Corn yield rapid estimation method, device and equipment based on corn plant morphology

Abstract

The application relates to the field of agricultural technology, and particularly discloses a corn yield rapid estimation method, device and equipment based on corn plant morphology, which comprises the following steps: obtaining corn plant morphology data 10 days before flowering to obtain a first above-ground biomass estimation result; obtaining corn plant morphology data 14 days after flowering to obtain a second above-ground biomass estimation result; based on the above estimation results, a plant growth rate estimation result in a yield formation critical period is obtained; and finally, based on a correlation model of the plant growth rate in the yield formation critical period and the corn yield and the growth rate estimation result, the corn yield is estimated. In this way, the corn yield can be directly estimated by using the corn plant morphology data surveyed in the field, and the method is simple and efficient. Moreover, since the plant growth rate in the yield formation critical period, i.e. 24 days before and after corn flowering, is determined, the most critical influence of drought stress on yield can be considered to the greatest extent, and the estimation accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of agricultural technology, specifically to a method, apparatus, and equipment for rapid estimation of maize yield based on maize plant morphology. Background Technology

[0002] Field surveys are a crucial tool for meteorological departments to understand crop growth, assess crop yield, and thus conduct disaster impact assessments and yield forecasts. However, currently, there is a lack of effective methods to directly estimate maize yield from field survey data, which significantly limits the application and value of this data. Therefore, developing a method for rapidly estimating maize yield based on maize plant morphology is of great significance for meteorological departments to conduct yield forecasting operations and for decision-making departments to promptly understand and grasp grain yield dynamics and rationally arrange production.

[0003] In existing technologies, the most commonly used yield forecasting methods in meteorological departments are statistical-based and crop growth model-based methods. However, both methods involve numerous parameters and meteorological data, and their prediction accuracy needs improvement under environmental stress. Therefore, there is currently a lack of technical methods that can quickly and efficiently estimate yields based on field survey data. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, apparatus and equipment for rapid estimation of maize yield based on maize plant morphology, so as to overcome the current problem of lack of a rapid and efficient method for estimating maize yield.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, this application provides a method for rapid estimation of maize yield based on maize plant morphology, including:

[0007] We obtained maize plant morphology data 10 days before flowering and estimated aboveground biomass to obtain the first aboveground biomass estimation result.

[0008] We obtained maize plant morphology data 14 days after flowering and estimated aboveground biomass to obtain the second aboveground biomass estimation result.

[0009] Based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, the plant growth rate during the critical period of yield formation is estimated, and the plant growth rate during the critical period of yield formation is estimated.

[0010] Based on the constructed correlation model between plant growth rate during the critical period of yield formation and maize yield, and the estimation results of plant growth rate during the critical period of yield formation, maize yield is estimated.

[0011] Furthermore, in some embodiments, the step of obtaining maize plant morphology data 10 days before flowering and estimating aboveground biomass to obtain a first aboveground biomass estimation result includes:

[0012] Select a first preset number of random corn plants, obtain the plant height and stem diameter of the random corn plants, and dry and weigh the stems and leaves of the random corn plants separately;

[0013] Based on the plant height, stem diameter, and weighing results, a first geometric model is constructed to determine the aboveground biomass using the plant height and stem diameter.

[0014] Select a second preset number of representative corn plants and obtain their plant height and stem diameter;

[0015] Based on the first geometric model and the plant height and stem diameter of representative maize plants, the first aboveground biomass estimation result was obtained.

[0016] Furthermore, in some embodiments, the step of obtaining maize plant morphology data 14 days after flowering and estimating aboveground biomass to obtain a second aboveground biomass estimation result includes:

[0017] Select a first preset number of random corn plants, obtain the plant height, stem diameter, ear length and ear diameter of the random corn plants, and dry and weigh the stems, leaves, ears and husks of the random corn plants separately;

[0018] Based on the plant height, stem diameter, ear diameter, ear length, and weighing results, a second geometric model is constructed to determine the aboveground biomass using the plant height, stem diameter, ear diameter, and ear length.

[0019] Select a second preset number of representative corn plants and obtain their plant height, stem diameter, ear diameter, and ear length;

[0020] Based on the second geometric model and the plant height, stem diameter, ear diameter, and ear length of representative maize plants, the second aboveground biomass estimation results were obtained.

