CN118446559B - Soybean unit production remote sensing estimation method and device based on data and knowledge dual driving - Google Patents

Abstract

The invention provides a soybean unit yield remote sensing estimation method and device based on data and knowledge dual drive, and relates to the technical field of agricultural production, wherein the method comprises the following steps: acquiring remote sensing data of crops, and determining a first average leaf area index and a second average leaf area index of the crops through the remote sensing data, wherein the first average leaf area index is an average leaf area index from seedling emergence to flowering, and the second average leaf area index is an average leaf area index from flowering to mature; inputting the first average leaf area index and the second average leaf area index into a crop unit yield estimation model to obtain the unit yield of the crop output by the crop unit yield estimation model; the crop yield per unit estimation model is obtained by training the recurrent neural network based on the first average leaf area index, the second average leaf area index and the yield per unit of a plurality of crop samples. By adopting the technical scheme, the problem of poor estimation effect on the unit yield of crops in the prior art is solved.

Description

Soybean yield remote sensing estimation method and device based on dual drive of data and knowledge

Technical Field

The present invention relates to the field of agricultural production technology, and in particular to a soybean yield remote sensing estimation method and device based on dual drive of data and knowledge.

Background Art

Early crop yield estimation models were mainly driven by data. Some machine learning models under data-driven backgrounds, such as distributed gradient boosting models, random forest models, support vector machine models, and artificial neural networks, have been widely used in crop yield remote sensing estimation applications. An empirical statistical model is constructed by combining crop growth state parameters inverted by remote sensing (such as Leaf Area Index (LAI), the proportion of photosynthetically active radiation absorbed by the canopy, and various vegetation indices (VIS)) with crop yield.

However, the accuracy of yield prediction by empirical statistical models depends on the data quality of a large number of training samples and the representativeness of the model. Although remote sensing data provides certain data support for the construction of yield estimation models, the accuracy of crop yield estimation is always limited and the estimation effect is poor. Furthermore, with the continuous in-depth understanding of the crop growth process, crop growth models driven by agronomic mechanism knowledge predict the evolution of crops from sowing to harvesting by simulating photosynthesis, gas exchange between the canopy and the atmosphere, phenology, soil moisture and temperature changes, biomass accumulation and grain formation. The prediction accuracy is compared with empirical statistical models, but the actual promotion and application of crop growth models depends on a large number of ground observation samples, consumes a lot of manpower and material resources, and is difficult to apply on a large scale.

Summary of the invention

In view of the problems existing in the prior art, the present invention provides a soybean yield remote sensing estimation method and device based on dual drive of data and knowledge, so as to solve the problem of poor crop yield estimation effect in the prior art, so as to achieve high-precision estimation of crop yield.

In a first aspect, the present invention provides a soybean yield remote sensing estimation method based on dual drive of data and knowledge, comprising: acquiring remote sensing data of crops, and determining a first average leaf area index and a second average leaf area index of the crops through the remote sensing data, wherein the first average leaf area index is the average leaf area index from seedling to flowering stage, and the second average leaf area index is the average leaf area index from flowering to maturity stage; inputting the first average leaf area index and the second average leaf area index of the crops into a crop yield estimation model to obtain the yield of the crops output by the crop yield estimation model; the crop yield estimation model is obtained by training a recurrent neural network based on the first average leaf area index, the second average leaf area index and the yield of multiple crop samples.

Optionally, the first average leaf area index, the second average leaf area index and the yield per unit area of the multiple crop samples are obtained by simulating a plurality of different growth scenarios with a crop growth model, each growth scenario corresponding to a set of scenario parameter combinations, and the scenario parameter combination includes a combination of meteorological parameters, crop parameters, soil parameters and management measure parameters.

Optionally, the input of the crop growth model is meteorological parameters, crop parameters, soil parameters and management measure parameters, and the output is leaf area index, date, development stage, total aboveground dry weight, storage organ dry weight, leaf dry weight, stem weight, root weight, transpiration rate, actual root depth, actual root zone soil moisture content and total water content in the soil profile.

Optionally, the acquiring of remote sensing data of crops includes: determining the maturity type of the crops based on the accumulated temperature zone; determining a first time range from the seedling stage to the flowering stage and a second time range from the flowering stage to the maturity stage of the crops based on the maturity type of the crops; and acquiring corresponding remote sensing data based on the first time range and the second time range.

Optionally, the recurrent neural network is a gated recurrent unit model.

Optionally, determining the first average leaf area index and the second average leaf area index of the crop through the remote sensing data includes: obtaining the first average leaf area index and the second average leaf area index of the crop based on inverting surface reflectivity data in the remote sensing data.

In a second aspect, the present invention provides a soybean yield remote sensing estimation device based on dual drive of data and knowledge, the device comprising: a first processing module, used to obtain remote sensing data of crops, and determine a first average leaf area index and a second average leaf area index of the crops through the remote sensing data, the first average leaf area index being the average leaf area index from seedling to flowering stage, and the second average leaf area index being the average leaf area index from flowering to maturity stage; a second processing module, used to input the first average leaf area index and the second average leaf area index of the crop into a crop yield estimation model, to obtain the yield of the crop output by the crop yield estimation model; wherein the crop yield estimation model is obtained by training a recurrent neural network based on the first average leaf area index, the second average leaf area index and the yield of multiple crop samples.

