A method for evaluating winter wheat mortality due to freezing damage during winter

Abstract

The present invention relates to the field of assessment of mortality rate of wheat overwintering frost damage, and in particular to a method for assessing mortality rate of winter wheat overwintering frost damage, comprising: obtaining first-stage cold resistance factors and second-stage cold resistance factors respectively; if the temperature of the wheat tillering node exceeds a preset threshold, calculating the cold hardening release rate; obtaining the critical cold resistance temperature of the wheat variety on the day before the assessment; constructing a low-temperature frost damage prediction model; determining the prediction accuracy, root mean square error, true deviation, and significant difference of the low-temperature frost damage prediction model; determining whether the simulation accuracy of the cold hardening release effect meets the requirements, and determining different cold hardening response methods based on the simulation accuracy results. The present invention detects the dynamic changes in the frost resistance of different varieties of wheat during the overwintering process through the cold hardening process and the cold hardening release process, thereby enhancing the simulation accuracy of the low-temperature frost damage prediction model for different types of frost damage.

Description

Technical Field

[0001] The present invention relates to the field of evaluation of mortality rate of winter wheat caused by frost damage, and in particular to a method for evaluating mortality rate of winter wheat caused by frost damage. Background Art

[0002] Wheat is the world's third-largest food crop, after corn and rice, with an annual global production exceeding 6 billion tons. 21% of the world's food supply depends on wheat production. Current research on wheat frost damage focuses on the mechanisms of its effects on growth and development, post-damage plant morphology, and the maximum freezing temperature during midwinter, along with corresponding risk assessments. With the integration and cross-fertilization of information science and agricultural science, significant progress has been made in agricultural information technology research both domestically and internationally. However, crop simulation technology remains incomplete, with descriptions of some plant physiological and ecological processes lacking scientific validity, and mechanistic research on crop disaster simulations lacking. Existing crop models, while relatively advanced in application, are at the third level of crop production. In particular, modules for simulating the impact of hazard factors are insufficient or lacking. Research on wheat frost damage impact models is limited, severely impacting their applicability.

[0003] Chinese Patent Publication No. CN108414695A discloses a device for simulating and evaluating wheat cold resistance, comprising a flowerpot, a bracket, a mesh cover, a rope, a wheat plant, a refrigerator, a refrigeration switch, a soil layer, a solar panel, a battery, a temperature sensor, a sensor bracket, and a controller. The flowerpot contains a soil layer, with the wheat plant planted in the center of the soil layer. The bracket's bottom is positioned around the flowerpot's opening, with the bracket's other end extending to a height sufficient to accommodate the wheat plant's growth. The sensor bracket's bottom is positioned adjacent to the wheat plant in the center of the soil layer, with the sensor bracket's top positioned midway along the bracket's height. The sensor bracket's top is provided with a temperature sensor. The device has a simple structure, intuitively compares the objects being evaluated, and delivers significant results. The solar panel is used to replenish the battery's charge, eliminating the need for repeated charging, making it clean and environmentally friendly. The mesh cover eliminates the influence of other natural factors, making the evaluation results more accurate and reliable. Therefore, the prior art suffers from an inability to detect the dynamic changes in the frost resistance of different wheat varieties during wintering, resulting in inaccurate simulations of different types of frost damage by low-temperature frost damage prediction models. Summary of the Invention

[0004] To this end, the present invention provides a method for evaluating the mortality rate of winter wheat due to frost damage during the wintering, so as to overcome the problem that the existing technology is unable to detect the dynamic changes in the frost resistance of different varieties of wheat during the wintering, thereby leading to inaccurate simulation of different types of frost damage by low temperature frost damage prediction models.

[0005] To achieve the above object, the present invention provides a method for evaluating winter wheat overwintering freeze injury mortality, comprising:

[0006] The first stage and the second stage of cold hardiness training of wheat were simulated respectively to obtain the first stage cold hardiness factors and the second stage cold hardiness factors respectively;

[0007] If the temperature of the wheat tillering node exceeds a preset threshold, the critical freezing temperature of the wheat that has not been cold-hardened is detected to determine the cold-hardening release rate in combination with the critical freezing temperature of the cold-hardened wheat;

[0008] Obtaining the critical freezing temperature of the wheat one day before the evaluation;

[0009] Constructing a low temperature freezing damage prediction model based on the first-stage cold resistance factor, the second-stage cold resistance factor, the cold resistance training release rate, and the critical freezing temperature on the day before the assessment;