[0021] Furthermore, in some embodiments, it also includes:

[0022] In the representative area, a third preset number of target areas of the same area are planned. In each target area, corn plants are selected and marked. The marked corn plants are used as representative corn plants.

[0023] Further, in some embodiments, the step of estimating the plant growth rate during the critical period of yield formation based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, to obtain the plant growth rate estimation result during the critical period of yield formation, includes:

[0024] For each maize plant, the difference between its second aboveground biomass estimate and its first aboveground biomass estimate is divided by the number of days between the two samplings to obtain the plant growth rate estimate for the critical period of yield formation.

[0025] The mean of the estimated plant growth rate during the critical period of yield formation for all representative maize plants is taken as the final estimated plant growth rate during the critical period of yield formation.

[0026] Furthermore, in some embodiments, the correlation model is implemented based on empirical statistical equations.

[0027] Secondly, this application provides a rapid corn yield estimation device based on corn plant morphology, comprising:

[0028] The first estimation module is used to obtain maize plant morphology data 10 days before flowering and to estimate aboveground biomass to obtain the first aboveground biomass estimation result.

[0029] The second estimation module is used to obtain maize plant morphology data 14 days after flowering and to estimate aboveground biomass, thus obtaining the second aboveground biomass estimation result.

[0030] The third estimation module is used to estimate the plant growth rate during the critical period of yield formation based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, and obtain the plant growth rate estimation result during the critical period of yield formation.

[0031] The fourth estimation module is used to estimate maize yield based on the constructed correlation model between plant growth rate during the yield formation critical period and maize yield, and the estimation results of plant growth rate during the yield formation critical period.

[0032] Thirdly, this application provides a rapid corn yield estimation device based on corn plant morphology, including a processor and a memory, wherein the processor is connected to the memory:

[0033] The processor is used to call and execute the program stored in the memory;

[0034] The memory is used to store the program, which is at least used to execute the above-described method for rapid estimation of maize yield based on maize plant morphology.

[0035] This invention relates to the field of agricultural technology, specifically to a method, apparatus, and equipment for rapid estimation of maize yield based on maize plant morphology. The method includes: acquiring maize plant morphology data 10 days before flowering and estimating aboveground biomass to obtain a first aboveground biomass estimation result; acquiring maize plant morphology data 14 days after flowering and estimating aboveground biomass to obtain a second aboveground biomass estimation result; estimating the plant growth rate during the critical period of yield formation based on the first and second aboveground biomass estimation results to obtain a plant growth rate estimation result during the critical period of yield formation; and finally, estimating the maize yield based on a constructed correlation model between the plant growth rate during the critical period of yield formation and maize yield, and the plant growth rate estimation result during the critical period of yield formation. Thus, maize yield can be rapidly and efficiently estimated and predicted using maize plant morphology data obtained from field surveys. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a method for rapid estimation of maize yield based on maize plant morphology provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of the rapid corn yield estimation device based on corn plant morphology provided in an embodiment of the present invention.

[0039] Figure 3 This is a schematic diagram of the structure of a rapid corn yield estimation device based on corn plant morphology provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0041] Figure 1 This is a flowchart of a rapid maize yield estimation method based on maize plant morphology provided in an embodiment of the present invention. Please refer to [link / reference]. Figure 1 This embodiment may include the following steps:

[0042] S101. Obtain maize plant morphology data 10 days before flowering and estimate aboveground biomass to obtain the first aboveground biomass estimation result.

[0043] This allows for the estimation of aboveground biomass of corn plants 10 days before flowering.

[0044] S102. Obtain maize plant morphology data 14 days after flowering and estimate aboveground biomass to obtain the second aboveground biomass estimation result.

[0045] This allows for the estimation of aboveground biomass 14 days after flowering in maize plants.

[0046] S103. Based on the first and second aboveground biomass estimation results, the plant growth rate during the critical period of yield formation is estimated, and the plant growth rate during the critical period of yield formation is estimated.

[0047] S104. Based on the constructed correlation model between plant growth rate during the critical period of yield formation and maize yield, and the estimation results of plant growth rate during the critical period of yield formation, estimate maize yield.

[0048] Among them, the correlation model can be an empirical statistical equation determined based on historical data. By substituting the estimated plant growth rate during the critical period of yield formation into the empirical statistical equation, the estimated corn yield can be obtained.

