CN110243406B - Crop yield estimation method and device, electronic equipment and storage medium - Google Patents
Crop yield estimation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a crop yield estimation method, a crop yield estimation device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring remote sensing images and meteorological data of crops to be estimated in each phenological period; determining a first net productivity of the crop for each phenological period based on the remote-sensed image and the meteorological data for that phenological period; estimating the yield of the crop based on the first net productivity and a preset yield estimation model of each phenological period. The remote sensing image spectral information of the crops in different phenological periods is different due to different growing environments, nutritional conditions or growth conditions of the crops in different phenological periods, meanwhile, meteorological data in different phenological periods have different influences on the growth of the crops, and the meteorological data and the remote sensing image data in different phenological periods are closely related to the estimation result of the crop yield, so that the reliability of the yield estimation result can be improved through the mode.
Description
Technical Field
The application relates to the technical field of crop production, in particular to a crop yield estimation method, a crop yield estimation device, electronic equipment and a storage medium.
Background
From the 20 th century and the 80 th era to the present, our country has made great progress in crop estimation, from traditional agronomic statistical methods to satellite remote sensing estimation, from single crop to multiple crop estimation, from small to large areas, and a number of remote sensing estimation systems have been established. With the rapid development of remote sensing technology, the remote sensing production estimation technology greatly innovates the crop yield investigation mode.
However, in the existing crop estimation method, the number of satellite remote sensing images of the crop is usually used to calculate the photosynthetically active radiation ratio, and then the yield of the crop is estimated based on the photosynthetically active radiation ratio and the solar radiation amount, and the yield estimation process takes a single factor into consideration, which results in low reliability of the yield estimation result.
Content of application
In view of the above, an object of the present invention is to provide a crop yield estimation method, apparatus, electronic device and storage medium, so as to improve reliability of crop yield estimation result.
In a first aspect, an embodiment of the present application provides a method for estimating crop yield, the method including: acquiring remote sensing images and meteorological data of crops to be estimated in each phenological period; determining a first net productivity of the crop for each phenological period based on the remote-sensed image and the meteorological data for that phenological period; estimating the yield of the crop based on the first net productivity and a preset yield estimation model of each phenological period.
In the implementation process, as the growing environments, the nutritional conditions or the growth conditions of the crops in different phenological periods are different, the remote sensing image spectral information of the crops in different phenological periods is different, meanwhile, the meteorological data of different phenological periods have different influences on the growth of the crops, and the meteorological data and the remote sensing image data of different phenological periods are closely related to the estimation result of the crop yield, therefore, the first net productivity of the crops in the phenological period is determined based on the remote sensing image of each phenological period and the meteorological data of each phenological period, then the crop yield is estimated based on the first net productivity and the preset yield estimation model of each phenological period, the influences of the growth conditions and the meteorological factors of the crops in different phenological periods on the growth and development of the crops are fully considered, and the reliability of the yield estimation result is improved.
In a possible design according to the first aspect, the meteorological data comprises: determining a first net productivity of said crop for each phenological period based on said remotely sensed image and said meteorological data for that phenological period at an actual temperature, an amount of solar radiation and a predetermined ideal temperature suitable for crop growth, comprising: determining a temperature stress coefficient for the phenological period based on the actual temperature of the phenological period and the ideal temperature of the phenological period; determining the product of the temperature stress coefficient of the phenological period and the predetermined ideal light energy utilization rate as a first light energy utilization rate of the phenological period; determining the photosynthetically active radiation absorption ratio of the phenological period based on the remote sensing image of the phenological period; determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the first light energy utilization ratio for the phenological period.
In the implementation process, because the yield of the crops is related to the actual temperature, the solar radiation amount and the ideal temperature suitable for the growth of the crops in each phenological period, the temperature stress coefficient of the phenological period is determined based on the actual temperature and the ideal temperature of the phenological period, then the product of the temperature stress coefficient of the phenological period and the preset light energy utilization rate is determined to be the first light energy utilization rate of the phenological period, and finally the first net productivity of the crops in the phenological period is determined based on the solar radiation amount, the photosynthetically active radiation absorption ratio and the first light energy utilization rate of the phenological period.
Based on the first aspect, in one possible design, determining the temperature stress coefficient of the phenological period based on the actual temperature of the phenological period and the ideal temperature of the phenological period includes: determining a first temperature stress coefficient of the phenological period based on the ideal temperature of the phenological period and a first preset algorithm; determining a second temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature of the phenological period and a second preset algorithm, wherein the second preset algorithm is different from the first preset algorithm; and determining the product of the first temperature stress coefficient and the second temperature stress coefficient of the phenological period as the temperature stress coefficient of the phenological period.
In the implementation process, because the ideal temperature of the phenological period affects the calculation result of the temperature stress coefficient, and both the ideal temperature and the actual temperature of the phenological period affect the calculation result of the temperature stress coefficient, the first temperature stress coefficient of the phenological period is determined based on the ideal temperature of the phenological period and a first preset algorithm; determining a second temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature of the phenological period and a second preset algorithm, wherein the second preset algorithm is different from the first preset algorithm; and determining the product of the first temperature stress coefficient and the second temperature stress coefficient of the phenological period as the temperature stress coefficient of the phenological period, wherein the mode considers the influence of temperature on the temperature stress coefficient from different aspects, and then the calculation result of the temperature stress coefficient is more objective.
In a possible design according to the first aspect, the meteorological data comprises: actual temperature, precipitation and solar radiation, determining a first net productivity of the crop for the phenological period based on the remote-sensed image and the meteorological data for the phenological period, the method comprising: determining a water stress coefficient of the phenological period based on the actual temperature of the phenological period and the precipitation of the phenological period; determining the product of the water stress coefficient of the phenological period and the predetermined ideal light energy utilization rate as a second light energy utilization rate of the phenological period; determining the photosynthetically active radiation absorption ratio of the phenological period based on the remote sensing image of the phenological period; determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the second light energy utilization ratio for the phenological period.
In the above implementation, since the yield of the crop is related to the actual temperature, precipitation and solar radiation amount of each phenological period, first, the water stress coefficient of the phenological period is determined based on the actual temperature of the phenological period and the precipitation of the phenological period; then, determining the product of the water stress coefficient of the phenological period and the predetermined ideal light energy utilization rate as a second light energy utilization rate of the phenological period; then, a first net productivity of the crop for the phenological period is determined based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the second light energy utilization ratio for the phenological period. The method fully considers the conditions that the influences of precipitation, solar radiation and temperature on the growth and development of crops in different phenological periods are different, and then the yield estimation result is more objective.
Based on the first aspect, in a possible design, determining a photosynthetically active radiation absorption ratio of the phenological period based on the remote sensing image of the phenological period includes: carrying out image processing on the remote sensing image of the phenological period to obtain a near infrared band reflection value and a red light band reflection value of the phenological period; determining the vegetation index of the phenological period based on the near-infrared band reflection value and the red-light band reflection value of the phenological period; determining the photosynthetically active radiation absorption ratio of the phenological period based on the vegetation index of the phenological period.
