CN117745004A - Corn nitrogen fertilizer recommendation method and system based on full-growth-period nitrogen nutrition diagnosis - Google Patents
Corn nitrogen fertilizer recommendation method and system based on full-growth-period nitrogen nutrition diagnosis Download PDFInfo
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Abstract
The invention relates to the field of crop growth monitoring, and provides a corn nitrogen fertilizer recommendation method and system based on full-growth-period nitrogen nutrition diagnosis. The method adopts a mode of combining remote sensing observation and crop growth models, forms a real-time dynamic monitoring and diagnosing method suitable for the nitrogen nutrition status of spring corn in full growth season, further utilizes estimated nitrogen nutrition status index data, constructs a recommended calculation method for nitrogen fertilizer dressing of spring corn in winter, has wide coverage period range, is simple and easy to understand, is convenient to operate, has strong practicability, is beneficial to realizing local accurate nitrogen fertilizer management in winter, can reduce nitrogen fertilizer investment while ensuring high yield of corn, realizes high-efficiency utilization of resources, and has environmental and economic benefits.
Description
Technical Field
The invention relates to the field of crop growth monitoring, in particular to a corn nitrogen fertilizer recommendation method and system based on full-growth-period nitrogen nutrition diagnosis.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Currently, the main problems with corn nitrogen nutrient status monitoring and diagnostic methods are:
(1) The construction of the nitrogen nutrition remote sensing estimation model is based on the remote sensing data of a single observation date, the remote sensing inversion model has strong period specificity, and the number of days in the covered growth period is small, so that the method is difficult to expand to any growth days in the whole growth period;
(2) The method based on the mechanistic crop growth model simulation is often limited in accuracy, needs to rely on real-time observation data for calibration, and is insufficient in acquiring space data;
(3) Most researches monitor the nitrogen nutrition status of plants through parameters such as leaf area, biomass and the like, but the plant parameters have large variation in the whole growth period, so that whether the standard diagnosis of nitrogen deficiency is difficult to be defined, and the plant parameters are difficult to be used for calculating the real-time nitrogen fertilizer demand of the plants.
The main problems of the accurate nitrogen fertilizer recommendation method based on the real-time nitrogen nutrition condition of corn are as follows:
the research of directly using the nitrogen nutrition index, which is the current reliable and widely used nitrogen nutrition condition diagnosis index, to conduct nitrogen fertilizer recommendation is insufficient;
compared with the nitrogen nutrition monitoring and diagnosis research of crops, the research of the field accurate nitrogen fertilizer recommended dosage calculation method based on modern high-efficiency data acquisition and processing means such as remote sensing and models is more limited, the established algorithm of few researches at present is generally complex, understanding is difficult, actual popularization and application are not facilitated, and sometimes an unrestricted field of nitrogen fertilizer is required to be additionally arranged as a reference, so that the actual operability is lower.
Disclosure of Invention
The invention provides a corn nitrogen fertilizer recommendation method and system based on full-growth-period nitrogen nutrition diagnosis, which combines the technology of remote sensing and mechanical crop growth model, solves the problem of difficult acquisition of data information of full-growth-period nitrogen nutrition conditions of corn, realizes monitoring and diagnosis of the daily nitrogen nutrition state of the whole growing season of corn, and further constructs a nitrogen fertilizer demand amount calculation method based on real-time nitrogen nutrition conditions, so as to serve the accurate nitrogen fertilizer management in corn fields, and achieve the purposes of stable yield and efficient utilization of resources.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a corn nitrogen fertilizer recommendation method based on full-growth-period nitrogen nutrition diagnosis.
A corn nitrogen fertilizer recommendation method based on full-growth-period nitrogen nutrition diagnosis comprises the following steps:
simulating plant nitrogen concentration and aboveground biomass in the full-growth season of the corn in the season by adopting a localized crop growth model based on meteorological data and management data acquired in the season;
based on the historical nitrogen nutrition index and nitrogen fertilizer consumption data of the corn for years, respectively constructing a first relation model of the corn in a growth stage when only the base fertilizer is applied and no nitrogen fertilizer topdressing is performed, and a second relation model of the corn in the growth stage after the nitrogen fertilizer topdressing is performed;
determining an optimized total nitrogen application amount based on different planting densities based on the relative yield and total nitrogen fertilizer consumption data under different planting densities for years;
based on remote sensing spectrum data of a key period covered in the current season, estimating corn aerial biomass and plant nitrogen concentration data of a current season remote sensing observation date by adopting a remote sensing quantitative inversion model;
coupling remote sensing estimation data with a crop growth model by adopting a particle swarm algorithm, acquiring real-time updated daily plant nitrogen concentration and aboveground biomass data of the whole growing period of the current season corn, and further calculating the daily nitrogen nutrition index of the whole growing period of the current season corn;
determining the date of corn nitrogen nutrition condition diagnosis and nitrogen fertilizer dressing in the season, and extracting the nitrogen nutrition index of the current date; if the nitrogen fertilizer is applied before the current date, calculating a theoretical basic fertilizer nitrogen supply level in a nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to a first relation model, and determining the recommended nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the current theoretical basic fertilizer nitrogen supply level;
if the nitrogen fertilizer topdressing is carried out before the current date, and the current corn nitrogen nutrition index is judged to be smaller than the nitrogen proper threshold lower limit, calculating the theoretical total nitrogen fertilizer supply level in the nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to the second relation model, and determining the recommended supplementary nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the theoretical total nitrogen fertilizer supply level.
