Corn variety density tolerance evaluation method, device and equipment and storage medium

Abstract

The invention provides a corn variety density tolerance evaluation method, device and equipment and a storage medium, and relates to the technical field of agricultural information.The density tolerance evaluation method comprises the steps that a density tolerance evaluation formula is established by collecting key data of the yield, the disease and pest occurrence rate and the corn population starch content mean value of corn under different planting conditions; according to the method, the density tolerance of the corn variety is systematically divided into a plurality of levels including a poor level, a medium level, a good level, an excellent level and the like in a mode of a preset density tolerance level threshold value, the density tolerance of the corn variety is evaluated, meanwhile, a plurality of adjustable parameters including coefficients and indexes are set in a model, and when a specific character is determined in practical application, the density tolerance of the corn variety is evaluated. And when the corn variety, such as the starch content or the disease and pest resistance, has special emphasis, the weight ratio of the character in the overall evaluation can be changed by scientifically adjusting the corresponding coefficient or index, so that a planter, a breeder and the like can be helped to screen or cultivate the corn variety more suitable for close planting according to actual requirements.

Description

Technical Field

[0001] The invention relates to the field of agricultural information technology, and in particular to a method, device, equipment and storage medium for evaluating the density tolerance of corn varieties. Background Art

[0002] In modern agricultural production, corn is an important food crop, and its efficient planting and management have always been the focus of agricultural research. Traditional corn planting methods usually rely on experience and extensive management, and cannot fully optimize planting density to improve yield and quality. With the growth of population and the limited land resources, strengthening the research on corn density tolerance to increase unit area yield has become an urgent problem to be solved in agricultural research.

[0003] Existing corn planting technologies mostly focus on optimizing a single indicator, such as simply pursuing yield increase or pest and disease control. These technologies often ignore the overall performance of corn under high-density planting conditions, including yield, quality, and disease resistance. In addition, when evaluating corn varieties, existing technologies lack a comprehensive method to measure their density tolerance level and cannot effectively guide the improvement of planting strategies.

[0004] The deficiency of existing technologies is the lack of a systematic evaluation system to integrate multiple key agricultural indicators, such as starch content, yield per unit area, and pest and disease incidence. On the one hand, the evaluation of a single indicator may lead to a one-sided understanding of the density tolerance of corn varieties; on the other hand, the lack of randomized and systematic data collection methods may lead to deviations in evaluation results, thus affecting the scientificity and effectiveness of planting decisions.

[0005] 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

[0006] The object of the present invention is to provide a method, device, equipment and storage medium for evaluating the density tolerance of corn varieties to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for evaluating the density tolerance of corn varieties, comprising the following specific steps:

[0009] Step 1: Plant the corn variety to be evaluated in the set planting area according to the pre-set planting density. After the corn matures, divide the planting area into equal areas to form multiple sub-plots of equal areas;

[0010] Step 2: In each sub-plot, a corn plant is selected by random sampling, and the starch content of each selected corn plant is counted respectively, and the result is recorded as the mean starch content of the corn population by calculating the average value;

[0011] Step 3: Determine the total ear weight of all corns in the planting area, calculate the yield per unit area by the total ear weight and the area of ​​the planting area, count the number of diseased corn plants and the total number of corn plants in the planting area, and calculate the incidence of diseases and insect pests by the ratio of the number of diseased corn plants to the total number;

[0012] Step 4: Construct a density tolerance evaluation formula based on unit area yield, pest and disease incidence rate, and mean starch content of corn population, and evaluate the density tolerance level of corn varieties based on the results.

[0013] Furthermore, a planting area is selected, its area is recorded as S, the planting density is set as D, and corn seeds are evenly sown in the planting area according to the planting density.

[0014] Furthermore, the sub-plot area is preset, and the planting area is divided into equal areas. The number of plot divisions is calculated by the sub-plot area and the planting area area:

[0015]

[0016] In the formula, N represents the number of plots divided, and the number of plots is determined by rounding down, S represents the planting area, and A represents the pre-set sub-plot area;

[0017] Taking the center of the planting area as the starting reference point, the planting area is evenly divided into N plots of area A.