In the implementation process, the photosynthetically active radiation absorption ratio of each phenological period is related to the near-infrared band reflection value and the red-light band reflection value of each phenological period, so that the remote sensing image of the phenological period is subjected to image processing to obtain the near-infrared band reflection value and the red-light band reflection value of the phenological period, and then the photosynthetically active radiation absorption ratio of the phenological period is determined based on the near-infrared band reflection value and the red-light band reflection value of the phenological period. When the photosynthetic effective radiation absorption ratio of each phenological period is calculated, the condition that the reflection value of the near infrared band and the reflection value of the red light band of each phenological period are different is fully considered, so that the calculation result of the photosynthetic effective radiation absorption ratio is more objective.
In a possible design based on the first aspect, the preset yield estimation model includes: estimating the yield of the crop based on the first net productivity and the preset yield estimation model in each phenological period, wherein the preset specific coefficient between the carbon content of dry matter of the crop and the dry matter, the preset specific coefficient between biomass of the crop on the ground and total biomass, the preset specific coefficient between the water content of grain of the crop in the storage period and the grain yield of the crop are preset, and the yield of the crop is estimated based on the first net productivity and the preset yield estimation model in each phenological period, and the method comprises the following steps: determining the sum of the first net productivity for all phenological sessions as a second net productivity; determining a yield of the crop based on the second net productivity, the crop type, and a preset yield estimation model.
In the implementation process, the relation between the yield estimation model and the type of the crop, the ratio coefficient between the carbon element content of the dry matter of the crop and the dry matter quantity, the ratio coefficient between the biomass of the crop above the ground and the total biomass, and the ratio coefficient between the moisture content of the grain of the crop and the grain yield of the crop during the storage period are considered, so that the yield estimation result is more accurate.
In a possible design according to the first aspect, estimating the yield of the crop based on the first net productivity and a preset yield estimation model for each phenological period comprises: determining a first harvest factor based on the harvest mode and the crop type; estimating the crop yield based on the first harvest factor, the first net productivity for each phenological period and the predetermined yield estimation model.
In the implementation process, since the yield estimation model is considered to be related to the harvesting mode and the crop type, the first harvesting coefficient is determined according to the harvesting mode and the crop type, and the crop yield is estimated based on the first harvesting coefficient, the first net productivity and the preset yield estimation model, so that the accuracy of the yield estimation result is higher.
In a second aspect, embodiments of the present application provide a crop yield assessment apparatus, the apparatus including: the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring a remote sensing image of a crop to be estimated in each phenological period and meteorological data of the phenological period; a determining unit for determining a first net productivity of the crop in each phenological period based on the remote sensing image and the meteorological data in that phenological period; and the estimation unit is used for estimating the yield of the crops based on the first net productivity and a preset yield estimation model of each phenological period.
Based on the second aspect, in one possible design, the meteorological data includes: the determination unit is specifically used for determining the temperature stress coefficient of the phenological period based on the actual temperature of the phenological period and the ideal temperature of the phenological period; the product of the temperature stress coefficient and the predetermined ideal light energy utilization rate of the phenological period is used for determining the first light energy utilization rate of the phenological period; the photosynthetically active radiation absorption ratio of the phenological period is determined based on the remote sensing image of the phenological period; and means for determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio and the first light energy utilization ratio for the phenological period.
Based on the second aspect, in a possible design, the determining unit is further configured to determine a first temperature stress coefficient of the phenological period based on the ideal temperature of the phenological period and a first preset algorithm; determining a second temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature of the phenological period and a second preset algorithm, wherein the second preset algorithm is different from the first preset algorithm; and determining the product of the first temperature stress coefficient and the second temperature stress coefficient of the phenological period as the temperature stress coefficient of the phenological period.
Based on the second aspect, in one possible design, the meteorological data includes: the determining unit is further used for determining a water stress coefficient of the phenological period based on the actual temperature of the phenological period and the precipitation of the phenological period; the product of the water stress coefficient used for determining the phenological period and the predetermined ideal light energy utilization rate is a second light energy utilization rate of the phenological period; the photosynthetically active radiation absorption ratio of the phenological period is determined based on the remote sensing image of the phenological period; and means for determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the second light energy utilization ratio for the phenological period.
Based on the second aspect, in a possible design, the determining unit is further configured to perform image processing on the remote sensing image in the phenological period to obtain a near-infrared band reflection value and a red-light band reflection value in the phenological period; determining the vegetation index of the phenological period based on the near-infrared band reflection value and the red-light band reflection value of the phenological period; and determining the photosynthetically active radiation absorption ratio of the phenological period based on the vegetation index of the phenological period.
Based on the second aspect, in one possible design, the preset yield estimation model includes: a predetermined ratio of carbon content to dry matter content in dry matter of the crop, a predetermined ratio of above-ground biomass to total biomass of the crop, a predetermined ratio of moisture content in grain of the crop during storage to grain yield of the crop, the prediction unit being further configured to determine a sum of the first net productivity for all climatic periods as a second net productivity; determining a yield of the crop based on the second net productivity, the crop type, and a preset yield estimation model.
Based on the second aspect, in a possible design, the pre-estimation unit is further configured to determine a first harvest factor based on the harvesting mode and the crop type; and estimating the crop production based on the first harvest factor, the first net productivity for each phenological period, and the predetermined production estimation model.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory connected to the processor, where a computer program is stored in the memory, and when the computer program is executed by the processor, the electronic device is caused to perform the method of the first aspect.
In a fourth aspect, an embodiment of the present application provides a storage medium, in which a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute the method of the first aspect.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a crop yield assessment method according to an embodiment of the present disclosure;
FIG. 3 is a schematic view illustrating a detailed process of a crop yield assessment method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a crop yield assessment device according to an embodiment of the present application.
Detailed Description
The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, an embodiment of the present application provides a schematic structural diagram of an electronic device 100, where the electronic device 100 may be a Personal Computer (PC), a tablet PC, a smart phone, a Personal Digital Assistant (PDA), or the like.
The electronic device 100 may include: memory 102, process 101, communication interface 103, and a communication bus for enabling the connection communications of these components.
The Memory 102 is used for storing various data such as a remote sensing image of a crop to be estimated in each phenological period, meteorological data of the phenological period, a preset yield estimation model, and a computer program instruction corresponding to the crop estimation method and apparatus provided in the embodiment of the present application, wherein the Memory 102 may be, but not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 101 is configured to execute the steps of the crop estimation method provided by the embodiment of the present application when reading and executing the computer program instructions stored in the memory, so as to obtain the remote sensing image of the crop to be estimated in each phenological period, the meteorological data of the phenological period, and the preset yield estimation model from the memory, determine the first net productivity of the crop in each phenological period based on the remote sensing image and the meteorological data of the phenological period, and finally estimate the yield of the crop based on the first net productivity and the preset yield estimation model of each phenological period.