Further, the localization process of the crop growth model comprises: acquiring historical multi-year weather, basic soil physicochemical properties, field management and data of overground biomass and plant nitrogen concentration of corn at different growth stages, regulating a crop growth model to participate in localization, simulating and outputting the plant nitrogen concentration and overground biomass of the whole growth season of the historical year, and further calculating the nitrogen nutrition index of the whole growth season.
Further, the construction process of the remote sensing quantitative inversion model comprises the following steps: remote sensing spectrum parameter data, the biomass on the ground and plant nitrogen concentration data of the key corn growth period for years are obtained, and a remote sensing quantitative inversion model of the plant nitrogen concentration and the biomass on the ground is constructed.
Further, the process for constructing the remote sensing quantitative inversion model of the plant nitrogen concentration and the aboveground biomass comprises the following steps: respectively fitting each remote sensing spectrum parameter with the mathematical quantitative relation of plant nitrogen concentration and overground biomass by adopting linear, logarithmic, power, exponent and quadratic curve functions, and adopting a decision coefficient R 2 The mathematical quantitative relationship is evaluated by the root mean square error RMSE, the relative error RE and the nuclear density curve, and the determination coefficient R is adopted 2 Modeling the mathematical quantitative relation of the maximum, root Mean Square Error (RMSE) and the nuclear density curve with the minimum Relative Error (RE) and the estimated value closest to the actual measured value is used as a remote sensing quantitative inversion model.
Further, the process of determining the optimized total nitrogen application amount based on different planting densities comprises the following steps: dividing each yield by the highest yield under the same planting density condition in the same year to obtain relative yield; based on the relative yield, a relation model of total nitrogen application amount and the relative yield under various planting densities is built by adopting a linear platform model, and the lowest total nitrogen application amount when the relative yield reaches an inflection point is selected to be the optimal total nitrogen application amount under the current density.
Further, if the current planting density does not correspond to the specific density in the step of optimally applying the total nitrogen amount, the optimally applying the total nitrogen amount corresponding to the current planting density is calculated in proportion according to the optimally applying the total nitrogen amount of two adjacent densities covering the current actual planting density.
Further, the first relation model is:
NNI＝-0.0000167×N b 2 +0.007015×N b +0.418
wherein NNI represents nitrogen nutrition index, N b Representing the amount of the base fertilizer;
further, the second relationship model is:
NNI＝-0.00000361×N t 2 +0.003716×N t +0.354
wherein NNI represents nitrogen nutrition index, N t Indicating the total amount of nitrogen fertilizer.
The second aspect of the invention provides a corn nitrogen fertilizer recommendation system based on full-growth nitrogen nutrition diagnosis.
A corn nitrogen fertilizer recommendation system based on full-growth nitrogen nutrition diagnosis, comprising:
a model simulation module configured to: simulating plant nitrogen concentration and aboveground biomass in the full-growth season of the corn in the season by adopting a localized crop growth model based on meteorological data and management data acquired in the season;
a model building module configured to: based on the historical nitrogen nutrition index and nitrogen fertilizer consumption data of the corn for years, respectively constructing a first relation model of the corn in a growth stage when only the base fertilizer is applied and no nitrogen fertilizer topdressing is performed, and a second relation model of the corn in the growth stage after the nitrogen fertilizer topdressing is performed;
an optimized total nitrogen application determination module configured to: determining an optimized total nitrogen application amount based on different planting densities based on the relative yield and total nitrogen fertilizer consumption data under different planting densities for years;
a remote sensing estimation module configured to: based on remote sensing spectrum data of a key period covered in the current season, estimating corn aerial biomass and plant nitrogen concentration data of a current season remote sensing observation date by adopting a remote sensing quantitative inversion model;
an assimilation module configured to: coupling remote sensing estimation data with a crop growth model by adopting a particle swarm algorithm, acquiring real-time updated daily plant nitrogen concentration and aboveground biomass data of the whole growing period of the current season corn, and further calculating the daily nitrogen nutrition index of the whole growing period of the current season corn;
a first recommendation module configured to: determining the date of corn nitrogen nutrition condition diagnosis and nitrogen fertilizer dressing in the season, and extracting the nitrogen nutrition index of the current date; if the nitrogen fertilizer is applied before the current date, calculating a theoretical basic fertilizer nitrogen supply level in a nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to a first relation model, and determining the recommended nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the current theoretical basic fertilizer nitrogen supply level;
a second recommendation module configured to: if the nitrogen fertilizer topdressing is carried out before the current date, and the current corn nitrogen nutrition index is judged to be smaller than the nitrogen proper threshold lower limit, calculating the theoretical total nitrogen fertilizer supply level in the nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to the second relation model, and determining the recommended supplementary nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the theoretical total nitrogen fertilizer supply level.
