Plant sprouting parameter adjustment system and method based on big data analysis

Abstract

The application relates to the technical field of plant growth regulation, in particular to a plant germination parameter regulation system and method based on big data analysis, which comprises the following steps: acquiring soil nutrient data, plant germination growth data and root system distribution data of a monitoring area; analyzing the soil nutrient distribution uniformity and the growth state consistency of plant germination of the monitoring area through the soil nutrient data and the plant germination growth data; analyzing the spatial expansion trend of the plant germination root system of the monitoring area through the root system distribution data; comprehensively analyzing the soil nutrient distribution uniformity, the growth state consistency and the spatial expansion trend analysis results to estimate the plant germination competition level of the monitoring area; and adjusting the fertilizer nutrient content of the monitoring area according to the plant germination competition level estimation results. The application quantifies the plant germination competition level to adjust the fertilizer nutrient content in a targeted manner, and effectively improves the accuracy and scientificity of the fertilizer nutrient content adjustment.

Description

Technical Field

[0001] This application relates to the field of plant growth regulation technology, and in particular to a system and method for regulating plant budding parameters based on big data analysis. Background Technology

[0002] Germination is a crucial stage in the plant's life cycle. In the early stages of germination, plant growth relies on nutrients stored inside the seed. However, as the germinating root system develops, the plant begins to absorb nutrients from the soil. The germinating root system can actively grow towards nutrient-rich areas and absorb water and nutrients such as nitrogen, phosphorus, and potassium through root hairs and root tips, thereby promoting the rapid growth of seedlings.

[0003] Sufficient soil nutrient supply during the germination period can promote robust plant growth and improve the plant's survival ability in early competition. Soil nutrient distribution directly determines the fairness of resource acquisition for the germination population and the consistency of germination growth. Rationally regulating soil nutrient distribution is an important regulatory target to avoid the phenomenon of dominant plants suppressing weak individuals and to achieve large-scale production.

[0004] Existing methods for regulating soil nutrients often rely on homogenization fertilization strategies. For example, after analyzing soil nutrient content through discrete sampling points, fertilizer is supplemented using a homogenization approach. However, such methods ignore the spatial heterogeneity of soil nutrients and the impact of the trend of sprouting root expansion on plant sprouting competition. When soil nutrients are unevenly distributed, sprouting individuals compete for limited resources, leading to excessive root concentration. In contrast, homogenization fertilization strategies exacerbate the ineffective supply of fertilizer in local areas, resulting in unreasonable fertilizer application. Summary of the Invention

[0005] To overcome the defects and shortcomings of existing technologies, this application provides a plant germination parameter adjustment system and method based on big data analysis. By analyzing the spatial distribution of soil nutrients, germination growth status, and germination root expansion trend, the system quantifies the plant germination competition level and then adjusts the fertilizer nutrient content in a targeted manner, effectively improving the accuracy and scientific nature of fertilizer nutrient content adjustment.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] In the first aspect, this application provides a method for adjusting plant germination parameters based on big data analysis, including the following steps:

[0008] Acquire soil nutrient data, plant germination and growth data, and root distribution data for the monitored area;

[0009] The uniformity of soil nutrient distribution and the consistency of plant bud growth status in the monitoring area were analyzed using soil nutrient data and plant bud growth data.

[0010] Analyze the spatial expansion trend of plant germination roots in the monitoring area using root distribution data;

[0011] Based on the analysis results of soil nutrient uniformity, growth consistency, and spatial expansion trend, the level of plant budding competition in the monitoring area is predicted.

[0012] Adjust the fertilizer nutrient content in the monitoring area based on the predicted results of plant budding competition.

[0013] Preferably, analyzing the uniformity of soil nutrient distribution includes:

[0014] Obtain soil nutrient data for the monitoring area, which includes nutrient content data for each nutrient detected at different monitoring points within the monitoring area;

[0015] Calculate the Gini coefficient of individual nutrient distribution for each nutrient to analyze the uniformity of distribution of individual nutrients within the monitoring area;

[0016] Using the average content percentage of each single nutrient as the weight, the weighted average of the individual nutrient distribution Gini coefficients of each nutrient is taken as the soil nutrient distribution Gini coefficient of the monitoring area.