The processor 101 may be an integrated circuit chip having signal processing capability. The Processor 101 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The communication interface 103 may use any transceiver or the like to send the estimated crop yield to the user terminal communicatively connected to the electronic device 100 for display.
Referring to fig. 2, fig. 2 is a flowchart illustrating a crop yield assessment method according to an embodiment of the present application, the method is applied to the electronic device 100 shown in fig. 1, and the flowchart shown in fig. 2 will be described in detail below, where the method includes:
s100: and acquiring remote sensing images of the crops to be estimated in each phenological period and meteorological data of the phenological period.
S200: determining a first net productivity of the crop for each phenological period based on the remote-sensed image and the meteorological data for that phenological period.
S300: estimating the yield of the crop based on the first net productivity and a preset yield estimation model of each phenological period.
The phenological period refers to the reaction of the growth, development and activity of animals and plants and the change of organisms on the phenological period, and is called the phenological period when the reaction is occurring.
Since the remote sensing image of the crop to be estimated in each phenological period and the meteorological data of the phenological period may be different, in one possible embodiment, S100 may be implemented as follows: the electronic device 100 receives the remote sensing image of the crop to be estimated in each phenological period shot by the unmanned aerial vehicle, stores the remote sensing image of the phenological period in the memory 102, the electronic device 100 acquires the meteorological data of the crop to be estimated in each phenological period from the meteorological bureau, stores the remote sensing image of the phenological period in the memory 102, and acquires the remote sensing image of the crop to be estimated in each phenological period and the meteorological data of the phenological period from the memory 102 when the yield of the crop to be estimated needs to be estimated. In this embodiment, the crop to be estimated is rice, and in other embodiments, the crop to be estimated may also be wheat.
In another possible embodiment, the remote sensing image of the crop to be estimated in each phenological period may be a remote sensing image taken by a satellite.
The resolution ratio of the remote sensing image acquired by the unmanned aerial vehicle is higher than that of the remote sensing image acquired by the satellite, so that the unmanned aerial vehicle is used for acquiring the image data of crops in the embodiment of the application.
In another possible embodiment, when the yield of the crop to be estimated needs to be estimated, meteorological data of the crop to be estimated in each phenological period is acquired from a meteorological office.
It should be noted that each of the phenological sessions in S100 may include all phenological sessions of the crop to be estimated, or may include only at least two key phenological sessions. The key phenological period is related to the crop species and is the phenological period which has a greater impact on the yield of the crop. If all the phenological periods are included, the result of the estimation is more accurate. If only the key phenological period is included, a more accurate estimation result than that in the prior art can be obtained, and the calculation complexity is smaller.
Specifically, in order to reduce the computational complexity of the yield estimation method on the basis of ensuring the accuracy of the yield estimation result, in another possible implementation manner, for each crop to be estimated, accurate estimation of the yield of the crop can be realized only by the remote sensing image of the crop in the key phenological stage and the meteorological data of the phenological stage, for example, rice can be estimated only by acquiring the data of the rice in the tillering stage, the incubation stage and the heading stage, and the data of the rice in the seedling stage, the milk stage, the wax stage and the mature stage are not required to be acquired. For example, the wheat can be estimated only by acquiring the data of the wheat in the jointing stage, the booting stage and the heading stage, and the data of the wheat in the sowing stage, the seedling stage, the tillering stage, the wintering stage, the green turning stage, the rising stage (biological jointing), the flowering stage, the filling stage and the mature stage are not required to be acquired.
The growth of rice in each phenological stage is illustrated below:
and (3) seedling stage: one leaf is produced every 3-4 days; the seed roots are replaced by secondary roots.
And (3) tillering stage: when the individual rice tillers, the tillering stage is started until the ear differentiation is started.
And (3) booting stage: the pillow of the rice's flag leaf is exposed to the first bare tip of the rice, about 1.2 leaves old (about 9 days).
In heading stage, 50% of the rice ears of the plants are exposed out of leaf sheaths.
And (3) milk stage: more than 50% of the middle part of the rice ear is filled with the glume, and when the content is in a milk state.
And (3) wax ripening stage: more than 50% of the rice head middle kernel content is concentrated, and when the rice head middle kernel content is not pulpy state substance.
And (3) mature period: 90% of the grains per ear are yellow-ripe, and the green grains at the base of the ear are also hard to the maturity stage.
The growth of wheat in each phenological stage is illustrated as follows:
and (3) sowing time: the day of sowing.
And (3) seedling stage: when 50% of first true leaves of the whole field grains are exposed out of coleoptile and grow 2cm above the ground, the spring wheat is grown for 3-4 months; winter wheat, about 10 months.
And (3) tillering stage: spring wheat in about 4 ten days when the first tillers of 50% of plants in the whole field extend 1.5-2cm out of leaf sheaths; winter wheat, about 10 months in the middle and last ten days.
And (3) overwintering period: the date that the average daily temperature is reduced to about 2 ℃ and the wheat plant basically stops growing, spring wheat, none; winter wheat, 11 at the end of the month and 1 month early.
And (3) a green turning period: in spring of the next year, the wheat starts to grow along with the rise of the air temperature, leaves newly grown after 50% of plants are in year (mostly winter-spring junction leaves) stretch out of leaf sheaths by 1-2cm, and spring wheat does not exist when the field is changed from dark green to green; winter wheat, 2 months later-3 months later.
Onset (biological jointing): the wheat seedlings grow upwards from the original creeping growth, the first leaves stretch after the year, the leaf sheaths stretch obviously, the distance between the leaf ear of the first stretched leaf and the leaf ear of the last leaf before the year reaches 1.5cm, the first internode of the base stretches slightly, and the spring wheat grows about 5 last ten days; winter wheat is about 4 months old.
Jointing stage (agronomic jointing): the first internode of the main stem of the wheat is 1.5-2cm away from the ground, the base part of the wheat is easy to break and make a sound by pinching with fingers, and the spring wheat is about 5 middle and last ten days of the month; winter wheat, in the middle and late April.
And (3) booting stage: flag leaves (last leaf) of the plant are fully extended (leaf ears are visible), spring wheat, about 6 last ten months; winter wheat, last 5 months.
Heading stage: the top end or one side of the spike (not referring to the awns), when the leaf sheath of the flag tree extends out of half of the length of the spike, the spring wheat, about 6 middle and last ten days-7 last ten days of the month; winter wheat, 5 middle and last-6 last-month.
And (3) flowering period: 50% of the plants in the whole field are bloomed in the first flower, spring wheat, about 7 last ten days; winter wheat, last 6 months.
Grouting stage: the shape of the grains is basically finished, the length is three quarters of the maximum value, the thickness is slightly increased, and the grains are spring wheat about 7 middle-of-month; winter wheat, which began to be grouted in the middle 6 months.
