CN114202438A - Sky and ground integrated agricultural remote sensing big data system based on wisdom agriculture - Google Patents
Sky and ground integrated agricultural remote sensing big data system based on wisdom agriculture Download PDFInfo
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Abstract
The invention discloses a sky and land integrated agricultural remote sensing big data system based on intelligent agriculture, which comprises an intelligent agricultural map display and query module, an agricultural soil environment information display module, a remote sensing seedling condition dynamic monitoring module, a growth model simulation analysis module, a soil moisture monitoring module and a meteorological data display and comparison module. The invention has the beneficial effects that: dynamic monitoring data such as crop growth vigor, flood and drought disasters, disease and pest disasters and the like are realized, and the optimized fertilization quantity, the base and top dressing distribution proportion, the fertilization period and the fertilization method of different crops of each fertilization unit are mastered through field experiments; basic parameters such as soil nutrient correction coefficient, soil fertilizer supply capacity, nutrient absorption of different crops, fertilizer utilization rate and the like are found out; and constructing a crop fertilization model, providing a basis for fertilization subareas and fertilizer formulas, and presenting the basis to all levels of governments and agricultural departments in a visual manner to realize the functions of real-time monitoring of global pest and disease occurrence conditions, risk early warning and data analysis.
Description
Technical Field
The invention relates to an agricultural remote sensing big data system, in particular to a sky and land integrated agricultural remote sensing big data system based on intelligent agriculture, and belongs to the technical field of intelligent agriculture.
Background
Agriculture is the foundation of national economy, with the improvement of agricultural industrialization and scale level, and the increasingly wide application of the internet of things technology, cloud computing technology, big data technology, geographic information system, remote sensing and global positioning system technology in the agricultural field, the traditional agricultural cultivation mode gradually exposes some defects, and the intelligent agriculture can fully apply modern information technology achievements, integrate and apply computer and network technology, internet of things technology, audio and video technology, 3S technology, wireless communication technology and expert wisdom and knowledge, and realize intelligent management such as agricultural visual remote diagnosis, remote control, disaster warning and the like.
For the existing intelligent agricultural management system, the following defects are often existed:
1. the existing intelligent agricultural management system generally considers the layered establishment of space resources and ecological agriculture, only monitors agricultural plants, but does not analyze planting soil, namely, a special required nitrogen-phosphorus-potassium formula ratio, a simple substance base fertilizer use ratio, topdressing and other schemes cannot be formulated according to different project plots;
2. the existing intelligent agricultural management system is lack of growth simulation on crops, namely, the targeted data information is only planting information of the current year, and the existing intelligent agricultural management system is lack of adjustment management on the overall growth trend of the second year and cannot help farmers to optimize the management scheme;
3. data that present wisdom agricultural was gathered are often only the required quality of water data of growth of crops, insect pest situation data and seedling situation data, and the data range of gathering promptly is incomplete, and then can't realize better management.
Disclosure of Invention
The invention aims to provide a sky and land integrated agricultural remote sensing big data system based on intelligent agriculture to solve the problems.
The invention realizes the purpose through the following technical scheme: sky and ground integrated agricultural remote sensing big data system based on wisdom agriculture includes
The intelligent agricultural map display and query module can display data such as an agricultural construction result map, a high-standard farmland construction result, a land contract operation right confirmation registration issuing result, a village-town boundary, a latest satellite remote sensing image and the like; the attribute data of the vector data can be inquired in space in a point, surface and other modes, and the inquiry can be carried out in a searching mode so as to quickly locate the attribute and the position of the target plot;
the agricultural soil environment information display module is used for making a space distribution map of nitrogen, phosphorus, potassium, organic matters and pH through space interpolation according to soil test data provided by an agricultural department, displaying the space distribution map in the system, switching different types through buttons, and inquiring the soil nutrient attribute of any point in the forms of points, surfaces and the like;
the remote sensing seedling condition dynamic monitoring module is used for establishing a correlation between vegetation index data of a satellite and agronomic data obtained by ground sampling point data after high-resolution satellite data are obtained through a space satellite, estimating a leaf area index of a plot scale, biomass and nitrogen absorption of overground parts of plants by using the correlation, importing the leaf area index, the biomass and the nitrogen absorption of the overground parts of the plants into a visualization system for displaying, and dynamically monitoring the crop growth condition of a project area in real time;
the growth model simulation analysis module is used for simulating the growth of the selected plot by adopting crop growth simulation according to the scale of the plot, the simulated data needs to set weather, soil, variety and management measures, and the data such as leaf area, biomass, nitrogen content, yield and the like which are simulated day by day can be obtained after simulation;
the soil moisture monitoring module is combined with a soil moisture monitoring system to store a large amount of soil temperature and humidity data, bidders call the data on the basis of the existing soil moisture monitoring station and interpolate all points into a map through spatial interpolation, and the system updates the data according to the soil moisture monitoring frequency to form an agricultural soil moisture map;
and the meteorological data display and comparison module is used for collecting meteorological data of 30 years in the project area, displaying and comparing the meteorological data, and performing predictive analysis on the meteorological data of the future year according to a comparison result.
