A method and system for predicting the suitable transplanting period of rape seedlings in pots

Abstract

The present invention provides a method and system for predicting a suitable transplanting period for rape seedlings, and relates to the technical field of crop transplanting. The present invention obtains the transplanting time situation, and analyzes to obtain the first transplanting period; obtains the meteorological data of the cold-soaked field, the rape seedling data and the environmental data of the cold-soaked field, comprehensively analyzes the rape seedling data to generate a rape adaptation coefficient, analyzes the environmental data of the cold-soaked field to generate a transplanting adaptation coefficient, constructs a meteorological transplanting prediction model, predicts the environmental data of the cold-soaked field in the first transplanting period through the transplanting prediction model, replaces the rape seedling data with the predicted first transplanting period with the rape seedlings planted earlier than the predicted rape seedlings, calculates the transplanting adaptation coefficient for each day in the first transplanting period, and analyzes to obtain the second transplanting period. The present invention accurately predicts the transplanting period, and improves the survival rate and adaptability of rape seedling transplanting.

Description

Technical Field

[0001] The invention relates to the technical field of crop transplanting, and in particular to a method and system for predicting a suitable transplanting period for rape seedlings in pots. Background Art

[0002] The prominent contradiction between crop rotation and tight farming time are the main factors affecting the expansion of rapeseed in Hunan's winter idle fields. Transplanting can effectively alleviate the seasonal contradiction between late rice harvest and rapeseed planting by raising seedlings in advance. To carry out transplanting, it is very important to determine the transplanting period, but a considerable part of the existing winter idle fields in Hunan are cold-soaked fields, with problems such as large previous crop stocks, perennial waterlogging, and heavy soil. These environmental problems will affect the adaptability of rapeseed after transplanting. Therefore, when choosing the transplanting period, it is necessary to comprehensively consider the changes in rapeseed's adaptability due to environmental changes.

[0003] In the prior art, publication number CN115545305A discloses a crop transplanting period time prediction method and system, including obtaining historical measured data; building a basic parameter database: establishing a time prediction model based on the basic parameter database, predicting the optimal transplanting time, and predicting the local suitable transplanting time in advance, which can provide a basis for growers to make advance reservations for machinery, land preparation, seedling management, etc., and provide a basis for agricultural machinery service companies to dispatch machinery, which can effectively ensure the orderly progress of land preparation and transplanting tasks; by determining the appropriate transplanting time, but the prior art still has defects. The prior art lacks a comprehensive analysis of the transplanted plants and the transplanting environment, which may lead to a reduced transplant survival rate, slow or blocked growth, and thus affect the normal development and adaptability of the plants.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for predicting the suitable transplanting period of rapeseed seedlings in pots, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for predicting a suitable transplanting period for rape seedlings in pots, specifically comprising:

[0008] To achieve the above object, the present invention provides the following technical solution: a method for predicting the suitable transplanting period of rape seedlings, specifically comprising:

[0009] Step 1: Obtain the earliest transplanting time and the latest transplanting time of rapeseed seedlings in the predicted area in the past 10 years, and the time interval consisting of the earliest transplanting time and the latest transplanting time is called the first transplanting period;

[0010] Step 2: Obtain the meteorological data of the cold-soaked field, rapeseed seedling data and environmental data of the cold-soaked field on each transplanting day in the past 10 years; the meteorological data include temperature, rainfall and rainfall time; the environmental data of the cold-soaked field include the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field; the rapeseed seedling data include the root conductivity and root respiration rate of the rapeseed seedlings before transplanting, and the chlorophyll content, conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting;

[0011] Step 3: Generate a leaf adaptation coefficient based on the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rape seedlings before transplanting; generate a root adaptation coefficient based on the root electrical conductivity and root respiration rate of the rape seedlings before transplanting; generate a rape adaptation coefficient based on the leaf adaptation coefficient and the root adaptation coefficient;

[0012] Step 4: Generate an environmental adaptation coefficient based on the water accumulation thickness, effective soil depth and soil viscosity strength; generate a transplanting adaptation coefficient based on the environmental adaptation coefficient and the rapeseed adaptation coefficient;

[0013] Step 5: Take meteorological data as input and the corresponding environmental data of cold-soaked fields as labels, train a meteorological transplanting prediction model, obtain the meteorological data of each day in the first transplanting period of the current year, use the meteorological transplanting prediction model to predict the environmental data of cold-soaked fields for each day in the first transplanting period of the current year, calculate the transplanting adaptation coefficient for each day in the first transplanting period to be predicted based on the rapeseed seedling data and the predicted environmental data of the cold-soaked fields, preset a transplanting adaptation threshold, and obtain the second transplanting period based on the comparison result of the transplanting adaptation coefficient and the transplanting adaptation threshold.