And (3) mature period: a wax ripening stage: the grain size and color are close to normal, the interior is waxy, the grain contains 22% of water, the cauline leaves are basically dried, the dry weight of the grain reaches the maximum value at the end of the waxing stage, and the harvest period is proper. The end stage: the seeds have the normal size and color of the variety, the interior becomes hard, the water content is reduced to below 20%, the accumulation of dry matters is stopped, the spring wheat is in the last ten days of about 8 months, the winter wheat is in the last 7 months.
S200: determining a first net productivity of the crop for each phenological period based on the remote-sensed image and the meteorological data for that phenological period.
In one possible implementation, the meteorological data includes: actual temperature, solar radiation amount and predetermined ideal temperature suitable for crop growth, S200 may include:
a1: determining a temperature stress coefficient for the phenological period based on the actual temperature of the phenological period and the ideal temperature of the phenological period.
In this embodiment, the actual temperature of the phenological period may be obtained by calculating an average of the maximum temperature and the minimum temperature of the phenological period per day, and the ideal temperature of the crop in each phenological period may be obtained according to actual empirical data.
For example, when the phenological period has 15 days, the average temperature value of each day is calculated according to the maximum air temperature and the minimum air temperature of each day, and then the average temperature values of each day in 15 days are summed and divided by 15 to obtain the actual temperature of the phenological period.
For example, when the phenological period has 15 days, the maximum air temperature and the minimum air temperature of 15 days are summed up first, and then the sum is divided by 30 to obtain the actual temperature of the phenological period.
As a possible implementation, the actual temperature of the waiting period may be obtained by calculating an average value of multiple sets of temperature data of each day of the waiting period, where the multiple sets of temperature data may be temperature data of each day at intervals of 1 hour, and the multiple sets of temperature data may be temperature data of each day at intervals of 2 hours.
For example, when the phenological period has 15 days, 24 sets of temperature data are available for each of the 15 days, the average temperature value of each day is calculated according to the temperature data of each day, and then the average temperature values of the 15 days are summed and divided by 15 to obtain the actual temperature of the phenological period.
For example, when the phenological period has 15 days, all temperature data of the 15 days are firstly summed, and then the sum is divided by 360 to obtain the actual temperature of the phenological period.
In one possible embodiment, a1 may be implemented as follows: determining the temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature of the phenological period and a preset algorithm, wherein the preset algorithm is as follows: t isε2-Pi(x,t)＝1.184/{1+exp[0.2×(Topt-Pi(x,t)-10-Temp_Pi(x,t))]}/{1+exp[0.3×(-Topt-Pi(x,t)-10+Temp_Pi(x,t)]In which T isε2-Pi(x, T) is the temperature stress coefficient of the ith phenological stage, Topt-Pi(x, t) is the ideal temperature of the ith phenological period, Temp _ Pi(x, t) is the actual temperature of the ith phenological period, where t is used to mark the year and month in which the phenological period is located, and x represents the actual area of the crop to be estimated represented by one pixel in the remote sensing image, and in this embodiment, one pixel represents a crop with an area of 1 square meter.
As another embodiment, at Temp _ Pi(x,t)-Topt-Pi(x,t)>10 ℃ or Topt-Pi(x,t)-Temp_Pi(x,t)>At 13 ℃ C，
Where TT is the actual temperature of the phenological period closest to the average of the actual temperatures of the phenological periods, for example, the actual temperature data of 3 phenological periods in total, 15 degrees, 20 degrees and 30 degrees respectively, then the average of the actual temperatures of the phenological periods is 21.66, then 20 degrees is closest to 21.66 degrees, and thus TT is 20 degrees.
As another possible embodiment, a1 includes:
determining a first temperature stress coefficient of the phenological period based on the ideal temperature of the phenological period and a first preset algorithm, wherein the first preset algorithm is as follows: t isε1-Pi(x,t)＝0.8+0.02×Topt-Pi(x,t)-0.0005×[Topt-Pi(x,t)]2Wherein, Tε1-Pi(x, t) is the first temperature stress coefficient of the ith phenological stage.
As an embodiment, at Temp _ PiWhen (x, T) is less than or equal to-10 ℃, making Tε1-Pi(x,t)＝0。
And determining a second temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature of the phenological period and a second preset algorithm, wherein the second preset algorithm is different from the first preset algorithm, and the second preset algorithm is the same as the preset algorithm.
And determining the product of the first temperature stress coefficient and the second temperature stress coefficient of the phenological period as the temperature stress coefficient of the phenological period.
And multiplying the first temperature stress coefficient and the second temperature stress coefficient of the phenological period, and taking the product as the temperature stress coefficient of the later period of the phenological period.
A2: and determining the product of the temperature stress coefficient of the phenological period and the predetermined ideal light energy utilization rate as the first light energy utilization rate of the phenological period.
In one possible embodiment, A2 can be implemented by adding the temperature stress coefficients of the phenological periodPredetermined ideal light energy utilization factor epsilonmaxMultiplying and taking the product as the first light energy utilization rate of the phenological period, wherein, under ideal conditions, epsilon of rice and wheatmaxThe value of (a) is 2.8 g/MJ.
A3: and determining the photosynthetically active radiation absorption ratio of the phenological period based on the remote sensing image of the phenological period.
As an embodiment, a3 includes:
and carrying out image processing on the remote sensing image in the phenological period to obtain a near infrared band reflection value and a red light band reflection value in the phenological period.
After the remote sensing image of the ith phenological period is subjected to radiation calibration, geometric calibration, image splicing and image registration processing, the near infrared band reflection value NIR of the ith phenological period is obtainedi(x, t) and red band reflectance Ri(x,t)。
And determining the vegetation index of the phenological period based on the near-infrared band reflection value and the red-light band reflection value of the phenological period, and determining the photosynthetically active radiation absorption ratio of the phenological period based on the vegetation index of the phenological period.
As an embodiment, first, the reflection value of the near infrared band, the reflection value of the red light band and the reflection value of the near infrared band passing through the ith phenological period
Determining a normalized vegetation index NDVI for the ith phenological periodi(x, t). By NDVIi(x, t) and
determining photosynthetically active radiation absorption ratio of ith phenological period
Wherein NDVIi,minAnd NDVIi,maxRelated to vegetation type, e.g. NDVI of rice and wheati,min＝0.023，NDVIi,max0.634, NDVI of bushi,min＝0.023，NDVIi,max0.636, evergreen broad leafNDVI of foresti,min＝0.023，NDVIi,max＝0.676，FPARi,maxAnd FPARi,minIndependent of vegetation type, FPARi,min＝0.001，FPARi,max＝0.95。
As an embodiment, the reflection value of the near infrared band, the reflection value of the red light band and the reflection value of the near infrared band passing through the ith phenological period
Or
Determining the ratio vegetation index RVI of the ith phenological periodi(x, t). By RVIi(x, t) and
determining photosynthetically active radiation absorption ratio of ith phenological period
Wherein, RVIi,minAnd RVIi,maxRelated to the type of vegetation, e.g. rice and wheat RVIi,min＝1.05，RVIi,max4.46 RVI of shrubsi,min＝1.05，RVIi,maxRVI of evergreen broadleaf forest ═ 5.17i,min＝0.95，RVIi,max＝5.17。
In order to increase the calculation accuracy of the photosynthetically active radiation absorption ratio in the phenological period, the photosynthetically active radiation absorption ratio in the ith phenological period is used as a further embodiment
And
determining photosynthetically active radiation absorption ratio FPAR _ P of ith phenological periodi(x, t) where α is a correction coefficient, 0<α<In this embodiment, α is 0.7, and in other embodiments, α is 0.6.