In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any of the above-mentioned methods for remote sensing estimation of soybean yield based on dual drive of data and knowledge.

In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for remote sensing estimation of soybean yield based on dual drive of data and knowledge.

In a fifth aspect, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for remote sensing estimation of soybean yield based on dual drive of data and knowledge.

The soybean yield remote sensing estimation method based on dual drive of data and knowledge provided by the present invention obtains the remote sensing data of the crop, determines the first average leaf area index and the second average leaf area index of the crop, and inputs the first average leaf area index and the second average leaf area index into the crop yield estimation model obtained by the recurrent neural network model, so as to realize the estimation of the crop yield. The problem of poor crop yield estimation effect in the prior art is solved. By using the leaf area index as the input indicator, the crop yield estimation process is simplified with the help of the recurrent neural network model, and the estimation accuracy is improved.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are 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.

FIG1 is a flow chart of a soybean yield remote sensing estimation method based on dual drive of data and knowledge provided by the present invention.

FIG2 is a schematic diagram of the GRU model structure provided by the present invention.

FIG3 is a schematic diagram of the structure of the WOFOST model provided by the present invention.

FIG4 is a schematic diagram of a flow chart of the hybrid modeling method for estimating crop yields based on data and knowledge driven hybrid modeling provided by the present invention.

FIG5 is a schematic diagram of soybean LAI simulation provided by the present invention.

FIG. 6 is a simulated diagram of the dry weight of soybean storage organs provided by the present invention.

FIG7 is a single-point scale soybean yield estimation accuracy verification diagram for 2022-2023 provided by the present invention.

FIG8 is a regional-scale soybean yield estimation accuracy verification diagram for 2019-2022 provided by the present invention.

FIG9 is a schematic diagram of the structure of a soybean yield remote sensing estimation device based on dual drive of data and knowledge provided by the present invention.

FIG. 10 is a schematic diagram of the structure of an electronic device provided by the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The above method provided by the present invention is illustrated below by examples of specific application scenarios.

FIG1 is a flow chart of a soybean yield remote sensing estimation method based on dual drive of data and knowledge provided by the present invention. As shown in FIG1 , the method includes the following steps S102-S104:

Step S102: Acquire remote sensing data of crops, and determine a first average leaf area index and a second average leaf area index of the crops through the remote sensing data, wherein the first average leaf area index is the average leaf area index from seedling emergence to flowering stage, and the second average leaf area index is the average leaf area index from flowering to maturity stage.

It should be noted that the crop described in this application is soybean.

It should be noted that the Leaf Area Index (LAI) is an important indicator for measuring the degree of vegetation coverage, which refers to the vegetation leaf area per unit projected area on unit ground.

In an exemplary embodiment, the acquisition of crop remote sensing data may be achieved by following steps S11-S13:

Step S11: determining the maturity type of the crop according to the accumulated temperature zone.

It should be noted that due to climate differences in different regions, the research area is divided according to the accumulated temperature zone in practice. The maturity types of soybeans include: early maturity, medium early maturity, medium maturity, medium late maturity, and late maturity. According to the division of accumulated temperature zones, combined with literature data, it is determined which type of soybeans should be planted in different accumulated temperature zones.

Step S12: Determine a first time range from the emergence to the flowering stage and a second time range from the flowering to the maturity stage of the crop according to the maturity type of the crop.

It should be noted that the stage division of crops is mainly defined by effective accumulated temperature. Effective accumulated temperature is the accumulation of daily average temperature above the base temperature of the crop. When the effective accumulated temperature reaches the accumulated temperature threshold required for a growth stage, the growth of the crop will enter the next growth stage. The time of the crop growth stage is usually expressed in calendar days. The time range is determined by converting calendar time into thermal time. The length and duration of the crop growth stage will be adjusted according to the temperature conditions in different years. The calculation formula for effective accumulated temperature is as follows:

Where T is the average daily temperature. is the lower limit of the critical temperature for development. It is the upper limit temperature for soybean development.

Step S13: Acquire corresponding remote sensing data based on the first time range and the second time range.

As an optional embodiment, the time range used by crops to reach different growth stages is mainly based on the ERA5-Land Daily Aggregated - ECMWF ClimateReanalysis data provided by the GEE (Google Earth Engine) platform, which provides daily average temperature data at an altitude of 2 meters above the surface from 1950 to the present.

In an exemplary embodiment, determining the first average leaf area index and the second average leaf area index of the crop by using the remote sensing data can be achieved by the following steps S21:

Step S21: obtaining a first average leaf area index and a second average leaf area index of the crop based on inversion of the surface reflectance data in the remote sensing data.