A third aspect of the present invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the corn nitrogen fertilizer recommendation method based on whole-growth nitrogen nutrition diagnosis as described in the first aspect above.
A fourth aspect of the invention provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the corn nitrogen fertilizer recommendation method based on whole-growth nitrogen nutrition diagnosis as described in the first aspect above when the program is executed.
Compared with the prior art, the invention has the beneficial effects that:
the method adopts a mode of combining remote sensing observation and crop growth models, forms a real-time dynamic monitoring and diagnosing method suitable for the nitrogen nutrition status of the whole growth season of corn, further utilizes the estimated nitrogen nutrition status index data, constructs a recommended calculation method for topdressing the nitrogen fertilizer of the corn in the current season, has wide coverage period range, is simple and easy to understand, is convenient to operate, has strong practicability, is beneficial to realizing local accurate nitrogen fertilizer management in the current season, can reduce nitrogen fertilizer investment while ensuring high yield of the corn, realizes high-efficiency utilization of resources, and has environmental and economic benefits.
The nitrogen nutrition state estimation and diagnosis provided by the invention is suitable for the whole growth period, can realize continuous and dynamic crop field growth and nitrogen nutrition estimation and diagnosis, and helps to grasp the crop growth condition in real time.
The nitrogen fertilizer recommendation method provided by the invention directly uses the nitrogen nutrition index which is the most reliable and widely applied nitrogen nutrition diagnosis index at present, and can fully calculate the nitrogen fertilizer demand according to the crop nitrogen deficiency state diagnosis information; the nitrogen fertilizer recommendation method is simple in algorithm, easy to understand and apply, applicable to nitrogen fertilizer recommendation calculation in any period in the growth process, wide in application time range and obviously stronger in applicability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of the corn nitrogen fertilizer recommendation method based on full-growth nitrogen nutrition diagnosis shown in the invention;
fig. 2 is a flow chart of crop growth model parameter adjustment based on particle swarm algorithm.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that 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 logical functions specified in the various embodiments. 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 flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.
Term interpretation:
the DSSAT is a crop growth simulation model based on a process, can quantitatively describe the crop growth and development and yield formation process and the relationship between the crop growth and development and yield formation process and climate factors, soil environment, variety types and technical measures, and provides a quantification tool for crop growth and development and yield prediction, cultivation management, environment evaluation, future climate change evaluation and the like under different conditions. The invention adopts an MAIZE submodel in the DSSAT, namely a DSSAT-CERES-MAIZE model.
Example 1
As shown in fig. 1, this embodiment provides a corn nitrogen fertilizer recommendation method based on nitrogen nutrition diagnosis in whole growth period, and this embodiment is illustrated by applying the method to a server, and it can be understood that the method can also be applied to a terminal, and can also be applied to a system and a terminal, and implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, which is not limited herein.