[0014] Furthermore, the temperature, rainfall and rainfall time are obtained through the meteorological station; the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field are measured at the center of the cold-soaked field, the water accumulation thickness is directly measured, and the effective soil depth is measured by a soil detector, specifically: the soil detector is inserted into the soil, and the soil moisture content is collected every one centimeter. A moisture deviation threshold is preset, and the difference between the soil moisture content collected last time and the soil moisture content collected this time is greater than the moisture deviation threshold, then the soil depth collected this time is defined as the effective depth. The soil viscosity strength is measured by a handheld shearing instrument, specifically, the rotating shear force is tested at intervals of 1 cm until the effective depth is reached, and the average value of the rotating shear force during the advancement is recorded as the soil viscosity strength. The chlorophyll content, electrical conductivity and stomatal conductance of leaves; the root electrical conductivity and root respiration rate of rape seedlings before transplanting are obtained by measuring the chlorophyll content, electrical conductivity and stomatal conductance of leaves of all rape seedlings in a unit area of ​​1 square meter in the center of the seedling raising area; the root electrical conductivity and root respiration rate of the rape seedlings before transplanting are measured, and the average value of the measured results is obtained; the root electrical conductivity of the rape seedlings before transplanting is obtained by a root electrolyte leakage sensor, the root respiration rate of the rape seedlings before transplanting is obtained by a micro oxygen sensor, the chlorophyll content of the leaves of the rape seedlings before transplanting is obtained by a portable chlorophyll sensor, the leaf electrical conductivity of the rape seedlings before transplanting is obtained by a leaf electrolyte leakage sensor, and the stomatal conductance of the leaves of the rape seedlings before transplanting is obtained by a stomatal conductance meter.

[0015] Furthermore, the specific logic for generating the leaf adaptation coefficient is: comparing the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting with the reference values, and generating the leaf adaptation coefficient according to the comparison results; the specific formula for generating the leaf adaptation coefficient is:

[0016]

[0017] Among them, LVO is the leaf adaptation coefficient, HY is the chlorophyll content of the leaves of rapeseed seedlings, HY0 is the reference chlorophyll content of the leaves of rapeseed seedlings, HE is the electrical conductivity of the leaves of rapeseed seedlings, HE0 is the reference electrical conductivity of the leaves of rapeseed seedlings, OD is the stomatal conductance of the leaves of rapeseed seedlings, and OD is the reference stomatal conductance of the leaves of rapeseed seedlings.

[0018] Furthermore, the specific logic for generating the root adaptation coefficient is: comparing the root conductivity and root respiration rate of the rape seedlings before transplanting with the root conductivity and root respiration rate of the standard rape seedlings before transplanting, analyzing the comparison results to generate the root adaptation coefficient. The specific formula for generating the root adaptation coefficient is:

[0019]

[0020] Among them, SOP is the root adaptation coefficient, GE is the root conductivity of rapeseed seedlings, GE0 is the reference root conductivity of rapeseed seedlings, GB is the respiration rate of the roots of rapeseed seedlings, and GB0 is the reference respiration rate of the roots of rapeseed seedlings;

[0021] The rapeseed adaptation coefficient is generated based on the leaf adaptation coefficient and the root adaptation coefficient. The specific formula for generating the rapeseed adaptation coefficient is:

[0022] OCO=LVO*SOP

[0023] Among them, OCO is the rapeseed adaptation coefficient, LVO is the leaf adaptation coefficient, and SOP is the root adaptation coefficient.

[0024] Furthermore, the specific logic for generating the environmental adaptation coefficient is: comprehensively analyzing the water accumulation thickness, soil effective depth and soil viscosity strength to generate the environmental adaptation coefficient; the specific formula for generating the environmental adaptation coefficient is:

[0025]

[0026] Among them, SEN is the environmental adaptation coefficient, WT is the water accumulation thickness, DC is the effective soil depth, and SN is the soil viscosity strength;

[0027] The transplanting adaptation coefficient is generated based on the environmental adaptation coefficient and the rapeseed adaptation coefficient; the specific formula for generating the transplanting adaptation coefficient is:

[0028] KIP=OCO-SEN

[0029] Among them, KIP is the transplanting adaptation coefficient, OCO is the rapeseed adaptation coefficient, and SEN is the environmental adaptation coefficient.