As an optional embodiment, the remote sensing data is usually Sentinel-2 Level-2A data obtained by searching, processing and downloading through the GEE (Google Earth Engine) platform, and the LA2-level data is the surface reflectance data that has completed radiation calibration and atmospheric correction.

As an optional embodiment, when performing inversion based on surface reflectivity data, the The vegetation index is implemented, where is defined as:

In the above formula, NIR is the reflectivity of the near-infrared band (B8A) of the Sentinel-2 image, and RE is the reflectivity of the red edge band (B5, 704nm) of the Sentinel-2 image.

Further, based on The LAI of soybean can be further expressed as:

Step S104: inputting the first average leaf area index and the second average leaf area index of the crop into a crop yield estimation model to obtain the yield of the crop output by the crop yield estimation model.

The crop yield estimation model is obtained by training a recurrent neural network based on the first average leaf area index, the second average leaf area index and the yield of multiple crop samples.

It should be noted that the recurrent neural network is a gated recurrent unit model. The gated recurrent unit (GRU) is mainly used to solve problems such as long-term memory and gradients in back propagation. GRU merges the gate structure of LSTM and improves the complex unit structure of the previous recurrent neural network. It can increase the training speed of the network and ensure that the training accuracy of the model will not decrease. Compared with the "three-gate" structure of LSTM, GRU only has two gate structures, the update gate and the reset gate, to control the flow of long-term information. The network structure is simplified and the training speed of the model can be increased. The calculation process of the GRU model is:

Among them, sigmoid() is the S-type activation function; tanh() represents the hyperbolic tangent activation function; represents the output of the reset gate at time t; represents the output of the update gate at time t; represents the weight matrix of the reset gate; represents the weight matrix of the update gate; The weight matrix representing the candidate hidden state; Represents the input of the GRU model at time t; Represents the hidden layer state output of the GRU model at time t; represents the candidate hidden state of the current input, Represents the hidden layer state output of the GRU model at time t-1.

It should be noted that the present invention uses the average LAI of crops from seedling to flowering stage (0<DVS≤1) and flowering to maturity stage (1<DVS≤2) as the time series input feature of the model, and the yield of the crop as the output feature of the model.

As an optional embodiment, in the process of training the model, the model optimizer is Adam, the loss function is the mean square error, and the network is trained multiple times to find the optimal GRU layer number, number of neurons units, epochs, batch size, dropout and other parameter values, as shown in Figure 2, which is a schematic diagram of the GRU model structure provided by the present invention.

It should be noted that the first average leaf area index, the second average leaf area index and the yield per unit area of the multiple crop samples are obtained by simulating a variety of growth scenarios with a crop growth model, and each growth scenario corresponds to a set of scenario parameter combinations, which include a combination of meteorological parameters, crop parameters, soil parameters and management measures parameters.

As an optional embodiment, the crop growth model can select the World Food Studies Model (abbreviated as WOFOST). FIG. 3 is a schematic diagram of the structure of the WOFOST model provided by the present invention. As shown in the figure, the model mainly includes four modules: meteorology, crops, soil and field management. The growth and development process of various crops is simulated with corresponding meteorological conditions, crop varieties, soil parameters, and field management measures as constraint mechanisms. The input of the crop growth model is meteorological parameters, crop parameters, soil parameters and management measure parameters, and the output is leaf area index, date, development stage, total aboveground dry weight, storage organ dry weight, leaf dry weight, stem weight, root weight, transpiration rate, actual root depth, actual root zone soil moisture content and total soil profile moisture.

It should be noted that the meteorological parameters include: date (DAY, d), incident shortwave radiation (IRRAD, KJ/m2/day), average vapor pressure (VAP, kpa), maximum temperature (TMAX, ℃), minimum temperature (TMIN, ℃), average wind speed (WIND, m/s), precipitation (RAIN, mm) and snow depth (SNOWDEPTH, cm).

It should be noted that the crop parameters include: low temperature threshold for emergence, high temperature threshold for emergence, accumulated temperature from sowing to emergence, accumulated temperature from emergence to flowering, accumulated temperature from flowering to maturity, initial total dry weight of crops, maximum growth rate of leaf area index, specific leaf area (DVS=0.0), specific leaf area (DVS=0.45), specific leaf area (DVS=0.90), specific leaf area (DVS=2.00), leaf life at 35 degrees Celsius, low temperature threshold of leaf age, extinction coefficient of visible light diffusion as a function of DVS, light energy utilization rate of a single leaf as a function of daily average temperature, maximum leaf Assimilation rate (DVS=0.0), maximum leaf Assimilation rate (DVS=1.7), maximum leaf Assimilation rate (DVS=2.0), assimilate conversion efficiency of leaves, assimilate conversion efficiency of storage organs, assimilate conversion efficiency of roots, assimilate conversion efficiency of stems, relative variable of respiration rate when temperature increases by 10℃, maintenance respiration rate of leaves, maintenance respiration rate of storage organs, maintenance respiration rate of roots, maintenance respiration rate of stems, ratio of total dry matter to roots as a function of DVS, ratio of aboveground dry matter to leaves as a function of DVS, ratio of aboveground dry matter to stems as a function of DVS, ratio of aboveground dry matter to organs as a function of DVS, maximum relative mortality of leaves caused by water stress, initial root depth, maximum daily increase in root depth, maximum root depth.