[0018] Furthermore, the starch content of each corn plant sampled is recorded as ω i , where i represents the number of the sub-field, i = 1, 2, 3, ..., N. The mean starch content of the corn population is calculated by the sampled corn starch content and the number of corn plants:

[0019]

[0020] In the formula, ω St It is expressed as the mean starch content of corn population,

[0021] Wherein, measuring the corn starch content of the sample comprises the following steps:

[0022] The corn kernels were peeled off from the cobs, and the kernels were ground into uniform and fine powder. The weight of the corn powder was recorded as m. After adding 2 mol / L dilute hydrochloric acid solution, the powder was heated and refluxed in a boiling water bath for 2 hours. After cooling to room temperature, the powder was neutralized with 2 mol / L sodium hydroxide solution. The pH value of the solution was monitored with a pH meter until the pH value of the solution reached neutrality.

[0023] After distilling the solution to 100 ml with distilled water, take 50 ml of the solution, add Fehling's reagent A and B and heat to boiling, then titrate with 0.1 mol / L glucose solution until the blue color of the solution just fades and a brick-red precipitate appears, and record the volume V of the standard glucose solution consumed;

[0024] According to n Glu =c·V, the amount of glucose consumed in the titration process is obtained, where c = 0.1 mol / L, and the starch mass is calculated:

[0025] m St =162·c·V

[0026] In the formula, m St Expressed as starch mass, 162 is the molar mass of starch, and then the starch content of each corn plant sampled is calculated:

[0027]

[0028] Furthermore, the total ear weight of corn in the planting area is measured and recorded, and the yield per unit area is calculated based on the total ear weight of the planting area and the set sub-plot area:

[0029]

[0030] Where Y represents the yield per unit area, and M represents the total corn ear weight in the planting area.

[0031] Furthermore, the total number of corn plants was calculated by the formula:

[0032] n=D·S

[0033] Where n represents the total number of corn plants in the planting area, D represents the planting density of the planting area, and S represents the area of ​​the planting area;

[0034] The number of diseased corn plants and the total number of corn plants in the planting area are counted, and the ratio of the two is recorded as the incidence rate of pests and diseases:

[0035]

[0036] In the formula, α PI Expressed as the incidence of pests and diseases, n DPIt is expressed as the number of diseased corn plants in the planting area.

[0037] Furthermore, extract the average data of unit area yield, incidence of pests and diseases, and corn population starch content, and construct a formula for evaluating density tolerance:

[0038]

[0039] In the formula, R represents the inhibitory effect of limiting factors such as soil fertility and climate conditions on density tolerance, the coefficient k represents the adjustment coefficient of the overall comprehensive index, the coefficient b represents the adjustment coefficient of pests and diseases on the comprehensive index, and the coefficient d represents the power adjustment coefficient of the average value of corn population starch content on the evaluation of density tolerance.

[0040] Preset the density tolerance level thresholds I1, I2, and I3. When I < I1, the density tolerance is evaluated as poor; when I1 ≤ I ≤ I2, the density tolerance is evaluated as medium; when I2 < I ≤ I3, the density tolerance is evaluated as good; when I > I3, the density tolerance is evaluated as excellent.

[0041] The present invention also provides a device for evaluating the density tolerance of corn varieties. The system is used to execute the above-mentioned method for evaluating the density tolerance of corn varieties, and includes:

[0042] A planting planning module, which is used to plant the corn variety to be evaluated in the set planting area according to the preset planting density. After the corn matures, the planting area is divided into equal areas to form a plurality of equal-area sub-field plots.

[0043] A content determination module, which is used to randomly select one corn plant in each sub-field plot, respectively count the starch content of each selected corn plant, and record the result as the average value of corn population starch content by calculating the average value.

[0044] A yield and pest and disease analysis module, which is used to measure the total ear weight of all corns in the planting area, calculate the unit area yield through the total ear weight and the area of the planting area, count the number of diseased corn plants and the total number of corn plants in the planting area, and calculate the incidence of pests and diseases through the ratio of the number of diseased corn plants to the total number.