[0030] Furthermore, the specific logic underlying the second transplanting period is as follows: the meteorological transplanting prediction model is used to predict the environmental data of the cold-soaked fields in the first transplanting period, the time interval from the start of cultivation of the rapeseed seedlings to be transplanted to each day in the first transplanting period of the current year is determined, a plurality of sample rapeseed seedlings with the same cultivation time and time interval are selected from the rapeseed seedlings, and the mean of their rapeseed seedling data is calculated, and the mean is used as the rapeseed seedling data of the rapeseed seedlings to be transplanted on that day in the first transplanting period of the current year, the transplanting adaptation coefficient is calculated through the environmental data of the cold-soaked fields in the first transplanting period and the rapeseed seedling data, a transplanting adaptation threshold is preset, the transplanting adaptation coefficient of each day is compared with the transplanting adaptation threshold, and the date on which the transplanting adaptation coefficient is greater than the transplanting adaptation threshold is defined as a suitable transplanting time, and if three consecutive days are suitable transplanting times, the first day is defined as the second transplanting period.

[0031] The present invention further provides a system for predicting a suitable transplanting period for rape seedlings in pots, and the method is used to implement the method for predicting a suitable transplanting period for rape seedlings in pots, and the specific steps include:

[0032] The preliminary prediction module is used to obtain the earliest transplanting time and the latest transplanting time of rape seedlings in the predicted area in the past 10 years. The time interval consisting of the earliest transplanting time and the latest transplanting time is called the first transplanting period;

[0033] The data acquisition module is used to obtain the meteorological data of the cold-soaked field, the rapeseed seedling data and the environmental data of the cold-soaked field on each day of transplanting in the past 10 years; the meteorological data include temperature, rainfall and rainfall time; the environmental data of the cold-soaked field include the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field; the rapeseed seedling data include the root conductivity and root respiration rate of the rapeseed seedlings before transplanting, and the chlorophyll content, conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting;

[0034] The rapeseed analysis module is used to generate a leaf adaptation coefficient according to the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting; to generate a root adaptation coefficient according to the root electrical conductivity and root respiration rate of the rapeseed seedlings before transplanting; and to generate a rapeseed adaptation coefficient according to the leaf adaptation coefficient and the root adaptation coefficient;

[0035] Comprehensive analysis module, used to generate environmental adaptability coefficient according to water accumulation thickness, effective soil depth and soil viscosity strength; generate transplanting adaptability coefficient according to environmental adaptability coefficient and rapeseed adaptability coefficient;

[0036] The final prediction module is used to take meteorological data as input and the corresponding cold-soaked field environmental data as labels, train a meteorological transplant prediction model, obtain the meteorological data of each day in the first transplant period of the current year, use the meteorological transplant prediction model to predict the environmental data of the cold-soaked field for each day in the first transplant period of the current year, calculate the transplant adaptation coefficient for each day in the first transplant period to be predicted based on the rapeseed seedling data and the predicted environmental data of the cold-soaked field, preset a transplant adaptation threshold, and obtain the second transplant period based on the comparison result of the transplant adaptation coefficient and the transplant adaptation threshold. Compared with the prior art, the beneficial effects of the present invention are:

[0037] The present invention generates a transplanting adaptation coefficient that can reflect the transplanting adaptability of the rapeseed seedlings based on a comprehensive analysis of the rapeseed seedlings and environmental data, predicts the transplanting adaptation coefficient through meteorological data, and more accurately defines the transplanting period based on the prediction result, which not only improves the survival rate and adaptability of the rapeseed seedlings transplanting, but also optimizes the planting time, reduces resource waste and management costs, and at the same time helps to improve the yield and quality of rapeseed, and ensures the scientificity and efficiency of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0039] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0041] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0042] Example:

[0043] See also Figure 1 , the present invention provides a technical solution:

[0044] A method for predicting a suitable transplanting period for rape seedlings in pots, specifically comprising:

[0045] Step 1: Obtain the earliest transplanting time and the latest transplanting time of rapeseed seedlings in the predicted area in the past 10 years, and the time interval consisting of the earliest transplanting time and the latest transplanting time is called the first transplanting period;

[0046] Step 2: Obtain the meteorological data of the cold-soaked field, rapeseed seedling data and environmental data of the cold-soaked field on each transplanting day in the past 10 years; the meteorological data include temperature, rainfall and rainfall time; the environmental data of the cold-soaked field include the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field; the rapeseed seedling data include the root conductivity and root respiration rate of the rapeseed seedlings before transplanting, and the chlorophyll content, conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting;

