CN115577951A - Summer corn lodging early warning algorithm based on corn growth mechanism model - Google Patents
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Abstract
The invention discloses a summer corn lodging early warning algorithm based on a corn growth mechanism model, and relates to the technical field of lodging early warning algorithms. The summer corn lodging early warning algorithm based on the corn growth mechanism model comprises the steps of building a server cluster; storing the data to a corresponding server, and simultaneously building an application service interface; and constructing a corn lodging disaster early warning platform. The method combines the requirements of the growth and development of the corn on light, temperature and water, establishes a growth period prediction model, accurately predicts the growth and development stage of the corn, establishes the growth period prediction model and accurately predicts the growth and development stage of the corn. The method has the advantages that the breeding stages of the corn easy to fall are determined by combining the classification of the breeding stages of the corn when the historical lodging occurs, the breeding stages of the corn easy to fall are used for judging whether the breeding stages of the corn reach the stage of the corn easy to fall or not in the follow-up breeding period, the early warning of the falling takes various factors such as corn breeding period nodes, farming management and meteorological environment into consideration, and the early warning accuracy of the falling disasters is improved.
Description
Technical Field
The invention relates to the technical field of lodging early warning algorithms, in particular to a summer corn lodging early warning algorithm based on a corn growth mechanism model.
Background
When early warning of lodging of summer corn is carried out, the stored predicted weather data including the weather data such as the highest temperature of day, the lowest temperature of day, the relative humidity of air, rainfall, wind speed and sunshine duration are directly obtained from the database after the point location is determined by inputting the longitude and latitude points, in addition, the sowing data including the sowing date, density and variety of the corn planted at the current longitude and latitude points are input, the weather data and the sowing data are transmitted to a corn growth mechanism model, the growing period is simulated, then the date corresponding to the growing period node 12 leaf period to the spinning period of the corn easy to lodging is taken out and input into a summer corn lodging disaster early warning algorithm, the lodging resistance characteristic of the variety and the weather prediction data of 15 days in the future are combined for predicting the lodging, and finally the predicted lodging occurrence degree and date are output. The corn growth simulation model comprises a photosynthetic module, a breathing module, a distribution module, a growth period module, a soil water module and the like, and needs to be optimized according to historical corn growth period data of an early warning area, and the prediction error of the growth period is ensured to be within +/-2 days; the early warning algorithm for the lodging disasters of summer corns combines the lodging resistance characteristics of different corn varieties, analyzes the lodging occurrence data of the different corn varieties and the current environmental data, sets an index threshold value by finding out relevant influence factors of the corn lodging, and then builds the lodging algorithm.
The early warning of the corn lodging in the prior art is basically to early warn the corn lodging in different growth stages based on meteorological conditions at fixed time stages, neglect the wind resistance of the corn in different growth stages, and inconvenience is brought to lodging prediction according to the wind resistance of the corn in different growth stages.
Disclosure of Invention
Solves the technical problem
Aiming at the defects of the prior art, the invention provides a summer corn lodging early warning algorithm based on a corn growth mechanism model, and solves the problems that the prior art ignores the wind resistance of the corn in different growth stages and is inconvenient to carry out lodging prediction according to the wind resistance of the corn in different growth stages.
Technical scheme
In order to realize the purpose, the invention is realized by the following technical scheme: a summer corn lodging early warning algorithm based on a corn growth mechanism model comprises the following steps:
s1, building a server cluster;
s2, storing the data to a corresponding server, and simultaneously building an application service interface;
and S3, constructing a corn lodging disaster early warning platform.
Further, the server cluster in step S1 includes a big data server for storing basic information of soil, weather, and crop varieties, an operation server for deploying model operations, a deployment interface, and an interface server for constructing an interface resource pool.
Further, the data in the step S2 comprises basic data and a simulation algorithm, the basic data comprises soil data, weather data, crop variety data and field operation data, and the simulation algorithm comprises a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm;
when the application service interface is built, the service interface is deployed through ngi nx, uWSG I and Django, wherein the ngi nx is used as the foremost end of the server.