Taking northeast spring corn production field as an example, 18 district field blocks (F1-F18) are arranged in total in selected land blocks, the planting density comprises 70000 and 80000 plants/ha, the sowing date is 28 days of 4 months in 2019, the nitrogen amount of the base fertilizer applied before sowing is 60kg/ha, and the harvesting date is 27 days of 9 months in 2019. According to a local area optimization field management mode, the nitrogen fertilizer is recommended to be applied mainly in the eight-leaf period, so that the embodiment selects to recommend the nitrogen fertilizer to the northeast spring corn in the period, and the technical scheme of the invention is further described in detail, and the method comprises the following steps:
step (1): crop growth model simulation: acquiring meteorological data, basic soil data, field management data and plant nitrogen concentration data of corn cultivars in different growth stages of a local history for years, wherein the meteorological data mainly comprises daily highest and lowest air temperatures, daily rainfall and daily solar radiation, the basic soil data mainly comprises basic physicochemical property data of different soil layers, the field management data mainly comprises date such as sowing, seedling emergence, fertilization, harvest, fertilization amount, planting density and the like, the input information data which are as perfect as possible are helpful for improving model simulation precision, an input file necessary for the operation of a crop growth model DSSAT-CERES-Maize is formed, the crop growth model is automatically adjusted by a particle swarm optimization algorithm, the adjusted parameters are variety parameters, and the adjustment range is manually set as shown in table 1:
TABLE 1 DSSAT-CERES-maximum model variety parameters and parameter tuning ranges
The particle swarm algorithm is then run with Python, a process as shown in fig. 2, comprising:
forming a data set of the measured plant nitrogen concentration and the aboveground biomass;
inputting meteorological data, soil data, field management data and crop data files, and outputting a simulated plant nitrogen concentration and overground biomass data set by adopting a DSSAT-CERES-size model;
calculating a cost function:
AGBs i and PNCs i Data sets respectively representing the aboveground biomass and plant nitrogen concentration simulated by the crop growth model, AGBm i And PNCm i An above-ground biomass and plant nitrogen concentration data set representing actual observations, respectively;
judging whether the maximum iteration number is reached by adopting a particle swarm algorithm;
if yes, terminating iteration, selecting each variety parameter value which enables the model simulation value to be closest to the actual observation value, namely completing model parameter adjustment, realizing model localization, and outputting a simulation result: plant nitrogen concentration and aboveground biomass in the whole growing season of the historical year;
otherwise, the breed parameters P1, P2, P5, G2, G3, PHINT are updated.
According to the dilution curve model of the nitrogen concentration of the northeast spring corn, N is c ＝36.5W -0.48 And nitrogen nutrition index calculation formula nni=n a /N c Further calculating nitrogen nutrition index of the historical year full-growth season based on plant nitrogen concentration and overground biomass data of the historical year full-growth season output by model simulation, wherein N c The critical plant nitrogen concentration (g/kg) of spring corn, W is the biomass (t/ha) of the overground part, NNI is the nitrogen nutrition index, N a In order to observe the nitrogen concentration (g/kg) of plants, when the biomass of the upper part is less than or equal to 1t/ha, the critical plant nitrogen concentration takes a value of 36.5g/kg, and when the biomass of the upper part is more than 1t/ha, the critical plant nitrogen concentration passes through the formula N c ＝36.5W -0.48 And (5) calculating.
The method comprises the steps of inputting meteorological and management data before 2019 eight-leaf period (6 months and 25 days) by using a localized crop growth model with adjusted parameters, and simulating and outputting plant nitrogen concentration and aboveground biomass data in the full-growth season of the current season of corn;
step (2): and determining the relationship between the nitrogen nutrition index and the nitrogen fertilizer consumption: based on historical data of the nitrogen nutrition index and the nitrogen fertilizer consumption of the corn for years, a quadratic curve relation model is adopted to construct a relation model of the nitrogen nutrition index and the nitrogen fertilizer consumption of the corn in a growth stage in which only the base fertilizer is applied and no nitrogen fertilizer topdressing is performed:
NNI＝-0.0000167×N b 2 +0.007015×N b +0.418，
NNI represents nitrogen nutrition index, N b Indicating the use of base fertilizerAmount (kg/ha);
and (3) a relationship model of the corn nitrogen nutrition index and the total nitrogen fertilizer consumption in the growth stage after the nitrogen fertilizer is applied:
NNI＝-0.00000361×N t 2 +0.003716×N t +0.354
NNI represents nitrogen nutrition index, N t Indicating the total amount of nitrogen fertilizer (kg/ha).
Step (3): optimizing total nitrogen application amount and determining: calculating relative yield by dividing each yield by the highest yield under the same planting density condition in the current year based on yield data of different planting densities (total three planting densities: 55000, 70000, 85000 plants/ha) in the history for years, adopting a linear platform model to fit and construct a relation model of the relative yield and total nitrogen fertilizer consumption under each density condition, determining the relation between the optimized nitrogen application total amount based on different planting densities, the related different densities, the linear platform model and the determined optimized nitrogen application total amount, wherein the first planting density is 55000 plants/ha, and the corresponding optimal nitrogen application total amount is 176kg/ha; the second planting density is 70000 plants/ha, and the corresponding optimal total nitrogen application amount is 193kg/ha; the second planting density is 85000 plants/ha, the corresponding optimal nitrogen application total amount is 203kg/ha, and when the densities are not the three densities, the optimal nitrogen application total amount corresponding to the current planting density is calculated in proportion according to the optimal nitrogen application total amount of the two adjacent densities covering the actual planting density;
TABLE 2 Linear additive platform relationship model for relative yield and total nitrogen fertilizer usage at different densities and optimal total nitrogen application
Step (4): remote sensing estimation: remote sensing spectrum parameter data and upper part biomass and plant nitrogen concentration data of corn in a key growth period of years are obtained, and the remote sensing spectrum parameter data obtained in the embodiment comprise leaf chlorophyll fluorescence parameters (Chl) of leaf fluorescence sensors in six-leaf period, eight-leaf period, twelve-leaf period and flowering period and canopy reflectivity data of a canopy reflection sensor in eight-leaf period.
