index between the planting information of the area to be planted and the standard growth information of the plants to be planted; and, analyzes the relative change value of the weather information within the predicted time period to analyze the change status of the associated soil information; obtains the required nutritional information based on the soil information with abnormal change status; effectively solves the matching between the area to be planted and the plants to be planted, and avoids the problems of crop growth stagnation or yield reduction caused by unsuitable planting environment; and, by predicting weather changes and evaluating future changes in the soil, it is helpful to timely discover potential problems in the soil environment, so as to take corresponding preventive measures to ensure that plants can grow under optimal soil conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0046] Figure 2 It is a schematic flow chart of the method for analyzing the compatibility between the area to be planted and the plants to be planted according to the present invention;

[0047] Figure 3 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] See also Figure 1 , the first aspect of the present invention provides a planting management system based on soil suitability, including a soil information prediction module, and a data analysis module and a maintenance implementation module connected thereto;

[0050] The data analysis module extracts the planting information of the area to be planted and the standard growth information of the plants to be planted;

[0051] Among them, planting information includes soil information and weather information;

[0052] The standard growth information includes the growth cycle and standard growth value of the plant to be planted; the standard growth value includes the standard value of weather information and the standard value of soil information suitable for the growth of the plant to be planted at each growth stage;

[0053] Weather information includes temperature, humidity, rainfall, etc.; soil information includes the content of trace elements such as nitrogen, phosphorus, potassium, as well as pH value, moisture, etc.

[0054] Specifically, standard growth information of several plants to be planted is extracted from historical data; and based on the growth cycle of the plants to be planted, planting information of the areas to be planted within several growth cycles is extracted.

[0055] It should be noted that the growth cycle is the time period suitable for the growth of the plant to be planted. For example, the planting time of early rice is different in different regions. Taking South China as an example, it is generally planted in March and harvested in August. The time from planting to maturity is the growth cycle.

[0056] If a certain planting area wants to plant early rice, the historical planting information of the planting area from March to August each year is extracted to evaluate whether it is suitable for planting early rice.

[0057] Based on the extracted planting information of the area to be planted and the standard growth information of the plants to be planted, analyzing whether the area to be planted is suitable for planting the plants to be planted; if yes, the area to be planted is suitable for planting the plants to be planted; if no, reselecting the plants to be planted or reselecting the planting area for the plants to be planted;

[0058] See also Figure 2 Specifically, the growth cycle of the plant to be planted is divided according to the growth stage, and the values ​​of each planting information in the same growth stage are respectively averaged to obtain the actual value of each planting information;

[0059] The growth stages include the seedling stage, the growing stage and the fruiting stage; the actual values ​​of each planting information include the actual values ​​corresponding to the soil information and the actual values ​​corresponding to each weather information.

[0060] For example, the growth cycle of early rice is from March to August, which is divided according to the growth stages, that is, the seedling period is from March to April, the growth period is from April to July, and the maturity period is from July to August. Assume that several temperatures of the area to be planted in the small growth cycle of March to April are extracted from historical data, and several temperatures are averaged to obtain the average temperature of the area to be planted in the small cycle of March to April, that is, the actual value of the temperature in the planting information.

[0061] Performing absolute difference processing between the actual value of each planting information at each growth stage and the corresponding standard growth value to obtain the absolute value of the difference, and then dividing it by the corresponding standard growth value to obtain the relative difference value between the actual value of each planting information and the corresponding standard growth value; wherein the relative difference value includes the relative difference value of weather information and the relative difference value of soil;

[0062] The relative difference values ​​of weather information and the relative difference values ​​of soil are accumulated respectively to obtain the total relative difference value of weather and the total relative difference value of soil;

[0063] The total relative difference values ​​of weather and soil at each growth stage are weighted and summed to obtain the difference index between the planting information at each growth stage and the standard growth information; wherein the weight coefficients are all greater than 0 and the sum is 1;

[0064] The difference index is multiplied by the proportional coefficient and the sum is calculated to obtain the planting matching index between the to-be-planted area and the to-be-planted plant. The proportional coefficient is greater than 0 and the sum is 1.

[0065] If the matching index is greater than the index threshold; if yes, the area to be planted is suitable for planting the plants to be planted; if no, the area to be planted is not suitable for planting the plants to be planted; wherein the growth stage includes the seedling stage, the growth stage and the fruiting stage.