[0010] Calculating the observed value, simulated value, average value of the observed value, average value of the simulated value, and number of samples of the low temperature damage prediction model to determine the prediction accuracy, root mean square error, true deviation, and significant difference of the low temperature damage prediction model;

[0011] Determining the simulation accuracy of the cold hardship release effect based on the true deviation to determine whether to adjust the number of segments of the vernalization temperature or to adjust the threshold of the tillering node temperature for the cold hardship release effect based on the average difference between the critical cold hardiness temperatures of the rhizomes and leaves of the wheat after thawing;

[0012] If the simulation accuracy still does not meet the requirements after the cold-resistant training coping method is executed, the ratio of data training to data replacement is adjusted according to the EF value of the low-temperature frost damage prediction model.

[0013] Furthermore, the calculation formula of the low temperature frost damage prediction model is:

[0014] LT i =LT i-1 +f hardningi +f dehardningi

[0015] Among them, LT i is the critical antifreeze temperature threshold on the i-th day during the wintering process, LT i-1 is the critical antifreeze temperature of the variety the day before. hardningi is the cold resistance training rate on day i, f dehardningi is the cold resistance training release rate on day i;

[0016] Among them, the lowest air temperature on that day is T min <LT i, it is determined that wheat has frost damage, when T min >LT i No frost damage occurs.

[0017] Furthermore, the calculation formula of the cold resistance factor in the first stage is:

[0018] f hardningi1 =f la ×ΔPot h1 ×min(f ti1 ,f shi )

[0019] Among them, f hardningi1 is the change in wheat temperature during the first stage of cold hardiness training, f ti1 is the temperature effect factor on the i-th day in the first stage of cold resistance training, f shi is the light effect factor on the i-th day, f la Is the leaf age factor before winter, with a value between 0 and 1, ΔPot h1 is the potential increase in daily wheat temperature after cold hardiness.

[0020] Furthermore, the calculation formula of the second stage cold resistance factor is:

[0021]

[0022] Among them, f hardningi2 is the change of wheat temperature in the second stage of cold hardiness training, HF2 is the cold resistance effect factor in the first stage, R c1 R is the critical temperature of wheat after the first stage of cold hardiness training. c2 LT is the critical temperature of wheat freezing resistance after the second stage of cold hardiness training. i is the critical antifreeze temperature threshold on the i-th day during the wintering process, L max It is the critical antifreeze temperature of wheat that has been hardened to cold.

[0023] Furthermore, the calculation formula for the prediction accuracy of the low temperature frost damage prediction model is:

[0024]

[0025] Among them, EF>0 means that the prediction accuracy of the low temperature freezing damage prediction model meets the requirements;

[0026] The root mean square error of the low temperature damage prediction model is the absolute error between the simulated value and the measured value, and the calculation formula is:

[0027]

[0028] Among them, RMSE ais the root mean square error of the low temperature damage prediction model;

[0029] The true deviation of the low temperature damage prediction model is the sum of the deviations between the simulated value and the measured value, and the calculation formula is:

[0030]

[0031] Among them, R 2 is the true deviation of the low temperature damage prediction model;

[0032] The significant difference of the low temperature damage prediction model is the difference between the simulated value and the measured value, and the calculation formula is:

[0033] ABS=S i -O i ;

[0034] Among them, ABS is the significant difference of low temperature damage prediction model;

[0035] Among them, O i is the observed value, S i is the analog value, is the mean of the observed values, is the average value of the simulation value, and n is the number of samples.

[0036] Furthermore, whether the simulation accuracy of the cold-resistance training release effect meets the requirements is determined based on the actual deviation of the low-temperature frost damage prediction model. When the actual deviation of the low-temperature frost damage prediction model is greater than the preset second deviation, it is determined that the simulation accuracy of the cold-resistance training release effect does not meet the requirements, and the number of segments of the vernalization temperature is increased.

[0037] Furthermore, when the actual deviation of the low temperature frost damage prediction model is greater than the preset first deviation and less than or equal to the preset second deviation, it is determined that the simulation accuracy of the cold hardening release effect does not meet the requirements, and a secondary judgment is made on the simulation accuracy of the cold hardening release effect based on the average difference in the critical cold hardiness temperature of the rhizomes and leaves of the wheat after thawing. When the average difference in the critical cold hardiness temperature of the rhizomes and leaves of the wheat after thawing is greater than the preset temperature difference, the tillering node threshold of the cold hardening release effect is adjusted.