As a still further scheme of the invention: in wisdom agricultural map show and query module, wisdom agricultural map includes:
one picture of agricultural information: the system is used for displaying meteorological data, water quality data, insect condition data, seedling condition data, industrial data, soil data, economic data and the like;
II, one drawing of the industry: the method comprises the steps that an agricultural geographic information system is utilized, information is collected through remote sensing data, the remote sensing data sent back by a ground satellite receiving station is processed and stored in a warehouse, GIS data is received and processed, manual reporting data is received and processed, and an agricultural information base is established; the method is characterized by comprising the steps of sorting the agricultural map database into an agricultural map database such as land utilization, landforms and remote sensing images, an agricultural basic database such as population, crop seeding area and yield, agricultural output values and rural basic conditions, dynamic monitoring data such as crop growth, flood and drought disasters and pest disasters.
Thirdly, one map of the soil: by obtaining the distribution of soil fertility data of the town area, constructing the distribution of soil fertility thermodynamic diagrams of the town area by relying on a GIS system, and constructing a fertilizer effect field test, the method is a fundamental way for obtaining the optimal fertilizing amount, fertilizing proportion, fertilizing period and fertilizing method of various crops, and is also a basic link for screening and verifying a soil nutrient testing method and establishing a fertilizing index system.
Fourthly, plant protection is as follows: the method is characterized in that a massive plant protection data resource library is established, an artificial intelligent image recognition technology is applied to the plant protection field of an agricultural planting link, agricultural pest field recognition, instant diagnosis and expert consultation services based on an AI technology are provided for agricultural producers, field acquisition information is collected into a massive data analysis system, the massive data analysis system is presented to all levels of governments and agricultural administrative departments in a visual mode, and the functions of real-time monitoring of global pest occurrence conditions, risk early warning and data analysis are achieved.
Base one picture: a reproduction base is utilized to deploy data acquisition equipment, monitoring equipment, a block chain source tracing client, an intelligent insect situation prediction device and the like, a base production management chart is constructed through a data center, point location information of each base can be displayed based on a GIS, and related condition introduction of each enterprise can be checked through clicking, wherein the method comprises the following steps of: enterprise name, profile, production mode, industrial scale, marketing mode, etc.
Sixthly, tracing to the source: a core agricultural product quality safety block chain traceability management information platform is constructed, comprises agricultural product supply chain informatization management of agricultural product main body of agricultural production and management, an agricultural product traceability system, an agricultural product distribution system, an agricultural product anti-counterfeiting early warning system and a big data analysis system, and is effectively docked with provincial traceability platform data.
As a still further scheme of the invention: after the remote sensing seedling condition dynamic monitoring module obtains the high-resolution satellite data, according to biomass and overground part nitrogen uptake obtained by seedling condition monitoring and a local crop nitrogen critical curve; according to the following steps: and (4) recommended fertilizing amount = regional optimized fertilizing amount- (real nitrogen absorption amount-critical nitrogen absorption amount of the overground part)/nitrogen utilization efficiency bulletin, and obtaining the ear fertilizer recommended fertilizing amount of the land scale.