For the test of leaf chlorophyll fluorescence parameters, if the leaf fluorescence sensor is used for testing and obtaining the latest chlorophyll fluorescence parameters of the fully unfolded three leaves before the flowering period, if the leaf fluorescence sensor is used for testing and obtaining the chlorophyll fluorescence parameters of the three leaves after the flowering period, taking the average value of the chlorophyll fluorescence parameters of the three leaves to represent the plant level, and further calculating the growth accumulated temperature from the sowing day to the chlorophyll fluorescence parameter data obtaining date, wherein the calculation method is as follows:
gdd= Σ ((daily maximum temperature + daily minimum temperature)/2-10 ℃),
wherein, GDD represents growth accumulation temperature (DEG C), and day maximum temperature and day minimum temperature are day maximum temperature (DEG C) and day minimum temperature (DEG C) from sowing day to current day by day;
further calculating the ratio between the chlorophyll fluorescence parameter and the growth heat accumulation, obtaining the corrected chlorophyll fluorescence parameter, and fitting the mathematical quantitative relation between the corrected chlorophyll fluorescence parameter and the plant nitrogen concentration by adopting linear, logarithmic, power, exponential and quadratic curve functions, wherein the quadratic curve function model has the highest determining coefficient R 2 And the lowest root mean square error RMSE and the lowest relative error RE are simultaneously based on the nuclear density curve of the estimated value of the model, which is closest to the nuclear density curve of the measured value, so that the model is selected as a remote sensing quantitative inversion model of the plant nitrogen concentration, and the specific formula is as follows:
PNC＝-39.896×dChl 2 +28.081×dChl-0.145，
wherein PNC represents plant nitrogen concentration (g/kg), dCHl represents modified chlorophyll fluorescence parameters.
For the canopy reflectivity data acquired based on the canopy reflection sensor, the canopy reflectivity data is further calculated as a normalized near infrared index (NNIRI), the calculation method is NIR/(NIR+RE+R), the R, RE and NIR are respectively the reflectivities of red light, red light and near infrared bands, and the mathematical quantitative relation between the normalized near infrared index and the aboveground biomass is respectively fitted by adopting linear, logarithmic, power, exponential and quadratic curve functions, wherein the quadratic curve function model has the highest decision coefficientR 2 Simultaneously, the nuclear density curve based on the estimated value of the model is closest to the nuclear density curve of the measured value, and the lowest root mean square error RMSE and the lowest relative error RE are used as the remote sensing quantitative inversion model of the biomass of the overground part, wherein the specific formula is as follows:
AGB＝51.277×NNIRI 2 –46.334×NNIRI+110.33，
wherein AGB represents aboveground biomass (t/ha), and NNIRI represents normalized near infrared index.
Substituting remote sensing spectrum data acquired in the current season (2019) into the remote sensing quantitative inversion model of the plant nitrogen concentration and the aboveground biomass respectively, and estimating the corn plant nitrogen concentration and the aboveground biomass data of the current season remote sensing observation date;
step (5): assimilation of remote sensing estimation data and crop growth models: coupling the plant nitrogen concentration and the aboveground biomass data estimated in the step (4) with the localized crop growth model in the step (1) by adopting a particle swarm algorithm, wherein the method comprises the following steps:
forming a remote sensing estimated plant nitrogen concentration and overground biomass data set;
inputting meteorological data, soil data, field management data and crop data files, and outputting a simulated plant nitrogen concentration and overground biomass data set by adopting a DSSAT-CERES-size model;
calculating a cost function:
AGBs i and PNCs i Data sets respectively representing the aboveground biomass and plant nitrogen concentration simulated by the crop growth model, AGBm i And PNCm i Respectively representing the remotely estimated aboveground biomass and plant nitrogen concentration data sets;
judging whether the maximum iteration number is reached by adopting a particle swarm algorithm;
if yes, terminating iteration, selecting each variety parameter value which enables the model simulation value to be closest to the remote sensing estimated value, namely completing model parameter adjustment, realizing model localization, and outputting a simulation result: plant nitrogen concentration and aboveground biomass throughout the growing season;
otherwise, the breed parameters P1, P2, P5, G2, G3, PHINT are updated.