[0017] The soil nutrient distribution uniformity index is obtained by subtracting 1 from the soil nutrient distribution Gini coefficient, which is used to analyze the soil nutrient distribution uniformity in the monitoring area.

[0018] Preferably, analyzing the consistency of the growth state includes:

[0019] Acquire plant budding and growth data in the monitored area, including budding time and budding height data;

[0020] The coefficient of variation for germination time and the coefficient of variation for germination height in the monitored area were calculated using germination time data and germination height data, respectively.

[0021] The growth status consistency index of plant germination is obtained by weighted summing of the coefficient of variation of germination time and the coefficient of variation of germination height, which is used to analyze the consistency of plant germination growth status within the monitoring area.

[0022] Preferably, analyzing the spatial expansion trend includes:

[0023] Acquire root distribution data for the monitoring area. The root distribution data includes root orientation angle data and root length data.

[0024] Root orientation angle data and root length data are converted into plant root orientation vectors, and the plant root orientation concentration is calculated using these vectors. The formula for calculating plant root orientation concentration is as follows:

[0025]

[0026] In the formula L i,j θ represents the length of the j-th root of plant i. i,j N represents the orientation angle of the j-th root of plant i. i R represents the number of roots in plant i. i This indicates the concentration of the root orientation of plant i.

[0027] Using the proportion of root length as the weight, the weighted average of the concentration of root orientation of all plants in the monitoring area is used as the spatial expansion trend index of the plant's sprouting root system.

[0028] Preferably, estimating the level of plant germination competition includes:

[0029] The soil nutrient distribution uniformity index, plant sprout growth status consistency index, and plant sprout root spatial expansion trend index were obtained in the monitoring area.

[0030] The plant budding competition index of the monitoring area is obtained by weighted summing of the reciprocal of the soil nutrient distribution evenness index, the reciprocal of the growth state uniformity index, and the spatial expansion trend index, which is used to predict the plant budding competition level in the monitoring area.

[0031] Preferably, the adjustment of fertilizer nutrient content in the monitoring area includes:

[0032] Obtain the basic fertilizer nutrient content and plant budding competition index of the monitoring area, and multiply the basic fertilizer nutrient content by the plant budding competition index to obtain the corrected value of fertilizer nutrient content;

[0033] The optimal fertilizer nutrient content for the monitored area is obtained by adding the base fertilizer nutrient content and the correction value of the fertilizer nutrient content, and then the fertilizer nutrient content of the monitored area is adjusted to the optimal fertilizer nutrient content.

[0034] Secondly, this application provides a plant germination parameter adjustment system based on big data analysis, including:

[0035] The data acquisition module is used to acquire soil nutrient data, plant germination and growth data, and root distribution data of the monitoring area;

[0036] The consistency analysis module is used to analyze the uniformity of soil nutrient distribution and the consistency of plant bud growth status in the monitored area by using soil nutrient data and plant bud growth data.

[0037] The expansion trend analysis module is used to analyze the spatial expansion trend of the sprouting roots of plants in the monitoring area by using root distribution data.

[0038] The competition level prediction module is used to predict the plant germination competition level in the monitoring area by comprehensively analyzing the results of soil nutrient distribution uniformity, growth status consistency and spatial expansion trend.

[0039] The nutrient content adjustment module is used to adjust the fertilizer nutrient content in the monitoring area based on the predicted results of plant germination competition level.

[0040] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a plant germination parameter adjustment method based on big data analysis by calling the computer program stored in the memory.

[0041] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for adjusting plant germination parameters based on big data analysis.