[0066] It should be noted that the weight coefficient is determined by the importance of the planting information or the standard growth information, and the proportional coefficient is determined by the importance of the growth stage, and both are set by those skilled in the art.

[0067] For example: Assuming that the early rice is in the seedling stage, that is, March-April, the required standard weather temperature is 26°C, which is the temperature suitable for the growth of early rice in the seedling stage; the actual temperature of the weather in the planting area in March-April is 30°C, and through |26-30| / 26≈0.15, the relative difference between the actual temperature value and the standard temperature value in the weather information is 0.15; thus, the relative difference between the actual value of other weather information in March-April and the standard value of the corresponding weather information, as well as the relative difference between the actual value of soil information and the standard value of the corresponding soil information can be calculated; and the relative difference values ​​of weather information and soil information are accumulated; the total relative difference value of weather and soil can be obtained;

[0068] The total relative difference values ​​from March to April are weighted and summed to obtain the difference index between the seedling planting information and the corresponding standard growth information; in this way, the difference index between the actual value of the planting information during the growth period and the mature period and the corresponding standard growth value is calculated;

[0069] The difference indexes of seedling stage difference, growth stage difference and maturity stage difference are marked as A, B and C respectively; the planting matching index G between the to-be-planted area and the to-be-planted plant is calculated by G=(α×A+β×B+λ×C); wherein α, β and λ are proportional coefficients.

[0070] The soil information prediction module analyzes the correlation between each soil information and each weather information to obtain the weather information associated with each soil information;

[0071] Specifically, some weather information and corresponding soil information are extracted from historical data, and the absolute value of the correlation coefficient between each weather information and each soil information is calculated using the Pearson correlation coefficient method; it is determined whether the absolute value of the correlation coefficient is greater than a preset threshold; if yes, the weather information is correlated with the soil information, that is, weather changes will have a strong impact on certain soil information, such as: the moisture content of the soil is affected by the temperature and rainfall in the weather information; if no, it is not correlated.

[0072] It should be noted that the Pearson correlation coefficient is a statistical indicator that measures the linear correlation between two variables. Its value range is -1 to 1. The closer the absolute value is to 1, the stronger the correlation between the two variables; the closer the absolute value is to 0, the weaker the correlation between the two variables.

[0073] The present invention sets a threshold value, which is close to 1 and much greater than 0, to judge the strength of the correlation; if the absolute value of the correlation coefficient is greater than the threshold value, it is considered that there is a significant correlation between the weather information and the soil information; if the absolute value of the correlation coefficient is less than or equal to the threshold value, it is considered that there is no correlation between the weather information and the soil information.

[0074] Extract weather information within the forecast period, and analyze the relative change value of the weather information within the forecast period relative to the corresponding standard weather information;

[0075] Specifically, the predicted average value of each weather information in the to-be-planted area within the predicted time period is subtracted from the standard value of the corresponding weather information, and then divided by the standard value of the corresponding weather information to obtain the relative change value of each weather information within the predicted time period relative to the standard value of the corresponding weather information; wherein the predicted average value of each weather information is the mean of the corresponding values ​​of each weather information within the predicted time period.

[0076] It should be noted that the forecast value of weather information for the area to be planted within the forecast period can be obtained from the Meteorological Bureau.

[0077] Analyzing the change status of the associated soil information based on the relative change value of the weather information within the forecast period; wherein the change status includes normal and abnormal;

[0078] Specifically, a number of weather information and corresponding relative change values, as well as associated soil information and corresponding change values ​​are extracted from historical data; the number of weather information and corresponding relative change values ​​are combined with the associated soil information and corresponding change values ​​to obtain a number of training data groups of a CNN neural network model; the CNN neural network model is trained by the several training groups to obtain a soil information prediction model; wherein the number of weather information and corresponding relative change values ​​are input data of the CNN neural network model, and the number of associated soil information and corresponding change values ​​are output data of the CNN neural network model;

[0079] The weather information and the corresponding relative change value within the forecast period are input into the soil information estimation model, and the associated soil information and the corresponding change value are output; if the change value of the soil information is greater than or less than the corresponding change threshold, the soil information is abnormal; otherwise, the soil information is normal.