[0038] Furthermore, if the actual deviation of the low-temperature frost damage prediction model is still greater than the preset second deviation after adjustment, the simulation accuracy of the cold-resistant training release effect is determined to meet the requirements based on the EF value of the low-temperature frost damage prediction model. If the EF value of the low-temperature frost damage prediction model is less than or equal to the EF value of the low-temperature frost damage model, it is determined that the simulation accuracy of the cold-resistant training release effect does not meet the requirements, and the ratio of data training to data replacement is adjusted.

[0039] Furthermore, the average difference in critical cold resistance temperatures of the rhizomes and leaves of the thawed wheat is the difference between the critical temperatures at which the rhizomes and leaves of the thawed wheat can withstand low temperatures without suffering frost damage; the wheat tillering node temperature refers to the extreme temperature condition that affects the growth and development of the wheat tillering node.

[0040] Furthermore, the ratio of data training to data replacement is the ratio of the amount of data used for training optimization problems in the data with problems when the low temperature damage prediction model is running to the amount of data used for search and replacement in the database;

[0041] The sum of the amount of data for the training optimization problem and the amount of data for search and replacement in the database is the amount of data that has problems when the low-temperature frost damage prediction model is running.

[0042] Compared with the prior art, the beneficial effects of the present invention are that, by simulating two stages of wheat cold hardening respectively, the present invention can consider the formation and changes of wheat's cold resistance at different stages in a more detailed and accurate manner, thereby improving the understanding of its cold resistance characteristics; by setting the cold hardening release rate to be calculated based on the tillering node temperature exceeding a preset threshold, the accuracy of capturing key nodes that affect changes in wheat's cold resistance can be improved; by combining the cold resistance factors, cold hardening release rate and critical antifreeze temperature at different stages to construct a low temperature frost damage prediction model, a number of key influencing factors are comprehensively considered, thereby improving the comprehensiveness of the prediction model; the present invention helps farmers take effective preventive measures in advance through frost damage prediction, reduce frost damage losses, ensure wheat yield and quality, provide a scientific decision-making basis for agricultural production, and provide effective evaluation means and data support for screening and breeding varieties with stronger frost resistance.

[0043] Furthermore, the present invention overcomes the problem of inaccurate wheat development process algorithm caused by inaccurate data sampling during wheat development process simulation, which in turn leads to inaccurate vernalization temperature, by setting the real deviation amount of the low temperature frost damage prediction model. When it is determined that the simulation does not meet the requirements, the number of segments of the vernalization temperature is increased, which helps to more carefully consider the impact of temperature changes on wheat frost damage mortality, thereby improving the accuracy and reliability of the model; by determining the accuracy of the frost damage mortality simulation based on the real deviation amount, it is possible to timely discover deviations in the simulation results, improve and optimize the model, and improve the adaptability of the prediction model under different environments and planting conditions.

[0044] Furthermore, the present invention overcomes the inaccuracy of the cold-resistance training release effect caused by part of the water being absorbed by the soil after thawing due to the sudden change of water, which leads to a slowdown in growth rate or failure to meet growth conditions. The surface soil thaws, and some wheat roots are exposed, interacting with the environment, thereby triggering different levels of cold-resistance training in some areas. The cold-resistance training release effect within the preset first deviation and the preset second deviation is secondary judged, and the accuracy of the model can be evaluated more carefully, which helps to improve the accuracy of the judgment model and further improve the simulation effect of the model.

[0045] Furthermore, the present invention sets the EF value of the low-temperature frost damage prediction model. When the critical cold resistance temperature difference of wheat is still too large, further judgment is made with the help of the EF value, which helps to more accurately find the problems with the model. By adjusting the ratio of data training to data replacement, the model's ability to learn and adapt to new data can be improved, thereby improving the model's generalization ability and prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a diagram showing the overall steps of a method for evaluating winter wheat overwintering freeze injury mortality according to an embodiment of the present invention;

[0047] Figure 2 A line graph showing the effect of pre-winter leaf age on the rate of cold hardening of wheat according to an embodiment of the present invention;

[0048] Figure 3 A line graph showing the actual deviation of the critical antifreeze temperature of wheat during the wintering period according to an embodiment of the present invention;

[0049] Figure 4 The present invention provides a flowchart of a method for evaluating winter wheat overwintering freeze injury mortality. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] It will be understood by those skilled in the art that, unless otherwise stated, the singular forms "a", "an", and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in this specification refers to the presence of features, integers, steps, operations, elements / components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements / components. It should be understood that when we say that a module is "connected" or "coupled" to another module, it can be directly connected or coupled to the other module, or there can be an intermediate unit. In addition, "connected" or "coupled" as used herein may include wireless connection or wireless coupling.