[0042] Compared with the prior art, this application has the following advantages and beneficial effects:

[0043] This application quantifies the level of plant bud competition by analyzing the uniformity of soil nutrient distribution, the consistency of plant bud growth status, and the spatial expansion trend of plant bud roots in the monitoring area. Based on the level of plant bud competition, fertilizer nutrient content is adjusted in a targeted manner to achieve early quantitative assessment of bud competition level and dynamic optimization of fertilizer supply, effectively improving the accuracy and scientific nature of fertilizer nutrient content adjustment. Attached Figure Description

[0044] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 This is a schematic diagram of the overall process of the plant germination parameter adjustment method based on big data analysis provided in the embodiments of this application;

[0046] Figure 2 This is a schematic diagram of the structure of the plant germination parameter adjustment system based on big data analysis provided in the embodiments of this application;

[0047] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0048] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0049] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of the plant germination parameter adjustment method based on big data analysis provided in the embodiments of this application, which specifically includes the following steps:

[0050] S110: Acquire soil nutrient data, plant sprouting growth data, and root distribution data of the monitoring area. In one embodiment of this application, the soil nutrient data can be acquired by any one of electrochemical sensor array, near-infrared spectrometer, and soil sampling laboratory analysis. The plant sprouting growth data can be acquired by automatically recording the breaking time and plant height dynamics of each sprouting point using time series image recognition technology (e.g., RGB camera or hyperspectral imaging device). The root distribution data can be acquired by any one of micro root canal observation and ground penetrating radar (GPR).

[0051] S120: Analyze the uniformity of soil nutrient distribution and the consistency of plant bud growth status in the monitoring area by analyzing soil nutrient data and plant bud growth data.

[0052] Analyzing the evenness of soil nutrient distribution can quantify the spatial heterogeneity of soil nutrient resources, providing a key basis for predicting the level of competition for plant buds. For example, when soil nutrients are unevenly distributed, high-nutrient areas will induce excessive concentration of bud roots in locally enriched areas, leading to spatial overlap and direct competition between the roots of adjacent plants. Conversely, low-nutrient areas will experience nutrient-depleting growth among plants due to insufficient supply, exacerbating differentiation within the bud population. By calculating the Gini coefficient of individual nutrient distribution and the soil nutrient evenness index, the risk of soil resource imbalance can be accurately identified, the intensity of root expansion conflict and nutrient competition during the budding stage can be predicted, and the evenness of soil nutrient distribution can be analyzed, including:

[0053] Obtain soil nutrient data for the monitoring area, which includes nutrient content data for each nutrient detected at different monitoring points within the monitoring area;

[0054] The Gini coefficient of individual nutrient distribution is calculated to analyze the uniformity of distribution of a single nutrient within the monitoring area. In one embodiment of this application, the formula for calculating the Gini coefficient of individual nutrient distribution can be:

[0055]

[0056] In the formula x k,p x represents the content of the k-th nutrient detected at the p-th monitoring point within the monitoring area. k,q This represents the content of the k-th nutrient detected at the q-th monitoring point within the monitoring area, where n represents the number of monitoring points within the monitoring area, and μ. k G represents the average content of the k-th nutrient detected at each monitoring point within the monitoring area. k This represents the Gini coefficient of the k-th nutrient distribution within the monitoring area;

[0057] Using the average content percentage of each individual nutrient as a weight, the weighted average of the individual nutrient distribution Gini coefficients is taken as the soil nutrient distribution Gini coefficient for the monitoring area. In one embodiment of this application, the formula for calculating the soil nutrient distribution Gini coefficient can be:

[0058]

[0059] In the formula μ total This represents the total content of the k-th nutrient detected at all monitoring points within the monitoring area. This indicates that the weighting is based on the average percentage of individual nutrients, where m represents the number of nutrient types, and G... total The Gini coefficient represents the distribution of soil nutrients in the monitored area.

[0060] The soil nutrient distribution uniformity index is obtained by subtracting 1 from the soil nutrient distribution Gini coefficient, which is used to analyze the soil nutrient distribution uniformity in the monitoring area.