As a still further scheme of the invention: the system also comprises a soil testing formula support decision, provides a soil testing formula on-line calculation function, inputs parameters such as target yield, the quantity of nutrients required by yield per hundred kilograms, non-fertilization yield, effective nutrient correction coefficient, fertilizer nutrient content, fertilizer utilization rate and the like, and can obtain nitrogen, phosphorus and potassium base fertilizer schemes with different scales of project plots and areas by calling GP service methods, wherein the schemes comprise the results of required nitrogen, phosphorus and potassium formula ratio, elemental base fertilizer use ratio, additional fertilization scheme and the like.
As a still further scheme of the invention: the system also comprises a yield estimation service, wherein in the flowering period of the wheat and the flowering period of the corn, the yield of the plot scale is estimated according to meteorological data and soil data and in combination with a crop growth model, the estimation data is automatically updated to a database, and the yield value in the project is calculated according to the grain price in the season.
As a still further scheme of the invention: the system also comprises field history image display, a real-time video visualization interface is reserved to support mainstream cameras such as Haikang cameras and Dahua cameras, and users can independently add a video recorder into the system and control the camera to take pictures, record videos and rotate.
The invention has the beneficial effects that:
1. by displaying and inquiring an intelligent agricultural map, dynamic monitoring data such as crop growth, flood and drought disasters, disease and pest disasters and the like are realized, and the optimized fertilization quantity, the base and topdressing distribution proportion, the fertilization period and the fertilization method of different crops of each fertilization unit are mastered through field experiments; basic parameters such as soil nutrient correction coefficient, soil fertilizer supply capacity, nutrient absorption of different crops, fertilizer utilization rate and the like are found out; a crop fertilization model is constructed, a basis is provided for fertilization subareas and fertilizer formulas, and the fertilizer fertilization model can be visually presented to all levels of governments and agricultural departments, so that the functions of real-time monitoring of global pest and disease occurrence conditions, risk early warning and data analysis are realized;
2. the method comprises the steps of supporting decision by a soil testing formula, providing an on-line calculation function of the soil testing formula, inputting parameters such as target yield, the amount of nutrients required by yield per hundred kilograms, non-fertilization yield, effective nutrient correction coefficient, fertilizer nutrient content, fertilizer utilization rate and the like, and obtaining nitrogen, phosphorus and potassium base fertilizer schemes with different scales of project plots and areas by calling GP service methods, wherein the schemes comprise the results of required nitrogen, phosphorus and potassium formula ratio, elemental base fertilizer use ratio, additional fertilization scheme and the like;
3. and estimating the yield of the plot scale by a yield estimation service according to meteorological data and soil data and in combination with a crop growth model, automatically updating estimation data to a database, and calculating the yield value in the project according to the grain price in the season.
Drawings
FIG. 1 is a schematic overall framework of the present invention;
FIG. 2 is a diagram of a framework of the intelligent agricultural map of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1-2, a sky-ground integrated agricultural remote sensing big data system based on intelligent agriculture includes
The intelligent agricultural map display and query module can display data such as an agricultural construction result map, a high-standard farmland construction result, a land contract operation right confirmation registration issuing result, a village-town boundary, a latest satellite remote sensing image and the like; the attribute data of the vector data can be inquired in space in a point, surface and other modes, and the inquiry can be carried out in a searching mode so as to quickly locate the attribute and the position of the target plot;
in an embodiment of the present invention, in the intelligent agricultural map displaying and querying module, the intelligent agricultural map includes:
one picture of agricultural information: the system is used for displaying meteorological data, water quality data, insect condition data, seedling condition data, industrial data, soil data, economic data and the like;
II, one drawing of the industry: the method comprises the steps that an agricultural geographic information system is utilized, information is collected through remote sensing data, the remote sensing data sent back by a ground satellite receiving station is processed and stored in a warehouse, GIS data is received and processed, manual reporting data is received and processed, and an agricultural information base is established; the method is characterized by comprising the steps of sorting the agricultural map database into an agricultural map database such as land utilization, landforms and remote sensing images, an agricultural basic database such as population, crop seeding area and yield, agricultural output values and rural basic conditions, dynamic monitoring data such as crop growth, flood and drought disasters and pest disasters.