[0053] See also Figure 1 、 Figure 2 、 Figure 3 as well as Figure 4 As shown, they are respectively a diagram of the overall steps of the method for assessing winter wheat overwintering freeze injury mortality according to an embodiment of the present invention, a line graph showing the effect of pre-winter leaf age on the rate of wheat cold hardening, a line graph showing the actual deviation of the critical freeze temperature during the wintering of wheat, and a flow chart. The method for assessing winter wheat overwintering freeze injury mortality according to the present invention comprises:

[0054] The first stage and the second stage of cold hardiness training of wheat were simulated respectively to obtain the first stage cold hardiness factors and the second stage cold hardiness factors respectively;

[0055] If the temperature of the wheat tillering node exceeds a preset threshold, the critical freezing temperature of the wheat that has not been cold-hardened is detected to determine the cold-hardening release rate in combination with the critical freezing temperature of the cold-hardened wheat;

[0056] Obtaining the critical freezing temperature of the wheat one day before the evaluation;

[0057] Constructing a low temperature freezing damage prediction model based on the first-stage cold resistance factor, the second-stage cold resistance factor, the cold resistance training release rate, and the critical freezing temperature on the day before the assessment;

[0058] Calculating the observed value, simulated value, average value of the observed value, average value of the simulated value, and number of samples of the low temperature damage prediction model to determine the prediction accuracy, root mean square error, true deviation, and significant difference of the low temperature damage prediction model;

[0059] Determining the simulation accuracy of the cold hardship release effect based on the true deviation to determine whether to adjust the number of segments of the vernalization temperature or to adjust the threshold of the tillering node temperature for the cold hardship release effect based on the average difference between the critical cold hardiness temperatures of the rhizomes and leaves of the wheat after thawing;

[0060] If the simulation accuracy still does not meet the requirements after the cold-resistant training coping method is executed, the ratio of data training to data replacement is adjusted according to the EF value of the low-temperature frost damage prediction model.

[0061] In practice, the present invention simulates two stages of wheat cold hardening respectively, which can more carefully and accurately consider the formation and changes of wheat's cold resistance at different stages, thereby improving the understanding of its cold resistance characteristics; by setting the cold hardening release rate to be calculated based on the tillering node temperature exceeding a preset threshold, the accuracy of capturing key nodes that affect the changes in wheat's cold resistance can be improved; by combining the cold resistance factors, cold hardening release rate and critical antifreeze temperature at different stages to construct a low-temperature frost damage prediction model, multiple key influencing factors are comprehensively considered, thereby improving the comprehensiveness of the prediction model; the present invention helps farmers take effective preventive measures in advance through frost damage prediction, reduce frost damage losses, ensure wheat yield and quality, provide a scientific decision-making basis for agricultural production, and provide effective evaluation means and data support for screening and breeding varieties with stronger frost resistance.

[0062] Specifically, the calculation formula of the low temperature frost damage prediction model is:

[0063] LT i =LT i-1 +f hardningi +f dehardningi

[0064] Among them, LT i is the critical antifreeze temperature threshold on the i-th day during the wintering process, LT i-1 is the critical antifreeze temperature of the variety the day before. hardningi is the cold resistance training rate on day i, f dehardningi is the cold resistance training release rate on day i;

[0065] Among them, the lowest air temperature on that day is T min <LT i , it is determined that wheat has frost damage, when T min >LT i No frost damage occurs.

[0066] Specifically, f hardningi Affected by variety, temperature and light, it has a promoting effect on improving the frost resistance of wheat; dehardningi It is mainly affected by variety type and temperature, and has an inhibitory effect on wheat's frost resistance.