Based on the remote sensing estimation data and plant nitrogen concentration and overground biomass data of the whole growth season output by assimilation of the crop growth model, calculating nitrogen nutrition index data of the whole growth season day by day, wherein the calculating method is the same as the method mentioned in the step (1), and redundant description is omitted, wherein N is not needed a Plant nitrogen concentration (g/kg) estimated by remote sensing, and aboveground biomass (t/ha) estimated by a model.
Step (6): and (3) recommending the nitrogen fertilizer topdressing amount: when the date of corn nitrogen nutrition condition diagnosis and nitrogen dressing is about 6 months and 25 days, extracting the nitrogen nutrition index of the current date, as shown in table 3:
TABLE 3 estimation of the upper biomass, plant Nitrogen concentration, nitrogen nutrient index for each field for which nitrogen nutrient diagnosis and Nitrogen dressing date are to be performed
If no additional nitrogenous fertilizer is applied before the current date, the method is based on a model of the relationship between the corn nitrogen nutrition index and the amount of the base fertilizer in the growth stage where only the base fertilizer is applied and no additional nitrogenous fertilizer is applied (nni= -0.0000167 ×n) b 2 +0.007015×N b +0.418), calculating a theoretical basal fertilizer nitrogen supply level in a nitrogen nutrient state corresponding to the current corn nitrogen nutrient index (table 4);
according to step (3), for the 70000 plants/ha planting density in this example, the corresponding optimized total nitrogen application amount is 193kg/ha;
for a planting density of 80000 plants/ha in this example, the optimum total nitrogen application amount corresponding to this planting density was calculated from the optimum nitrogen application amounts at 70000 and 85000 plants/ha densities, namely: 203kg/ha- (203 kg/ha-193 kg/ha)/3, approximately equal to 200kg/ha. The recommended topdressing nitrogen fertilizer usage was determined by subtracting the theoretical basal nitrogen supply level corresponding to the current plant nutrient status from the optimized total nitrogen application 193 and 200kg/ha at the above-mentioned 70000 and 80000 plants/ha planting densities, respectively (table 4).
Table 4 recommended Nitrogen fertilizer topdressing amount for each field
The effect is as follows: the corn yield obtained according to the conventional management mode of farmers (the planting density 62000 strain/ha, the total nitrogen application amount is 250 kg/ha) is 11.75t/ha, the corn yield obtained according to the conventional management mode of region-optimized nitrogen fertilizer (the planting density 70000 strain/ha, the total nitrogen application amount is 220 kg/ha) is 11.12t/ha, the average value of the corn yield obtained according to the nitrogen fertilizer recommendation algorithm of the invention is 11.71t/ha, the corn yield obtained according to the conventional management mode of farmers has no statistical difference with the corn yield obtained according to the conventional management mode of farmers, the corn yield obtained according to the conventional management mode of farmers is superior to the corn yield obtained according to the conventional management mode of region-optimized nitrogen fertilizer, the nitrogen fertilizer consumption is reduced by 49-89kg/ha compared with the conventional management mode of farmers, and the nitrogen fertilizer consumption is reduced by 19-59kg/ha compared with the conventional management mode of region-optimized nitrogen fertilizer.
Example two
The embodiment provides a corn nitrogen fertilizer recommendation system based on full-growth-period nitrogen nutrition diagnosis.
A corn nitrogen fertilizer recommendation system based on full-growth nitrogen nutrition diagnosis, comprising:
a model simulation module configured to: simulating plant nitrogen concentration and aboveground biomass in the full-growth season of the corn in the season by adopting a localized crop growth model based on meteorological data and management data acquired in the season;
a model building module configured to: based on the historical nitrogen nutrition index and nitrogen fertilizer consumption data of the corn for years, respectively constructing a first relation model of the corn in a growth stage when only the base fertilizer is applied and no nitrogen fertilizer topdressing is performed, and a second relation model of the corn in the growth stage after the nitrogen fertilizer topdressing is performed;
an optimized total nitrogen application determination module configured to: determining an optimized total nitrogen application amount based on different planting densities based on the relative yield and total nitrogen fertilizer consumption data under different planting densities for years;
a remote sensing estimation module configured to: based on remote sensing spectrum data of a key period covered in the current season, estimating corn aerial biomass and plant nitrogen concentration data of a current season remote sensing observation date by adopting a remote sensing quantitative inversion model;
an assimilation module configured to: coupling remote sensing estimation data with a crop growth model by adopting a particle swarm algorithm, acquiring real-time updated daily plant nitrogen concentration and aboveground biomass data of the whole growing period of the current season corn, and further calculating the daily nitrogen nutrition index of the whole growing period of the current season corn;
a first recommendation module configured to: determining the date of corn nitrogen nutrition condition diagnosis and nitrogen fertilizer dressing in the season, and extracting the nitrogen nutrition index of the current date; if the nitrogen fertilizer is applied before the current date, calculating a theoretical basic fertilizer nitrogen supply level in a nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to a first relation model, and determining the recommended nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the current theoretical basic fertilizer nitrogen supply level;
a second recommendation module configured to: if the nitrogen fertilizer topdressing is carried out before the current date, and the current corn nitrogen nutrition index is judged to be smaller than the nitrogen proper threshold lower limit, calculating the theoretical total nitrogen fertilizer supply level in the nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to the second relation model, and determining the recommended supplementary nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the theoretical total nitrogen fertilizer supply level.
