CN112561367A - Intelligent planting decision control system - Google Patents
Intelligent planting decision control system Download PDFInfo
- Publication number
- CN112561367A CN112561367A CN202011532285.5A CN202011532285A CN112561367A CN 112561367 A CN112561367 A CN 112561367A CN 202011532285 A CN202011532285 A CN 202011532285A CN 112561367 A CN112561367 A CN 112561367A
- Authority
- CN
- China
- Prior art keywords
- information
- real
- agricultural
- planting
- plate
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 claims abstract description 50
- 238000003860 storage Methods 0.000 claims abstract description 20
- 238000013500 data storage Methods 0.000 claims abstract description 10
- 210000001503 joint Anatomy 0.000 claims abstract description 5
- 239000002689 soil Substances 0.000 claims description 52
- 230000004888 barrier function Effects 0.000 claims description 16
- 230000012010 growth Effects 0.000 claims description 14
- 238000003973 irrigation Methods 0.000 claims description 13
- 230000002262 irrigation Effects 0.000 claims description 13
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 12
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 9
- 229910052700 potassium Inorganic materials 0.000 claims description 9
- 239000011591 potassium Substances 0.000 claims description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 6
- 238000009795 derivation Methods 0.000 claims description 6
- 229910052757 nitrogen Inorganic materials 0.000 claims description 6
- 229910052698 phosphorus Inorganic materials 0.000 claims description 6
- 239000011574 phosphorus Substances 0.000 claims description 6
- 238000011897 real-time detection Methods 0.000 claims description 6
- 241000607479 Yersinia pestis Species 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 5
- 230000004720 fertilization Effects 0.000 claims description 5
- 201000010099 disease Diseases 0.000 claims description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 4
- 239000000575 pesticide Substances 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 230000006855 networking Effects 0.000 claims description 3
- 239000005416 organic matter Substances 0.000 claims description 3
- 230000003749 cleanliness Effects 0.000 claims description 2
- 238000010411 cooking Methods 0.000 claims description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims 2
- ZOXJGFHDIHLPTG-UHFFFAOYSA-N Boron Chemical compound [B] ZOXJGFHDIHLPTG-UHFFFAOYSA-N 0.000 claims 1
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 claims 1
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 claims 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 claims 1
- ZOKXTWBITQBERF-UHFFFAOYSA-N Molybdenum Chemical compound [Mo] ZOKXTWBITQBERF-UHFFFAOYSA-N 0.000 claims 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims 1
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 claims 1
- 229910052785 arsenic Inorganic materials 0.000 claims 1
- RQNWIZPPADIBDY-UHFFFAOYSA-N arsenic atom Chemical compound [As] RQNWIZPPADIBDY-UHFFFAOYSA-N 0.000 claims 1
- 229910052796 boron Inorganic materials 0.000 claims 1
- 229910052793 cadmium Inorganic materials 0.000 claims 1
- BDOSMKKIYDKNTQ-UHFFFAOYSA-N cadmium atom Chemical compound [Cd] BDOSMKKIYDKNTQ-UHFFFAOYSA-N 0.000 claims 1
- 239000011575 calcium Substances 0.000 claims 1
- 229910052791 calcium Inorganic materials 0.000 claims 1
- 229910052804 chromium Inorganic materials 0.000 claims 1
- 239000011651 chromium Substances 0.000 claims 1
- 229910052802 copper Inorganic materials 0.000 claims 1
- 239000010949 copper Substances 0.000 claims 1
- 229910052742 iron Inorganic materials 0.000 claims 1
- 229910052749 magnesium Inorganic materials 0.000 claims 1
- 239000011777 magnesium Substances 0.000 claims 1
- WPBNNNQJVZRUHP-UHFFFAOYSA-L manganese(2+);methyl n-[[2-(methoxycarbonylcarbamothioylamino)phenyl]carbamothioyl]carbamate;n-[2-(sulfidocarbothioylamino)ethyl]carbamodithioate Chemical compound [Mn+2].[S-]C(=S)NCCNC([S-])=S.COC(=O)NC(=S)NC1=CC=CC=C1NC(=S)NC(=O)OC WPBNNNQJVZRUHP-UHFFFAOYSA-L 0.000 claims 1
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 claims 1
- 229910052753 mercury Inorganic materials 0.000 claims 1
- 229910052750 molybdenum Inorganic materials 0.000 claims 1
- 239000011733 molybdenum Substances 0.000 claims 1
- 238000005457 optimization Methods 0.000 claims 1
- 229910052710 silicon Inorganic materials 0.000 claims 1
- 239000010703 silicon Substances 0.000 claims 1
- 239000011593 sulfur Substances 0.000 claims 1
- 229910052717 sulfur Inorganic materials 0.000 claims 1
- 239000011701 zinc Substances 0.000 claims 1
- 229910052725 zinc Inorganic materials 0.000 claims 1
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 6
- 235000015097 nutrients Nutrition 0.000 description 6
- 230000008635 plant growth Effects 0.000 description 4
- 229910002092 carbon dioxide Inorganic materials 0.000 description 3
