Method, device and equipment for quickly estimating corn yield based on corn plant morphology

Abstract

The invention relates to the technical field of agriculture, and particularly discloses a corn yield rapid estimation method, device and equipment based on corn plant morphology, the method comprises the following steps: obtaining corn plant morphology data 10 days before flowering, and estimating to obtain a first above-ground biomass estimation result; obtaining corn plant morphology data 14 days after flowering, and estimating to obtain a second aboveground biomass estimation result; estimating a plant growth rate estimation result in a yield formation critical period based on the estimation result; and finally, based on the constructed yield, forming a correlation model of the plant growth rate and the corn yield in the critical period and an estimation result of the growth rate, and estimating the corn yield. Thus, the corn plant morphology data investigated in the field can be directly utilized to perform yield estimation, the method is simple and efficient, meanwhile, the key period of yield formation, namely the plant growth rate of 24 days before and after corn flowering, is determined, the most critical influence of drought stress on the yield can be considered to the greatest extent, and the estimation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to a method, a device and equipment for quickly estimating corn yield based on corn plant morphology. Background Art

[0002] Field survey is an important means for meteorological departments to understand crop growth, assess crop yields, and carry out disaster impact assessment and yield forecasting. However, there is currently a lack of effective methods to directly estimate corn yields from field survey data, which greatly limits the application and value of field survey data. Therefore, developing a method for quickly estimating yields based on corn plant morphology is of great significance for meteorological departments to carry out yield forecasting business and for decision-making departments to timely understand and grasp the dynamics of grain yields and arrange production reasonably.

[0003] In the existing technology, among the technical methods currently used by meteorological departments to carry out yield forecasting business, the most commonly used methods in meteorological departments are the yield forecasting method based on statistics and the yield forecasting method based on crop growth model. However, both methods involve many parameters and meteorological data when applied, and the prediction accuracy needs to be improved under environmental stress. Therefore, there is currently a lack of technical methods for fast and efficient yield estimation based on field survey data. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a method, device and equipment for quickly estimating corn yield based on corn plant morphology, so as to overcome the problem of the current lack of a method for quickly and efficiently estimating corn yield.

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

[0006] In a first aspect, the present application provides a method for quickly estimating corn yield based on corn plant morphology, comprising:

[0007] Acquire the morphological data of corn plants 10 days before flowering, and estimate the aboveground biomass to obtain the first aboveground biomass estimation result;

[0008] Obtaining the morphological data of corn plants 14 days after flowering, and estimating the aboveground biomass, to obtain a second aboveground biomass estimation result;

[0009] Based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, estimating the plant growth rate during the key period of yield formation, and obtaining an estimation result of the plant growth rate during the key period of yield formation;

[0010] The corn yield is estimated based on the constructed correlation model between the plant growth rate during the critical period of yield formation and the corn yield, and the estimated result of the plant growth rate during the critical period of yield formation.

[0011] Further, in some embodiments, the step of obtaining the morphological data of corn plants 10 days before flowering and estimating the aboveground biomass to obtain a first aboveground biomass estimation result includes:

[0012] Selecting a first preset number of random corn plants, obtaining plant heights and stem diameters of the random corn plants, and drying and weighing the stems and leaves of the random corn plants;

[0013] Based on the plant height, the stem diameter and the weighing result, constructing a first geometric model for determining aboveground biomass from the plant height and the stem diameter;

[0014] Selecting a second preset number of representative corn plants and obtaining their plant heights and stem diameters;

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

[0016] Further, in some embodiments, the obtaining of corn plant morphological data 14 days after flowering and performing aboveground biomass estimation to obtain a second aboveground biomass estimation result includes:

[0017] Selecting a first preset number of random corn plants, obtaining plant height, stem thickness, ear length and ear thickness of the random corn plants, and drying and weighing the stems, leaves, ears and husks of the random corn plants;