Further, the early warning platform in the step S3 includes a PC terminal and a mobile phone terminal, and the application service of the PC terminal and the mobile phone terminal adopts a front-end and back-end separation technology.
Further, the corn growth mechanism model algorithm comprises the steps of calculating the net dry matter mass and calculating the nodes in the growth period by using the accumulated temperature.
Further, the calculating the net dry matter quality in the corn growth mechanism model algorithm comprises:
the method comprises the following steps: calculating the daily assimilated dry matter quantity of the corn leaves;
step two: calculating the dry matter quantity of the corn leaf respiration consumption;
step three: and calculating the net dry matter according to the daily assimilated dry matter of the corn leaves and the dry matter consumed by the corn leaves in respiration.
Further, the calculation formula of the daily assimilated dry matter quantity of the corn leaves is as follows:
wherein A is CO per square meter per day 2 Amount of assimilation, LAI is leaf area index, A max For maximum rate of assimilation, carbons gross The dry matter per square meter per day, P the number of plants per square meter, L the depth of canopy of maize plants, PAR i，L The effective solar radiation amount of different canopy depths is obtained, and epsilon is the effective utilization rate of initial light energy;
the calculation formula of the dry matter quantity of the corn leaf respiration consumption is as follows:
mResp＝DM live *Coef resp *Coef T
wherein Coef T The coefficient of influence of temperature on respiratory consumption, T mean Is the average daily temperature, T ref For reference temperature, mrresp maintains respiratory consumption, DM, for different tissue organs live Is the weight of the organ, coef T Organ-specific maintenance breathing coefficients;
the calculation formula of the net dry matter is as follows:
Carbo＝Carbon gross -∑mResp。
further, the calculation formula for calculating the nodes in the growth period by using the accumulated temperature is as follows:
when the lowest temperature of the day is lower than the lower limit of the growth temperature of the corns and the highest temperature of the day is higher than the upper limit of the growth temperature of the corns,
when the lowest temperature of the day is within the range of the upper limit and the lower limit of the growth temperature of the corns and the highest temperature of the day is higher than the upper limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both in the range of the upper limit and the lower limit of the growth temperature of the corn,
GDD＝T mean -T L ；
when the lowest temperature of the day is lower than the lower limit of the growth temperature of the corns and the highest temperature of the day is in the range of the upper limit and the lower limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both higher than the lower limit of the growth temperature of the corn,
GDD＝T U -T L ；
when the lowest temperature of the day and the highest temperature of the day are both lower than the lower limit of the growth temperature of the corn,
GDD＝0。
furthermore, the growth period nodes comprise a seedling stage, an elongation stage, a 12-leaf stage, a spinning stage, a grouting stage, a milk stage and a mature stage.
Further, the summer corn lodging early warning model algorithm has the following calculation formula:
wherein a1, a2, a3, b1, b2, b3, C1, C2, C3, D1, D2, D3, e1, e2 and e3 are system parameters, Y is lodging occurrence degree, P is growth period stage, W is wind speed, R is rainfall, F is crop variety lodging resistance coefficient, C is agricultural chemical control management and D is density.
Advantageous effects
The invention has the following beneficial effects:
the summer corn lodging early warning algorithm based on the corn growth mechanism model combines the requirements of the corn growth and development on light, temperature and water, establishes a growth period prediction model, and accurately predicts the corn growth and development stage. Classifying by combining with the growth stages of the corn when the historical lodging occurs, determining the growth stage of the corn easy to lodging, judging whether the growth stage of the corn reaches the stage of easy lodging or not in the follow-up manner, and giving early warning of lodging by considering various factors such as corn growth stage nodes, farming management, meteorological environment and the like; the same strong wind and rainfall environment has different influences on the corns planted in different densities, and the higher the density is, the higher the lodging probability is; if the maize plants are subjected to chemical control to cause short stalks, the same heavy wind rainfall can have different lodging degrees; the lodging resistance of the variety needs to be taken into consideration, most factors influencing lodging of corn plants are taken into consideration, and the early warning accuracy of lodging disasters is improved.