- 239000001569 carbon dioxide Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000005286 illumination Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000012271 agricultural production Methods 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 239000003673 groundwater Substances 0.000 description 2
- 238000000034 method Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000000243 photosynthetic effect Effects 0.000 description 1
- 230000008636 plant growth process Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000005070 ripening Effects 0.000 description 1
- 238000004856 soil analysis Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Marine Sciences & Fisheries (AREA)
- Primary Health Care (AREA)
- Animal Husbandry (AREA)
- Mining & Mineral Resources (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent planting decision control system, which comprises an agricultural information input storage plate, a real-time monitoring information collection plate and a planting decision plate, wherein the agricultural information input storage plate is in butt joint with agricultural databases of various regions and is used for inputting and storing agricultural information; the real-time monitoring information collecting plate is used for collecting real-time agricultural information of each region; the planting decision plate is used for comparing and analyzing agricultural information input storage plate and real-time monitoring information collection plate to generate corresponding planting decisions, and issuing decision instructions through the Internet of things. The decision-making system not only can collect various agricultural basic information to obtain real-time monitoring data, but also has massive historical agricultural data storage to provide powerful data support for planting schemes, and moreover, the decision-making system also collects planting scheme information of all regions in the country, and can obtain the optimal planting decision-making scheme of the land where the field is located through retrieval at any time.
Description
Technical Field
The invention relates to the technical field of agricultural intelligent production, in particular to an intelligent planting decision control system.
Background
The application of the technologies such as the Internet of things and cloud computing breaks through the space-time geographic limitation of the original agriculture, and the intellectualization of agricultural production becomes a new way for increasing the income of farmers and a new normal state of modern agriculture. The existing intelligent planting system is designed for a single crop or a single plant or designed for a single performance, and the existing intelligent planting system only simply collects basic cloud information and combines artificial experience to make a decision to send an instruction to a mechanized facility, so that although manpower resources can be saved to a great extent, if data information is insufficient and artificial experience is not sufficient, agricultural production benefits are also influenced to a great extent.
Disclosure of Invention
Aiming at the technical problems, the invention provides an intelligent planting decision control system, which not only can collect various agricultural basic information to obtain real-time monitoring data, but also has massive historical agricultural data storage to provide powerful data support for planting schemes, and moreover, the system also collects the planting scheme information of various regions in the country, and can obtain the optimal planting decision scheme of the land where the field is located by retrieval at any time.
An intelligent planting decision control system comprises an agricultural information input storage plate, a real-time monitoring information collection plate and a planting decision plate,
the agricultural information input storage plate is in butt joint with agricultural databases of various regions, can directly call and download data in the agricultural databases of various regions, and can be synchronously updated in real time;
the real-time monitoring information collecting plate is used for collecting real-time agricultural information of each region through a GPS satellite positioning system, a meteorological monitoring system and a soil moisture content system;
the planting decision plate comprises an information analysis unit and a derivation data storage library, the derivation data storage library comprises full-growth-cycle ecological models of various plants in various regions, the full-growth-cycle ecological models are established according to historical agricultural information input into the storage plate, the information analysis unit analyzes the agricultural information normal threshold of the full-growth-cycle ecological models through comparison and collects real-time agricultural information of the various regions collected by the real-time monitoring information collection plate, corresponding planting decisions are generated, and decision instructions are issued through the Internet of things.