It should be noted that soil parameters mainly include the wilting coefficient (SMW), saturated water content (SM0) and field holding capacity (SMFCF), and the values of these parameters mainly depend on the soil texture and structure.

It should be noted that management measure parameters include planting date, etc.

As an optional embodiment, the present invention can widely set the four types of input data based on the "lookup table" concept to generate numerous growth scenarios through their permutations and combinations, and use the model to simulate the crop growth and development and yield formation process under each growth scenario one by one, thereby constructing a huge agronomic knowledge base for the yield formation process. The huge agronomic knowledge base for the yield formation process constructed through crop samples solves the problem that the existing estimation method relies on a large number of ground observation samples, and the acquisition of observation samples requires a lot of manpower and material resources. The constructed agronomic knowledge base for the yield formation process can be more suitable for large-scale crop yield estimation scenarios.

It should be noted that the storage organ dry weight on the last day of the growth period is used to characterize the crop yield per unit area in this application.

As an optional embodiment, after obtaining the crop yield per unit area output by the yield estimation model, the estimation accuracy can be further verified. The estimation accuracy verification includes two aspects: actual sample point verification and statistical data verification. Among them, the actual sample point verification data comes from sample data collected in the field, and the accuracy verification indicators include the determination coefficient (Coefficient of Determination), root mean square error RMSE (Root Mean Square Error) and mean relative error MRE (Mean Relative Error). The higher it is, the smaller the RMSE and MRE are, which means the performance of the yield estimation model is better. The calculation formula is as follows:

in, and Represent the actual yield and estimated yield, respectively. is the mean of the actual yield.

Through the above steps S102-S104, by acquiring the remote sensing data of the crop, the first average leaf area index and the second average leaf area index of the crop are determined, and the first average leaf area index and the second average leaf area index are input into the crop yield estimation model obtained by the recurrent neural network model, so as to realize the estimation of the crop yield. The problem of poor crop yield estimation effect in the prior art is solved, and by using the leaf area index as an input indicator, the crop yield estimation process is simplified with the help of the recurrent neural network model, and the estimation accuracy is improved.

The executor of each step in the method can be a soybean yield remote sensing estimation device based on dual drive of data and knowledge. The device can be implemented through software and/or hardware. The device can be integrated in an electronic device. The electronic device can be a terminal device (such as a smart phone, a personal computer, etc.), or a server (such as a local server or a cloud server, or a server cluster, etc.), or a processor, or a chip, etc.

Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. In order to better understand the above method, the above process is described below in conjunction with the embodiments, but the technical solutions of the embodiments of the present invention are not limited. Specifically:

Based on agronomic mechanisms, the present invention couples machine learning methods and crop growth models to establish a hybrid modeling method driven by data and knowledge to estimate crop yields. FIG4 is a flow chart of the hybrid modeling method driven by data and knowledge provided by the present invention to estimate crop yields. For ease of understanding, the present invention takes soybeans as an example. The present invention solves the problems of sample scarcity and poor crop yield estimation in yield estimation research, and expands the spatiotemporal generalization capability of the yield estimation model.

1. Construction of agronomic knowledge base on soybean yield formation process

WOFOST can quantitatively simulate the crop growth process under the influence of meteorological and other environmental factors with a day-long step, and the model's description of the process is universal, and different crops in different geographical locations can be simulated by changing parameters. The WOFOST model is based on the simulation of crop physiological and ecological processes such as assimilation, respiration, transpiration, and dry matter distribution, mainly including the simulation of crop growth under potential growth conditions, water-limited conditions, and nutrient-limited conditions. Taking the Northeast Black Soil Region as an example, the model parameters are set as follows.

(1) Meteorological parameters

The meteorological parameters required by the WOFOST model include date (DAY, d), incident shortwave radiation (IRRAD, KJ/m2/day), average vapor pressure (VAP, kpa), maximum temperature (TMAX, ℃), minimum temperature (TMIN, ℃), average wind speed (WIND, m/s), precipitation (RAIN, mm) and snow depth (SNOWDEPTH, cm). The meteorological observation dataset used in the study comes from the meteorological website, and 319 meteorological stations in the Northeast Black Soil Region are selected, with a time range of 1980-2023. In order to further screen meteorological stations, based on the 2023 soybean planting distribution map, a total of 51 meteorological stations within 1 km of the soybean planting plot were selected to provide meteorological input data for the model. The meteorological observation dataset contains daily observation data, including: maximum temperature (℃), average temperature (℃), minimum temperature (℃), average wind speed (m/sec), precipitation (mm), average vapor pressure (kPa) and sunshine hours (h).