A4: determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the first light energy utilization ratio for the phenological period.
Wherein, the solar radiation amount of the phenological period can be obtained by multiplying the total radiation amount of the month in which the phenological period is located by the ratio of the number of days of the phenological period to the number of days of the month, for example, when the number of days of the ith phenological period is 15 days, the number of days of the month in which the ith phenological period is located is 30 days, the ratio of the number of days of the ith phenological period to the number of days of the month is 1/2, and when the total radiation amount of the month in which the ith phenological period is located is SOL (x, t), the solar radiation amount of the ith phenological period is
As a possible embodiment, the solar radiation amount of the phenological period may be obtained by calculating the sum of the solar radiation amounts of the phenological period.
In one possible embodiment, A4 can be implemented as follows, by the sum of the solar radiation of the ith weather period
Determination of photosynthetically active radiation PAR _ P in the ith phenological stagei(x, t) determining the photosynthetically active radiation APAR _ P actually absorbed in the ith phenological stage based on the product of the photosynthetically active radiation in the ith phenological stage and the photosynthetically active radiation absorption ratio in the ith phenological stagei(x, t) photosynthetically active radiation APAR _ P based on the actual absorption in the ith phenological phasei(x, t) and the product of the first light energy utilization rate of the ith phenological stage, obtaining the first net productivity NPP _ P of said crop of the ith phenological stagei(x,t)。
In one possible embodiment, a4 may be implemented by determining the actual absorbed photosynthetically active radiation for the ith phenological period based on the product of the amount of solar radiation for the ith phenological period and the photosynthetically active radiation absorption ratio for the ith phenological period, and determining the first net productivity of the crop for the ith phenological period based on the product of the actual absorbed photosynthetically active radiation for the ith phenological period and the first solar energy utilization ratio for the ith phenological period.
In another possible implementation, the meteorological data includes: the actual temperature, the amount of solar radiation and the predetermined ideal temperature suitable for crop growth, S200 includes:
b1: determining a water stress coefficient for the phenological period based on the actual temperature of the phenological period and the precipitation of the phenological period.
In one possible implementation, B1 may be implemented by obtaining the local latent heat of vaporization E of the ith physical period based on the actual temperature of the ith physical period and a third preset algorithmpo-Pi(x, t) at 0 ℃ C<Temp_Pi(x,t)<At 26.5 ℃, the third preset algorithm is: epo-Pi(x,t)＝16×[10×Temp_Pi(x,t)/I]β[ 2 ] wherein [0.675 ] I3-77.1×I2+17920×I+492390]×10-6Wherein, when the number of the phenological period is only 4, i is 1,2,3,4, when the number of the phenological period is only 3, i is 1,2,3, in this embodiment, the phenological period used for the estimation of the crop is 3,
at Temp _ Pi(x, t) ≧ 26.5 ℃, the local latent boil-off increases only with temperature rise and is independent of the value of I, the third predetermined algorithm is:
at Temp _ PiWhen (x, t) is less than or equal to 0 ℃, Epo-Pi(x, t) ═ 0. Precipitation Precip _ P based on ith phenological periodi(x, t), local latent heat of vaporization E of ith key phenological periodpo-Pi(x, t) and a fourth preset algorithm to obtain the net surface radiation R of the ith phenological periodn-Pi(x, t), wherein the fourth preset algorithm is: rn-Pi(x,t)＝[Epo-Pi(x,t)×Precip_Pi(x,t)]0.5×{0.369+0.598×[Epo-Pi(x,t)/Precip_Pi(x,t)]0.5}. Precipitation E based on ith phenological periodpo-Pi(x, t) and the net surface radiation R of the ith phenological periodn-Pi(x, t) and a fifth preset algorithm to obtain the actual evaporation amount EET _ P of the area of the ith phenological periodi(x, t), wherein the fifth preset algorithm is:
at Temp _ PiEET _ P when (x, t) is less than or equal to 0 DEG Ci(x, t) ═ 0. Local latent heat of vaporization E based on ith phenological periodpo-Pi(x, t) and the actual evaporation amount EET _ P of the area of the ith phenological periodi(x, t) and a sixth preset algorithm to obtain the potential evapotranspiration amount PET _ P of the ith phenological period areai(x, t), wherein the sixth preset algorithm is: PET _ Pi(x,t)＝[Epo-Pi(x,t)+EET_Pi(x,t)]/2. Potential evapotranspiration amount PET _ P based on ith phenological period areai(x, t) and the actual evapotranspiration amount EET _ P of the ith phenological period areai(x, t) and a seventh preset algorithm, and calculating to obtain the water stress coefficient W of the ith phenological periodε-Pi(x,t)。Wε-PiThe larger (x, t) is, the wetter the ground is. Wherein, the seventh preset algorithm is as follows: wε-Pi(x,t)＝0.5+0.5×EET_Pi(x,t)/PET_Pi(x,t)。
As an embodiment, the water stress coefficient W at the ith phenological stage is calculatedε-PiAt (x, t), at EET _ Pi(x,t)≥PET_Pi(x, t), determining EET _ Pi(x,t)＝PET_Pi(x, t) at EET _ Pi(x,t)<PET_Pi(x, t), determining EET _ Pi(x,t)＝EET_Pi(x, t). When W isε-PiWhen (x, t) ═ 0.5, it means extreme drought, Wε-PiWhen (x, t) ═ 1, it indicates very wet.
B2: and determining the product of the water stress coefficient of the phenological period and the predetermined ideal light energy utilization rate as the second light energy utilization rate of the phenological period.
As an embodiment, B2 can be implemented according to the water stress coefficient of the phenological period and the ideal light energy utilization rate epsilon determined in advancemaxDetermining a second light energy utilization rate of the phenological period, wherein, under ideal conditions, epsilon of rice and wheatmaxThe value of (a) is 2.8 g/MJ.
Alternatively, B2 may be implemented by determining the stress factor of the phenological period based on the product of the water stress factor and the temperature stress factor of the phenological period, and by determining the ideal light energy utilization factor ε based on the stress factor and a predetermined valuemaxDetermining a second light energy utilization rate of the phenological period.