[0067] Specifically, the calculation formula for the first stage cold resistance factor is:

[0068] f hardningi1 =f la ×ΔPoth1 ×min(f ti1 ,f shi )

[0069] Among them, f hardningi1 is the change in wheat temperature during the first stage of cold hardiness training, f ti1 is the temperature effect factor on the i-th day in the first stage of cold resistance training, f shi is the light effect factor on the i-th day, f la Is the leaf age factor before winter, with a value between 0 and 1, ΔPot h1 is the potential increase in daily wheat temperature after cold hardiness.

[0070] Specifically, the temperature effect factor f on the i-th day in the first stage of the cold resistance training is ti1 The calculation formula is:

[0071]

[0072] Among them, T a The temperature during the first phase of cold-resistance training.

[0073] Specifically, this study assumes that when the sunshine hours are less than 5 hours, it is considered that the sunshine is insufficient, which in turn affects the cold resistance of wheat; the light effect factor f on the i-th day is shi The calculation formula is:

[0074]

[0075] Where ssd is the number of hours of sunshine on that day.

[0076] Specifically, the pre-winter leaf age factor f la The calculation formula is:

[0077]

[0078] Among them, L a It is the leaf age before winter.

[0079] Specifically, the calculation formula for the second stage cold resistance factor is:

[0080]

[0081] Among them, f hardningi2 is the change of wheat temperature in the second stage of cold hardiness training, HF2 is the cold resistance effect factor in the first stage, R c1 R is the critical temperature of wheat after the first stage of cold hardiness training. c2 LT is the critical temperature of wheat freezing resistance after the second stage of cold hardiness training. iis the critical antifreeze temperature threshold on the i-th day during the wintering process, L max It is the critical antifreeze temperature of wheat that has been hardened to cold.

[0082] Specifically, after the first stage of cold resistance training, the critical temperature of wheat freezing resistance R c1 The calculation formula is:

[0083]

[0084] Among them, N1 is the number of days in the first stage of cold resistance training.

[0085] Specifically, the change in wheat temperature during the second stage of cold hardiness training is f hardning2 The calculation formula is:

[0086] f hardning2 =[(L max -L c ) / N2]×f ti2

[0087] Among them, f ti2 Temperature effect factor of the second stage of cold resistance training, L c L is the maximum freezing temperature that a specific type of wheat can reach after the first stage of cold hardiness training. max is the critical freezing temperature of wheat after cold hardening, N2 is the number of days in the second stage of cold hardening;

[0088] When the temperature during the first stage of cold resistance training is T a When the value range is [-5℃, 0℃), f ti2 The value is 1, when the temperature during the first stage of cold resistance training is T a When the value range of f is less than or greater than or equal to the value range of [-5℃, 0℃), f ti2 The value of is 0.

[0089] Specifically, if the temperature of the wheat tillering node exceeds a preset threshold, the cold hardening release rate is calculated according to the critical freezing temperature of the cold hardened wheat;

[0090] The preset threshold value can be in the range of [0°C, 10°C]. In a specific embodiment, the temperature of the wheat tillering node is 12°C, which exceeds the preset threshold value. The cold resistance training release rate f dehardning The calculation formula is:

[0091]

[0092] Among them, L max is the critical freezing temperature of wheat after cold hardening, L min is the critical freezing temperature of wheat that has not been hardened to cold, Ta The temperature during the first phase of cold-resistance training.

[0093] Specifically, the calculation formula for the prediction accuracy of the low temperature frost damage prediction model is:

[0094]

[0095] Among them, EF>0 means that the prediction accuracy of the low temperature freezing damage prediction model meets the requirements;

[0096] The root mean square error of the low temperature damage prediction model is the absolute error between the simulated value and the measured value, and the calculation formula is:

[0097]

[0098] Among them, RMSE a is the root mean square error of the low temperature damage prediction model;

[0099] The true deviation of the low temperature damage prediction model is the sum of the deviations between the simulated value and the measured value, and the calculation formula is:

[0100]

[0101] Among them, R 2 is the true deviation of the low temperature damage prediction model;

[0102] The significant difference of the low temperature damage prediction model is the difference between the simulated value and the measured value, and the calculation formula is:

[0103] ABS=S i -O i ;

[0104] Among them, ABS is the significant difference of low temperature damage prediction model;

[0105] Among them, O i is the observed value, S i is the analog value, is the mean of the observed values, is the average value of the simulation value, and n is the number of samples.