(2) Crop parameters

The crop variety parameters required by the WOFOST model are shown in Table 1. Since the growth process of soybeans is regulated by accumulated temperature, the present invention has graded the accumulated temperature from emergence to flowering (TSUM1) and the accumulated temperature from flowering to maturity (TSUM2) of soybeans according to existing research, and divided the soybeans planted in the Northeast Black Soil Region into five maturity types: early, medium-early, medium, medium-late and late. Among them, early-maturing soybeans require less accumulated temperature to reach different growth stages, and late-maturing soybeans have a greater demand for accumulated temperature. For other non-sensitive crop parameters, such as the low temperature threshold (TBASEM) of soybean emergence, the high temperature threshold (TEFFMX), the initial crop total dry weight (TDWI), and the specific leaf area (SLATB), they are mainly set with reference to the soybean default parameter values provided by the WOFOST model and the calibration values of related studies in the same study area.

Table 1. Crop parameter settings of WOFOST model

Table 1. WOFOST model crop parameter settings

Table 1. WOFOST model crop parameter settings

Table 1. WOFOST model crop parameter settings

(3) Soil parameters

The main soil parameters required by the WOFOST model include the wilting coefficient (SMW), saturated water content (SM0) and field water holding capacity (SMFCF), etc. The values of these parameters mainly depend on soil texture and structure. It mainly includes soil spatial distribution, soil physical properties, soil chemical properties and soil nutrient data. The soil types in the Northeast Black Soil Region are divided into four types: sandy loam, light loam, medium loam and heavy loam. The parameter values of different soil types are shown in Tables 2 and 3. The parameters involved mainly include the wilting coefficient (SMW), field water holding capacity (SMFCF), saturated water content (SM0), saturated hydraulic conductivity (K0), critical air content of soil ventilation (CRAIRC), deep seedling bed first layer topsoil seepage parameters (SPADS), deep seedling bed second layer topsoil seepage parameters (SPODS), shallow seedling bed first layer topsoil seepage parameters (SPASS), shallow seedling bed second layer topsoil seepage parameters (SPOSS), etc. The values of other parameters refer to the existing literature research and the default parameter values provided by the WOFOST model.

Table 2 Main soil parameter settings of WOFOST model

Table 3 Main soil parameter settings of WOFOST model (continued)

(4) Management measures parameters

The WOFOST model is based on the simulation of crop physiological and ecological processes such as assimilation, respiration, transpiration and dry matter distribution, mainly including the simulation of crop growth under potential growth conditions, water limitation conditions and nutrient limitation conditions. Since soybeans planted in the black soil region of Northeast China are basically rain-fed, the study used the water stress model to simulate the soybean growth process. According to the ground survey results, four soybean sowing dates were set for crop growth simulation, namely April 20, April 30, May 10 and May 20.

In order to simulate the growth process of soybeans under various agricultural scenarios and solve the problem of sample dependence of existing models, a wide range of settings for four types of input data were made based on the "lookup table" idea, and their arrangement and combination generated many growth scenarios. The model was used to simulate the growth and yield formation process of soybeans under each growth scenario one by one, with a total of more than 150,000 (38 51 4 5 4) soybean planting scenarios and finally constructed a large multi-scenario simulation data set (as shown in Table 4).

Table 4 WOFOST model simulation scenarios

Further, as shown in Table 5, the input simulation parameters of the model mainly include date (day), development stage (DVS), leaf area index (LAI), total aboveground dry weight (TAGP), storage organ dry weight (TWSO), leaf dry weight (TWLV), stem weight (TWST), root weight, transpiration rate (TRA), actual root depth (RD), actual root zone soil moisture content (SM) and soil profile total water content (WWLOW). After the parameters are given, the WOFOST model can realize continuous simulation of the crop growth process with a step length of days starting from the emergence of crops, wherein the storage organ dry weight on the last day of the growth period is the soybean yield of concern in the present invention.

Table 5. Simulation parameters output by WOFOST model

2. Intelligent Modeling of Soybean Yield Estimation

The present invention uses a recurrent neural network GRU model to perform intelligent modeling for soybean yield estimation. The calculation process of the GRU model is:

Among them, sigmoid() is the S-type activation function; tanh() represents the hyperbolic tangent activation function; represents the output of the reset gate at time t; represents the output of the update gate at time t; represents the weight matrix of the reset gate; represents the weight matrix of the update gate; The weight matrix representing the candidate hidden state; Represents the input of the GRU model at time t; Represents the hidden layer state output of the GRU model at time t; represents the candidate hidden state of the current input, Represents the hidden layer state output of the GRU model at time t-1.

In the process of model construction, considering that the WOFOST model divides the growth stage of crops into three stages: emergence, flowering, and maturity, the average LAI (LAImean) of soybeans from emergence to flowering (0<DVS≤1) and flowering to maturity (1<DVS≤2) is used as the temporal input feature of the model, and the yield of soybeans is used as the output feature of the model. The simulated data set is divided into training samples and test samples in a ratio of 9:1. The model optimizer is Adam (Adaptive MomentEstimation), and the loss function is the mean square error. The network is trained multiple times to find the optimal GRU layer number, number of neurons units, epochs, batch size, dropout and other parameter values.