It should be noted that the model simulation module, the model construction module, the optimized nitrogen application total amount determination module, the remote sensing estimation module, the assimilation module, the first recommendation module and the second recommendation module are the same as the examples and application scenarios implemented in the steps (1) to (6) in the first embodiment, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the corn nitrogen fertilizer recommendation method based on whole-growth nitrogen nutrition diagnosis as described in the above embodiment.
Example IV
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps in the corn nitrogen fertilizer recommendation method based on total growth period nitrogen nutrition diagnosis according to the embodiment.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A corn nitrogen fertilizer recommendation method based on full-growth-period nitrogen nutrition diagnosis is characterized by comprising the following steps:
simulating plant nitrogen concentration and aboveground biomass in the full-growth season of the corn in the season by adopting a localized crop growth model based on meteorological data and management data acquired in the season;
based on the historical nitrogen nutrition index and nitrogen fertilizer consumption data of the corn for years, respectively constructing a first relation model of the corn in a growth stage when only the base fertilizer is applied and no nitrogen fertilizer topdressing is performed, and a second relation model of the corn in the growth stage after the nitrogen fertilizer topdressing is performed;
determining an optimized total nitrogen application amount based on different planting densities based on the relative yield and total nitrogen fertilizer consumption data under different planting densities for years;
based on remote sensing spectrum data of a key period covered in the current season, estimating corn aerial biomass and plant nitrogen concentration data of a current season remote sensing observation date by adopting a remote sensing quantitative inversion model;
coupling remote sensing estimation data with a crop growth model by adopting a particle swarm algorithm, acquiring real-time updated daily plant nitrogen concentration and aboveground biomass data of the whole growing period of the current season corn, and further calculating the daily nitrogen nutrition index of the whole growing period of the current season corn;
determining the date of corn nitrogen nutrition condition diagnosis and nitrogen fertilizer dressing in the season, and extracting the nitrogen nutrition index of the current date; if the nitrogen fertilizer is applied before the current date, calculating a theoretical basic fertilizer nitrogen supply level in a nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to a first relation model, and determining the recommended nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the current theoretical basic fertilizer nitrogen supply level;
if the nitrogen fertilizer topdressing is carried out before the current date, and the current corn nitrogen nutrition index is judged to be smaller than the nitrogen proper threshold lower limit, calculating the theoretical total nitrogen fertilizer supply level in the nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to the second relation model, and determining the recommended supplementary nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the theoretical total nitrogen fertilizer supply level.
2. The corn nitrogen fertilizer recommendation method based on full-growth nitrogen nutrition diagnosis of claim 1, wherein the localization process of the crop growth model comprises: acquiring historical multi-year weather, basic soil physicochemical properties, field management and data of overground biomass and plant nitrogen concentration of corn at different growth stages, regulating a crop growth model to participate in localization, simulating and outputting the plant nitrogen concentration and overground biomass of the whole growth season of the historical year, and further calculating the nitrogen nutrition index of the whole growth season.
3. The corn nitrogen fertilizer recommendation method based on full-growth nitrogen nutrition diagnosis according to claim 1, wherein the construction process of the remote sensing quantitative inversion model comprises the following steps: remote sensing spectrum parameter data, the biomass on the ground and plant nitrogen concentration data of the key corn growth period for years are obtained, and a remote sensing quantitative inversion model of the plant nitrogen concentration and the biomass on the ground is constructed.