B3: and determining the photosynthetically active radiation absorption ratio of the phenological period based on the remote sensing image of the phenological period. Here, B3 is the same as A3, please refer to the content described in the embodiment of A3, and therefore, the description thereof is omitted.
B4: determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the second light energy utilization ratio for the phenological period.
In one possible embodiment, B4 can be implemented as follows, based on the sum of the solar radiation of the ith weather period
Obtaining photosynthetically active radiation PAR _ P of the ith phenological periodi(x, t) obtaining the photosynthetically active radiation APAR _ P actually absorbed in the ith phenological stage based on the product of the photosynthetically active radiation in the ith phenological stage and the photosynthetically active radiation absorption ratio in the ith phenological stagei(x, t), finally, photosynthetically active radiation APAR _ P based on the actual absorption in the ith phenological phasei(x, t) and the product of the second light energy utilization rate of the ith phenological stage to obtain the first net productivity NPP _ P of the crop of the ith phenological stagei(x,t)。
In one possible embodiment, B4 may be implemented by obtaining the photosynthetically active radiation actually absorbed in the ith phenological stage based on the product of the amount of solar radiation in the ith phenological stage and the photosynthetically active radiation absorption ratio in the ith phenological stage, and obtaining the first net productivity of the crop in the ith phenological stage based on the product of the photosynthetically active radiation actually absorbed in the ith phenological stage and the second solar energy utilization ratio in the ith phenological stage.
S300: estimating the yield of the crop based on the first net productivity and a preset yield estimation model of each phenological period.
In one possible implementation, the preset yield estimation model includes: a predetermined ratio of carbon content to dry matter content in dry matter of the crop, a predetermined ratio of biomass of the crop above ground to total biomass, a predetermined ratio of moisture content in grain of the crop during storage, and grain yield of the crop, S300 comprising:
determining the sum of the first net productivity of all phenolics periods as the second net productivity NPP (x, t).
For example, in predicting the yield of a crop to be estimated, if only the data of 3 phenological phases of the data of all phenological phases of the crop are required to achieve accurate prediction of the yield of the crop, the first net productivities of the 3 phenological phases are added and the sum is taken as the second net productivity
In predicting a crop to be estimated, it is assumed that only data for 4 phenological sessions of the crop are required, and therefore, by adding the first net productivities of 4 phenological sessions, and taking the sum as the second net productivity
Determining a yield of the crop based on the second net productivity, the crop type, and a preset yield estimation model.
Firstly, the methodDetermining, according to the type of the crop, a coefficient of ratio T between the carbon content in dry matter of the crop and the dry matter, a coefficient of ratio ρ between the above-ground biomass of the crop and the total biomass, a coefficient of ratio ω between the moisture content in the grain of the crop during storage and the grain yield of the crop, and a harvest coefficient HI of the crop, and then determining the yield Y of the crop based on the above coefficients and the second net productivity and the preset yield estimation model, wherein the preset yield estimation model is: y ═ a × B × HI × 0.001+ B, where,
a and b are regression coefficients, HI is a harvest coefficient, the harvest coefficients of different crops are different, and the value range of the harvest coefficient is 0.3 to 0.8, for example, the harvest coefficient of rice is 0.45; the harvest factor for wheat was 0.37. a and b are solved simultaneously by constructing two equations through historical experimental data, wherein only a and b are unknown numbers, and the different crops T are not necessarily the same, for example, the T of rice is 0.38, the T of wheat is 0.39, and the different crops rho are not necessarily the same, for example, the rho of rice is 0.91, the rho of wheat is 0.9, and the omega of different crops is not necessarily the same, for example, the omega of rice is 13.0%, and the omega of wheat is 12.5%.
As another possible embodiment, S300 includes:
since the harvest factor is different for different harvest styles of different types of crops depending on the harvest style and the crop, the first harvest factor HII for the crop is determined based on the harvest style and the crop type in order to improve the accuracy of the yield estimation.
Wherein, when the harvesting mode is a harvester, the first harvesting coefficient of the rice is 0.48; the first harvest factor for wheat was 0.4.
When the harvesting mode is hand cutting, the first harvesting coefficient of the rice is 0.42; the first harvest factor for wheat was 0.36.
Estimating the crop yield based on the harvest factor, the first net productivity for each phenological period and the preset yield estimation model, wherein the preset yield estimation model is: y ═ c [ B × HI × 0.001 × 60% + (a1 × B × HI × 0.001+ B1) × 39% ] + d, wherein a1, B1, c, and d are regression coefficients that can be solved simultaneously by constructing four equations from historical data, wherein only a1, B1, c, and d are unknowns, after the regression coefficients are obtained, Y ═ c [ B × HI × 0.001 × 60% + (a1 × B × HI × 0.001+ B1) × 39% ] + d is determined based on the regression coefficients, and the crop yield is estimated based on the harvest coefficients, the first net productivity for each season, and the estimated yield model. Referring to fig. 3, a detailed flowchart of a crop yield estimation method according to an embodiment of the present application is provided, wherein an operation process of the method includes:
the first stage is as follows: and acquiring remote sensing images and meteorological data of the crops in each phenological period.
Wherein, the remote sensing image of every phenological period includes: multispectral image and RGB image, the meteorological data of every phenological period includes: the rainfall amount of the phenological period, the actual temperature of the phenological period, the preset ideal temperature suitable for the growth of crops in the phenological period and the solar radiation of the phenological period; the rainfall amount of the phenological period is the sum of the daily rainfall amounts of the phenological period, the actual temperature of the phenological period is the average value of the highest temperature and the lowest temperature of the daily air temperature of the phenological period, and the solar radiation of the phenological period is the sum of the daily solar radiation amounts of the phenological period.
And in the second stage, acquiring a water stress coefficient, a first temperature stress coefficient, a second temperature stress coefficient, a predetermined ideal light energy utilization rate of the crops, photosynthetically active radiation and photosynthetically active radiation absorption ratio of the crops in each phenological period based on the remote sensing image and meteorological data of each phenological period.
Wherein, the calculation process of the water stress coefficient of each phenological period is as follows: first, a local potential evapotranspiration amount of the phenological period is calculated based on an actual temperature of the phenological period, then, a net surface radiation amount of the phenological period is calculated based on a total precipitation amount and the local potential evapotranspiration amount of the phenological period, then, a regional actual evapotranspiration amount of the phenological period is calculated based on the precipitation amount and the net surface radiation amount of the phenological period, then, a regional potential evapotranspiration amount of the phenological period is calculated based on the regional actual evapotranspiration amount and the local potential evapotranspiration amount of the phenological period, and finally, a water stress coefficient of the phenological period is calculated based on the regional potential evapotranspiration amount and the regional actual evapotranspiration amount of the phenological period.
Calculating a first temperature stress coefficient and a second temperature stress coefficient of each phenological period according to an ideal temperature suitable for crop growth of the phenological period and a first preset algorithm; and calculating a second temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature suitable for crop growth of the phenological period and a second preset algorithm.