[0047] Furthermore, the temperature, rainfall and rainfall time are obtained through the meteorological station; the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field are measured at the center of the cold-soaked field, the water accumulation thickness is directly measured, and the effective soil depth is measured by a soil detector, specifically: the soil detector is inserted into the soil, and the soil moisture content is collected every one centimeter. A moisture deviation threshold is preset, and the difference between the soil moisture content collected last time and the soil moisture content collected this time is greater than the moisture deviation threshold, then the soil depth collected this time is defined as the effective depth. The soil viscosity strength is measured by a handheld shearing instrument, specifically, the rotating shear force is tested at intervals of 1 cm until the effective depth is reached, and the average value of the rotating shear force during the advancement is recorded as the soil viscosity strength. The leaves of the rapeseed seedlings before transplanting The chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rapeseed seedlings in pots before transplanting are measured; the root electrical conductivity and root respiration rate of the rapeseed seedlings before transplanting are measured by measuring the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of all the rapeseed seedlings in pots within a unit area of ​​1 square meter in the center of the seedling raising area; the root electrical conductivity and root respiration rate of the rapeseed seedlings before transplanting are measured, and the average value of the measured results is obtained; the root electrical conductivity of the rapeseed seedlings before transplanting is obtained by a root electrolyte leakage sensor, the root respiration rate of the rapeseed seedlings before transplanting is obtained by a micro oxygen sensor, the chlorophyll content of the leaves of the rapeseed seedlings before transplanting is obtained by a portable chlorophyll sensor, the leaf electrical conductivity of the rapeseed seedlings before transplanting is obtained by a leaf electrolyte leakage sensor, and the stomatal conductance of the leaves of the rapeseed seedlings before transplanting is obtained by a stomatal conductance meter.

[0048] Step 3: Generate a leaf adaptation coefficient based on the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rape seedlings before transplanting; generate a root adaptation coefficient based on the root electrical conductivity and root respiration rate of the rape seedlings before transplanting; generate a rape adaptation coefficient based on the leaf adaptation coefficient and the root adaptation coefficient;

[0049] Furthermore, the specific logic for generating the leaf adaptation coefficient is: comparing the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting with the reference values, and generating the leaf adaptation coefficient according to the comparison results; the specific formula for generating the leaf adaptation coefficient is:

[0050]

[0051] Among them, LVO is the leaf adaptation coefficient, HY is the chlorophyll content of the leaves of rapeseed seedlings, HY0 is the reference chlorophyll content of the leaves of rapeseed seedlings, HE is the electrical conductivity of the leaves of rapeseed seedlings, HE0 is the reference electrical conductivity of the leaves of rapeseed seedlings, OD is the stomatal conductance of the leaves of rapeseed seedlings, and OD is the reference stomatal conductance of the leaves of rapeseed seedlings.

[0052] The leaf adaptation coefficient reflects the adaptability of leaves to environmental conditions. The larger the value, the stronger the adaptability of leaves to environmental conditions. The leaf chlorophyll content is a key indicator of the photosynthetic capacity of plant leaves. The higher the chlorophyll content, the stronger the photosynthesis, the better the adaptability, and the greater the contribution to LVO. The reference chlorophyll content refers to the reference chlorophyll content under ideal conditions and is used as a comparison benchmark. The conductivity of the leaves reflects the integrity and permeability of the cell membrane and is an indicator of the degree of stress on plant leaves. The higher the conductivity of the leaves, the more serious the damage to the cell membrane and the poorer the adaptability. Stomatal conductance reflects the degree of stomatal opening and closing, which is related to the transpiration rate and gas exchange capacity of the plant. When the stomatal conductance is moderate, it means that the plant has a good ability to regulate water and gas in the environment. Too large or too small will have an adverse effect on plant growth, so |OD-OD0| is used to reflect this adverse effect. It is a comprehensive consideration of electrical conductivity and stomatal conductance. Using square root can also slow down data volatility, making LVO changes smoother. Leaf adaptability Leaf adaptability is an important part of the adaptability of rapeseed seedlings. The generation of leaf adaptability coefficient can provide an important basis for judging whether rapeseed seedlings are suitable for transplanting.

[0053] Furthermore, the specific logic for generating the root adaptation coefficient is: comparing the root conductivity and root respiration rate of the rape seedlings before transplanting with the root conductivity and root respiration rate of the standard rape seedlings before transplanting, analyzing the comparison results to generate the root adaptation coefficient. The specific formula for generating the root adaptation coefficient is:

[0054]

[0055] Among them, SOP is the root adaptation coefficient, GE is the root conductivity of rapeseed seedlings, GE0 is the reference root conductivity of rapeseed seedlings, GB is the respiration rate of the roots of rapeseed seedlings, and GB0 is the reference respiration rate of the roots of rapeseed seedlings;

[0056] The root adaptation coefficient expresses the adaptability of the root system in the current soil environment. The larger the value, the stronger the adaptability of the root system to the soil environment. The root conductivity of rapeseed seedlings indicates the ability of the root system to absorb water and minerals, and is an important indicator to measure the physiological state of the root system. The root conductivity of rapeseed seedlings reflects the absorption function of the root system. The respiration rate of the root system of rapeseed seedlings indicates the respiratory metabolism level of the root system. The appropriate root conductivity and respiration rate of rapeseed seedlings are conducive to the growth of rapeseed seedlings and adaptation to the new environment. These two values ​​are too large or too small, which are not conducive to the growth of rapeseed seedlings. The reference root conductivity and reference respiration rate are the ideal values ​​of conductivity and respiration rate, respectively. |GE-GE0| and |GB-GB0| respectively reflect the deviation between conductivity and respiration rate and the ideal. The greater the deviation, the smaller the adaptability of the root system. The adaptability of the root system is an important part of the adaptability of rapeseed seedlings. The generation of the root adaptation coefficient can provide an important basis for judging whether the rapeseed seedlings are suitable for transplanting.