[0018] Based on the plant height, the stem diameter, the ear diameter, the ear length and the weighing results, constructing a second geometric model for determining aboveground biomass by the plant height, the stem diameter, the ear diameter and the ear length;

[0019] Selecting a second preset number of representative corn plants and obtaining the plant height, stem diameter, ear diameter, and ear length thereof;

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

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

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

[0023] Further, in some embodiments, the estimating the plant growth rate in 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 in the critical period of yield formation includes:

[0024] For each corn plant, the difference between the second aboveground biomass estimation result and the first aboveground biomass estimation result is divided by the number of days between the two corresponding samplings to serve as the estimation result of the plant growth rate in the key period of yield formation of the corn plant;

[0025] The average of the estimated results of the plant growth rate during the critical period of yield formation of all representative corn plants is taken as the final estimated result of the plant growth rate during the critical period of yield formation.

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

[0027] In a second aspect, the present application provides a device for quickly estimating corn yield based on corn plant morphology, comprising:

[0028] The first estimation module is used to obtain the morphological data of corn plants 10 days before flowering, and to estimate the aboveground biomass to obtain a first aboveground biomass estimation result;

[0029] The second estimation module is used to obtain the morphological data of corn plants 14 days after flowering, and to estimate the aboveground biomass to obtain a second aboveground biomass estimation result;

[0030] A third estimation module is used to estimate the plant growth rate in the key period of yield formation based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, so as to obtain the plant growth rate estimation result in the key period of yield formation;

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

[0032] In a third aspect, the present application provides a device for quickly estimating corn yield based on corn plant morphology, including a processor and a memory, wherein the processor is connected to the memory:

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

[0034] The memory is used to store the program, and the program is at least used to execute the above-mentioned method for quickly estimating corn yield based on corn plant morphology.

[0035] The present invention relates to the field of agricultural technology, and in particular to a method, device and equipment for quickly estimating corn yield based on corn plant morphology. The method comprises: obtaining corn plant morphology data 10 days before flowering, and performing aboveground biomass estimation to obtain a first aboveground biomass estimation result; obtaining corn plant morphology data 14 days after flowering, and performing aboveground biomass estimation to obtain a second aboveground biomass estimation result; based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, estimating the plant growth rate in the key period of yield formation to obtain the plant growth rate estimation result in the key period of yield formation; finally, estimating corn yield based on a constructed correlation model between the plant growth rate in the key period of yield formation and corn yield, and the plant growth rate estimation result in the key period of yield formation. In this way, the corn plant morphology data obtained from field surveys can be directly used to achieve a fast and efficient estimation and prediction of corn yield. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 is a flow chart of a method for rapidly estimating corn yield based on corn plant morphology provided by an embodiment of the present invention;

[0038] Figure 2 is a schematic structural diagram of a device for rapidly estimating corn yield based on corn plant morphology provided by an embodiment of the present invention;

[0039] Figure 3 It is a schematic diagram of the structure of a device for quickly estimating corn yield based on corn plant morphology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0041] Figure 1 is a flowchart of a method for rapidly estimating corn yield based on corn plant morphology provided by an embodiment of the present invention. Figure 1 , this embodiment may include the following steps:

[0042] S101. Acquire morphological data of corn plants 10 days before flowering, and estimate aboveground biomass to obtain a first aboveground biomass estimation result.

[0043] That is, the aboveground biomass of corn plants 10 days before flowering can be estimated.

[0044] S102, obtaining morphological data of corn plants 14 days after flowering, and estimating aboveground biomass to obtain a second aboveground biomass estimation result.

[0045] That is, the aboveground biomass of corn plants 14 days after flowering can be estimated.

[0046] S103. 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 an estimation result of the plant growth rate during the critical period of yield formation.

[0047] S104. Estimate corn yield based on the constructed correlation model between plant growth rate during the critical period of yield formation and corn yield, and the estimated results of plant growth rate during the critical period of yield formation.