[0080] For example, taking soil moisture changes as an example, assuming that soil is affected by weather temperature and rainfall; assuming that early rice is in the seedling stage, extract the relative change values ​​of several temperatures in the weather information of the expected planting area at that time from historical data, and the corresponding soil moisture change values ​​relative to the standard moisture content under the influence of temperature;

[0081] Assume the weather conditions during a certain period of the seedling stage: the average temperature is 30°C, the standard temperature value is 26°C, and the relative change value between the two is -0.15; the soil moisture decreases by 2% relative to the standard moisture, and if the rainfall during this period is 1mm, the soil moisture increases by 4% relative to the standard moisture; and other historical data; a number of corresponding historical data of temperature and moisture are collected to obtain a number of training data sets, such as a training data set of temperature and moisture: [temperature: 30°C, moisture: -2%; rainfall: 1mm, moisture: 4%]; several sets of such training data sets are input into the CNN neural network model for training; among them, temperature and rainfall are the input data of the CNN neural network model, and moisture is the output data of the CNN neural network model; and in this way, training data sets between other soil information and weather information are constructed;

[0082] The CNN neural network model is trained through several training data sets to obtain a soil information estimation model.

[0083] Based on the soil information and the corresponding change value output by the soil information estimation model, the change values ​​of each soil information are accumulated respectively to obtain the comprehensive change value of each soil information.

[0084] The maintenance implementation module analyzes the nutritional information required for abnormally changing soil information; wherein the nutritional information includes nutrient fertilizers and corresponding usage amounts.

[0085] Specifically, a lookup table of comprehensive change values ​​of soil information and corresponding nutritional information is constructed based on historical data; and the corresponding required nutritional information is found through keyword search of soil information.

[0086] For example: the header of the lookup table consists of three parts: soil information change value, nutrients to be supplemented, and corresponding nutrient amounts; other required information columns can be added to the lookup table; assuming that the standard pH of the soil during the early rice seedling stage is between 5 and 6, during the predicted time period, due to weather changes, the soil pH increases by 2 relative to the standard pH. By searching for the keyword "PH, +2", the lookup table jumps to the column corresponding to the soil "PH, +2", and the corresponding "nutrients to be supplemented, corresponding nutrient amounts" related information can be obtained.

[0087] See also Figure 3 The second aspect of the present invention provides a planting management method based on soil suitability, comprising the following steps:

[0088] Step 1: Analyze the compatibility between the area to be planted and the plants to be planted;

[0089] Step 2: Analyze the correlation between each soil information and each weather information;

[0090] Step 3: Extract weather information within the forecast period, and analyze the relative change value of the weather information within the forecast period relative to the corresponding standard weather information;

[0091] Step 4: Analyze the change status of the associated soil information based on the relative change value of the weather information within the forecast period;

[0092] Step 5: Analyze the nutritional information required for abnormal soil information; the nutritional information includes nutritional fertilizers and corresponding dosages.

[0093] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0094] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A planting management system based on soil suitability, characterized in that: Including data analysis module, soil information prediction module and maintenance implementation module; Data analysis module: used to extract the planting information of the area to be planted and the standard growth information of the plants to be planted, and analyze whether the area to be planted is suitable for planting the plants to be planted; if yes, the area to be planted is suitable for planting the plants to be planted; If not, reselect the plants to be planted or reselect the planting area for the plants to be planted; Among them, the planting information includes soil information and weather information; the standard growth information includes the growth cycle and the standard growth value; the standard growth value includes the standard value of weather information and the standard value of soil information at each growth stage; Soil information prediction module: used to analyze the correlation between each soil information and each weather information, and obtain the weather information associated with each soil information; Extracting weather information within the forecast period, analyzing the relative change value of the weather information within the forecast period relative to the corresponding standard weather information; analyzing the change status of the associated soil information based on the relative change value of the weather information within the forecast period; wherein the change status includes normal and abnormal; Maintenance implementation module: used to analyze the nutritional information required for abnormal soil changes; the nutritional information includes nutritional fertilizers and corresponding dosages.

2. A planting management system based on soil suitability according to claim 1, characterized in that: The step of extracting the planting information of the area to be planted and the standard growth information of the plants to be planted includes: Extract standard growth information of several plants to be planted from historical data; based on the growth cycle of the plants to be planted, extract planting information of the areas to be planted within several growth cycles.

3. A planting management system based on soil suitability according to claim 2, characterized in that: The analyzing whether the area to be planted is suitable for planting the plants to be planted includes: The growth cycle of the plant to be planted is divided into growth stages, and the values ​​of each planting information in the same growth stage are respectively averaged to obtain the actual value of each planting information; wherein the actual value of each planting information includes the actual value corresponding to the soil information and the actual value corresponding to each weather information; Based on the actual value of the planting information and the standard growth value, the planting matching index between the area to be planted and the plant to be planted is calculated; whether the matching index is greater than the index threshold is determined; if yes, the area to be planted is suitable for planting the plant to be planted; if no, the area to be planted is not suitable for planting the plant to be planted; wherein the growth stage includes the seedling stage, the growth stage and the fruiting stage.