[0045] A density tolerance evaluation module, which is used to construct a density tolerance evaluation formula through the unit area yield, incidence of pests and diseases, and average value of corn population starch content, and evaluate the density tolerance level of the corn variety according to the result.

[0046] The present invention also provides a device. The system is used to execute the above-mentioned method for evaluating the density tolerance of corn varieties, and includes:

[0047] A processor and a memory connected to the processor;

[0048] The memory is used to store a computer program, and the computer program is at least used to execute the above-mentioned corn variety density tolerance evaluation method;

[0049] The processor is used to call and execute the computer program in the memory.

[0050] The present invention further provides a storage medium storing a computer program. When the computer program is executed by a processor, each step of the above-mentioned corn variety density tolerance evaluation method is implemented.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention systematically collects and analyzes key indicators such as starch content, unit area yield and pest and disease incidence of corn populations by introducing equal area division and random sampling methods, and establishes a comprehensive density tolerance evaluation formula, which effectively overcomes the one-sidedness and data deviation problems of the prior art in evaluating corn density tolerance, and uses equal area sub-field division and random sampling to ensure the scientificity and representativeness of data collection. By integrating the starch content, unit area yield and pest and disease incidence of corn, a comprehensive density tolerance evaluation formula is constructed, making the evaluation of the density tolerance level of corn varieties more comprehensive and reliable. This not only helps to guide the optimization of planting density and improve unit area yield, but also can reduce the losses caused by pests and diseases by early identification of varieties with weak density tolerance, and ultimately maximize the benefits of corn planting. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0054] Figure 2 It is a schematic diagram of the overall system module of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] 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.

[0057] Example:

[0058] See also Figure 1 , the present invention provides a technical solution:

[0059] A method for evaluating the density tolerance of corn varieties, comprising the following specific steps:

[0060] Step 1: Plant the corn variety to be evaluated in the set planting area according to the pre-set planting density. After the corn matures, divide the planting area into equal areas to form multiple sub-plots of equal areas;

[0061] The sub-field area is set in advance, and the number of field divisions is calculated by dividing the planting area by the sub-field area. The set sub-field area can make the number of field divisions 30 or more. Reasonable setting of the planting area and planting density can simulate the actual planting situation. The field division can ensure the randomness and representativeness of the sampling, providing a basis for the subsequent evaluation of density tolerance.

[0062] At the same time, the number of plots is determined by rounding down to ensure that the area of ​​each plot is not less than the set sub-plot area. Then, the center of the planting area is used as the starting reference point to evenly divide the planting area into N plots with an area of ​​A.

[0063] For example, select a block with an area of ​​S = 1000m 2 The planting area is set at a planting density of D = 5000 plants / hectare and a sub-plot area of ​​A = 100m 2 , then the number of divided plots is:

[0064]

[0065] Taking the center of the planting area as the starting reference point, the planting area is evenly divided into 10 areas with an area of ​​100m 2The corn seeds are evenly sown in the planting area according to the set planting density.

[0066] Step 2: In each sub-plot, a corn plant is selected by random sampling, and the starch content of each selected corn plant is counted respectively, and the result is recorded as the mean starch content of the corn population by calculating the average value;

[0067] Starch is one of the main components of corn, and its content directly affects the taste and edible quality of corn. A higher starch content usually means better taste and processing performance. Therefore, taking starch content as one of the important evaluation indicators can help breeders screen out varieties that are more in line with market demand and actual requirements of agricultural production. In the subsequent density tolerance evaluation formula, the power adjustment coefficient can be used to regulate the degree of influence of starch content on the density tolerance evaluation, thereby reflecting the different emphasis on starch content in different actual demand scenarios.