[0048] The correlation model may be an empirical statistical equation determined based on historical data, and the estimated result of corn yield is obtained by substituting the estimated result of plant growth rate in the critical period of yield formation to be estimated into the empirical statistical equation.

[0049] The method for rapidly estimating corn yield based on corn plant morphology provided in the present application obtains the corn plant morphology data 10 days before flowering and estimates the aboveground biomass to obtain a first aboveground biomass estimation result; obtains the corn plant morphology data 14 days after flowering and estimates the aboveground biomass to obtain a second aboveground biomass estimation result; estimates the plant growth rate in the key period of yield formation based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, and obtains the plant growth rate estimation result in the key period of yield formation; finally, estimates the corn yield based on the constructed correlation model between the plant growth rate in the key period of yield formation and the corn yield, and the plant growth rate estimation result in the key period of yield formation. In this way, the corn plant morphology data of the field survey can be directly used for yield estimation, which is simple and efficient. At the same time, because the plant growth rate in the key period of yield formation, i.e., 24 days before and after corn flowering, is determined, the most critical impact of drought stress on yield can be considered to the greatest extent, thereby improving the estimation accuracy.

[0050] It should be noted that the 24 days before and after the flowering of corn is the critical period for yield formation. The meteorological conditions before and after the flowering period of corn determine the accumulation of dry matter in the cob, thereby determining the silking time. Corn is a sink-limited crop. The 12 to 15 days after the female flower is fertilized is the grain formation period. The meteorological conditions and plant growth conditions during this period determine the number and size of corn grains. Drought stress during this period will affect the expansion of the grain volume and is not conducive to continued filling. Therefore, the change in corn grain weight is highly dependent on the potential formation of grains during the grain formation period. In summary, the meteorological conditions about 24 days before and after the flowering of corn (flowering period and grain formation period) play an important role in the formation of corn grain number and grain weight. Therefore, this period (i.e., about 24 days before and after the flowering of corn) is called the "critical period for yield formation."

[0051] The plant growth rate during the critical period of yield formation largely determines the corn yield. The silking time is mainly determined by the accumulation of dry matter in the cob, and is therefore largely affected by the plant growth rate (PGR) before and after the flowering period. Environmental stresses that affect PGR, such as drought, will delay the silking time. At the canopy scale, the responses of corn flowering and silking time to the decline in PGR are different. The decline in PGR has little effect on the flowering time of the male ear, but has a delayed effect on the silking time of the female ear. The smaller the PGR, the more days of silking are delayed. The effect of drought stress on silking delay is more serious than that on flowering, resulting in flowering and silking intervals ranging from 3 to 15 days. In addition, the key stages that determine the number of corn kernels and potential kernel size are the same (i.e., the kernel formation period). Therefore, the plant growth rate during the critical period of yield formation largely determines the corn yield.

[0052] In summary, the aboveground biomass of corn can be estimated by plant morphology. Combined with previous research reports and related field test data, it is confirmed that the aboveground biomass of corn 10 days before flowering and 14 days after flowering can be fitted by morphological parameters such as plant stem thickness, ear length, and ear thickness, so as to achieve the purpose of estimating the aboveground biomass of corn plants by non-destructive sampling. This application is based on the above-mentioned principle, from the perspective of convenient application of field survey data to estimate yield, proposes to estimate the aboveground biomass of corn from 10 days before flowering to 14 days after flowering, and then calculates the growth rate of the plant during the critical period of yield formation to quickly estimate the yield, making full use of field survey data to achieve quantitative estimation of corn yield.