[0061] Significant differences in germination time lead to earlier germinating plants preferentially occupying light and heat resources and dominant ecological niches, creating time-difference competition and thus inhibiting the growth space of later germinating individuals. The discrete distribution of germination height reflects an imbalance in nutrient absorption rates among plants; the greater the height difference, the stronger the monopoly of nutrient resources by dominant plants, intensifying stratified competition within the population. By calculating and weighting the coefficients of variation of germination time and height, the degree of growth synchronicity defects in the germination population can be effectively quantified, accurately identifying the sources of competitive pressure and providing a basis for differentiated regulation. Simultaneously, by quantifying the risk of resource plunder caused by misaligned growth rhythms and dynamically adjusting nutrient supply strategies, the growth potential difference between individuals can be reduced, thereby weakening asymmetric competition, promoting coordinated development within the population, and analyzing the consistency of growth status, including:

[0062] Acquire plant budding and growth data in the monitored area, including budding time and budding height data;

[0063] The coefficient of variation for germination time and the coefficient of variation for germination height in the monitored area are calculated using germination time data and germination height data, respectively. Taking the coefficient of variation for germination time as an example, in one embodiment of this application, the formula for calculating the coefficient of variation for germination time can be: CV T =σ T / μ T In the formula σ T The standard deviation of germination time, μ T CV represents the mean of germination time. T Indicates the coefficient of variation of germination time;

[0064] The growth status consistency index of plant germination is obtained by weighted summing of the coefficient of variation of germination time and the coefficient of variation of germination height, which is used to analyze the consistency of plant germination growth status within the monitoring area.

[0065] S130: Analyze the spatial expansion trend of plant sprouting roots in the monitoring area using root distribution data;

[0066] Plant sprouting roots exhibit nutrient tropism, meaning they actively grow towards areas of nutrient-rich soil. This characteristic makes the root spatial expansion trend a key indicator for predicting the level of competition for sprouting. When nutrients are evenly distributed and sprouting competition is mild within the monitoring area, the orientation of sprouting roots should show a random distribution due to the lack of nutrient tropism stimulation. Conversely, if the root orientation shows a highly concentrated trend, it indicates intensified root space encroachment and increased competition for sprouting. Analysis of spatial expansion trends includes:

[0067] Acquire root distribution data for the monitoring area. The root distribution data includes root orientation angle data and root length data.

[0068] The root orientation angle data and root length data are converted into a plant root orientation vector. In one embodiment of this application, the plant root orientation vector can be:

[0069]

[0070] In the formula L i,j θ represents the length of the j-th root of plant i. i,j This represents the orientation angle of the j-th root of plant i. This represents the projection of the orientation angles of all plant roots onto the horizontal direction, i.e., the horizontal component of the root system. This represents the projection of the orientation angles of all plant roots onto the vertical direction, i.e., the vertical component of the root system, N. i This represents the number of roots in plant i. The vector representing the orientation of the root system of plant i;

[0071] The concentration of plant root orientation is calculated using the root orientation vector. The formula for calculating the concentration of plant root orientation is as follows:

[0072]

[0073] In the formula L i,j θ represents the length of the j-th root of plant i. i,j N represents the orientation angle of the j-th root of plant i. i R represents the number of roots in plant i. i The denominator represents the concentration of the root orientation of plant i. The numerator is the total modulus of the composite root orientation vectors. If all root orientations are completely consistent, the composite modulus is the largest. If the root orientations are completely random, the composite modulus approaches zero. The denominator is the total root length.

[0074] Using the proportion of root length as the weight, the weighted average of the root orientation concentration of all plants in the monitoring area is used as the spatial expansion trend index of the plant's sprouting roots. In one embodiment of this application, the formula for calculating the spatial expansion trend index can be:

[0075]

[0076] In the formula R i L represents the concentration of root orientation of plant i. i The total root length of plant i is represented by M, where the greater the total root length, the greater the weight of the plant. The number of plants in the monitoring area is represented by M, and C represents the spatial expansion trend index of the plant's sprouting root system.

[0077] S140: Based on the analysis results of soil nutrient uniformity, growth consistency and spatial expansion trend, the level of plant budding competition in the monitoring area is predicted.