4. A planting management system based on soil suitability according to claim 3, characterized in that: The step of calculating the planting matching index between the to-be-planted area and the to-be-planted plant based on the actual value and the standard growth value of the planting information includes: Performing absolute difference processing between the actual value of each planting information at each growth stage and the corresponding standard growth value to obtain the absolute value of the difference, and then dividing it by the corresponding standard growth value to obtain the relative difference value between the actual value of each planting information and the corresponding standard growth value; wherein the relative difference value includes the relative difference value of weather information and the relative difference value of soil; The relative difference values ​​of weather information and the relative difference values ​​of soil are accumulated respectively to obtain the total relative difference value of weather and the total relative difference value of soil; The total relative difference values ​​of weather and soil at each growth stage are weighted and summed to obtain the difference index between the planting information at each growth stage and the standard growth information; wherein the weight coefficients are all greater than 0 and the sum is 1; The difference index is multiplied by the proportional coefficient and the sum is calculated to obtain the planting matching index between the to-be-planted area and the to-be-planted plant. The proportional coefficient is greater than 0 and the sum is 1.

5. The planting management system based on soil suitability according to claim 1, characterized in that: The analysis of the correlation between each soil information and each weather information includes: Extract some weather information and corresponding soil information from historical data, and use Pearson correlation coefficient method to calculate the absolute value of correlation coefficient between each weather information and each soil information; judge whether the absolute value of correlation coefficient is greater than a preset threshold; if yes, the weather information and soil information are correlated; if no, they are not correlated.

6. A planting management system based on soil suitability according to claim 5, characterized in that: The step of extracting weather information of the area to be planted within the forecast period and analyzing the relative change value of the weather information within the forecast period relative to the standard weather information includes: Subtract the standard value of the corresponding weather information from the predicted average value of each weather information in the planting area within the predicted time period, and then divide it by the standard value of the corresponding weather information to obtain the relative change value of each weather information within the predicted time period relative to the standard value of the corresponding weather information; among which, the predicted average value of each weather information is the mean of the corresponding values ​​of each weather information within the predicted time period.

7. The planting management system based on soil suitability according to claim 5, characterized in that: The analyzing the change status of the associated soil information based on the relative change value of the weather information within the forecast period includes: Extracting a number of weather information and corresponding relative change values, as well as associated soil information and corresponding change values ​​from historical data; combining the number of weather information and corresponding relative change values ​​with the associated soil information and corresponding change values ​​to obtain a number of training data groups of a CNN neural network model; training the CNN neural network model through the several training groups to obtain a soil information prediction model; wherein the number of weather information and corresponding relative change values ​​are input data of the CNN neural network model, and the number of associated soil information and corresponding change values ​​are output data of the CNN neural network model; The weather information and the corresponding relative change value within the forecast period are input into the soil information estimation model, and the associated soil information and the corresponding change value are output; if the change value of the soil information is greater than or less than the corresponding change threshold, the soil information is abnormal; otherwise, the soil information is normal.

8. The planting management system based on soil suitability according to claim 7, characterized in that: Based on the soil information and the corresponding change value output by the soil information estimation model, the change values ​​of each soil information are accumulated respectively to obtain the comprehensive change value of each soil information.

9. A planting management system based on soil suitability according to claim 8, characterized in that: Based on the comprehensive change value of each soil information, the nutritional information required by each soil information is analyzed, including: An electronic lookup table of comprehensive change values ​​of soil information and corresponding nutritional information is constructed based on historical data; the required nutritional information is found by searching the soil information with keywords.

10. A planting management method based on soil suitability, based on the planting management system based on soil suitability according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Analyze the compatibility between the area to be planted and the plants to be planted; Step 2: Analyze the correlation between each soil information and each weather information; Step 3: Extract weather information within the forecast period, and analyze the relative change value of the weather information within the forecast period relative to the corresponding standard weather information; Step 4: Analyze the change status of the associated soil information based on the relative change value of the weather information within the forecast period; Step 5: Analyze the nutritional information required for abnormal soil information; the nutritional information includes nutritional fertilizers and corresponding dosages.

Images