[0057] The rapeseed adaptation coefficient is generated based on the leaf adaptation coefficient and the root adaptation coefficient. The specific formula for generating the rapeseed adaptation coefficient is:

[0058] OCO=LVO*SOP

[0059] Among them, OCO is the rapeseed adaptation coefficient, LVO is the leaf adaptation coefficient, and SOP is the root adaptation coefficient. The adaptability of plant leaves and roots comprehensively reflects the overall adaptability of rapeseed seedlings to the current environmental conditions. The larger the value, the stronger the adaptability; the leaf adaptation coefficient and the root adaptation coefficient reflect the adaptability of the above-ground and underground parts of the rapeseed seedlings respectively. The generation of this coefficient can provide an important basis for judging whether the rapeseed seedlings are suitable for transplanting.

[0060] Step 4: Generate an environmental adaptation coefficient based on the water accumulation thickness, effective soil depth and soil viscosity strength; generate a transplanting adaptation coefficient based on the environmental adaptation coefficient and the rapeseed adaptation coefficient;

[0061] Furthermore, the specific logic for generating the environmental adaptation coefficient is: comprehensively analyzing the water accumulation thickness, soil effective depth and soil viscosity strength to generate the environmental adaptation coefficient; the specific formula for generating the environmental adaptation coefficient is:

[0062]

[0063] Among them, SEN is the environmental adaptation coefficient, WT is the water accumulation thickness, DC is the effective soil depth, and SN is the soil viscosity strength;

[0064] The environmental adaptability coefficient is used to measure the comprehensive adaptability of plants in the current soil and water environment. The larger the value, the less suitable the environmental conditions are and the weaker the ability of plants to adapt to the environment. The generation of this coefficient can provide an important basis for judging whether rapeseed seedlings are suitable for transplanting. Reflects the relative distribution of water accumulation. If WT is larger (deeper water accumulation) and DC is smaller (shallower effective soil layer), the ratio will be larger, indicating that the soil drainage is poor, plant roots are easily stressed by hypoxia, and the SEN value will increase. If WT is smaller (less water accumulation) and DC is larger (deeper effective soil layer), the ratio will be smaller, indicating that the soil moisture condition is more suitable, the plant root environment is healthier, and the SEN value will decrease. Soil cohesion strength indicates the cohesion between soil particles. The larger the value, the soil may be too dense, with poor air permeability and drainage, which limits the growth of roots and oxygen supply.

[0065] The transplanting adaptation coefficient is generated based on the environmental adaptation coefficient and the rapeseed adaptation coefficient; the specific formula for generating the transplanting adaptation coefficient is:

[0066] KIP=OCO-SEN

[0067] Among them, KIP is the transplanting adaptation coefficient, OCO is the rapeseed adaptation coefficient, and SEN is the environmental adaptation coefficient.

[0068] The transplanting adaptation coefficient comprehensively considers the rapeseed seedlings' own adaptability and the difficulty of environmental adaptation intensity. It is obtained by subtracting the environmental adaptation coefficient, which reflects the spontaneous adaptation of the rapeseed seedlings, from the environmental adaptation coefficient. The larger the value, the easier it is for the rapeseed seedlings to adapt to the new environment and the more suitable it is for transplanting.

[0069] Step 5: Take meteorological data as input and the corresponding environmental data of cold-soaked fields as labels, train a meteorological transplanting prediction model, obtain the meteorological data of each day in the first transplanting period of the current year, use the meteorological transplanting prediction model to predict the environmental data of cold-soaked fields for each day in the first transplanting period of the current year, calculate the transplanting adaptation coefficient for each day in the first transplanting period to be predicted based on the rapeseed seedling data and the predicted environmental data of the cold-soaked fields, preset a transplanting adaptation threshold, and obtain the second transplanting period based on the comparison result of the transplanting adaptation coefficient and the transplanting adaptation threshold.