Preferably, the agricultural information acquired by the agricultural information recording memory board includes information such as province (city) name, city name, county (district, city, farm) name, town name, village name, group (society), year of sampling, longitude and latitude, soil class, subclass, soil genus, soil species, parent soil, landscape type, terrain part, available soil layer thickness, plough layer texture, plough layer soil volume weight, texture configuration, perennial cultivation system, maturity, biological diversity, farmland forestation degree, barrier factors, barrier layer type, barrier layer depth, barrier layer thickness, irrigation capacity, irrigation manner, water source type, drainage capacity, cleanliness degree, soil nutrients (soil PH, organic matter, quick-acting total nitrogen, available phosphorus, potassium, slow-acting potassium), main cultivated plants and yield thereof.
Preferably, the real-time agricultural information collected by the real-time detection information collection board includes local agricultural information, crop growth image data, meteorological information, and soil moisture information.
Preferably, the planting decisions stored in the planting decision plate include pest and disease prediction and accurate pesticide application decisions, fertigation decisions and plant cultivation decisions, and each planting decision corresponds to a full-growth-cycle ecological model.
Preferably, the real-time monitoring information collecting board and the agricultural information recording and storing board realize information sharing, the agricultural information recording and storing board synchronously records and stores the real-time detection information of the real-time monitoring information collecting board, and data support is provided for optimizing the ecological model in the full growth cycle.
Preferably, the real-time monitoring information collecting board further supports an early warning function, and when the detection values of the meteorological monitoring system and the sensor exceed a predetermined threshold value, the real-time monitoring information collecting board timely gives an early warning indication.
Preferably, the agricultural information entry memory board supports manual entry and storage, and the planting decision board supports manual input of planting decisions.
The invention has the beneficial effects that:
the decision making system not only can collect various agricultural basic information to obtain real-time monitoring data, but also has massive historical agricultural data storage, and provides powerful data support for planting schemes. Moreover, the system also collects the planting scheme information of all regions in the country, can obtain the optimal planting decision scheme of the land where the field is located at any time through retrieval, and makes a powerful support for improving the productivity.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Detailed Description
The technical scheme of the invention is clearly and completely described below by combining the attached drawings of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, belong to the protection of the present invention.
An intelligent planting decision control system comprises an agricultural information input storage plate, a real-time monitoring information collection plate and a planting decision plate,
the agricultural information input storage plate is in butt joint with agricultural databases of various regions, can directly call and download data in the agricultural databases of various regions, and can be synchronously updated in real time;
the real-time monitoring information collecting plate is used for collecting real-time agricultural information of each region through a GPS satellite positioning system, a meteorological monitoring system and a soil moisture monitoring system;
the information of the meteorological monitoring system and the soil moisture content system is mainly acquired by various sensors, and the information of the meteorological monitoring system is mainly acquired by a plurality of sensors such as wind speed, wind direction, rainfall, air temperature, air humidity, illumination intensity, soil temperature, soil humidity, evaporation capacity, atmospheric pressure and the like; the collection of the soil moisture content system information mainly depends on the sensors corresponding to soil temperature, soil conductivity, soil PH, groundwater level, groundwater quality and related meteorological elements to collect the information.
The planting decision plate comprises an information analysis unit and a derivation data storage library, the derivation data storage library comprises full-growth-cycle ecological models of various plants in various regions, the full-growth-cycle ecological models are established according to historical agricultural information input into the storage plate, the information analysis unit analyzes the agricultural information normal threshold of the full-growth-cycle ecological models through comparison and collects real-time agricultural information of the various regions collected by the real-time monitoring information collection plate, corresponding planting decisions are generated, and decision instructions are issued through the Internet of things.