Wherein, the calculation process of the photosynthetically active radiation absorption ratio of each phenological period is as follows: firstly, performing radiation calibration, geometric correction, image splicing and image-sound registration on a multispectral image and an RGB image in a remote sensing image of each phenological period to obtain a ratio vegetation index RVI of the phenological periodi(x, t) and normalized vegetation index NDVIi(x, t), then based on RVIi(x, t) and NDVIi(x, t), calculating the photosynthetically active radiation absorption ratio of the phenological period.
Wherein the photosynthetically active radiation for each phenological stage is obtained on the basis of the solar radiation for that phenological stage.
And a third stage, acquiring the first net productivity of the crops in the phenological period based on the water stress coefficient, the first temperature stress coefficient, the second temperature stress coefficient, the predetermined ideal light energy utilization rate, the photosynthetically active radiation and the photosynthetically active radiation absorption ratio of the crops in each phenological period.
Wherein the first net productivity for each phenological period is calculated as follows: firstly, the actual light energy utilization rate of each phenological period is determined based on the water stress coefficient, the first temperature stress coefficient, the second temperature stress coefficient and the predetermined ideal light energy utilization rate of the crops, then the actual absorbed photosynthetically active radiation of the phenological period is determined based on the photosynthetically active radiation and photosynthetically active radiation absorption ratio, and finally the first net productivity of the phenological period is determined based on the actual light energy utilization rate and the actual absorbed photosynthetically active radiation of the phenological period.
A fourth stage of estimating the yield of the crop based on the first net productivity and a preset yield estimation model for each phenological period, wherein the preset yield estimation model comprises: the method comprises the following steps of presetting a ratio coefficient between the content of carbon element in dry matter of the crop and the quality of the dry matter, presetting a ratio coefficient between biomass of the crop above the ground and total biomass, presetting a ratio coefficient between the content of water in grains of the crop and the yield of the grains of the crop during a storage period, and presetting a harvesting coefficient of the crop.
Wherein the yield of the crop is calculated by first determining the sum of the first net productivity of all phenological sessions as the second net productivity; then, based on the second net productivity, the crop species and a preset yield estimation model, the yield of the crop is determined.
Referring to fig. 4, fig. 4 is a block diagram illustrating a crop assessment apparatus 400 according to an embodiment of the present disclosure. The block diagram of fig. 4 will be explained, and the apparatus shown comprises:
the obtaining unit 410 is configured to obtain a remote sensing image of a crop to be estimated in each phenological period and meteorological data of the phenological period.
A determining unit 420 for determining a first net productivity of the crop for each phenological period based on the remote sensing image and the meteorological data for that phenological period.
An estimating unit 430 for estimating the yield of the crop based on the first net productivity and a preset yield estimation model for each phenological period.
The meteorological data includes: actual temperature, solar radiation amount and a predetermined ideal temperature suitable for crop growth, a determining unit 420 for determining a temperature stress coefficient of the phenological period; the product of the temperature stress coefficient and the predetermined ideal light energy utilization rate of the phenological period is used for determining the first light energy utilization rate of the phenological period; the photosynthetically active radiation absorption ratio of the phenological period is determined based on the remote sensing image of the phenological period; and means for determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio and the first light energy utilization ratio for the phenological period.
The determining unit 420 is further configured to determine a first temperature stress coefficient of the phenological period based on the ideal temperature of the phenological period and a first preset algorithm; determining a second temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature of the phenological period and a second preset algorithm, wherein the second preset algorithm is different from the first preset algorithm; and determining the product of the first temperature stress coefficient and the second temperature stress coefficient of the phenological period as the temperature stress coefficient of the phenological period.
The meteorological data includes: an actual temperature, precipitation and solar radiation amount determining unit 420 for determining a water stress coefficient of the phenological period based on the actual temperature of the phenological period and the precipitation of the phenological period; the product of the water stress coefficient used for determining the phenological period and the predetermined ideal light energy utilization rate is a second light energy utilization rate of the phenological period; the photosynthetically active radiation absorption ratio of the phenological period is determined based on the remote sensing image of the phenological period; and means for determining a first net productivity of the crop for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the second light energy utilization ratio for the phenological period.
The determining unit 420 is further configured to perform image processing on the remote sensing image in the phenological period to obtain a near-infrared band reflection value and a red-light band reflection value in the phenological period; determining the vegetation index of the phenological period based on the near-infrared band reflection value and the red-light band reflection value of the phenological period; and determining the photosynthetically active radiation absorption ratio of the phenological period based on the vegetation index of the phenological period.
The preset yield estimation model includes: a predetermined ratio of carbon content to dry matter content in dry matter of the crop, a predetermined ratio of above-ground biomass to total biomass of the crop, a predetermined ratio of moisture content in grain of the crop to grain yield of the crop during storage, and a predetermined harvest factor of the crop, the pre-estimation unit 430, and the sum of the first net productivity and the second net productivity for all climatic periods; determining a yield of the crop based on the second net productivity, the crop type, and a preset yield estimation model.
An estimation unit 430, further configured to determine a first harvest coefficient based on the harvest mode and the crop type; and estimating the crop yield based on the first harvest factor, the first net productivity for each phenological period and the preset yield estimation model.
Please refer to the content described in the embodiment shown in fig. 2 for the process of implementing each function of each functional unit in this embodiment, which is not described herein again.
In addition, a storage medium is provided in an embodiment of the present application, and a computer program is stored in the storage medium, and when the computer program runs on a computer, the computer is caused to execute the method provided in any embodiment of the present application.
In summary, embodiments of the present application provide a crop yield estimation method, apparatus, electronic device and storage medium, where the method includes: acquiring a remote sensing image of a crop to be estimated in each phenological period and meteorological data of the phenological period; determining a first net productivity of the crop for each phenological period based on the remote-sensed image and the meteorological data for that phenological period; estimating the yield of the crop based on the first net productivity and a preset yield estimation model of each phenological period.
The method comprises the steps of determining the first net productivity of crops in different phenological periods based on remote sensing images of the phenological periods and meteorological data of the phenological periods, estimating the crop yield based on the first net productivity of the phenological periods and a preset yield estimation model, fully considering the growth conditions of the crops in different phenological periods and the influence of the meteorological factors on the growth and development of the crops, and improving the reliability of a yield estimation result.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Claims (9)
1. A method of estimating crop yield, the method comprising:
acquiring remote sensing images and meteorological data of crops to be estimated in each phenological period;
determining a first net productivity of the crop for each phenological period based on the remote-sensed image and the meteorological data for that phenological period;
predicting the yield of the crop based on the first net productivity and a preset yield estimation model of each phenological period;
wherein predicting the yield of the crop based on the first net productivity and a preset yield estimation model for each phenological period comprises:
determining a first harvest factor based on the harvest mode and the crop type;
estimating said yield based on said first harvest factor, said first net productivity for each phenological period and said predetermined yield estimation model.