[0068] A corn plant was selected from each plot using random sampling according to the divided fields. For each corn plant, the corn kernels were peeled from the cobs and ground into uniform and fine powder. The weight of the corn powder was recorded, m. After adding 2 mol / L dilute hydrochloric acid solution, the powder was heated under reflux in a boiling water bath for 2 hours. The powder was cooled to room temperature and neutralized with 2 mol / L sodium hydroxide solution. After monitoring with a pH meter until the pH value reached neutrality, the powder was diluted to 100 ml with distilled water. 50 ml of the diluted solution was taken, and Fehling's reagent A solution and B solution were added and heated to boiling. The powder was titrated with 0.1 mol / L glucose solution, and the volume V of the standard glucose solution consumed was recorded.

[0069] According to n Glu =c·V, the amount of glucose consumed in the titration process is obtained, where c = 0.1 mol / L, and the starch mass is calculated:

[0070] m St =162·c·V

[0071] Then calculate the starch content of each corn plant sampled:

[0072]

[0073] Then the starch content of the corn samples was averaged to get the mean starch content of the corn population:

[0074]

[0075] For example, divide the planting area evenly into 10 areas, each with an area of ​​100m 2According to the divided fields, a corn plant was selected from each field by random sampling. A total of 10 corn plants were selected, of which the weight of corn powder was m = 50g, and the volume of standard glucose solution consumed was V = 10ml. The starch mass was calculated according to the formula:

[0076] m St =162·c·V=162*0.1*10=162

[0077] The sampled corn starch content is:

[0078]

[0079] Using the same starch detection method, the starch content of the other 9 samples of corn was calculated. Here, assuming that the total starch content of the other 9 samples of corn is 30g, the average starch content of the corn population is:

[0080]

[0081] The reason for using the random sampling method is that within the same field, there are genetic and growth and development differences among individual corn plants. Random sampling of each field can give corn plants in different growth states the opportunity to be selected, thereby more comprehensively reflecting the diversity and overall characteristics of the corn population in the field.

[0082] Step 3: Determine the total ear weight of all corns in the planting area, calculate the yield per unit area by the total ear weight and the area of ​​the planting area, count the number of diseased corn plants and the total number of corn plants in the planting area, and calculate the incidence of diseases and insect pests by the ratio of the number of diseased corn plants to the total number;

[0083] The yield per unit area directly reflects the production capacity of a corn variety under a specific planting density, and is also directly related to the economic benefits of corn planting. The higher the index value, the more effectively light, heat, water, fertilizer and other resources can be utilized under a certain planting density. Therefore, the yield per unit area is used as one of the core indicators for evaluating the density tolerance of corn varieties.

[0084] When the corn matures, the corn ears in the planting area are collected and weighed. The result is recorded as the total ear weight, and the yield per unit area is calculated by the formula:

[0085]

[0086] Assume that the total corn ear weight in the planting area is M = 500kg and the planting area is S = 1000m 2 , then the yield per unit area

[0087]

[0088] Corn varieties with strong density tolerance are usually better able to cope with pest and disease problems that may be caused by intensified competition among plants under high-density planting conditions. If the incidence of pests and diseases is high, the density tolerance assessment will also be reduced. This can avoid selecting varieties that have high yields under dense planting conditions but are susceptible to pests and diseases. Counting the total number of corn plants and the number of diseased corn plants can accurately calculate the incidence of pests and diseases.

[0089] Count the number of infected corn plants n in the planting area DP , the total number of corn plants n, where the total number of corn plants is calculated by the formula:

[0090] n=D·S

[0091] Calculate the incidence of pests and diseases:

[0092]

[0093] Step 4: Construct a density tolerance evaluation formula based on unit area yield, pest and disease incidence, and the average starch content of corn populations, and evaluate the density tolerance level of corn varieties based on the results;

[0094] Obtain the yield per unit area Y and the incidence rate of pests and diseases α from the yield and pests and diseases analysis module PI , obtain the mean starch content of the corn population ω from the content determination module St , construct the seal resistance evaluation formula:

[0095]

[0096] Among them, the value of R is based on the actual situation of the planting area, that is, the inhibitory effect of limiting factors such as soil fertility and climatic conditions on density tolerance. Different corn varieties have different responses to environmental factors such as soil fertility and climatic conditions, as well as fertilizer utilization efficiency. For example, varieties with strong environmental adaptability and high fertilizer absorption and utilization efficiency can appropriately reduce the value of the inhibition coefficient R, reflecting the advantages of this variety over other varieties under the same planting conditions. In this way, under the conditions of soils with different fertility levels or different climatic characteristics, the impact of actual planting conditions on the density tolerance of the variety can be reflected, which helps to more accurately screen the most suitable corn varieties in specific planting area environments.