Of course, it is not necessary for any product to practice the invention to achieve all of the above-described advantages at the same time.
Drawings
FIG. 1 is a diagram of the early warning process of lodging of summer corn according to the present invention;
fig. 2 is a flow chart of the summer corn lodging early warning in the prior art.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.
Referring to fig. 1, an embodiment of the present invention provides a technical solution: a summer corn lodging early warning algorithm based on a corn growth mechanism model comprises the following steps:
s1, building a server cluster;
s2, storing the data to a corresponding server, and simultaneously building an application service interface;
and S3, constructing a corn lodging disaster early warning platform.
Specifically, the server cluster in step S1 includes a big data server for storing basic information of soil, weather, and crop varieties, an operation server for deploying model operations, a deployment interface, and an interface server for constructing an interface resource pool.
Specifically, the data in the step S2 comprises basic data and a simulation algorithm, wherein the basic data comprises soil data, weather data, crop variety data and field operation data, and the simulation algorithm comprises a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm;
when the application service interface is built, the service interface is deployed through ngi nx, uWSG I and Django, wherein the ngi nx is used as the foremost end of the server.
Specifically, the early warning platform in step S3 includes a PC end and a mobile phone end, and the application service of the PC end and the mobile phone end adopts a front-end and back-end separation technology.
In the embodiment, a big data server for storing basic information of soil, weather and crop varieties is built, an operation server for model operation and a deployment interface are deployed, an interface server for an interface resource pool is built, then basic data including soil data, weather data, crop variety data, field operation data and the like are stored in the big data server, a simulation algorithm including a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm is stored in the operation server, an application service interface is built at the same time, a service interface is deployed through ngi nx, uWSG I and Django, wherein ngi nx is used as the frontmost end of the server, all web requests are received, requests are managed in a unified mode, a corn lodging disaster early warning platform including a PC end and a mobile phone end is built, the APP application service adopts a front-end and back-end separation technology, when the PC end page and the APP need to display contents, the interface server is logged in to obtain token information, and when the interface service is requested, the service is accessed together with token; and the server end receives the request and verifies the token information to pass, processes the request and returns result data.
Specifically, the corn growth mechanism model algorithm comprises the steps of calculating the net dry matter mass and calculating the nodes in the growth period by using accumulated temperature;
the net dry matter mass calculation in the corn growth mechanism model algorithm comprises the following steps:
the method comprises the following steps: calculating the daily assimilated dry matter quantity of the corn leaves;
step two: calculating the dry matter quantity of the corn leaf respiration consumption;
step three: and calculating the net dry matter according to the daily assimilated dry matter of the corn leaves and the dry matter consumed by the corn leaves in respiration.
Specifically, the calculation formula of the daily assimilated dry matter amount of the corn leaves is as follows:
wherein A is CO per square meter per day 2 Amount of assimilation, LAI is leaf area index, A max For maximum rate of assimilation, carbon gross The dry matter per square meter per day, P is the number of plants per square meter, L is the depth of the canopy of the corn plant, and PAR i，L The effective solar radiation amount of different canopy depths is obtained, and epsilon is the effective utilization rate of initial light energy;
the calculation formula of the dry matter quantity of the corn leaf respiration consumption is as follows:
mResp＝DM live *Coef resp *Coef T
wherein Coef T The coefficient of influence of temperature on respiratory consumption, T mean Is the average daily temperature, T ref For reference temperature, mrresp maintains respiratory consumption, DM, for different tissue organs live Is the weight of the organ, coef T Organ-specific maintenance breathing coefficients;
the calculation formula of the net dry matter is as follows:
Carbo＝Carbon gross -∑mResp。
specifically, the calculation formula for calculating the nodes in the growth period by using the accumulated temperature is as follows:
when the lowest daily temperature is lower than the lower limit of the growth temperature of the corns and the highest daily temperature is higher than the upper limit of the growth temperature of the corns,
when the lowest temperature of the day is within the range of the upper limit and the lower limit of the growth temperature of the corns and the highest temperature of the day is higher than the upper limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both in the upper and lower limits of the growth temperature of the corn,
GDD＝T mean -T L ；
when the lowest temperature of the day is lower than the lower limit of the growth temperature of the corns and the highest temperature of the day is in the range of the upper limit and the lower limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both higher than the lower limit of the growth temperature of the corn,
GDD＝T U -T L ；
when the lowest temperature of the day and the highest temperature of the day are both lower than the lower limit of the growth temperature of the corn,
GDD＝0。
specifically, the nodes in the growth period comprise the seedling stage, the jointing stage, the 12-leaf stage, the spinning stage, the grouting stage, the milk stage and the mature stage.