[0049] The rapid maize yield estimation method based on maize plant morphology provided in this application obtains maize plant morphology data 10 days before flowering and estimates aboveground biomass to obtain a first aboveground biomass estimation result; it then obtains maize plant morphology data 14 days after flowering and estimates aboveground biomass to obtain a second aboveground biomass estimation result; based on the first and second aboveground biomass estimation results, it estimates the plant growth rate during the critical period of yield formation to obtain the plant growth rate estimation result during the critical period of yield formation; finally, based on the constructed correlation model between the plant growth rate during the critical period of yield formation and maize yield, and the plant growth rate estimation result during the critical period of yield formation, it estimates the maize yield. In this way, yield estimation can be directly performed using maize plant morphology data obtained from field surveys, which is simple and efficient. Furthermore, since the plant growth rate during the critical period of yield formation (24 days before and after maize flowering) is determined, the most critical impact of drought stress on yield can be considered to the greatest extent, improving the accuracy of the estimation.

[0050] It should be noted that the approximately 24 days before and after corn flowering are the critical period for yield formation. Meteorological conditions before and after flowering determine the amount of dry matter accumulated in the ear, thus determining the silking time. Corn is a sink-limited crop; the 12-15 days after female flower fertilization is the kernel formation period. Meteorological conditions and plant growth during this period determine the number and size of corn kernels. Drought stress during this period will affect kernel volume expansion and is detrimental to continued grain filling. Therefore, changes in corn kernel weight are highly dependent on the potential kernel formation during the kernel formation period. In summary, meteorological conditions during the approximately 24 days before and after corn flowering (flowering and kernel formation period) play a crucial role in the formation of corn kernel number and weight; therefore, this period (approximately 24 days before and after corn flowering) is called the "critical period for yield formation."

[0051] The plant growth rate during the critical period of yield formation largely determines maize yield. Silking time is primarily determined by the accumulation of dry matter in the ear, and is therefore significantly influenced by the plant growth rate (PGR) before and after flowering. Environmental stresses affecting PGR, such as drought, delay silking time. At the canopy scale, the responses of maize flowering and silking times to a decrease in PGR differ. A decrease in PGR has little effect on the flowering time of the tassel, but it delays the silking time of the female ear. The lower the PGR, the greater the delay in silking. Drought stress has a greater impact on silking delay than on flowering, resulting in flowering-silking intervals ranging from 3 to 15 days. Furthermore, the critical stage determining the number of kernels and potential kernel size in maize is the same (i.e., the kernel formation period). Therefore, the plant growth rate during the critical period of yield formation largely determines maize yield.

[0052] In summary, aboveground biomass of maize can be estimated through plant morphology. Previous research reports and relevant field trial data confirm that aboveground biomass 10 days before and 14 days after flowering can be fitted using morphological parameters such as stem diameter, ear length, and ear diameter, thus achieving the goal of estimating aboveground biomass of maize plants through non-destructive sampling. Based on the above principle, this application proposes a method to estimate yield by calculating the aboveground biomass from 10 days before to 14 days after flowering, and then calculating the growth rate during the critical period of yield formation. This fully utilizes field survey data to achieve quantitative estimation of maize yield.

[0053] Furthermore, in this application, the aboveground biomass estimation can be divided into two parts: 10 days before flowering and 14 days after flowering. Each of these parts is further divided into two components: determining the geometric model through destructive sampling observation, and then combining the geometric model and non-destructive sampling observation data through non-destructive sampling observation. The aboveground biomass estimation will be described in detail below:

[0054] First, for the 10 days before flowering, the following steps are included:

[0055] First, a first predetermined number of random maize plants are selected, and their plant height and stem diameter are obtained. The stems and leaves of the random maize plants are then dried and weighed (i.e., destructive sampling observation). Then, based on the plant height, stem diameter, and weighing results, a first geometric model for determining aboveground biomass using plant height and stem diameter is constructed (the model includes at least one of plant height and stem diameter). Next, a second predetermined number of representative maize plants are selected, and their plant height and stem diameter are obtained (i.e., non-destructive sampling observation). Finally, based on the first geometric model and the plant height and stem diameter of the representative maize plants, the first aboveground biomass estimation result is obtained.