[0097] Including yield Y in the calculation makes the impact of yield on density tolerance assessment more intuitive, providing the most direct basis for selecting high-yield and high-quality varieties and optimizing planting plans.

[0098] α PI It reflects the extent to which corn is damaged by pests and diseases during its growth. The occurrence of pests and diseases often interferes with the normal physiological metabolism of corn plants, ultimately affecting yield and quality. PIWith the gradual increase of (1 - α PI ), the value gradually decreases, indicating that the negative impact of pests and diseases on density tolerance increases gradually, and the density tolerance evaluation will be reduced due to the negative impact of pests and diseases, which is in line with the law of the impact of pests and diseases on maize growth and density tolerance in actual production.

[0099] ω St The maize variety is described in terms of quality characteristics. Starch is an important component of maize. Its content not only affects the taste and edible value of maize, but also is closely related to the processing uses of maize. In the density tolerance evaluation, it appears in the form of ω St d where the exponent d is used as an adjustment coefficient to meet the differences in the emphasis on starch content under different market demands and application scenarios. For example, for enterprises with high requirements for starch content, the exponent d can be set to a larger value, and the influence weight of the average starch content of the maize population in the density tolerance evaluation will increase significantly; while for some application scenarios that do not pay much attention to starch content, d can be set to a value close to 0, or directly set d = 0 to make the value of ω St d equal to 1, which is equivalent to ignoring the factor of starch content.

[0100] Finally, the density tolerance evaluation I can be scaled globally through the adjustment coefficient k to scale the result to a suitable range. Higher values of the result indicate varieties with better density tolerance, and lower values indicate varieties with poorer density tolerance. Through multiple experiments, data of several maize varieties were collected. The minimum value is 0.1 and the maximum value is 10. Since it is desired to scale to the target range [0, 100], the value of the adjustment coefficient is:

[0101]

[0102] Set the density tolerance level thresholds I1 = 30, I2 = 60, and I3 = 90.

[0103] When I < 30, the density tolerance is evaluated as poor; when 30 ≤ I ≤ 60, the density tolerance is evaluated as medium; when 60 < I ≤ 90, the density tolerance is evaluated as good; when I > 90, the density tolerance is evaluated as excellent.

[0104] For any given maize variety, its corresponding value directly reflects its density tolerance level. If a maize variety has a higher value, it means that the variety shows strong comprehensive advantages in terms of yield, pest and disease resistance, starch content maintenance, and coping with environmental and fertility limitations.

[0105] For example, in an evaluation scenario that focuses on starch content and regards yield as a relatively minor factor, by increasing the coefficient weight of the mean starch content of the corn population in the evaluation formula and evaluating the corn varieties, it is possible to accurately screen out varieties that have both good density tolerance and high starch content. The varieties determined in this way are more in line with actual application scenarios that have higher requirements for starch content.

[0106] If growers are in an area with a high incidence of pests and diseases and establish pest and disease resistance as the primary consideration, and pay relatively less attention to starch content, by targetedly increasing the weight of the influence coefficient of pest and disease incidence in the formula and conducting a comprehensive evaluation of each corn variety, they can find corn varieties that not only exhibit excellent density tolerance but also have pest and disease resistance.