4. The corn nitrogen fertilizer recommendation method based on full-growth nitrogen nutrition diagnosis according to claim 3, wherein the process of constructing a remote sensing quantitative inversion model of plant nitrogen concentration and aboveground biomass comprises the following steps: respectively fitting each remote sensing spectrum parameter with the mathematical quantitative relation of plant nitrogen concentration and overground biomass by adopting linear, logarithmic, power, exponent and quadratic curve functions, and adopting a decision coefficient R 2 The mathematical quantitative relationship is evaluated by the root mean square error RMSE, the relative error RE and the nuclear density curve, and the determination coefficient R is adopted 2 Modeling the mathematical quantitative relation of the maximum, root Mean Square Error (RMSE) and the nuclear density curve with the minimum Relative Error (RE) and the estimated value closest to the actual measured value is used as a remote sensing quantitative inversion model.
5. The corn nitrogen fertilizer recommendation method based on total growth phase nitrogen nutrition diagnosis according to claim 1, wherein the process of determining the optimized total nitrogen application amount based on different planting densities comprises: dividing each yield by the highest yield under the same planting density condition in the same year to obtain relative yield; based on the relative yield, a relation model of total nitrogen application amount and the relative yield under various planting densities is built by adopting a linear platform model, and the lowest total nitrogen application amount when the relative yield reaches an inflection point is selected to be the optimal total nitrogen application amount under the current density.
6. The corn nitrogen fertilizer recommendation method based on full-growth period nitrogen nutrition diagnosis according to claim 5, wherein if the current planting density does not correspond to the specific density in the optimal nitrogen application total amount step, the optimal nitrogen application total amount corresponding to the current planting density is calculated proportionally according to the optimal nitrogen application total amount of two adjacent densities covering the current actual planting density.
7. The corn nitrogen fertilizer recommendation method based on full-life nitrogen nutrition diagnosis of claim 1, wherein the first relationship model is:
NNI＝-0.0000167×N b 2 +0.007015×N b +0.418
wherein NNI represents nitrogen nutrition index, N b Representing the amount of the base fertilizer;
or alternatively, the first and second heat exchangers may be,
the second relationship model is:
NNI＝-0.00000361×N t 2 +0.003716×N t +0.354
wherein NNI represents nitrogen nutrition index, N t Indicating the total amount of nitrogen fertilizer.
8. Corn nitrogen fertilizer recommendation system based on full-growth-period nitrogen nutrition diagnosis, which is characterized by comprising:
a model simulation module configured to: simulating plant nitrogen concentration and aboveground biomass in the full-growth season of the corn in the season by adopting a localized crop growth model based on meteorological data and management data acquired in the season;
a model building module configured to: based on the historical nitrogen nutrition index and nitrogen fertilizer consumption data of the corn for years, respectively constructing a first relation model of the corn in a growth stage when only the base fertilizer is applied and no nitrogen fertilizer topdressing is performed, and a second relation model of the corn in the growth stage after the nitrogen fertilizer topdressing is performed;
an optimized total nitrogen application determination module configured to: determining an optimized total nitrogen application amount based on different planting densities based on the relative yield and total nitrogen fertilizer consumption data under different planting densities for years;
a remote sensing estimation module configured to: based on remote sensing spectrum data of a key period covered in the current season, estimating corn aerial biomass and plant nitrogen concentration data of a current season remote sensing observation date by adopting a remote sensing quantitative inversion model;
an assimilation module configured to: coupling remote sensing estimation data with a crop growth model by adopting a particle swarm algorithm, acquiring real-time updated daily plant nitrogen concentration and aboveground biomass data of the whole growing period of the current season corn, and further calculating the daily nitrogen nutrition index of the whole growing period of the current season corn;
a first recommendation module configured to: determining the date of corn nitrogen nutrition condition diagnosis and nitrogen fertilizer dressing in the season, and extracting the nitrogen nutrition index of the current date; if the nitrogen fertilizer is applied before the current date, calculating a theoretical basic fertilizer nitrogen supply level in a nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to a first relation model, and determining the recommended nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the current theoretical basic fertilizer nitrogen supply level;
a second recommendation module configured to: if the nitrogen fertilizer topdressing is carried out before the current date, and the current corn nitrogen nutrition index is judged to be smaller than the nitrogen proper threshold lower limit, calculating the theoretical total nitrogen fertilizer supply level in the nitrogen nutrition state corresponding to the current corn nitrogen nutrition index according to the second relation model, and determining the recommended supplementary nitrogen fertilizer application dosage according to the difference value between the optimized total nitrogen application amount corresponding to the current planting density and the theoretical total nitrogen fertilizer supply level.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the method for corn nitrogen fertilizer recommendation based on whole-growth nitrogen nutrition diagnosis according to any one of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps in the method of corn nitrogen fertilizer recommendation based on whole-growth nitrogen nutrition diagnosis as claimed in any one of claims 1 to 7 when the program is executed.
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