3. Remote sensing estimation of soybean yield based on multi-scenario simulation dataset

Due to climate differences in different regions, it is necessary to divide the regions according to accumulated temperature, so as to download remote sensing images in different regions. Taking the Northeast Black Soil Area as an example, referring to the accumulated temperature zone division standard of Heilongjiang Province in the 1990s, taking the daily average temperature ≥ 10℃ active accumulated temperature as the heat resource indicator, based on the daily average temperature data of meteorological stations in the Northeast Black Soil Area from 1981 to 2019, using the five-day sliding average method, with 200℃·d as the difference, using the empirical frequency method with an 80% guarantee rate to take the value, the Northeast Black Soil Area is divided into 10 accumulated temperature zones.

With reference to the classification of soybean planting maturity types in different accumulated temperature zones in Heilongjiang Province, the present invention sets five different soybean planting maturity types, namely early-maturing, medium-early-maturing, medium-maturing, medium-late-maturing, and late-maturing, for different accumulated temperature zones in the Northeast Black Soil Region, thereby calculating the soybean phenological period in planting areas with different soybean maturity types. Among them, provinces such as Jilin and Liaoning mainly plant medium-maturing, medium-late-maturing, and late-maturing soybeans, and the northern region tends to plant early-maturing and medium-early-maturing soybeans.

The division of different growth stages of soybean is mainly based on the effective accumulated temperature ( ) is used for judgment, and the calculation formula for effective accumulated temperature is as follows:

Where T is the average daily temperature. is the lower limit of the critical temperature for development. is the upper limit temperature of soybean development. Referring to the setting of soybean variety parameters in the WOFOST model when constructing the simulation data set, the accumulated temperature thresholds corresponding to the growth stages for different soybean planting maturity types are shown in Table 6, where TSUM is the effective accumulated temperature required for soybean from sowing to germination, TSUM1 is the effective accumulated temperature required for soybean from germination to flowering, and TSUM2 is the effective accumulated temperature required for soybean from flowering to maturity. According to the results of field surveys, the soybean sowing time in Heilongjiang Province and Inner Mongolia Autonomous Region was uniformly set to May 5 in the study, while the soybean sowing time in Jilin Province and Liaoning Province was earlier and uniformly set to May 1. The soybean emergence time in the Northeast Black Soil Region is set to no later than June 1, and the maturity time is set to no later than October 1.

Table 6 Accumulated temperature thresholds corresponding to different soybean maturity types

The calculation of soybean phenology is mainly based on the ERA5-LandDaily Aggregated - ECMWF Climate Reanalysis data provided by the GEE (Google Earth Engine) platform. Based on the calculation results of soybean phenological period, the study used the K-means clustering method (K-means) to divide the phenological extraction results into 10 categories, obtained remote sensing images for different regions, inverted the soybean LAI, and completed the data download.

4. Results and Analysis

1. Analysis of simulated data sets

FIG5 is a schematic diagram of soybean LAI simulation provided by the present invention, and FIG6 is a simulation diagram of soybean storage organ dry weight provided by the present invention. It can be seen from the results that the diversified setting of parameters provides rich simulation results for the simulation. The growth process of soybeans under different production scenarios is also different. The simulation data provides a wide range of variability, ensuring the completeness of the training sample data set and the representativeness of the soybean growth conditions in the target area, providing data support for the subsequent intelligent modeling of yield estimation.

2. Yield accuracy evaluation

The single-point scale accuracy of the model prediction results was verified using the ground-measured soybean yield data for 2022 and 2023. Figure 7 is a single-point scale soybean yield estimation accuracy verification diagram for 2022-2023 provided by the present invention. It can be seen from the results that the prediction model achieves a relatively accurate estimate of soybean yield at a single-point scale. The accuracy verification results show that the determination coefficient is 0.73, and the significance test (P<0.01, where P is used to characterize probability) reaches an extremely significant level. The root mean square error is 288.72 kg/ha, the average relative error is 10.06%, and the overall prediction accuracy is higher than 85%. The regional scale accuracy of the model prediction results was verified using the municipal soybean yield statistical data from 2019 to 2022. As shown in Figure 8, it is a regional scale soybean yield estimation accuracy verification diagram for 2019-2022 provided by the present invention. The model's overall estimation accuracy determination coefficient for 2019-2022 is 0.62, the root mean square error is 272.36 kg/ha, and the average relative error is 12.08%.

In general, the pixel-scale yield estimation not only obtains yield information at a finer scale, but also performs well at the municipal scale. In addition, this method can realize dynamic and flexible yield estimation based on the available data, showing great potential in large-scale soybean yield estimation.