[0107] See also Figure 2 The present invention further provides a device for evaluating the density tolerance of corn varieties, the device being used to perform the above-mentioned method for evaluating the density tolerance of corn varieties, comprising:

[0108] The planting planning module is used to plant the corn variety to be evaluated in the set planting area according to the pre-set planting density. After the corn matures, the planting area is divided into equal areas to form multiple sub-fields of equal area;

[0109] The content determination module is used to select a corn plant in each sub-field by random sampling, count the starch content of each selected corn plant, and record the result as the mean starch content of the corn population by calculating the average value;

[0110] The yield and pest analysis module is used to determine the total ear weight of all corns in the planting area, calculate the yield per unit area by the total ear weight and the area of ​​the planting area, count the number of diseased corn plants and the total number of corn plants in the planting area, and calculate the incidence of pests and diseases by the ratio of the number of diseased corn plants to the total number;

[0111] The density tolerance evaluation module is used to construct a density tolerance evaluation formula based on unit area yield, pest and disease incidence and average starch content of corn population, and evaluate the density tolerance level of corn varieties based on the results.

[0112] The present invention also provides a device for executing the above-mentioned method for evaluating the density tolerance of corn varieties, comprising:

[0113] A processor, and a memory connected to the processor;

[0114] The memory is used to store a computer program, and the computer program is used to execute at least one of the above-mentioned methods for evaluating the density tolerance of corn varieties;

[0115] The processor is used to call and execute the computer program in the memory.

[0116] The present invention also provides a storage medium, which stores a computer program. When the computer program is executed by a processor, each step of the method for evaluating the density tolerance of corn varieties in this embodiment is implemented.

[0117] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0118] 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.

[0119] 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.

[0120] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for evaluating the density tolerance of corn varieties, characterized in that: The specific steps include: Step 1: Plant the corn variety to be evaluated in the set planting area according to the pre-set planting density. After the corn matures, divide the planting area into equal areas to form multiple sub-plots of equal areas; Step 2: In each sub-plot, a corn plant is selected by random sampling, and the starch content of each selected corn plant is counted respectively, and the result is recorded as the mean starch content of the corn population by calculating the average value; Step 3: Determine the total ear weight of all corns in the planting area, calculate the yield per unit area by the total ear weight and the area of ​​the planting area, count the number of diseased corn plants and the total number of corn plants in the planting area, and calculate the incidence of diseases and insect pests by the ratio of the number of diseased corn plants to the total number; Step 4: Construct a density tolerance evaluation formula based on unit area yield, pest and disease incidence rate, and mean starch content of corn population, and evaluate the density tolerance level of corn varieties based on the results.

2. The method for evaluating the density tolerance of corn varieties according to claim 1, characterized in that: In step 1, the method of planting the corn variety to be tested according to the preset planting density is: Select a planting area, record its area as S, set the planting density as D, and sow corn seeds evenly in the planting area according to the planting density.

3. The method for evaluating the density tolerance of corn varieties according to claim 1, characterized in that: In step 1, the method for dividing the planting area into plots is: The sub-plot area is preset, and the planting area is divided into equal areas. The number of plot divisions is calculated by the sub-plot area and the planting area area: In the formula, N represents the number of plots divided, and the number of plots is determined by rounding down, S represents the planting area, and A represents the pre-set sub-plot area; Taking the center of the planting area as the starting reference point, the planting area is evenly divided into N plots of area A.

4. A method for evaluating the density tolerance of corn varieties according to claim 3, characterized in that: In step 2, the method for calculating the mean starch content of the corn population is: The starch content of each corn sampled is recorded as ω i , where i represents the number of the sub-field, i = 1, 2, 3, ..., N. The mean starch content of the corn population is calculated by the sampled corn starch content and the number of corn plants: In the formula, ω St It is expressed as the mean starch content of corn population, Wherein, measuring the corn starch content of the sample comprises the following steps: The corn kernels were peeled off from the cobs, and the kernels were ground into uniform and fine powder. The weight of the corn powder was recorded as m. After adding 2 mol / L dilute hydrochloric acid solution, the powder was heated and refluxed in a boiling water bath for 2 hours. After cooling to room temperature, the powder was neutralized with 2 mol / L sodium hydroxide solution. The pH value of the solution was monitored with a pH meter until the pH value of the solution reached neutrality. After distilling the solution to 100 ml with distilled water, take 50 ml of the solution, add Fehling's reagent A and B and heat to boiling, then titrate with 0.1 mol / L glucose solution until the blue color of the solution just fades and a brick-red precipitate appears, and record the volume V of the standard glucose solution consumed; According to n Glu =c·V, the amount of glucose consumed in the titration process is obtained, where c = 0.1 mol / L, and the starch mass is calculated: m St =162·c·V In the formula, m St Expressed as starch mass, 162 is the molar mass of starch, and then the starch content of each corn plant sampled is calculated:

5. The method for evaluating the density tolerance of corn varieties according to claim 1, characterized in that: In step 3, the method for calculating the yield per unit area is: Measure and record the total ear weight of corn in the planting area, and calculate the yield per unit area based on the total ear weight of the planting area and the set sub-plot area: Where Y represents the yield per unit area, and M represents the total corn ear weight in the planting area.

6. The method for evaluating the density tolerance of corn varieties according to claim 1, characterized in that: In step 3, the method for calculating the incidence of pests and diseases is: The total number of corn plants is calculated using the formula: n=D·S Where n represents the total number of corn plants in the planting area, D represents the planting density of the planting area, and S represents the area of ​​the planting area; Count the number of diseased corn plants and the total number of corn plants in the planting area, and record the ratio of the two as the incidence of pests and diseases: In the formula, α PI Expressed as the incidence of pests and diseases, n DP Expressed as the number of diseased corn plants in a growing area.

7. The method for evaluating the density tolerance of corn varieties according to claim 1, characterized in that: In step 4, an evaluation formula for density tolerance is constructed by using the average yield per unit area, the incidence of pests and diseases, and the average starch content of the corn population. The method for evaluating the density tolerance level is as follows: Extract the data of the average yield per unit area, the incidence of pests and diseases, and the average starch content of the corn population, and construct an evaluation formula for density tolerance: In the formula, R represents the inhibitory effect of limiting factors such as soil fertility and climatic conditions on density tolerance, the coefficient k represents the adjustment coefficient of the whole on the comprehensive index, the coefficient b represents the adjustment coefficient of pests and diseases on the comprehensive index, and the coefficient d represents the power adjustment coefficient of the average starch content of the corn population on the evaluation of density tolerance. Preset the density tolerance level thresholds I1, I2, and I3. When I < I1, the density tolerance is evaluated as poor; when I1 ≤ I ≤ I2, the density tolerance is evaluated as medium; when I2 < I ≤ I3, the density tolerance is evaluated as good; when I > I3, the density tolerance is evaluated as excellent.

8. A device for evaluating the density tolerance of corn varieties, characterized in that: The system is used to execute a method for evaluating the density tolerance of a corn variety according to any one of claims 1-7: A planting planning module, configured to plant the corn variety to be evaluated in a set planting area according to a preset planting density. After the corn matures, divide the planting area into equal-area subplots; A content measurement module, configured to randomly select one corn plant in each subplot, respectively count the starch content of each selected corn plant, and record the result as the average starch content of the corn population by calculating the average value; A yield and pest and disease analysis module, configured to measure the total ear weight of all corns in the planting area, calculate the yield per unit area through the total ear weight and the area of the planting area, count the number of diseased corn plants and the total number of corn plants in the planting area, and calculate the incidence of pests and diseases through the ratio of the number of diseased corn plants to the total number; A density tolerance evaluation module, configured to construct an evaluation formula for density tolerance by using the yield per unit area, the incidence of pests and diseases, and the average starch content of the corn population, and evaluate the density tolerance level of the corn variety according to the result.

9. A device, characterized in that: Including: A processor and a memory connected to the processor; The memory is used to store a computer program, and the computer program is at least used to execute the method for evaluating the density tolerance of a corn variety according to any one of claims 1-7; The processor is used to call and execute the computer program in the memory.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating the density tolerance of a corn variety according to any one of claims 1-7 is implemented.

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