Thirdly, one map of the soil: by obtaining the distribution of soil fertility data of the town area, constructing the distribution of soil fertility thermodynamic diagrams of the town area by relying on a GIS system, and constructing a fertilizer effect field test, the method is a fundamental way for obtaining the optimal fertilizing amount, fertilizing proportion, fertilizing period and fertilizing method of various crops, and is also a basic link for screening and verifying a soil nutrient testing method and establishing a fertilizing index system. Through field experiments, the optimized fertilization quantity, the base and additional fertilization distribution proportion, the fertilization period and the fertilization method of different crops of each fertilization unit are mastered; basic parameters such as soil nutrient correction coefficient, soil fertilizer supply capacity, nutrient absorption of different crops, fertilizer utilization rate and the like are found out; and (4) constructing a crop fertilization model, and providing a basis for fertilization subareas and fertilizer formulas.
Fourthly, plant protection is as follows: the method is characterized in that a massive plant protection data resource library is established, an artificial intelligent image recognition technology is applied to the plant protection field of an agricultural planting link, agricultural pest field recognition, instant diagnosis and expert consultation services based on an AI technology are provided for agricultural producers, field acquisition information is collected into a massive data analysis system, the massive data analysis system is presented to all levels of governments and agricultural administrative departments in a visual mode, and the functions of real-time monitoring of global pest occurrence conditions, risk early warning and data analysis are achieved.
Base one picture: a reproduction base is utilized to deploy data acquisition equipment, monitoring equipment, a block chain source tracing client, an intelligent insect situation prediction device and the like, a base production management chart is constructed through a data center, point location information of each base can be displayed based on a GIS, and related condition introduction of each enterprise can be checked through clicking, wherein the method comprises the following steps of: enterprise name, profile, production mode, industrial scale, marketing mode, etc.
Sixthly, tracing to the source: a core agricultural product quality safety block chain traceability management information platform is constructed, comprises agricultural product supply chain informatization management of agricultural product main body of agricultural production and management, an agricultural product traceability system, an agricultural product distribution system, an agricultural product anti-counterfeiting early warning system and a big data analysis system, and is effectively docked with provincial traceability platform data.
Example two
Referring to fig. 1-2, a sky-ground integrated agricultural remote sensing big data system based on intelligent agriculture includes
The agricultural soil environment information display module is used for making a space distribution map of nitrogen, phosphorus, potassium, organic matters and pH through space interpolation according to soil test data provided by an agricultural department, displaying the space distribution map in the system, switching different types through buttons, and inquiring the soil nutrient attribute of any point in the forms of points, surfaces and the like;
the remote sensing seedling condition dynamic monitoring module is used for establishing a correlation between vegetation index data of a satellite and agronomic data obtained by ground sampling point data after high-resolution satellite data are obtained through a space satellite, estimating a leaf area index of a plot scale, biomass and nitrogen absorption of overground parts of plants by using the correlation, importing the leaf area index, the biomass and the nitrogen absorption of the overground parts of the plants into a visualization system for displaying, and dynamically monitoring the crop growth condition of a project area in real time;
the growth model simulation analysis module is used for simulating the growth of the selected plot by adopting crop growth simulation according to the scale of the plot, the simulated data needs to set weather, soil, variety and management measures, and the data such as leaf area, biomass, nitrogen content, yield and the like which are simulated day by day can be obtained after simulation;
the soil moisture monitoring module is combined with a soil moisture monitoring system to store a large amount of soil temperature and humidity data, bidders call the data on the basis of the existing soil moisture monitoring station and interpolate all points into a map through spatial interpolation, and the system updates the data according to the soil moisture monitoring frequency to form an agricultural soil moisture map;
and the meteorological data display and comparison module is used for collecting meteorological data of 30 years in the project area, displaying and comparing the meteorological data, and performing predictive analysis on the meteorological data of the future year according to a comparison result.
In the embodiment of the invention, after the remote sensing seedling condition dynamic monitoring module obtains the high-resolution satellite data, biomass and the nitrogen absorption amount of the overground part and the nitrogen critical curve of local crops are obtained according to the seedling condition monitoring; according to the following steps: and (4) recommended fertilizing amount = regional optimized fertilizing amount- (real nitrogen absorption amount-critical nitrogen absorption amount of the overground part)/nitrogen utilization efficiency bulletin, and obtaining the ear fertilizer recommended fertilizing amount of the land scale.