2. The method of claim 1, wherein the meteorological data comprises: determining a first net productivity of said crop for each phenological period based on said remotely sensed image and said meteorological data for that phenological period at an actual temperature, an amount of solar radiation and a predetermined ideal temperature suitable for crop growth, comprising:
determining a temperature stress coefficient of the phenological period based on the actual temperature and the ideal temperature of the phenological period;
determining the product of the temperature stress coefficient of the phenological period and the predetermined ideal light energy utilization rate as a first light energy utilization rate of the phenological period;
determining the photosynthetically active radiation absorption ratio of each phenological period based on the remote sensing image of the phenological period;
determining the first net productivity for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio and the first light energy utilization ratio for the phenological period.
3. The method of claim 2, wherein determining the temperature stress coefficient for the phenological period based on the actual temperature and the ideal temperature for the phenological period comprises:
determining a first temperature stress coefficient of the phenological period based on the ideal temperature of the phenological period and a first preset algorithm;
determining a second temperature stress coefficient of the phenological period based on the actual temperature of the phenological period, the ideal temperature of the phenological period and a second preset algorithm, wherein the second preset algorithm is different from the first preset algorithm;
determining the product of the first temperature stress coefficient and the second temperature stress coefficient of the phenological period as the temperature stress coefficient of the phenological period.
4. The method of claim 1, wherein the meteorological data comprises: actual temperature, precipitation and solar radiation, determining a first net productivity of the crop for the phenological period based on the remote-sensed image and the meteorological data for the phenological period, the method comprising:
determining a water stress coefficient of the phenological period based on the actual temperature of the phenological period and the precipitation of the phenological period;
determining the product of the water stress coefficient of the phenological period and the predetermined ideal light energy utilization rate as a second light energy utilization rate of the phenological period;
determining the photosynthetically active radiation absorption ratio of the phenological period based on the remote sensing image of the phenological period;
determining the first net productivity for the phenological period based on the amount of solar radiation, the photosynthetically active radiation absorption ratio, and the second light energy utilization ratio for the phenological period.
5. The method of claim 2 or 4, wherein determining the photosynthetically active radiation absorption ratio for the phenological period comprises:
carrying out image processing on the remote sensing image of the phenological period to obtain a near infrared band reflection value and a red light band reflection value of the phenological period;
determining the vegetation index of the phenological period based on the near-infrared band reflection value and the red-light band reflection value of the phenological period;
determining the photosynthetically active radiation absorption ratio of the phenological period based on the vegetation index of the phenological period.
6. The method of claim 1, wherein the pre-set yield estimation model comprises: estimating the yield of the crop based on the first net productivity and the preset yield estimation model for each phenological period, wherein the preset ratio coefficient between the carbon content in the dry matter of the crop and the dry matter, the preset ratio coefficient between the biomass of the crop on the ground and the total biomass, the preset ratio coefficient between the water content in the grain of the crop during the storage period and the grain yield of the crop and the preset harvesting coefficient of the crop comprise:
determining the sum of the first net productivity for all phenological sessions as a second net productivity;
determining the yield based on the second net productivity, crop species, and a preset yield estimation model.
7. An apparatus for assessing crop yield, the apparatus comprising:
the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring a remote sensing image of a crop to be estimated in each phenological period and meteorological data of the phenological period;
a determining unit for determining a first net productivity of the crop in each phenological period based on the remote sensing image and the meteorological data in that phenological period;
an estimation unit for estimating the yield of the crop based on the first net productivity and a preset yield estimation model of each phenological period;
the pre-estimating unit is further used for determining a first harvesting coefficient based on the harvesting mode and the crop type; and estimating the crop production based on the first harvest factor, the first net productivity for each phenological period, and the predetermined production estimation model.
8. An electronic device comprising a memory and a processor, the memory having stored therein computer program instructions, wherein the computer program instructions, when read and executed by the processor, perform the steps of the method according to any one of claims 1-6.
9. A storage medium having stored thereon computer program instructions which, when read and executed by a computer, perform the steps of the method according to any one of claims 1-6.
Priority Applications (1)
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| CN113743819B (en) * | 2021-09-15 | 2024-03-26 | 二十一世纪空间技术应用股份有限公司 | Crop yield estimation method, device, electronic equipment and storage medium |
| CN114510528B (en) * | 2022-02-15 | 2023-11-17 | 平安科技（深圳）有限公司 | Crop yield display method, device electronic equipment and storage medium |
| CN115577866A (en) * | 2022-12-09 | 2023-01-06 | 中化现代农业有限公司 | Method and device for predicting waiting period, electronic equipment and storage medium |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| CN102650587A (en) * | 2012-05-11 | 2012-08-29 | 中国农业大学 | Crop biomass inversion method based on SEBAL-HJ model |
| CN104732299A (en) * | 2015-04-03 | 2015-06-24 | 中国农业科学院农业信息研究所 | Maize yield combined prediction system and method |
| CN106295865A (en) * | 2016-08-02 | 2017-01-04 | 沈阳农业大学 | A kind of Forecasting Methodology of rice yield |
| CN108985588A (en) * | 2018-06-28 | 2018-12-11 | 中国科学院遥感与数字地球研究所 | A kind of crop yield prediction remote sensing estimation method, device and system |
| CN109063893A (en) * | 2018-06-25 | 2018-12-21 | 浙江大学 | A kind of rice yield estimating and measuring method of dynamic harvest index in conjunction with net primary productivity |
| CN109272161A (en) * | 2018-09-18 | 2019-01-25 | 三亚中科遥感研究所 | Rice yield estimation method based on dynamic HI |
| CN109614891A (en) * | 2018-11-27 | 2019-04-12 | 北京师范大学 | Crops recognition methods based on phenology and remote sensing |
-
2019
- 2019-06-21 CN CN201910547885.XA patent/CN110243406B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| CN102650587A (en) * | 2012-05-11 | 2012-08-29 | 中国农业大学 | Crop biomass inversion method based on SEBAL-HJ model |
| CN104732299A (en) * | 2015-04-03 | 2015-06-24 | 中国农业科学院农业信息研究所 | Maize yield combined prediction system and method |
| CN106295865A (en) * | 2016-08-02 | 2017-01-04 | 沈阳农业大学 | A kind of Forecasting Methodology of rice yield |
| CN109063893A (en) * | 2018-06-25 | 2018-12-21 | 浙江大学 | A kind of rice yield estimating and measuring method of dynamic harvest index in conjunction with net primary productivity |
| CN108985588A (en) * | 2018-06-28 | 2018-12-11 | 中国科学院遥感与数字地球研究所 | A kind of crop yield prediction remote sensing estimation method, device and system |
| CN109272161A (en) * | 2018-09-18 | 2019-01-25 | 三亚中科遥感研究所 | Rice yield estimation method based on dynamic HI |
| CN109614891A (en) * | 2018-11-27 | 2019-04-12 | 北京师范大学 | Crops recognition methods based on phenology and remote sensing |
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