[0078] By integrating soil nutrient distribution evenness index, growth status uniformity index, and root space expansion trend index to construct a plant budding competition index, it is possible to quantitatively assess multi-dimensional competition mechanisms and predict the level of plant budding competition, including:

[0079] The soil nutrient distribution uniformity index, plant sprout growth status consistency index, and plant sprout root spatial expansion trend index were obtained in the monitoring area.

[0080] The plant budding competition index of the monitoring area is obtained by weighting and summing the reciprocal of the soil nutrient distribution evenness index, the reciprocal of the growth state uniformity index, and the spatial expansion trend index. This index is used to estimate the plant budding competition level in the monitoring area. In one embodiment of this application, the weight values ​​can be obtained as follows:

[0081] A dataset was constructed by acquiring soil nutrient data, plant budding growth data, and root distribution data. The plant budding competition index was calculated by substituting these data into the dataset. At the same time, the expert judgment results on the plant budding competition level were obtained. The calculated plant budding competition index and the judgment results were imported into the fitting software, and the weight values ​​that meet the maximum judgment accuracy were output.

[0082] S150: Adjust the fertilizer nutrient content in the monitoring area based on the predicted results of plant budding competition level;

[0083] The plant germination competition index, as a comprehensive quantitative indicator of environmental stress and resource competition, indicates greater regional competitive pressure with higher values. This necessitates measures such as increasing nutrient supply to compensate for resource consumption and alleviate competition intensity, and adjusting fertilizer nutrient content in the monitored area, including:

[0084] Obtain the basic fertilizer nutrient content and plant budding competition index of the monitoring area, and multiply the basic fertilizer nutrient content by the plant budding competition index to obtain the corrected value of fertilizer nutrient content;

[0085] The optimal fertilizer nutrient content for the monitored area is obtained by adding the base fertilizer nutrient content and the correction value of the fertilizer nutrient content, and then the fertilizer nutrient content of the monitored area is adjusted to the optimal fertilizer nutrient content.

[0086] Please see Figure 2 , Figure 2 This is a schematic diagram of the plant germination parameter adjustment system based on big data analysis provided in this application embodiment. This embodiment provides a plant germination parameter adjustment system based on big data analysis, including:

[0087] Data acquisition module 210 is used to acquire soil nutrient data, plant germination and growth data and root distribution data of the monitoring area;

[0088] The consistency analysis module 220 is used to analyze the uniformity of soil nutrient distribution and the consistency of plant germination growth status in the monitoring area through soil nutrient data and plant germination growth data.

[0089] The expansion trend analysis module 230 is used to analyze the spatial expansion trend of the sprouting roots of plants in the monitoring area through root distribution data.

[0090] The competition level prediction module 240 is used to predict the plant germination competition level in the monitoring area by comprehensively analyzing the results of soil nutrient distribution uniformity, growth status consistency and spatial expansion trend.

[0091] Nutrient content adjustment module 250 is used to adjust the fertilizer nutrient content in the monitoring area based on the predicted results of plant germination competition level.

[0092] In one embodiment of this application, the consistency analysis module 220 is used to analyze the uniformity of soil nutrient distribution and the consistency of plant bud growth status in the monitoring area through soil nutrient data and plant bud growth data, and the analysis of soil nutrient distribution uniformity includes:

[0093] Obtain soil nutrient data for the monitoring area, which includes nutrient content data for each nutrient detected at different monitoring points within the monitoring area;

[0094] Calculate the Gini coefficient of individual nutrient distribution for each nutrient to analyze the uniformity of distribution of individual nutrients within the monitoring area;

[0095] Using the average content percentage of each single nutrient as the weight, the weighted average of the individual nutrient distribution Gini coefficients of each nutrient is taken as the soil nutrient distribution Gini coefficient of the monitoring area.

[0096] The difference between 1 and the Gini coefficient of soil nutrient distribution is used to obtain the soil nutrient distribution uniformity index, which is used to analyze the soil nutrient distribution uniformity in the monitoring area.