[0070] The meteorological transplanting prediction model adopts a feedforward neural network, with meteorological data as input and the corresponding cold-soaked field environmental data as labels. The existing technology can be used to train the meteorological transplanting prediction model, including: input layer, hidden layer, output layer and activation function. The input layer is responsible for receiving meteorological data; the hidden layer is used for data processing of meteorological data; it is composed of multiple layers, each layer contains 4 time nodes, and the time nodes of each hidden layer are connected to the previous layer through weights, which are used for feature abstraction and nonlinear transformation of input meteorological data; by using the ReLu activation function, nonlinear relationships are introduced so that the model can fit complex feature relationships; an independent neuron is set in the output layer, which is responsible for converting the local and high-level feature representations extracted by the hidden layer for outputting the transplanting adaptation coefficient; the root mean square error loss function is adopted; the input data is calculated once through the network to obtain the output result, the loss function is calculated according to the predicted value and the true value, the gradient of the loss function for each weight and bias is calculated by the chain rule, and the weights and biases of the network are updated using the gradient descent algorithm to minimize the loss function.

[0071] The specific logic for obtaining the second transplanting period is as follows: use the meteorological transplanting prediction model to predict the environmental data of the cold-soaked fields in the first transplanting period, determine the time interval from the start of cultivation to each day in the first transplanting period of the current year for the rapeseed seedlings to be transplanted, screen out multiple sample rapeseed seedlings with the same cultivation time and time interval from the rapeseed seedlings, and calculate the mean of their rapeseed seedling data, and use the mean as the rapeseed seedling data of the rapeseed seedlings to be transplanted on that day in the first transplanting period of the current year, calculate the transplanting adaptation coefficient through the environmental data of the cold-soaked fields in the first transplanting period and the rapeseed seedling data, preset a transplanting adaptation threshold, compare the transplanting adaptation coefficient and the transplanting adaptation threshold for each day, define the date when the transplanting adaptation coefficient is greater than the transplanting adaptation threshold as a suitable transplanting time, and if three consecutive days are suitable transplanting times, the first day is defined as the second transplanting period.

[0072] Furthermore, a batch of rapeseed seedlings is sown on October 1st, and this batch of rapeseed seedlings reaches the first transplanting period on October 20th. The first transplanting period is from October 20th to October 30th. It is now necessary to predict the rapeseed seedling data from October 20th to October 30th to determine the second transplanting period. Since rapeseed seedlings are usually grown in greenhouses, and the environmental conditions of greenhouses usually do not change much, the growth deviation of rapeseed seedlings during the transplanting period is not large. Therefore, the rapeseed seedling data of the rapeseed seedlings sown on September 1st for each day between September 20th and September 30th can be collected, and the rapeseed seedling data of the rapeseed seedlings sown on September 1st for each day between September 20th and September 30th can be used as the rapeseed seedling data from October 20th to October 30th.

[0073] The present invention further provides a system for predicting a suitable transplanting period for rape seedlings in pots, and the method is used to implement the method for predicting a suitable transplanting period for rape seedlings in pots, and the specific steps include:

[0074] The preliminary prediction module is used to obtain the earliest transplanting time and the latest transplanting time of rape seedlings in the predicted area in the past 10 years. The time interval consisting of the earliest transplanting time and the latest transplanting time is called the first transplanting period;

[0075] The data acquisition module is used to obtain the meteorological data of the cold-soaked field, the rapeseed seedling data and the environmental data of the cold-soaked field on each day of transplanting in the past 10 years; the meteorological data include temperature, rainfall and rainfall time; the environmental data of the cold-soaked field include the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field; the rapeseed seedling data include the root conductivity and root respiration rate of the rapeseed seedlings before transplanting, and the chlorophyll content, conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting;

[0076] The rapeseed analysis module is used to generate a leaf adaptation coefficient according to the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting; to generate a root adaptation coefficient according to the root electrical conductivity and root respiration rate of the rapeseed seedlings before transplanting; and to generate a rapeseed adaptation coefficient according to the leaf adaptation coefficient and the root adaptation coefficient;

[0077] Comprehensive analysis module, used to generate environmental adaptability coefficient according to water accumulation thickness, effective soil depth and soil viscosity strength; generate transplanting adaptability coefficient according to environmental adaptability coefficient and rapeseed adaptability coefficient;