In this embodiment, the corn growth and development model dynamically simulates the growth state of corn based on climate data (temperature, rainfall, humidity, wind speed, sunshine hours) in combination with physiological processes of photosynthesis, respiration, growth and development of crops.
Specifically, the calculation formula of the summer corn lodging early warning model algorithm is as follows:
wherein a1, a2, a3, b1, b2, b3, C1, C2, C3, D1, D2, D3, e1, e2 and e3 are system parameters, Y is lodging occurrence degree, P is growth period stage, W is wind speed, R is rainfall, F is crop variety lodging resistance coefficient, C is agricultural chemical control management and D is density.
In the embodiment, the lodging degree of the plants is different under the same strong wind, rainfall and farming management environment in different growth periods, which indicates that the lodging resistance of the plants is different in different periods, so that three periods, namely 12 leaves period-androgenesis period, androgenesis period-grouting period, grouting period-mature period, are divided.
Building a big data server for storing soil, weather and crop variety basic information, deploying an operation server for model operation, a deployment interface and an interface server for constructing an interface resource pool, then storing basic data including soil data, weather data, crop variety data, field operation data and the like into the big data server, storing a simulation algorithm including a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm into the operation server, simultaneously building an application service interface, deploying the service interface through nginx, uWSG and Django, wherein the nginx is used as the foremost end of the server, receiving all requests of a web, uniformly managing the requests, constructing a corn lodging disaster early warning platform including a PC end and a mobile phone end, adopting a front-end and back-end separation technology for APP application service, logging in the interface server to obtain token information when the PC end page and the APP need to display contents, and accessing the service with the token when requesting the interface service; the server side receives the request and verifies that the token information passes, processes the request and returns result data;
the corn growth mechanism model is based on climate data (temperature, rainfall, humidity, wind speed and sunshine hours), combines physiological processes of photosynthesis, respiration, growth and development and the like of crops to dynamically simulate the growth state of corn, and comprises two parts of calculating the net dry matter quantity and calculating a growth period node by using accumulated temperature, wherein the step of calculating the net dry matter quantity comprises three steps of calculating the dry matter quantity assimilated by corn leaves every day, and calculating the dry matter quantity consumed by the corn leaves in respiration and the net dry matter quantity;
first pass through
Calculating to obtain C02 assimilation A per square meter per day, and then obtaining the C02 assimilation A
Calculating the dry matter quantity carbon of each square meter of the corn leaves per day gross (wherein, LAI is leaf area index, A) max For maximum assimilation rate, P is the number of plants per square meter, L is the depth of the canopy of maize plants, PAR i，L Effective solar radiation dose at different canopy depths, and epsilon is the initial light energy effective utilization rate), the corn plant can breathe and consume part of the dry matter every day in addition to photosynthesis
Calculating the influence coefficient Coef of temperature on respiration consumption T Then through mrep = DM live *Coef resp *Coef T The respiratory consumption mResp (wherein, T) for maintaining different tissues and organs can be calculated mean Is the average daily temperature, T ref For reference temperature, DM live Is the weight of the organ, coef T Organ-specific maintenance breathing factor), finally by Carbo = Carbon gros The net dry matter amount can be calculated by s-sigma mrresp (wherein, sigma mrresp is the sum of the respiratory consumption of all the tissue organs;
the growth and development stage of the corn plant is dominated by accumulated temperature, and the calculation of nodes in the growth period needs to be carried out according to different conditions:
when the lowest temperature of the day is lower than the lower limit of the growth temperature of the corns and the highest temperature of the day is higher than the upper limit of the growth temperature of the corns,