In this embodiment, the agricultural information acquired by the agricultural information recording memory board includes information such as province (city) name, city name, county (district, city, farm) name, town name, village name, group (society), year of sampling, longitude and latitude, soil type, subclass, soil genus, soil species, soil matrix, landform type, terrain part, available soil layer thickness, plough layer texture, plough layer soil volume weight, texture configuration, perennial cultivation system, ripening, biological diversity, farmland forest networking degree, barrier factor, barrier layer type, barrier layer depth, barrier layer thickness, irrigation ability, irrigation manner, water source type, drainage ability, cleaning degree, soil nutrient (soil PH, organic matter, total nitrogen, available phosphorus, available potassium), main cultivated crop and annual output thereof.
In this embodiment, the real-time agricultural information collected by the real-time detection information collecting block includes local agricultural information, crop growth image data, meteorological information, and soil moisture information.
In this embodiment, the planting decisions stored in the planting decision block include pest prediction and precise pesticide application decisions, fertigation decisions, and plant cultivation decisions, each corresponding to a full growth cycle ecological model.
In this embodiment, the real-time monitoring information collecting board and the agricultural information recording memory board realize information sharing, and the agricultural information recording memory board synchronously records and stores the real-time detection information of the real-time monitoring information collecting board, so as to provide data support for optimizing the full-growth-cycle ecological model.
In this embodiment, the real-time monitoring information collecting board further supports an early warning function, and when the detection values of the weather monitoring system and the sensor exceed a predetermined threshold value, the real-time monitoring information collecting board timely gives an early warning indication.
In this embodiment, the agricultural information entry memory board supports manual entry and storage, and the planting decision board supports manual input of a planting decision.
The regional agricultural information input and storage plate can be used for inputting basic agricultural information of each region at any time, and mainly comprises information of province (city) names, city names, county (district, city and farm) names, town names, village names, groups (society), year of sampling, longitude and latitude, soil types, subclasses, soil genera, soil seeds, soil matrix, landform types, terrain parts, effective soil layer thickness, plough layer texture, plough layer soil volume weight, texture configuration, perennial cultivation system, cooking, biological diversity, farmland and forest networking degree, barrier factors, barrier layer types, barrier layer depths, barrier layer thicknesses, irrigation capacities, irrigation modes, water source types, drainage capacities, cleaning degrees, soil nutrients (soil pH, organic matters, total nitrogen, available phosphorus, available potassium, slow-acting potassium), main cultivated crops, yield of the main cultivated crops and the like of each region. The plate is in butt joint with agricultural databases of various regions, so that data can be updated in time, the basic agricultural information of the plate is directly called by a GPS satellite positioning system, and the plate is mainly recorded into the accumulation of the databases in the later period.
The real-time monitoring information collecting plate comprises meteorological information, soil moisture content information and crop growth image data acquisition information of a monitoring area, and has a real-time early warning function. The system can quickly respond to an early warning instruction and take emergency measures when the system is used as an object with limited growth due to factors, and the field crop growth condition can be mastered in time by using the ground temperature, humidity, illumination and photosynthetic active radiation sensors to acquire information; a large amount of meteorological information can be collected by using the rainfall, wind speed, wind direction and air pressure sensors, and when the information exceeds the normal value range, a user can take precautionary measures in time, so that the loss caused by natural disasters is reduced. The plate can realize the information acquisition of underground or underwater soil temperature, moisture, water level, nitrogen, phosphorus, potassium, dissolved oxygen and PH value. The soil temperature, water content and water level are detected to realize reasonable irrigation and prevent water source waste and soil nutrient loss caused by a large amount of irrigation. The detection of the information of nitrogen, phosphorus, potassium, dissolved oxygen and pH value aims at comprehensively detecting the content of soil nutrients, accurately guiding reasonable fertilization of a paddy field, improving the yield and avoiding the environmental problem caused by excessive fertilization. The plant growth information can be acquired in real time through the image acquisition, and the growth condition of the crops can be seen at any time on a monitoring center or a remote internet. A reasonable range can be set for each sensor on the system, and when information on the ground, underground or underwater exceeds the set range, the alarm system can give an early warning to the field information in time.