[0106] Specifically, whether the simulation accuracy of the cold-hardening release effect meets the requirements is determined according to the actual deviation of the low-temperature frost damage prediction model. When the actual deviation of the low-temperature frost damage prediction model is greater than a preset second deviation, it is determined that the simulation accuracy of the cold-hardening release effect does not meet the requirements, and the number of segments of the vernalization temperature is increased.

[0107] Among them, the vernalization temperature segmentation is divided according to the temperature range required for winter wheat to complete the vernalization process. Generally, the vernalization temperature segmentation intervals are [0℃, 3℃], [3℃, 6℃] and [6℃, 15℃].

[0108] In practice, the present invention overcomes the problem of inaccurate wheat development process algorithm caused by inaccurate data sampling during wheat development process simulation, which in turn leads to inaccurate vernalization temperature, by setting the real deviation amount of the low temperature frost damage prediction model. When it is determined that the simulation does not meet the requirements, the number of segments of the vernalization temperature is increased, which helps to more finely consider the impact of temperature changes on wheat frost damage mortality, thereby improving the accuracy and reliability of the model; by determining the accuracy of the frost damage mortality simulation based on the real deviation amount, it is possible to timely discover deviations in the simulation results, improve and optimize the model, and improve the adaptability of the prediction model under different environments and planting conditions.

[0109] Optionally, the preset second deviation value may be in the range of [5%, 10%];

[0110] Preferably, the second deviation is preset to be 7%.

[0111] In one possible embodiment, the true deviation of the low-temperature frost damage prediction model is 13%, which is greater than the preset second deviation. It is determined that the simulation accuracy of the cold-resistance training release effect does not meet the requirements. For every 2% by which the true deviation of the low-temperature frost damage prediction model is greater than the preset second deviation, the number of segments of the vernalization temperature is increased by 1. At this time, the number of segments of the vernalization temperature is 3. The increased number of segments of the vernalization temperature is: 3+(13-7) / 2=6.

[0112] Specifically, when the actual deviation of the low temperature frost damage prediction model is greater than the preset first deviation and less than or equal to the preset second deviation, it is determined that the simulation accuracy of the cold hardening release effect does not meet the requirements, and a secondary judgment is made on the simulation accuracy of the cold hardening release effect based on the average difference in the critical cold hardiness temperature of the rhizomes and leaves of the wheat after thawing. When the average difference in the critical cold hardiness temperature of the rhizomes and leaves of the wheat after thawing is greater than the preset temperature difference, the tillering node threshold of the cold hardening release effect is adjusted.

[0113] In practice, the present invention overcomes the inaccuracy of the cold-resistance training release effect caused by part of the water being absorbed by the soil after thawing due to the sudden change of water, and the growth rate slows down or the growth conditions cannot be met. The surface soil thaws, and some wheat roots are exposed and interact with the environment, thereby triggering different levels of cold-resistance training in some areas. The cold-resistance training release effect within the preset first deviation and the preset second deviation is secondary judged, and the accuracy of the model can be evaluated more carefully, which helps to improve the accuracy of the judgment model and further improve the simulation effect of the model.

[0114] Optionally, the preset first deviation value may be in the range of [2%, 5%], and the preset temperature difference value may be in the range of [3°C, 5°C];

[0115] Preferably, the preferred embodiment of the preset first deviation amount is 3%, and the preferred embodiment of the preset temperature difference amount is 4°C;

[0116] In one possible embodiment, the actual deviation of the low temperature frost damage prediction model is 6%, which is greater than the preset first deviation and less than the preset second deviation, and it is determined that the simulation accuracy of the cold hardship training release effect does not meet the requirements; the average difference in the critical cold hardship temperature of the rhizome and leaves of the wheat after thawing is 6°C, which is greater than the preset temperature difference. For every 1°C increase in the average difference in the critical cold hardship temperature of the rhizome and leaves of the wheat after thawing that is greater than the preset first deviation, the maximum value of the tiller node threshold for the cold hardship training release effect is reduced by 1°C. At this time, the tiller node threshold is [0°C, 10°C], and the maximum value of the reduced tiller node threshold is 10-(6-4)=8, and the reduced tiller node threshold is [0°C, 8°C].