[0053] Further, in this application, the estimation of aboveground biomass can be divided into two parts, namely, 10 days before flowering and 14 days after flowering. Each of the above parts is further divided into two parts, namely, determining the geometric model through destructive sampling observation, and then conducting non-destructive sampling observation to combine the obtained geometric model and non-destructive sampling observation data. For the estimation of aboveground biomass, the following is a detailed introduction:

[0054] First, for the first 10 days of flowering, include the following steps:

[0055] First, a first preset number of random corn plants are selected, the plant height and stem diameter of the random corn plants are obtained, and the random corn plants are separated into stems and leaves for drying and weighing (i.e., destructive sampling observation); then, based on the plant height, stem diameter and weighing results, a first geometric model for determining the aboveground biomass by plant height and stem diameter is constructed (the model includes at least one of plant height and stem diameter); then, a second preset number of representative corn 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 corn plants, a first aboveground biomass estimation result is obtained.

[0056] For example, firstly, destructive sampling observation is carried out, that is, 10 days before flowering (no fruit ears at this time), 10-12 (i.e., the first preset number) plants of the aboveground part of corn plants are randomly selected, and their plant height and stem thickness are observed, and they are divided into stems and leaves, and dried and weighed; and an aboveground biomass geometric model, i.e., the first geometric model, is established. The reference model is as follows: S0=a+b SD*PH, where PH represents plant height, SD represents stem thickness, and S0 represents the aboveground biomass 10 days before flowering, i.e., the first aboveground biomass estimation result (where a and b are constants, which can be calculated and statistically determined based on the above data).

[0057] Taking a certain variety in a certain region, such as Danyu 405, a sparsely planted large-ear corn variety grown in western Liaoning, as an example, the aboveground biomass of this variety 10 days before flowering had a good linear relationship with stem diameter, and its 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 millimeter. After the above model is established, it can continue to be used if the maturity of the corn variety remains unchanged, and destructive sampling is no longer necessary.

[0060] Then, non-destructive sampling observation is performed, such as setting 3 plots of 10 meters * 10 meters in the representative area (i.e., the target area of ​​the same area), and selecting 10 corn plants in each plot in turn and marking them, for a total of 30 plants (i.e., the second preset number of representative corn plants). The plant height and stem thickness are measured 10 days before flowering, and the aboveground biomass is estimated using formula (1) to obtain the above-mentioned first aboveground biomass estimation result. In addition, in other embodiments of the present application, the above-ground biomass model established 10 days before flowering can also be used to replace the above-mentioned formula (1) for estimation.

[0061] Second, based on the same principle (but different data were collected), the morphological data of corn plants 14 days after flowering were obtained, and the aboveground biomass was estimated to obtain a second aboveground biomass estimation result, specifically including: selecting a first preset number of random corn plants, obtaining the plant height, stem thickness, ear length and ear thickness of the random corn plants, and drying and weighing the stems, leaves, ears and bracts of the random corn plants; based on the plant height, stem thickness, ear thickness, ear length and weighing results, constructing a second geometric model for determining the aboveground biomass by plant height, stem thickness, ear thickness and ear length; selecting a second preset number of representative corn plants, and obtaining their plant height, stem thickness, ear thickness and ear length; based on the second geometric model, and the plant height, stem thickness, ear thickness and ear length of the representative corn plants, obtaining the second aboveground biomass estimation result.

[0062] For example, firstly, destructive sampling observation is carried out, that is, 14 days after flowering, 10-12 corn plants are randomly selected from the aboveground parts, and the plant height, stem thickness, ear length, and ear thickness are observed; the stems, leaves, ears, and bracts are divided, dried and weighed; and the geometric model of aboveground biomass and ear weight is established, that is, the second geometric model. Reference model: S1 = a + b (SD PH) d +c(ED EL) f , where ED represents the diameter of the ear, EL represents the length of the ear, and S1 represents the aboveground biomass 14 days after flowering, i.e., the first aboveground biomass estimation result.

[0063] Taking a certain variety in a certain region, such as Danyu 405, a sparsely planted large-ear corn variety 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] The units of SD and ED in the formula are millimeters, and the unit of EL is centimeters. Similarly, after the above model is established, it can be used continuously without destructive sampling if the maturity type of corn varieties remains unchanged.