The sensor is an important component of the whole detection link of the system, and is used for converting non-electrical physical quantities such as farmland environmental factors and the like into electric signals which can be identified by the control system and providing judgment and processing basis for system management and control. The common sensors mainly comprise a temperature sensor, a humidity sensor, an illumination sensor, a carbon dioxide sensor, a soil moisture sensor, a soil temperature sensor, a salinity and acidity sensor of nutrient solution and the like. The acquired data are transmitted to a decision control system center through the Internet of things, and the real-time monitoring of the planting environment is realized. The information collected by the sensor and the decision given by the manager are mixed by using the modern network, and the monitoring and the display of the terminal and the server are realized through a series of serial communication and TCP/IP protocols. The system comprehensively analyzes the data, gives out control decisions according to rules, sends instructions through the Internet of things, and the field controller controls various facilities in real time to guarantee the growth of plants. The management center can set various environmental parameters and decision rules in software, and store the monitoring data as the basis for subsequent inquiry and quick decision. Specifically, the method comprises the following steps:
1) monitoring air temperature and humidity, surface temperature and humidity, soil temperature and humidity and the like in plant growth through a temperature and humidity sensor, and acquiring, analyzing, calculating, controlling, storing, sending and the like data;
2) monitoring and recording the light intensity of plant growth through a light sensor and a photosensitive sensor, and transmitting related data to a user monitoring terminal through a wireless transmission technology;
3) the carbon dioxide concentration sensor can monitor the content of carbon dioxide in real time, and when the concentration exceeds the set threshold range of the system, related data are transmitted to the user monitoring terminal through a wireless transmission technology and are correspondingly adjusted by related workers;
4) in the field microclimate station, a large amount of meteorological information is collected by using rainfall, wind speed, wind direction and air pressure sensors, and when the information exceeds the normal value range, precautionary measures are taken in time, so that the loss caused by natural disasters is reduced, and the plant production is guided better;
5) and alarm control, namely setting the upper limit and the lower limit of certain parameter indexes, generating alarm information when the temperature is higher or lower than a certain temperature range, and displaying the alarm information on a control platform and a field control node in the system. And checking the field condition through video monitoring, and then adopting a reasonable mode to deal with the specific occurrence condition in the field.
And the planting decision plate can obtain relevant agricultural information of a certain area after regional agricultural information is input and stored in the plate and the information of the real-time monitoring information collection plate, and then performs information analysis, generation and issuing of a planting decision instruction through the planting decision plate.
The planting decision plate comprises a disease and pest prediction and accurate pesticide application decision, an irrigation and fertilization decision and a plant cultivation decision, and a series of processes such as soil analysis of a planting field, a planting field cultivation system, irrigation and fertilization disease and pest protection in the plant growth process, mechanized collection in the harvest period and the like are carried out according to information collected by the regional agricultural information input and storage plate and the real-time monitoring information collection plate.
The precise application scheme comprises the following steps:
taking the plant cultivation decision in the northHubei region as an example, the data adopted by the full growth cycle ecological model of the plant cultivation decision comprises:
the data are integrated by using the existing model mode, the full-growth-cycle production cultivation model (taking the northHubei area as an example) of each plant according with the region is worked out, different cultivation instructions are issued in different time periods, farmers cultivate in real time after receiving the cultivation instructions, the growth of the plants is ensured to be in the optimal growth period of the local region, and the growth and development of the plants are promoted.