when the lowest temperature of the day is within the range of the upper limit and the lower limit of the growth temperature of the corns and the highest temperature of the day is higher than the upper limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both in the range of the upper limit and the lower limit of the growth temperature of the corn, GDD = T mean -T L ；
When the lowest temperature of the day is lower than the lower limit of the growth temperature of the corns and the highest temperature of the day is in the range of the upper limit and the lower limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both higher than the lower limit of the growth temperature of the corn, GDD = T U -T L ；
When the lowest temperature and the highest temperature are lower than the lower limit of the growth temperature of the corn, GDD =0
Wherein, T mean Is the average of the highest and lowest temperatures, T U 、T L Is the upper and lower limits of the growth temperature of the corn, alpha is half of the temperature difference between the highest temperature and the lowest temperature of the day, and theta 1 、θ 2 The intersection angle of the daily temperature change curve and the upper and lower growth temperature limits is shown, and alpha is an intermediate parameter;
predicting key growth period nodes including a seedling emergence period, a jointing stage, a 12-leaf stage, a spinning stage, a filling stage, a milk stage and a mature stage according to the requirements of different growth stages of the corn on accumulated temperature;
the summer corn lodging early warning model algorithm can be combined with the corn growth stage to carry out lodging early warning; through the annual lodging data of the summer corn area, including lodging occurrence areas, varieties, density, occurrence time, growth period stages, occurrence degree, meteorological environment data during occurrence and whether chemical control agents are activated, factors which are highly related to the lodging occurrence degree are found by using a spearman correlation analysis method, wherein the factors include the growth period stages (P), wind speed (W), rainfall (R), crop variety lodging resistance coefficients (F), agricultural chemical control management (C) and density (D). Establishing a lodging disaster early warning regression curve by combining the correlation of 6 main influence factors and lodging disaster occurrence degree (no (0), mild degree (0-10%), moderate degree (10-30%) and severe degree (30-100%):
wherein a1, a2, a3, b1, b2, b3, c1, c2, c3, d1, d2, d3, e1, e2 and e3 are system parameters, and Y is the lodging occurrence degree;
and (3) inputting the corn growth period stages predicted by the mechanism model into an early warning algorithm, judging whether the three stages of lodging are reached, and if the growth period reaches the three stages of lodging easily, respectively predicting the lodging disaster occurrence degree according to the lodging resistance coefficient, rainfall, wind speed, density and chemical control management data of the input variety and a lodging disaster early warning regression curve formula.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. The summer corn lodging early warning algorithm based on the corn growth mechanism model is characterized in that: the method comprises the following steps:
s1, building a server cluster;
s2, storing the data to a corresponding server, and simultaneously building an application service interface;
and S3, constructing a corn lodging disaster early warning platform.
2. The summer corn lodging early warning algorithm based on the corn growth mechanism model as claimed in claim 1, wherein: the server cluster in the step S1 comprises a big data server for storing basic information of soil, weather and crop varieties, an operation server for deploying model operation, a deployment interface and an interface server for constructing an interface resource pool.
3. The summer corn lodging early warning algorithm based on the corn growth mechanism model as claimed in claim 1, wherein: the data in the step S2 comprise basic data and simulation algorithms, wherein the basic data comprise soil data, weather data, crop variety data and field operation data, and the simulation algorithms comprise a corn growth mechanism model algorithm and a summer corn lodging early warning model algorithm;
when the application service interface is built, the service interface is deployed through nginx, uWSGI and Django, wherein the nginx is used as the foremost end of the server.
4. The summer corn lodging early warning algorithm based on the corn growth mechanism model as claimed in claim 1, wherein: the early warning platform in the step S3 comprises a PC end and a mobile phone end, and the application service of the PC end and the mobile phone end adopts a front-end and back-end separation technology.