[0097] Analysis of growth state consistency includes:

[0098] Acquire plant budding and growth data in the monitored area, including budding time and budding height data;

[0099] The coefficient of variation for germination time and the coefficient of variation for germination height in the monitored area were calculated using germination time data and germination height data, respectively.

[0100] The growth status consistency index of plant germination is obtained by weighted summing of the coefficient of variation of germination time and the coefficient of variation of germination height, which is used to analyze the consistency of plant germination growth status within the monitoring area.

[0101] In one embodiment of this application, the expansion trend analysis module 230 is used to analyze the spatial expansion trend of the sprouting roots of plants in the monitoring area through root distribution data, and the analysis of the spatial expansion trend includes:

[0102] Acquire root distribution data for the monitoring area. The root distribution data includes root orientation angle data and root length data.

[0103] Root orientation angle data and root length data are converted into plant root orientation vectors, and the plant root orientation concentration is calculated using these vectors. The formula for calculating plant root orientation concentration is as follows:

[0104]

[0105] In the formula L i,j θ represents the length of the j-th root of plant i. i,jN represents the orientation angle of the j-th root of plant i. i R represents the number of roots in plant i. i This indicates the concentration of the root orientation of plant i.

[0106] Using the proportion of root length as the weight, the weighted average of the concentration of root orientation of all plants in the monitoring area is used as the spatial expansion trend index of the plant's sprouting root system.

[0107] In one embodiment of this application, the competition level prediction module 240 is used to predict the plant germination competition level in the monitoring area by comprehensively analyzing the soil nutrient distribution uniformity, growth status consistency, and spatial expansion trend. The prediction of the plant germination competition level includes:

[0108] The soil nutrient distribution uniformity index, plant sprout growth status consistency index, and plant sprout root spatial expansion trend index were obtained in the monitoring area.

[0109] The plant budding competition index of the monitoring area is obtained by weighted summing of the reciprocal of the soil nutrient distribution evenness index, the reciprocal of the growth state uniformity index, and the spatial expansion trend index, which is used to predict the plant budding competition level in the monitoring area.

[0110] In one embodiment of this application, the nutrient content adjustment module 250 is used to adjust the fertilizer nutrient content of the monitoring area according to the predicted results of plant germination competition level. Adjusting the fertilizer nutrient content of the monitoring area includes:

[0111] Obtain the basic fertilizer nutrient content and plant budding competition index of the monitoring area, and multiply the basic fertilizer nutrient content by the plant budding competition index to obtain the corrected value of fertilizer nutrient content;

[0112] The optimal fertilizer nutrient content for the monitored area is obtained by adding the base fertilizer nutrient content and the correction value of the fertilizer nutrient content, and then the fertilizer nutrient content of the monitored area is adjusted to the optimal fertilizer nutrient content.

[0113] The steps for implementing the corresponding functions of each parameter and each unit module in the plant germination parameter adjustment system based on big data analysis described above can be referred to the parameters and steps in the embodiments of the plant germination parameter adjustment method based on big data analysis above, and will not be repeated here.

[0114] Please refer to Figure 3 The present invention also provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a plant germination parameter adjustment method based on big data analysis, which can be loaded and executed by the processor 320 as provided in the above embodiments.

[0115] The memory 310 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the plant germination parameter adjustment method based on big data analysis provided in the above embodiments, etc. The data storage area may store data involved in the plant germination parameter adjustment method based on big data analysis provided in the above embodiments, etc.

[0116] Processor 320 may include one or more processing cores. Processor 320 executes instructions, programs, code sets, or instruction sets stored in memory 310, and calls data stored in memory 310 to perform various functions and process data as described in this application. Processor 320 may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 320 may also be other types, and this application embodiment does not specifically limit the specific devices used.

[0117] The communication bus 330 may include a path for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0118] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a method for adjusting plant germination parameters based on big data analysis.