The decision making system not only can collect various agricultural basic information to obtain real-time monitoring data, but also has massive historical agricultural data storage, and provides powerful data support for planting schemes. Moreover, the system also collects the planting scheme information of all regions in the country, can obtain the optimal planting decision scheme of the land where the field is located at any time through retrieval, and makes a powerful support for improving the productivity.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The utility model provides an intelligence planting decision-making control system which characterized in that: comprises an agricultural information recording and storing plate, a real-time monitoring information collecting plate and a planting decision plate,
the agricultural information input storage plate is in butt joint with agricultural databases of various regions, can directly call and download data in the agricultural databases of various regions, and can be synchronously updated in real time;
the real-time monitoring information collecting plate is used for collecting real-time agricultural information of each region through a GPS satellite positioning system, a meteorological monitoring system and a soil moisture monitoring system;
the planting decision plate comprises an information analysis unit and a derivation data storage library, the derivation data storage library comprises full-growth-cycle ecological models of various plants in various regions, the full-growth-cycle ecological models are established according to historical agricultural information input into the storage plate, the information analysis unit analyzes the agricultural information normal threshold of the full-growth-cycle ecological models through comparison and collects real-time agricultural information of the various regions collected by the real-time monitoring information collection plate, corresponding planting decisions are generated, and decision instructions are issued through the Internet of things.
2. The intelligent planting decision control system of claim 1, wherein: the agricultural information obtained by the agricultural information recording storage plate comprises province names, city names, county names, town names, village names, groups, sampling years, longitude and latitude, soil types, subclasses, soil genera, soil seeds, soil matrix, landform types, terrain parts, effective soil layer thicknesses, plough layer textures, plough layer soil volume weights, texture configurations, perennial cultivation systems, cooking, biological diversity, farmland forest networking degrees, barrier factors, barrier layer types, barrier layer depths and barrier layer thicknesses, irrigation capacity, irrigation mode, water source type, drainage capacity, cleanliness, soil PH, organic matter, available phosphorus, total nitrogen, available potassium, slow-release potassium, available copper, available zinc, available iron, available manganese, available boron, available molybdenum, available sulfur, available silicon, exchangeable calcium, exchangeable magnesium, chromium, cadmium, lead, arsenic, mercury, main crops and annual yields thereof.
3. The intelligent planting decision control system of claim 1, wherein: the real-time agricultural information collected by the real-time detection information collecting plate comprises local agricultural information, crop growth image data, meteorological information and soil moisture content information.
4. The intelligent planting decision control system of claim 1, wherein: the planting decisions stored in the planting decision plate comprise pest and disease damage prediction and accurate pesticide application decisions, irrigation and fertilization decisions and plant cultivation decisions, and each planting decision corresponds to a full-growth-cycle ecological model.
5. The intelligent planting decision control system of claim 1, wherein: the real-time monitoring information collecting plate and the agricultural information input storage plate realize information sharing, the agricultural information input storage plate synchronously inputs and stores the real-time detection information of the real-time monitoring information collecting plate, and data support is provided for optimization of the full growth cycle ecological model.
6. The intelligent planting decision control system of claim 1, wherein: the real-time monitoring information collecting plate also has a support early warning function, and when the detection values of the meteorological monitoring system and the sensor exceed a preset threshold value, the real-time monitoring information collecting plate timely gives an early warning indication.