5. The summer corn lodging early warning algorithm based on the corn growth mechanism model as claimed in claim 3, wherein: the corn growth mechanism model algorithm comprises the steps of calculating the net dry matter mass and calculating the nodes in the growth period by using accumulated temperature.
6. The summer corn lodging early warning algorithm based on the corn growth mechanism model as claimed in claim 5, wherein: the net dry matter mass calculation in the corn growth mechanism model algorithm comprises the following steps:
the method comprises the following steps: calculating the daily assimilated dry matter quantity of the corn leaves;
step two: calculating the dry matter quantity of the corn leaf respiration consumption;
step three: and calculating the net dry matter according to the daily assimilated dry matter of the corn leaves and the dry matter consumed by the corn leaves in respiration.
7. The summer corn lodging early warning algorithm based on corn growth mechanism model as claimed in claim 6, characterized in that: the calculation formula of the daily assimilated dry matter quantity of the corn leaves is as follows:
wherein A is CO per square meter per day 2 Amount of assimilation, LAI is leaf area index, A max For maximum rate of assimilation, carbon gross The dry matter per square meter per day, P is the number of plants per square meter, L is the depth of the canopy of the corn plant, and PAR i，L The effective solar radiation amount of different canopy depths is obtained, and epsilon is the effective utilization rate of initial light energy;
the calculation formula of the dry matter quantity of the corn leaf respiration consumption is as follows:
mResp＝DM live *Coef resp *Coef T
wherein Coef T The coefficient of influence of temperature on respiratory consumption, T mean Is the average daily temperature, T ref For reference temperature, mrresp maintains respiratory consumption, DM, for different tissue organs live Is the weight of the organ, coef T Organ-specific maintenance breathing coefficients;
the calculation formula of the net dry matter is as follows:
Carbo＝Carbon gross -∑mResp。
8. the summer corn lodging early warning algorithm based on corn growth mechanism model as claimed in claim 5, characterized in that: the calculation formula for calculating the nodes in the growth period by using the accumulated temperature is as follows:
when the lowest temperature of the day is lower than the lower limit of the growth temperature of the corns and the highest temperature of the day is higher than the upper limit of the growth temperature of the corns,
when the lowest daily temperature is within the range of the upper limit and the lower limit of the growth temperature of the corns and the highest daily temperature is higher than the upper limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both in the range of the upper limit and the lower limit of the growth temperature of the corn,
GDD＝T mean -T L ；
when the lowest temperature of the day is lower than the lower limit of the growth temperature of the corns and the highest temperature of the day is in the range of the upper limit and the lower limit of the growth temperature of the corns,
when the lowest temperature of the day and the highest temperature of the day are both higher than the lower limit of the growth temperature of the corn,
GDD＝T U -T L ；
when the lowest temperature of the day and the highest temperature of the day are both lower than the lower limit of the growth temperature of the corn,
GDD＝0；
wherein, T mean Is the average of the highest and lowest temperatures, T U 、T L Is the upper and lower limits of the growth temperature of the corn, alpha is half of the temperature difference between the highest temperature and the lowest temperature of the day, and theta 1 、θ 2 Is the intersection angle of the daily temperature change curve and the upper and lower limits of the growth temperature, and alpha is an intermediate parameter.
9. The summer corn lodging early warning algorithm based on the corn growth mechanism model as claimed in claim 8, wherein: the growth period nodes comprise a seedling emergence period, a jointing stage, a 12-leaf stage, a spinning stage, a grouting stage, a milk stage and a mature stage.
10. The summer corn lodging early warning algorithm based on the corn growth mechanism model as claimed in claim 3, wherein: the summer corn lodging early warning model algorithm has the following calculation formula:
wherein a1, a2, a3, b1, b2, b3, C1, C2, C3, D1, D2, D3, e1, e2 and e3 are system parameters, Y is the lodging incidence degree, P is the growth period stage, W is the wind speed, R is the rainfall, F is the lodging resistance coefficient of the crop variety, C is the agricultural chemical control management, and D is the density.
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