[0078] The final prediction module is used to take meteorological data as input and the corresponding environmental data of cold-soaked fields as labels, train the meteorological transplanting prediction model, obtain the meteorological data of each day in the first transplanting period of the current year, use the meteorological transplanting prediction model to predict the environmental data of cold-soaked fields for each day in the first transplanting period of the current year, calculate the transplanting adaptation coefficient for each day in the first transplanting period to be predicted based on the rapeseed seedling data and the predicted environmental data of the cold-soaked fields, preset the transplanting adaptation threshold, and obtain the second transplanting period based on the comparison result of the transplanting adaptation coefficient and the transplanting adaptation threshold. The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0079] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0080] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] The above is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technical personnel familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for predicting the suitable transplanting period of rape seedlings, characterized in that: Specifically include: Step 1: Obtain the earliest transplanting time and the latest transplanting time of rapeseed seedlings in the predicted area in the past 10 years, and the time interval consisting of the earliest transplanting time and the latest transplanting time is called the first transplanting period; Step 2: Obtain the meteorological data of the cold-soaked field, rapeseed seedling data and environmental data of the cold-soaked field on each transplanting day in the past 10 years; the meteorological data include temperature, rainfall and rainfall time; the environmental data of the cold-soaked field include the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field; the rapeseed seedling data include the root conductivity and root respiration rate of the rapeseed seedlings before transplanting, and the chlorophyll content, conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting; Step 3: Generate a leaf adaptation coefficient based on the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rape seedlings before transplanting; generate a root adaptation coefficient based on the root electrical conductivity and root respiration rate of the rape seedlings before transplanting; generate a rape adaptation coefficient based on the leaf adaptation coefficient and the root adaptation coefficient; Step 4: Generate an environmental adaptation coefficient based on the water accumulation thickness, effective soil depth and soil viscosity strength; generate a transplanting adaptation coefficient based on the environmental adaptation coefficient and the rapeseed adaptation coefficient; Step 5: Take the meteorological data as input and the corresponding environmental data of the cold-soaked field as labels, train the meteorological transplanting prediction model, obtain the meteorological data of each day in the first transplanting period of the current year, use the meteorological transplanting prediction model to predict the environmental data of the cold-soaked field for each day in the first transplanting period of the current year, calculate the transplanting adaptation coefficient for each day in the first transplanting period to be predicted based on the rapeseed seedling data and the predicted environmental data of the cold-soaked field, preset the transplanting adaptation threshold, and obtain the second transplanting period based on the comparison result of the transplanting adaptation coefficient and the transplanting adaptation threshold; The specific logic for generating the leaf adaptation coefficient is: the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting are compared with the reference values, and the leaf adaptation coefficient is generated according to the comparison results; the specific formula for generating the leaf adaptation coefficient is: Wherein, LVO is the leaf adaptation coefficient, HY is the chlorophyll content of the leaves of rapeseed seedlings, HY0 is the reference chlorophyll content of the leaves of rapeseed seedlings, HE is the electrical conductivity of the leaves of rapeseed seedlings, HE0 is the reference electrical conductivity of the leaves of rapeseed seedlings, OD is the stomatal conductance of the leaves of rapeseed seedlings, and OD is the reference stomatal conductance of the leaves of rapeseed seedlings; The specific logic for generating the root adaptation coefficient is: compare the root conductivity and root respiration rate of the rape seedlings before transplanting with the root conductivity and root respiration rate of the standard rape seedlings before transplanting, analyze the comparison results to generate the root adaptation coefficient, and the specific formula for generating the root adaptation coefficient is: Among them, SOP is the root adaptation coefficient, GE is the root conductivity of rapeseed seedlings, GE0 is the reference root conductivity of rapeseed seedlings, GB is the respiration rate of the roots of rapeseed seedlings, and GB0 is the reference respiration rate of the roots of rapeseed seedlings; The rapeseed adaptation coefficient is generated based on the leaf adaptation coefficient and the root adaptation coefficient. The specific formula for generating the rapeseed adaptation coefficient is: OCO=LVO*SOP Among them, OCO is the rapeseed adaptation coefficient, LVO is the leaf adaptation coefficient, and SOP is the root adaptation coefficient; The specific logic for generating the environmental adaptability coefficient is: comprehensively analyzing the water accumulation thickness, effective soil depth and soil viscosity to generate the environmental adaptability coefficient; the specific formula for generating the environmental adaptability coefficient is: Among them, SEN is the environmental adaptation coefficient, WT is the water accumulation thickness, DC is the effective soil depth, and SN is the soil viscosity strength; The transplanting adaptation coefficient is generated based on the environmental adaptation coefficient and the rapeseed adaptation coefficient; the specific formula for generating the transplanting adaptation coefficient is: KIP=OCO-SEN Among them, KIP is the transplanting adaptation coefficient, OCO is the rapeseed adaptation coefficient, and SEN is the environmental adaptation coefficient.