[0056] For example, destructive sampling observation is first conducted, that is, 10 days before flowering (when there are no ears), randomly select 10-12 (i.e., the first preset number) of aboveground parts of corn plants, observe their plant height and stem diameter, separate them into stems and leaves, and dry and weigh them; establish a geometric model of aboveground biomass, i.e., the first geometric model. The reference model is as follows: S0 = a + b SD * PH, where PH represents plant height, SD represents stem diameter, and S0 represents the aboveground biomass 10 days before flowering, i.e., the estimated result of the first aboveground biomass (where a and b are constants that can be calculated and determined based on the above data).

[0057] Taking a certain variety, such as the sparsely planted, large-ear maize variety Danyu 405 grown in western Liaoning, as an example, the aboveground biomass and stem diameter of this variety in the 10 days before flowering showed a good linear relationship. The fitting formula (S0: n=36, P<0.01, R 2 =0.60) is:

[0058] s0=6.057SD-40.635 (1)

[0059] The unit of SD is millimeters. Once the above model is established, it can be used continuously without destructive sampling, provided that the maturity type of the corn variety remains unchanged.

[0060] Then, non-destructive sampling observation is carried out. For example, three plots of 10m*10m are set up in the generation area (i.e., target areas of the same area). Ten corn plants are selected consecutively in each plot and marked, for a total of 30 plants (i.e., the second preset number of representative corn plants). Ten days before flowering, the plant height and stem diameter are measured, and the aboveground biomass is estimated using formula (1) to obtain the above-mentioned first aboveground biomass estimation result. In addition, in some other embodiments of this application, the above-mentioned formula (1) can also be replaced by a self-established aboveground biomass model 10 days before flowering for estimation.

[0061] Second, based on the same principle (but with different data), morphological data of maize plants 14 days after flowering were obtained, and aboveground biomass was estimated to obtain a second aboveground biomass estimation result. Specifically, this included: selecting a first preset number of random maize plants, obtaining the plant height, stem diameter, ear length, and ear diameter of the random maize plants, and drying and weighing the stems, leaves, ears, and husks of the random maize plants; based on the plant height, stem diameter, ear diameter, ear length, and weighing results, constructing a second geometric model that determines aboveground biomass by plant height, stem diameter, ear diameter, and ear length; selecting a second preset number of representative maize plants, and obtaining their plant height, stem diameter, ear diameter, and ear length; based on the second geometric model and the plant height, stem diameter, ear diameter, and ear length of the representative maize plants, obtaining the second aboveground biomass estimation result.

[0062] For example, destructive sampling observation is first conducted, that is, 14 days after flowering, 10-12 corn plants are randomly selected from the aboveground parts to observe plant height, stem diameter, ear length, and ear diameter; the aboveground parts are then separated into stems, leaves, ears, and husks, dried, and weighed; a geometric model of aboveground biomass and ear weight, i.e., the second geometric model, is established. Reference model: S1 = a + b(SD PH) d +c(ED EL) f In this context, ED represents ear diameter, EL represents ear length, and S1 represents the estimated aboveground biomass 14 days after flowering, i.e., the first aboveground biomass estimate.

[0063] Taking a certain variety, such as the sparsely planted, large-ear maize variety Danyu 405 grown in western Liaoning, as an example, the fitting formula for the aboveground biomass of this variety 14 days after flowering (S1: n=36, P<0.01, R 2 =0.54) is:

[0064] S1 = 147.544 + 2.185E-8SD 6087 +7.316(ED×EL) 0.29 (2)

[0065] In the formula, SD and ED are in millimeters, and EL is in centimeters. Similarly, once the above model is established, it can be used continuously without destructive sampling, provided that the maturity type of the maize variety remains unchanged.

[0066] Then, non-destructive sampling and observation were carried out. 14 days after flowering, the 30 marked corn plants were observed, and the plant height, stem diameter, ear length, and ear diameter were observed. The above-ground biomass was estimated using the above formula (2) to obtain the above-mentioned first above-ground biomass estimation result. In addition, in some other embodiments of this application, the above-mentioned formula (2) can also be used to estimate the above-mentioned biomass by using a self-established above-ground biomass model 10 days before flowering.

[0067] Based on this, the plant growth rate during the critical period of yield formation is estimated using the first and second aboveground biomass estimation results. Specifically, for each maize plant, the difference between its second and first aboveground biomass estimation results is divided by the number of days between the two samplings to obtain the plant growth rate estimation result during the critical period of yield formation. The mean of the plant growth rate estimation results during the critical period of yield formation for all representative maize plants is taken as the final plant growth rate estimation result during the critical period of yield formation.