In the embodiment of the invention, the system also comprises a soil testing formula support decision, provides a soil testing formula on-line calculation function, inputs parameters such as target yield, the quantity of nutrients required by per hundred kilograms of yield, non-fertilization yield, effective nutrient correction coefficient, fertilizer nutrient content, fertilizer utilization rate and the like, and can obtain nitrogen, phosphorus and potassium base fertilizer schemes with different scales of project plots and areas by calling GP service methods, wherein the schemes comprise the results of required nitrogen, phosphorus and potassium formula ratio, elemental base fertilizer use ratio, additional fertilization scheme and the like.
In the embodiment of the invention, the system also comprises a yield estimation service, wherein in the flowering period of the wheat and the flowering period of the corn, the yield of the plot scale is estimated according to meteorological data and soil data and in combination with a crop growth model, the estimation data is automatically updated to a database, and the in-project yield value is calculated according to the current season grain price.
In the embodiment of the invention, the system also comprises field history image display, a real-time video visualization interface is reserved to support mainstream cameras such as Haikang and Dahua cameras, and a user can independently add a video recorder into the system and control the functions of photographing, recording and rotating of the camera.
The working principle is as follows: according to a fixed biological cycle of crop production, the existing internet of things, big data and cloud technologies are combined, and the full life cycle monitoring, the automatic detection and matching of natural environment, the automatic detection of plant diseases and insect pests and the monitoring of the quality of agricultural products are realized through 4G data transmission; by utilizing the monitoring and control functions of intelligent agriculture and a crop growth data model, the system can ensure that plants are kept in the optimal growth state, meets the growth requirements of crops at different stages, utilizes natural conditions to the maximum extent and saves the cost; through a video analysis technology, automatic snapshot of plant diseases and insect pests is realized, the function of automatically spraying plant diseases and insect pests and drugs is realized after the snapshot is completed, the yield of the plot scale can be estimated according to the yield estimation service, the estimation data is automatically updated to a database, and the yield value in the project is calculated according to the grain price in the season.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. The utility model provides a sky ground integration agricultural remote sensing big data system based on wisdom agricultural, its characterized in that: comprises that
The intelligent agricultural map display and query module can display data such as an agricultural construction result map, a high-standard farmland construction result, a land contract operation right confirmation registration issuing result, a village-town boundary, a latest satellite remote sensing image and the like; the attribute data of the vector data can be inquired in space in a point, surface and other modes, and the inquiry can be carried out in a searching mode so as to quickly locate the attribute and the position of the target plot;
the agricultural soil environment information display module is used for making a space distribution map of nitrogen, phosphorus, potassium, organic matters and pH through space interpolation according to soil test data provided by an agricultural department, displaying the space distribution map in the system, switching different types through buttons, and inquiring the soil nutrient attribute of any point in the forms of points, surfaces and the like;
the remote sensing seedling condition dynamic monitoring module is used for establishing a correlation between vegetation index data of a satellite and agronomic data obtained by ground sampling point data after high-resolution satellite data are obtained through a space satellite, estimating a leaf area index of a plot scale, biomass and nitrogen absorption of overground parts of plants by using the correlation, importing the leaf area index, the biomass and the nitrogen absorption of the overground parts of the plants into a visualization system for displaying, and dynamically monitoring the crop growth condition of a project area in real time;
the growth model simulation analysis module is used for simulating the growth of the selected plot by adopting crop growth simulation according to the scale of the plot, the simulated data needs to set weather, soil, variety and management measures, and the data such as leaf area, biomass, nitrogen content, yield and the like which are simulated day by day can be obtained after simulation;
the soil moisture monitoring module is combined with a soil moisture monitoring system to store a large amount of soil temperature and humidity data, bidders call the data on the basis of the existing soil moisture monitoring station and interpolate all points into a map through spatial interpolation, and the system updates the data according to the soil moisture monitoring frequency to form an agricultural soil moisture map;
and the meteorological data display and comparison module is used for collecting meteorological data of 30 years in the project area, displaying and comparing the meteorological data, and performing predictive analysis on the meteorological data of the future year according to a comparison result.