2. The method for predicting the suitable transplanting period of rape seedlings according to claim 1, characterized in that: The temperature, rainfall and rainfall time are obtained through the meteorological station; the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field are measured at the center of the cold-soaked field. The water accumulation thickness is directly measured, and the effective soil depth is measured by a soil detector, specifically: the soil detector is inserted into the soil, and the soil moisture content is collected every one centimeter. A moisture deviation threshold is preset. If the difference between the soil moisture content collected last time and the soil moisture content collected this time is greater than the moisture deviation threshold, the soil depth collected this time is defined as the effective depth. The soil viscosity strength is measured by a handheld shearing instrument, specifically, the rotating shear force is tested at intervals of 1 cm until the effective depth is reached, and the average value of the rotating shear force during the advancement is recorded as the soil viscosity strength. The leaf thickness of the leaves of the rapeseed seedlings before transplanting Chlorophyll content, electrical conductivity and stomatal conductance; the root electrical conductivity and root respiration rate of the rape seedlings before transplanting are obtained by measuring the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of all the rape seedlings in a unit area of ​​1 square meter in the center of the seedling raising area; the root electrical conductivity and root respiration rate of the rape seedlings before transplanting are measured, and the average value of the measured results is obtained; the root electrical conductivity of the rape seedlings before transplanting is obtained by a root electrolyte leakage sensor, the root respiration rate of the rape seedlings before transplanting is obtained by a micro oxygen sensor, the chlorophyll content of the leaves of the rape seedlings before transplanting is obtained by a portable chlorophyll sensor, the leaf electrical conductivity of the rape seedlings before transplanting is obtained by a leaf electrolyte leakage sensor, and the stomatal conductance of the leaves of the rape seedlings before transplanting is obtained by a stomatal conductance meter.

3. The method for predicting the suitable transplanting period of rape seedlings according to claim 1, characterized in that: The specific logic for obtaining the second transplanting period is as follows: using the meteorological transplanting prediction model to predict the environmental data of the cold-soaked field in the first transplanting period, determining the time interval from the start of cultivation to each day in the first transplanting period of the current year for the rapeseed seedlings to be transplanted, selecting multiple sample rapeseed seedlings with the same cultivation time and time interval from the rapeseed seedlings, and calculating the mean of their rapeseed seedling data, and using the mean as the rapeseed seedling data for the day in the first transplanting period of the current year for the rapeseed seedlings to be transplanted; The transplanting adaptation coefficient is calculated through the environmental data of the cold-soaked field in the first transplanting period and the rapeseed seedling data, and the transplanting adaptation threshold is preset. The transplanting adaptation coefficient and the transplanting adaptation threshold are compared every day, and the date when the transplanting adaptation coefficient is greater than the transplanting adaptation threshold is defined as the suitable transplanting time. If three consecutive days are suitable transplanting times, the first day will be defined as the second transplanting period.

4. A prediction system for the suitable transplanting period of rape seedlings, characterized in that: The method is used to implement the method for predicting the suitable transplanting period of rape seedlings in pots as described in any one of claims 1 to 3, and the specific steps include: The preliminary prediction module is used to obtain the earliest transplanting time and the latest transplanting time of rape seedlings in the predicted area in the past 10 years. The time interval consisting of the earliest transplanting time and the latest transplanting time is called the first transplanting period; The data acquisition module is used to obtain the meteorological data of the cold-soaked field, the rapeseed seedling data and the environmental data of the cold-soaked field on each day of transplanting in the past 10 years; the meteorological data include temperature, rainfall and rainfall time; the environmental data of the cold-soaked field include the water accumulation thickness, soil effective depth and soil viscosity strength of the cold-soaked field; the rapeseed seedling data include the root conductivity and root respiration rate of the rapeseed seedlings before transplanting, and the chlorophyll content, conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting; The rapeseed analysis module is used to generate a leaf adaptation coefficient according to the chlorophyll content, electrical conductivity and stomatal conductance of the leaves of the rapeseed seedlings before transplanting; to generate a root adaptation coefficient according to the root electrical conductivity and root respiration rate of the rapeseed seedlings before transplanting; and to generate a rapeseed adaptation coefficient according to the leaf adaptation coefficient and the root adaptation coefficient; Comprehensive analysis module, used to generate environmental adaptability coefficient according to water accumulation thickness, effective soil depth and soil viscosity strength; generate transplanting adaptability coefficient according to environmental adaptability coefficient and rapeseed adaptability coefficient; The final prediction module is used to take meteorological data as input and the corresponding environmental data of cold-soaked fields as labels, train a meteorological transplanting prediction model, obtain the meteorological data of each day in the first transplanting period of the current year, use the meteorological transplanting prediction model to predict the environmental data of cold-soaked fields for each day in the first transplanting period of the current year, calculate the transplanting adaptation coefficient for each day in the first transplanting period to be predicted based on the rapeseed seedling data and the predicted environmental data of the cold-soaked fields, preset a transplanting adaptation threshold, and obtain the second transplanting period based on the comparison result of the transplanting adaptation coefficient and the transplanting adaptation threshold.

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