[0068] The yield-forming critical period plant growth rate (PGR) of each representative maize plant is estimated by dividing the difference in aboveground biomass (g / plant) between 14 days after flowering and 10 days before flowering by the number of days between the two samplings. Based on this, the PGR of maize plants in the representative area is taken as the average of the PGR of 30 marked maize plants.

[0069] Then, based on the constructed correlation model between plant growth rate during the yield formation critical period and maize yield, and the estimated results of plant growth rate during the yield formation critical period obtained above, maize yield is estimated. In practical applications, the correlation model can be implemented based on empirical statistical equations.

[0070] For example, taking the experimental data obtained from the drought stress experiment in Jinzhou area, the formula for estimating maize yield (i.e., Y) from PGR is as follows (Y: n=5, P<0.01, R 2 =0.53):

[0071] Y = 1736.52PGR - 1480.8 (3)

[0072] The rapid yield estimation method for maize based on maize plant morphology provided in this application offers a highly practical method for estimating yield based on yield survey data (i.e., maize plant morphology), starting from the perspective of the environmental control mechanism during the critical period of maize yield formation.

[0073] After back-substitution and independent sample testing of four years of field trial data in actual production environments, this method has been confirmed to have high accuracy in estimating maize yield under drought stress, solving the problem of low yield forecast accuracy of other methods such as crop models in drought years. Furthermore, this method can provide a relatively accurate yield estimate 14 days after maize flowering (i.e., early August, approximately 40 days before maturity), providing strong support for agricultural meteorological yield forecasting. Four years of data modeling and testing have demonstrated that this method is quick, convenient, and accurate in estimating maize yield. Specific verification data are as follows:

[0074] The yield estimation back-substitution accuracy rate was 94%: Based on the drought stress control experiment data from 2020 to 2022, the PGR of the two experimental treatments in 2020 were 3.23 and 3.48 g / plant / day, respectively; the PGR of one experimental treatment in 2021 was 3.6 g / plant / day; and the PGR of the two experimental treatments in 2022 were 3.52 and 3.85 g / plant / day, respectively. Using the above formula (3), the yield estimation back-substitution accuracy rates of maize for each experimental treatment were obtained as 98%, 91%, 87%, 98%, and 98%, respectively, with an average back-substitution accuracy rate of 94%.

[0075] The accuracy rate of yield estimation in 2023 was 92%: The drought stress test data in 2023 was used as an independent test sample. The critical period PGR of maize yield formation calculated by formula (1) and (2) was 3.43 g / plant / day. After substituting into formula (3), the yield estimate was obtained. After comparison with the actual yield, the accuracy rate was 92%.

[0076] Based on the same inventive concept, this application also provides a rapid corn yield estimation device based on corn plant morphology, used to implement the above-described method embodiments. Figure 2 This is a schematic diagram of the structure of the rapid corn yield estimation device based on corn plant morphology provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the rapid corn yield estimation device based on corn plant morphology provided in this application specifically includes:

[0077] The first estimation module 21 is used to acquire maize plant morphology data 10 days before flowering and estimate aboveground biomass to obtain the first aboveground biomass estimation result; the second estimation module 22 is used to acquire maize plant morphology data 14 days after flowering and estimate aboveground biomass to obtain the second aboveground biomass estimation result; the third estimation module 23 is used to estimate the plant growth rate during the yield formation critical period based on the first and second aboveground biomass estimation results to obtain the plant growth rate estimation result during the yield formation critical period; the fourth estimation module 24 is used to estimate maize yield based on the constructed correlation model between the plant growth rate during the yield formation critical period and maize yield, and the plant growth rate estimation result during the yield formation critical period.

[0078] Based on the same inventive concept, this application also provides a rapid corn yield estimation device based on corn plant morphology, used to implement the above-described method embodiments. Figure 3 This is a schematic diagram of the structure of the rapid corn yield estimation device based on corn plant morphology provided in an embodiment of the present invention, as shown below. Figure 3As shown, the rapid corn yield estimation device based on corn plant morphology provided in this application specifically includes: a processor 31 and a memory 32, with the processor 31 connected to the memory 32. The processor 31 is used to call and execute the program stored in the memory 32; the memory 32 is used to store the program, which is at least used to execute the rapid corn yield estimation method based on corn plant morphology in the above embodiments.