[0066] Then, non-destructive sampling observation is performed, and the 30 marked corn plants are observed 14 days after flowering, and the plant height, stem thickness, ear length, and ear thickness are observed. The aboveground biomass is estimated using the aboveground biomass formula (2) to obtain the aboveground biomass estimation result. In addition, in other embodiments of the present application, the aboveground biomass model established 10 days before flowering can be used instead of the aboveground biomass model (2) for estimation.

[0067] On this basis, based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, the plant growth rate in the critical period of yield formation is estimated to obtain the estimated result of the plant growth rate in the critical period of yield formation, specifically including, for each corn plant, taking the difference between its second aboveground biomass estimation result and its first aboveground biomass estimation result, dividing the difference by the number of days between the two corresponding samplings, as the estimated result of the plant growth rate in the critical period of yield formation of the corn plant; and taking the average of the estimated results of the plant growth rate in the critical period of yield formation of all representative corn plants as the final estimated result of the plant growth rate in the critical period of yield formation.

[0068] That is, the estimated growth rate of each representative corn plant during the critical period of yield formation is calculated by dividing the difference in aboveground biomass (g / plant) 14 days after flowering and 10 days before flowering by the number of days between the two samplings. On this basis, the PGR of corn plants in the representative area is taken as the average PGR of the 30 marked corn plants.

[0069] Then, based on the constructed correlation model between plant growth rate and corn yield during the key period of yield formation and the above-estimated plant growth rate estimation result, corn 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 as an example, the formula for estimating corn yield (i.e., Y) by PGR is as follows (Y: n = 5, P < 0.01, R 2 =0.53):

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

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

[0073] After back-testing and independent sample testing of 4-year field test data in actual production environment, it is confirmed that this method has a high accuracy in estimating corn yield under drought stress, solving the problem of low yield forecast accuracy of other methods such as drought year crop models. At the same time, this method can give a relatively high accuracy yield estimate 14 days after corn flowering (i.e. early August, about 40 days before maturity), which is a strong support for agricultural meteorological yield forecasting business. After 4 years of data modeling and testing, it is confirmed that this method has the characteristics of quick use, convenient application, and high accuracy in estimating corn yield. The specific verification data are as follows:

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

[0075] The accuracy of the yield estimate in 2023 is 92%: Taking the 2023 drought stress test data as an independent test sample, the PGR of the key period of corn yield formation calculated by formula (1) (2) is 3.43 g / plant / day. After substituting it into formula (3), the yield estimate is obtained. After comparing it with the actual yield, the accuracy is 92%.

[0076] Based on the same inventive concept, the present application also provides a device for quickly estimating corn yield based on corn plant morphology, which is used to implement the above method embodiment. Figure 2 is a schematic diagram of the structure of a device for rapidly estimating corn yield based on corn plant morphology provided by an embodiment of the present invention, such as Figure 2 As shown, the corn yield rapid estimation device based on corn plant morphology provided by the present application specifically includes:

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

[0078] Based on the same inventive concept, the present application also provides a device for quickly estimating corn yield based on corn plant morphology, which is used to implement the above method embodiment. Figure 3 is a schematic diagram of the structure of a device for rapidly estimating corn yield based on corn plant morphology provided by an embodiment of the present invention, such as Figure 3As shown, the device for rapidly estimating corn yield based on corn plant morphology provided by the present application specifically comprises: a processor 31 and a memory 32, wherein the processor 31 is 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, and the program is at least used to execute the method for rapidly estimating corn yield based on corn plant morphology in the above embodiment.

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

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

[0081] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0082] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0083] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0084] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0085] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0086] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0087] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0088] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for rapidly estimating corn yield based on corn plant morphology, characterized in that: include: Acquire the morphological data of corn plants 10 days before flowering, and estimate the aboveground biomass to obtain the first aboveground biomass estimation result; Obtaining the morphological data of corn plants 14 days after flowering, and estimating the aboveground biomass, to obtain a second aboveground biomass estimation result; Based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, estimating the plant growth rate during the key period of yield formation, and obtaining an estimation result of the plant growth rate during the key period of yield formation; The corn yield is estimated based on the constructed correlation model between the plant growth rate during the critical period of yield formation and the corn yield, and the estimated result of the plant growth rate during the critical period of yield formation.