The present invention is driven by meteorological data, crop varieties, soil parameters and management measures data, and uses the WOFOST model to simulate various scenarios of soybean planting, so as to realize the construction of an agronomic knowledge base for the soybean yield formation process. The simulation scenarios are rich, the simulation process is continuous, and the simulation results are scientific and reasonable, which provides agronomic knowledge support and sufficient sample data for the intelligent modeling of yield estimation. Based on the soybean multi-scenario simulation data set, the deep learning method is used to explore the complex relationship between the key indicator factors of soybean yield formation and the yield, and the intelligent modeling of yield estimation is realized. The modeling does not rely on ground measured samples, which provides theoretical guidance for the realization of early high-precision yield estimation, agricultural production and agricultural decision-making. At the same time, the study area is divided into accumulated temperature zones, and different soybean planting maturity types are set. Combined with medium and high-resolution remote sensing data, meteorological data, statistical yields, and measured yield data, the hybrid modeling method proposed in the present invention is used to carry out pixel-scale soybean yield remote sensing estimation, and the demonstration application and accuracy evaluation of the method are realized. The results show that the prediction model achieves high yield estimation accuracy at both single point and regional scales. The accuracy of the model was evaluated using the measured yield data of the Northeast Black Soil Region in 2022 and 2023. The coefficient of determination was 0.73, which passed the significance test (P<0.01) and reached an extremely significant level. The root mean square error was 288.72 kg/ha, the average relative error was 10.06%, and the overall prediction accuracy was higher than 85%. Compared with the municipal statistical data, the overall performance of the Northeast Black Soil Region's yield estimation is , RMSE =272.36 kg/ha, MRE = 12.08%, passing the significance test (P<0.01) and reaching an extremely significant level. The yield estimation model showed a high spatiotemporal continuity in regional scale demonstration applications, and was able to achieve large-scale, precise, and flexible yield estimation.

In short, the present invention proposes a hybrid modeling method that combines remote sensing data, crop growth models and deep learning to estimate soybean yield at the pixel scale. The method is supported by agronomic mechanisms and gives full play to the advantages of process model mechanisms and data mining advantages of deep learning. The research method meets the requirements of agronomic knowledge support and intelligent level for yield estimation modeling. Combined with high-resolution remote sensing images, it can achieve a relatively ideal soybean yield estimation performance at the pixel scale, providing a new idea and method for crop yield estimation. It solves the problem of poor crop yield estimation in the existing technology under the premise of insufficient ground samples.

The following is a description of the soybean yield remote sensing estimation device based on dual drive of data and knowledge provided by the present invention. The soybean yield remote sensing estimation device based on dual drive of data and knowledge described below and the soybean yield remote sensing estimation method based on dual drive of data and knowledge described above can be referenced to each other.

FIG9 is a schematic diagram of the structure of a soybean yield remote sensing estimation device based on dual drive of data and knowledge provided by the present invention. As shown in FIG9 , the device includes:

The first processing module 902 is used to obtain remote sensing data of crops, and determine a first average leaf area index and a second average leaf area index of the crops through the remote sensing data, wherein the first average leaf area index is an average leaf area index from seedling to flowering stage, and the second average leaf area index is an average leaf area index from flowering to maturity stage;

The second processing module 904 is used to input the first average leaf area index and the second average leaf area index of the crop into the crop yield estimation model to obtain the yield of the crop output by the crop yield estimation model; wherein the crop yield estimation model is obtained by training a recurrent neural network based on the first average leaf area index, the second average leaf area index and the yield of multiple crop samples.

By using the above-mentioned soybean yield remote sensing estimation device based on dual drive of data and knowledge, the first average leaf area index and the second average leaf area index of the crop are determined by acquiring the remote sensing data of the crop, and the first average leaf area index and the second average leaf area index are input into the crop yield estimation model obtained by the recurrent neural network model, so as to realize the estimation of the crop yield. The problem of poor crop yield estimation effect in the prior art is solved. By using the leaf area index as the input indicator, the crop yield estimation process is simplified with the help of the recurrent neural network model, and the estimation accuracy is improved.

Optionally, the first average leaf area index, the second average leaf area index and the yield per unit area of the multiple crop samples are obtained by simulating a plurality of different growth scenarios with a crop growth model, each growth scenario corresponding to a set of scenario parameter combinations, and the scenario parameter combination includes a combination of meteorological parameters, crop parameters, soil parameters and management measure parameters.

Optionally, the input of the crop growth model is meteorological parameters, crop parameters, soil parameters and management measure parameters, and the output is leaf area index, date, development stage, total aboveground dry weight, storage organ dry weight, leaf dry weight, stem weight, root weight, transpiration rate, actual root depth, actual root zone soil moisture content and total water content in the soil profile.

Optionally, the first processing module 902 is also used to determine the maturity type of the crop based on the accumulated temperature zone; determine a first time range from the seedling stage to the flowering stage and a second time range from the flowering stage to the maturity stage of the crop based on the maturity type of the crop; and obtain corresponding remote sensing data based on the first time range and the second time range.

Optionally, the recurrent neural network is a gated recurrent unit model.

Optionally, the first processing module 902 is further used to invert the first average leaf area index and the second average leaf area index of the crop based on the surface reflectance data in the remote sensing data.