[0119] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0120] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0121] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for adjusting plant germination parameters based on big data analysis, characterized in that, Includes the following steps: Acquire soil nutrient data, plant germination and growth data, and root distribution data for the monitoring area. Soil nutrient data includes nutrient content data of each nutrient detected at different monitoring points within the monitoring area. Plant germination and growth data includes germination time data and germination height data. Root distribution data includes root orientation angle data and root length data. The analysis of soil nutrient distribution uniformity and plant bud growth consistency in the monitored area was conducted using soil nutrient data and plant bud growth data, including: The Gini coefficient of individual nutrient distribution is calculated using soil nutrient data to analyze the uniformity of distribution of individual nutrients within the monitoring area. Using the average content percentage of each single nutrient as the weight, the weighted average of the individual nutrient distribution Gini coefficients of each nutrient is taken as the soil nutrient distribution Gini coefficient of the monitoring area. The difference between 1 and the Gini coefficient of soil nutrient distribution is used to obtain the soil nutrient distribution uniformity index, which is used to analyze the soil nutrient distribution uniformity in the monitoring area. The coefficient of variation for germination time and the coefficient of variation for germination height in the monitored area were calculated using germination time data and germination height data, respectively. The growth status consistency index of plant germination is obtained by weighted summing of the coefficient of variation of germination time and the coefficient of variation of germination height, which is used to analyze the consistency of plant germination growth status within the monitoring area. Analysis of root distribution data was used to monitor the spatial expansion trend of germinating roots in the monitoring area, including: Root orientation angle data and root length data are converted into plant root orientation vectors, and the plant root orientation concentration is calculated using these vectors. The formula for calculating plant root orientation concentration is as follows: ； In the formula Indicates plant The Root length, Indicates plant The The angle of each root system Indicates plant The number of roots, Indicates plant The degree of concentration of the plant's root system orientation; Using the proportion of root length as the weight, the weighted average of the concentration of root orientation of all plants in the monitoring area is used as the spatial expansion trend index of the plant's sprouting root system. Based on the analysis of soil nutrient distribution uniformity, growth consistency, and spatial expansion trend, the level of plant budding competition in the monitoring area was predicted, including: The plant budding competition index of the monitoring area is obtained by weighted summing of the reciprocal of the soil nutrient distribution evenness index, the reciprocal of the growth state uniformity index, and the spatial expansion trend index, which is used to predict the plant budding competition level in the monitoring area. Adjust the fertilizer nutrient content in the monitoring area based on the predicted plant budding competition level, including: Obtain the basic fertilizer nutrient content and plant budding competition index of the monitoring area, and multiply the basic fertilizer nutrient content by the plant budding competition index to obtain the corrected value of fertilizer nutrient content; The optimal fertilizer nutrient content for the monitored area is obtained by adding the base fertilizer nutrient content and the correction value of the fertilizer nutrient content, and then the fertilizer nutrient content of the monitored area is adjusted to the optimal fertilizer nutrient content.

2. A plant germination parameter adjustment system based on big data analysis, applied to the plant germination parameter adjustment method based on big data analysis as described in claim 1, characterized in that, The system includes: The data acquisition module is used to acquire soil nutrient data, plant germination and growth data, and root distribution data of the monitoring area; The consistency analysis module is used to analyze the uniformity of soil nutrient distribution and the consistency of plant bud growth status in the monitored area by using soil nutrient data and plant bud growth data. The expansion trend analysis module is used to analyze the spatial expansion trend of the sprouting roots of plants in the monitoring area by using root distribution data. The competition level prediction module is used to predict the plant germination competition level in the monitoring area by comprehensively analyzing the results of soil nutrient distribution uniformity, growth status consistency and spatial expansion trend. The nutrient content adjustment module is used to adjust the fertilizer nutrient content in the monitoring area based on the predicted results of plant germination competition level.

3. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the plant germination parameter adjustment method based on big data analysis as described in claim 1 by calling the computer program stored in the memory.

4. A computer-readable storage medium, characterized in that, The device stores instructions that, when executed on a computer, cause the computer to perform the plant germination parameter adjustment method based on big data analysis as described in claim 1.

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