2. The intelligent agriculture-based sky-ground integrated agricultural remote sensing big data system according to claim 1, wherein: in wisdom agricultural map show and query module, wisdom agricultural map includes:
one picture of agricultural information: the system is used for displaying meteorological data, water quality data, insect condition data, seedling condition data, industrial data, soil data and economic data;
II, one drawing of the industry: the method comprises the steps that an agricultural geographic information system is utilized, information is collected through remote sensing data, the remote sensing data sent back by a ground satellite receiving station is processed and stored in a warehouse, GIS data is received and processed, manual reporting data is received and processed, and an agricultural information base is established; the method comprises the following steps of sorting the agricultural land map database into an agricultural land map database such as land utilization, landforms and remote sensing images, an agricultural basic data base such as population, crop seeding area and yield, agricultural output value and rural basic conditions, and dynamic monitoring data such as crop growth, flood and drought disasters and disease and pest disasters;
thirdly, one map of the soil: by obtaining the distribution of soil fertility data of a town area, constructing the distribution of soil fertility thermodynamic diagrams of the town area by relying on a GIS system, and constructing a fertilizer effect field test, the method is a fundamental way for obtaining the optimal fertilizing amount, fertilizing proportion, fertilizing period and fertilizing method of various crops and is also a basic link for screening and verifying a soil nutrient testing method and establishing a fertilizing index system;
fourthly, plant protection is as follows: by constructing a massive plant protection data resource library, applying the artificial intelligent image recognition technology to the plant protection field of an agricultural planting link, providing agricultural producers with agricultural pest field recognition, instant diagnosis and expert consultation services based on AI technology, gathering field acquisition information into a massive data analysis system, and visually presenting the massive data analysis system to all levels of governments and agricultural departments to realize the functions of real-time monitoring of global pest occurrence, risk early warning and data analysis;
base one picture: a reproduction base is utilized to deploy data acquisition equipment, monitoring equipment, a block chain source tracing client, an intelligent insect situation prediction device and the like, a base production management chart is constructed through a data center, point location information of each base can be displayed based on a GIS, and related condition introduction of each enterprise can be checked through clicking, wherein the method comprises the following steps of: enterprise name, brief introduction, production mode, industrial scale, marketing mode and other information;
sixthly, tracing to the source: a core agricultural product quality safety block chain traceability management information platform is constructed, comprises agricultural product supply chain informatization management of agricultural product main body of agricultural production and management, an agricultural product traceability system, an agricultural product distribution system, an agricultural product anti-counterfeiting early warning system and a big data analysis system, and is effectively docked with provincial traceability platform data.
3. The intelligent agriculture-based sky-ground integrated agricultural remote sensing big data system according to claim 1, wherein: after the remote sensing seedling condition dynamic monitoring module obtains the high-resolution satellite data, according to biomass and overground part nitrogen uptake obtained by seedling condition monitoring and a local crop nitrogen critical curve; according to the following steps: and (4) recommended fertilizing amount = regional optimized fertilizing amount- (real nitrogen absorption amount-critical nitrogen absorption amount of the overground part)/nitrogen utilization efficiency bulletin, and obtaining the ear fertilizer recommended fertilizing amount of the land scale.
4. The intelligent agriculture-based sky-ground integrated agricultural remote sensing big data system according to claim 1, wherein: the system also comprises a soil testing formula support decision, provides a soil testing formula on-line calculation function, inputs parameters such as target yield, the quantity of nutrients required by yield per hundred kilograms, non-fertilization yield, effective nutrient correction coefficient, fertilizer nutrient content, fertilizer utilization rate and the like, and can obtain nitrogen, phosphorus and potassium base fertilizer schemes with different scales of project plots and areas by calling GP service methods, wherein the schemes comprise required nitrogen, phosphorus and potassium formula ratio, elemental base fertilizer use ratio and topdressing scheme results.
5. The intelligent agriculture-based sky-ground integrated agricultural remote sensing big data system according to claim 1, wherein: the system also comprises a yield estimation service, wherein in the flowering period of the wheat and the flowering period of the corn, the yield of the plot scale is estimated according to meteorological data and soil data and in combination with a crop growth model, the estimation data is automatically updated to a database, and the yield value in the project is calculated according to the grain price in the season.
6. The intelligent agriculture-based sky-ground integrated agricultural remote sensing big data system according to claim 1, wherein: the system also comprises field history image display, a real-time video visualization interface is reserved to support mainstream cameras such as Haikang cameras and Dahua cameras, and users can independently add a video recorder into the system and control the camera to take pictures, record videos and rotate.
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