It should be noted here that the above-mentioned device provided by the present invention can implement all the method steps implemented by the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as the method embodiment in this embodiment will not be described in detail here.

FIG10 is a schematic diagram of the structure of the electronic device provided by the present invention. As shown in FIG10 , the electronic device may include: a processor 1010, a communications interface 1020, a memory 1030 and a communication bus 1040, wherein the processor 1010, the communications interface 1020 and the memory 1030 communicate with each other through the communication bus 1040. The processor 1010 may call the logic instructions in the memory 1030 to execute the soybean yield remote sensing estimation method based on dual drive of data and knowledge.

In addition, the logic instructions in the above-mentioned memory 1030 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

It should be noted here that the electronic device provided by the present invention can implement all the method steps implemented by the above-mentioned method embodiment, and can achieve the same technical effects. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the soybean yield remote sensing estimation method based on dual drive of data and knowledge provided by the above-mentioned methods.

It should be noted here that the non-transitory computer-readable storage medium provided by the present invention can implement all the method steps implemented by the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as the method embodiment in this embodiment will not be described in detail here.

The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the soybean yield remote sensing estimation method based on dual data and knowledge drive provided by the above-mentioned methods.

It should be noted here that the computer program product provided by the present invention can implement all the method steps implemented by the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those of the method embodiment will not be described in detail here.

The device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. The soybean unit production remote sensing estimation method based on data and knowledge dual drive is characterized by comprising the following steps of:

Acquiring remote sensing data of crops, and determining a first average leaf area index and a second average leaf area index of the crops through the remote sensing data, wherein the first average leaf area index is an average leaf area index from seedling emergence to flowering, and the second average leaf area index is an average leaf area index from flowering to mature;

Inputting the first average leaf area index and the second average leaf area index of the crop to a crop unit yield estimation model to obtain the unit yield of the crop output by the crop unit yield estimation model;

The crop unit yield estimation model is obtained by training a circulating neural network based on a first average leaf area index, a second average leaf area index and unit yield of a plurality of crop samples;

The first average leaf area index, the second average leaf area index and the unit yield of the plurality of crop samples are obtained by simulating a plurality of different growth scenes through a crop growth model, each growth scene corresponds to a set of scene parameter combinations, and the scene parameter combinations comprise combinations of meteorological parameters, crop parameters, soil parameters and management measure parameters.

2. The data and knowledge based dual-driven soybean yield per unit remote sensing estimation method according to claim 1, wherein the crop growth model is input with meteorological parameters, crop parameters, soil parameters and management measure parameters, and the output is leaf area index, date, development stage, total dry weight on the ground, dry weight of storage organ, she Ganchong, stem weight, root weight, transpiration rate, actual root depth, actual root zone soil moisture content and total soil profile water quantity.

3. The method for estimating soybean yield per unit based on dual driving of data and knowledge according to claim 1, wherein the acquiring the remote sensing data of the crop comprises:

Determining the ripeness type of the crop according to the heat accumulation zone;

determining a first time range from seedling emergence to flowering and a second time range from flowering to maturity of the crop according to the maturity of the crop;

And acquiring corresponding remote sensing data based on the first time range and the second time range.

4. A data and knowledge based dual driven soybean production remote sensing estimation method according to any one of claims 1 to 3, wherein the recurrent neural network is a gated recurrent unit model.

5. A data and knowledge based dual drive soybean production remote sensing estimation method according to claim 1 or 3, wherein said determining a first average leaf area index and a second average leaf area index of said crop from said remote sensing data comprises:

And inverting to obtain a first average leaf area index and a second average leaf area index of the crop based on the earth surface reflectivity data in the remote sensing data.

6. A data and knowledge based dual-driven soybean unit production remote sensing estimation device, the device comprising:

The first processing module is used for acquiring remote sensing data of crops and determining a first average leaf area index and a second average leaf area index of the crops according to the remote sensing data, wherein the first average leaf area index is an average leaf area index from seedling emergence to flowering, and the second average leaf area index is an average leaf area index from flowering to mature;

The second processing module is used for inputting the first average leaf area index and the second average leaf area index of the crops to a crop unit yield estimation model to obtain the unit yield of the crops output by the crop unit yield estimation model;

The crop unit yield estimation model is obtained by training a circulating neural network based on a first average leaf area index, a second average leaf area index and unit yield of a plurality of crop samples;

The first average leaf area index, the second average leaf area index and the unit yield of the plurality of crop samples are obtained by simulating a plurality of different growth scenes through a crop growth model, each growth scene corresponds to a set of scene parameter combinations, and the scene parameter combinations comprise combinations of meteorological parameters, crop parameters, soil parameters and management measure parameters.

7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the data and knowledge based dual drive soybean unit remote sensing estimation method of any one of claims 1 to 5 when the program is executed.

8. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the data and knowledge based dual drive soybean unit remote sensing estimation method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program which, when executed by a processor, implements a data and knowledge based dual drive soybean production remote sensing estimation method according to any one of claims 1 to 5.