[0079] The specific implementation scheme of the corn yield rapid estimation device based on corn plant morphology provided in this application embodiment can refer to the implementation scheme of the corn yield rapid estimation method based on corn plant morphology in any of the above embodiments, and will not be repeated here.

[0080] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0081] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0082] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0083] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0084] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0085] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0086] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0087] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0088] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A rapid method for estimating maize yield based on maize plant morphology, characterized in that, include: We obtained maize plant morphology data 10 days before flowering and estimated aboveground biomass to obtain the first aboveground biomass estimation result. We obtained maize plant morphology data 14 days after flowering and estimated aboveground biomass to obtain the second aboveground biomass estimation result. Based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, the plant growth rate during the critical period of yield formation is estimated, and the plant growth rate during the critical period of yield formation is estimated. Based on the constructed correlation model between plant growth rate during the critical period of yield formation and maize yield, and the estimation results of plant growth rate during the critical period of yield formation, maize yield is estimated. The process of obtaining maize plant morphology data 10 days before flowering and estimating aboveground biomass to obtain the first aboveground biomass estimation result includes: Select a first preset number of random corn plants, obtain the plant height and stem diameter of the random corn plants, and dry and weigh the stems and leaves of the random corn plants separately; Based on the plant height, stem diameter, and weighing results, a first geometric model is constructed to determine the aboveground biomass using the plant height and stem diameter. Select a second preset number of representative corn plants and obtain their plant height and stem diameter; Based on the first geometric model and the plant height and stem diameter of representative maize plants, the first aboveground biomass estimation result is obtained. The estimation of plant growth rate during the critical period of yield formation is performed based on the first and second aboveground biomass estimation results, resulting in the following: For each maize plant, the difference between its second aboveground biomass estimate and its first aboveground biomass estimate is divided by the number of days between the two samplings to obtain the plant growth rate estimate for the critical period of yield formation. The mean of the estimated plant growth rate during the yield formation critical period of all representative maize plants is taken as the final estimated plant growth rate during the yield formation critical period. The correlation model is based on empirical statistical equations.

2. The method for rapid estimation of maize yield based on maize plant morphology according to claim 1, characterized in that, The process of obtaining maize plant morphology data 14 days after flowering and estimating aboveground biomass to obtain a second aboveground biomass estimation result includes: Select a first preset number of random corn plants, obtain the plant height, stem diameter, ear length and ear diameter of the random corn plants, and dry and weigh the stems, leaves, ears and husks of the random corn plants separately; Based on the plant height, stem diameter, ear diameter, ear length, and weighing results, a second geometric model is constructed to determine the aboveground biomass using the plant height, stem diameter, ear diameter, and ear length. Select a second preset number of representative corn plants and obtain their plant height, stem diameter, ear diameter, and ear length; Based on the second geometric model and the plant height, stem diameter, ear diameter, and ear length of representative maize plants, the second aboveground biomass estimation results were obtained.

3. The method for rapid estimation of maize yield based on maize plant morphology according to claim 1, characterized in that, Also includes: In the representative area, a third preset number of target areas of the same size are planned. In each target area, corn plants are selected and marked. The marked corn plants are used as representative corn plants.

4. A rapid yield estimation device for maize based on maize plant morphology, used to perform the method as described in any one of claims 1-3, characterized in that, include: The first estimation module is used to obtain maize plant morphology data 10 days before flowering and to estimate aboveground biomass to obtain the first aboveground biomass estimation result. The second estimation module is used to obtain maize plant morphology data 14 days after flowering and to estimate aboveground biomass, thus obtaining the second aboveground biomass estimation result. The third estimation module is used to estimate the plant growth rate during the critical period of yield formation based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, and obtain the plant growth rate estimation result during the critical period of yield formation. The fourth estimation module is used to estimate maize yield based on the constructed correlation model between plant growth rate during the yield formation critical period and maize yield, and the estimation results of plant growth rate during the yield formation critical period.

5. A rapid yield estimation device for maize based on maize plant morphology, characterized in that, It includes a processor and a memory, wherein the processor is connected to the memory: The processor is used to call and execute the program stored in the memory; The memory is used to store the program, which is at least used to execute the rapid corn yield estimation method based on corn plant morphology as described in any one of claims 1-3.

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