[0117] Specifically, if the actual deviation of the low-temperature frost damage prediction model is still greater than the preset second deviation after adjustment, whether the simulation accuracy of the cold-resistant training release effect meets the requirements is determined according to the EF value of the low-temperature frost damage prediction model; if the EF value of the low-temperature frost damage prediction model is less than or equal to the EF value of the preset low-temperature frost damage model, it is determined that the simulation accuracy of the cold-resistant training release effect does not meet the requirements, and the ratio of data training to data replacement is adjusted;

[0118] In a specific embodiment, the EF value of the preset low-temperature freezing damage model is 0, the EF value of the low-temperature freezing damage model is -1, and the simulation accuracy of the cold-resistance training release effect is determined to be unsatisfactory. For every 0.5 by which the EF value of the low-temperature freezing damage model is less than the EF value of the low-temperature freezing damage model, the proportion of data training is increased by 1% compared with the current data training proportion. The current data training proportion is 34%, and the increased data training proportion is 34% + |-1 / 0.5| = 36%.

[0119] In implementation, the present invention sets the EF value of the low-temperature frost damage prediction model. When the critical cold resistance temperature difference of wheat is still too large, further judgment is made with the help of the EF value, which helps to more accurately find the problems in the model. By adjusting the ratio of data training to data replacement, the model's ability to learn and adapt to new data can be improved, thereby improving the model's generalization ability and prediction accuracy.

[0120] Specifically, the average difference in critical cold resistance temperature between the rhizomes and leaves of the wheat after thawing is the average value of the difference between the critical temperatures at which the rhizomes and leaves of the wheat after thawing can withstand low temperatures without being damaged by frost; the wheat tillering node temperature refers to the extreme temperature condition that affects the growth and development of the wheat tillering node.

[0121] Specifically, the ratio of data training to data replacement is the ratio of the amount of data used for training optimization problems in the data with problems when the low temperature damage prediction model is running to the amount of data used for search and replacement in the database;

[0122] The sum of the amount of data for the training optimization problem and the amount of data for search and replacement in the database is the amount of data that has problems when the low temperature damage prediction model is running;

[0123] Among them, the data with problems when the low-temperature frost damage prediction model is running are the data used by the prediction model in the prediction process when the low-temperature frost damage prediction model has prediction errors. For example, when the low-temperature frost damage prediction model has errors in predicting low-temperature frost damage, the critical antifreeze temperature threshold of wheat on a certain day, the data used to calculate the cold resistance training rate, and the data used to calculate the cold resistance training release rate are the data with problems.

[0124] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for evaluating winter wheat overwintering freeze injury mortality, characterized in that: include: The first stage and the second stage of cold hardiness training of wheat were simulated respectively to obtain the first stage cold hardiness factors and the second stage cold hardiness factors respectively; If the temperature of the wheat tillering node exceeds a preset threshold, the critical freezing temperature of the wheat that has not been cold-hardened is detected to determine the cold-hardening release rate in combination with the critical freezing temperature of the cold-hardened wheat; Obtaining the critical freezing temperature of the wheat one day before the evaluation; Constructing a low temperature freezing damage prediction model based on the first-stage cold resistance factor, the second-stage cold resistance factor, the cold resistance training release rate, and the critical freezing temperature on the day before the assessment; Wherein, the cold resistance training release rate f dehardning The calculation formula is: Among them, L max is the critical freezing temperature of wheat after cold hardening, L min is the critical freezing temperature of wheat that has not been hardened to cold, T a The temperature during the first phase of cold-resistance training; Calculating the observed value, simulated value, average value of the observed value, average value of the simulated value, and number of samples of the low temperature damage prediction model to determine the prediction accuracy, root mean square error, true deviation, and significant difference of the low temperature damage prediction model; Determining the simulation accuracy of the cold hardship release effect based on the true deviation to determine whether to adjust the number of segments of the vernalization temperature or to adjust the threshold of the tillering node temperature for the cold hardship release effect based on the average difference between the critical cold hardiness temperatures of the rhizomes and leaves of the wheat after thawing; If the simulation accuracy still does not meet the requirements after the cold-resistant training is completed, the ratio of data training to data replacement is adjusted according to the EF value of the low temperature frost damage prediction model. The calculation formula of the low temperature frost damage prediction model is: LT i =LT i-1 +f hardningi +f dehardningi Among them, LT i is the critical antifreeze temperature threshold on the i-th day during the wintering process, LT i-1 is the critical antifreeze temperature of the variety the day before, f hardningi is the cold resistance training rate on day i, f dehardningi is the cold resistance training release rate on day i; Among them, the lowest air temperature on that day is T min <LT i , it is determined that wheat has frost damage, when T min >LT i No frost damage occurs.

2. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 1, wherein: The calculation formula of the cold resistance factor in the first stage is: f hardningi1 =f la ×ΔPot h1 ×min(f ti1 ,f shi ) Among them, f hardningi1 is the change in wheat temperature during the first stage of cold hardiness training, f ti1 is the temperature effect factor on the i-th day in the first stage of cold resistance training, f shi is the light effect factor on the i-th day, f la Is the leaf age factor before winter, with a value between 0 and 1, ΔPot h1 is the potential increase in daily wheat temperature after cold hardiness.

3. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 2, characterized in that: The calculation formula of the cold resistance factor in the second stage is: Among them, f hardningi2 is the change of wheat temperature in the second stage of cold hardiness training, HF2 is the cold resistance effect factor in the first stage, R c1 R is the critical temperature of wheat after the first stage of cold hardiness training. c2 LT is the critical temperature of wheat freezing resistance after the second stage of cold hardiness training. i is the critical antifreeze temperature threshold on the i-th day during the wintering process, L max is the critical freezing temperature of wheat after cold hardening, N1 is the number of days in the first stage of cold hardening, and N2 is the number of days in the second stage of cold hardening.

4. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 3, characterized in that: The calculation formula for the prediction accuracy of the low temperature frost damage prediction model is: Among them, EF>0 means that the prediction accuracy of the low temperature freezing damage prediction model meets the requirements; The root mean square error of the low temperature damage prediction model is the absolute error between the simulated value and the measured value, and the calculation formula is: Among them, RMSE a is the root mean square error of the low temperature damage prediction model; The true deviation of the low temperature damage prediction model is the sum of the deviations between the simulated value and the measured value, and the calculation formula is: Among them, R 2 is the true deviation of the low temperature damage prediction model; The significant difference of the low temperature damage prediction model is the difference between the simulated value and the measured value, and the calculation formula is: ABS=S i -O i ; Among them, ABS is the significant difference of low temperature damage prediction model; Among them, O i is the observed value, S i is the analog value, is the mean of the observed values, is the average value of the simulation value, and n is the number of samples.

5. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 4, characterized in that: Whether the simulation accuracy of the cold-hardness training release effect meets the requirements is determined based on the actual deviation of the low-temperature frost damage prediction model. When the actual deviation of the low-temperature frost damage prediction model is greater than the preset second deviation, it is determined that the simulation accuracy of the cold-hardness training release effect does not meet the requirements, and the number of segments of the vernalization temperature is increased.

6. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 5, characterized in that: When the actual deviation of the low temperature frost damage prediction model is greater than the preset first deviation and less than or equal to the preset second deviation, it is determined that the simulation accuracy of the cold hardening release effect does not meet the requirements, and a secondary judgment is made on the simulation accuracy of the cold hardening release effect based on the average difference in the critical cold hardiness temperature of the rhizomes and leaves of the wheat after thawing. When the average difference in the critical cold hardiness temperature of the rhizomes and leaves of the wheat after thawing is greater than the preset temperature difference, the tillering node threshold of the cold hardening release effect is adjusted.

7. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 6, characterized in that: If the actual deviation of the low-temperature frost damage prediction model is still greater than the preset second deviation after adjustment, the simulation accuracy of the cold-resistant training release effect is determined to meet the requirements based on the EF value of the low-temperature frost damage prediction model. If the EF value of the low-temperature frost damage prediction model is less than or equal to the EF value of the low-temperature frost damage model, it is determined that the simulation accuracy of the cold-resistant training release effect does not meet the requirements, and the ratio of data training to data replacement is adjusted.

8. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 7, characterized in that: The average difference in critical cold resistance temperature between the rhizomes and leaves of the thawed wheat is the difference between the critical temperatures at which the rhizomes and leaves of the thawed wheat can withstand low temperatures without suffering frost damage; the wheat tillering node temperature refers to the extreme temperature condition that affects the growth and development of the wheat tillering node.

9. The method for evaluating winter wheat overwintering freeze injury mortality according to claim 8, characterized in that: The ratio of data training to data replacement is the ratio of the amount of data used for training optimization problems in the data with problems when the low temperature damage prediction model is running to the amount of data used for search and replacement in the database; The sum of the amount of data for the training optimization problem and the amount of data for search and replacement in the database is the amount of data that has problems when the low-temperature frost damage prediction model is running.

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