2. The method for rapidly estimating corn yield based on corn plant morphology according to claim 1, characterized in that: The step of obtaining the morphological data of the corn plants 10 days before flowering and estimating the aboveground biomass to obtain the first aboveground biomass estimation result includes: Selecting a first preset number of random corn plants, obtaining plant heights and stem diameters of the random corn plants, and drying and weighing the stems and leaves of the random corn plants; Based on the plant height, the stem diameter and the weighing result, constructing a first geometric model for determining aboveground biomass from the plant height and the stem diameter; Selecting a second preset number of representative corn plants and obtaining their plant heights and stem diameters; Based on the first geometric model, and the plant height and stem diameter of representative corn plants, the first aboveground biomass estimation result is obtained.

3. The method for rapidly estimating corn yield based on corn plant morphology according to claim 1, characterized in that: The step of obtaining the morphological data of the corn plant 14 days after flowering and estimating the aboveground biomass to obtain the second aboveground biomass estimation result includes: Selecting a first preset number of random corn plants, obtaining plant height, stem thickness, ear length and ear thickness of the random corn plants, and drying and weighing the stems, leaves, ears and husks of the random corn plants; Based on the plant height, the stem diameter, the ear diameter, the ear length and the weighing results, constructing a second geometric model for determining aboveground biomass by the plant height, the stem diameter, the ear diameter and the ear length; Selecting a second preset number of representative corn plants and obtaining the plant height, stem diameter, ear diameter, and ear length thereof; Based on the second geometric model and the plant height, stem diameter, ear diameter, and ear length of representative corn plants, the second aboveground biomass estimation result is obtained.

4. The method for rapidly estimating corn yield based on corn plant morphology according to claim 2, characterized in that: Also includes: A third preset number of target areas of the same area are planned in the representative area, corn plants are selected and marked in each of the target areas, and the marked corn plants are used as the representative corn plants.

5. The method for rapidly estimating corn yield based on corn plant morphology according to claim 2, characterized in that: The estimating the plant growth rate in the key 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 in the key period of yield formation includes: For each corn plant, the difference between the second aboveground biomass estimation result and the first aboveground biomass estimation result is divided by the number of days between the two corresponding samplings to serve as the estimation result of the plant growth rate in the key period of yield formation of the corn plant; The average of the estimated results of the plant growth rate during the critical period of yield formation of all representative corn plants is taken as the final estimated result of the plant growth rate during the critical period of yield formation.

6. The method for rapid estimation of corn yield based on corn plant morphology according to claim 1, characterized in that: The correlation model is implemented based on an empirical statistical equation.

7. A device for rapidly estimating corn yield based on corn plant morphology, characterized in that: include: The first estimation module is used to obtain the morphological data of corn plants 10 days before flowering, and to estimate the aboveground biomass to obtain a first aboveground biomass estimation result; The second estimation module is used to obtain the morphological data of corn plants 14 days after flowering, and to estimate the aboveground biomass to obtain a second aboveground biomass estimation result; A third estimation module is used to estimate the plant growth rate in the key period of yield formation based on the first aboveground biomass estimation result and the second aboveground biomass estimation result, so as to obtain the plant growth rate estimation result in the key period of yield formation; The fourth estimation module is used to estimate the corn yield based on the constructed correlation model between the plant growth rate in the critical period of yield formation and the corn yield, and the estimation result of the plant growth rate in the critical period of yield formation.

8. A device for quickly estimating corn yield based on corn plant morphology, characterized in that: The invention comprises a processor and a memory, wherein the processor is connected to the memory: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the method for quickly estimating corn yield based on corn plant morphology as described in any one of claims 1-7.

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