7. The intelligent planting decision control system of claim 1, wherein: the agricultural information input storage plate supports manual input storage, and the planting decision plate supports manual input of planting decisions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011532285.5A CN112561367A (en) | 2020-12-22 | 2020-12-22 | Intelligent planting decision control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011532285.5A CN112561367A (en) | 2020-12-22 | 2020-12-22 | Intelligent planting decision control system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112561367A true CN112561367A (en) | 2021-03-26 |
Family
ID=75031420
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011532285.5A Pending CN112561367A (en) | 2020-12-22 | 2020-12-22 | Intelligent planting decision control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112561367A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113295208A (en) * | 2021-05-19 | 2021-08-24 | 天津现代职业技术学院 | Intelligent agricultural planting data monitoring method |
| CN113807750A (en) * | 2021-11-19 | 2021-12-17 | 中国气象局公共气象服务中心(国家预警信息发布中心) | Service decision device and method based on environment elements |
| CN114041408A (en) * | 2021-12-20 | 2022-02-15 | 巫溪县八步云核桃种植有限公司 | Walnut seedling is grown seedlings and is used self-adaptation intelligent irrigation system |
| CN114545833A (en) * | 2022-03-02 | 2022-05-27 | 腾圣福(广州)农业科技有限公司 | Facility agriculture intelligent interactive processing system based on internet of things technology |
| CN115984028A (en) * | 2023-03-21 | 2023-04-18 | 山东科翔智能科技有限公司 | AI technology-based intelligent agricultural production data decision management system |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103927685A (en) * | 2014-04-23 | 2014-07-16 | 广西力源宝科技有限公司 | Agricultural (forestal) intelligent fertilizer applying system |
| CN104766153A (en) * | 2015-02-03 | 2015-07-08 | 中国科学院合肥物质科学研究院 | Agricultural things-internet platform architecture |
| CN105959396A (en) * | 2016-06-15 | 2016-09-21 | 北京华泰德丰技术有限公司 | Agricultural information sending method, device and system |
| CN106254476A (en) * | 2016-08-09 | 2016-12-21 | 沈阳君农哥科技有限公司 | Agroecological environment information management based on Internet of Things, big data and cloud computing and monitoring method and system |
| CN106779414A (en) * | 2016-12-19 | 2017-05-31 | 山西奥克斯电子***工程中心 | Industrialized agriculture remote monitoring and intelligent decision system |
| CN108077042A (en) * | 2017-12-04 | 2018-05-29 | 北京农业智能装备技术研究中心 | A kind of winter wheat time of infertility irrigates early warning decision method |
| CN108094370A (en) * | 2017-12-15 | 2018-06-01 | 河北省科学院应用数学研究所 | Control method of irrigation and device |
| CN109191074A (en) * | 2018-08-27 | 2019-01-11 | 宁夏大学 | Wisdom orchard planting management system |
| CN110347127A (en) * | 2019-06-26 | 2019-10-18 | 北京农业智能装备技术研究中心 | Crop planting mandatory system and method based on cloud service |
| CN110955212A (en) * | 2019-12-05 | 2020-04-03 | 河南工业大学 | Wisdom agricultural information processing system based on thing networking |
| CN111507857A (en) * | 2020-04-22 | 2020-08-07 | 青岛逸景数字科技有限公司 | Digital agricultural planting system and method based on Internet of things technology |
-
2020
- 2020-12-22 CN CN202011532285.5A patent/CN112561367A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103927685A (en) * | 2014-04-23 | 2014-07-16 | 广西力源宝科技有限公司 | Agricultural (forestal) intelligent fertilizer applying system |
| CN104766153A (en) * | 2015-02-03 | 2015-07-08 | 中国科学院合肥物质科学研究院 | Agricultural things-internet platform architecture |
| CN105959396A (en) * | 2016-06-15 | 2016-09-21 | 北京华泰德丰技术有限公司 | Agricultural information sending method, device and system |
| CN106254476A (en) * | 2016-08-09 | 2016-12-21 | 沈阳君农哥科技有限公司 | Agroecological environment information management based on Internet of Things, big data and cloud computing and monitoring method and system |
| CN106779414A (en) * | 2016-12-19 | 2017-05-31 | 山西奥克斯电子***工程中心 | Industrialized agriculture remote monitoring and intelligent decision system |
| CN108077042A (en) * | 2017-12-04 | 2018-05-29 | 北京农业智能装备技术研究中心 | A kind of winter wheat time of infertility irrigates early warning decision method |
| CN108094370A (en) * | 2017-12-15 | 2018-06-01 | 河北省科学院应用数学研究所 | Control method of irrigation and device |
| CN109191074A (en) * | 2018-08-27 | 2019-01-11 | 宁夏大学 | Wisdom orchard planting management system |
| CN110347127A (en) * | 2019-06-26 | 2019-10-18 | 北京农业智能装备技术研究中心 | Crop planting mandatory system and method based on cloud service |
| CN110955212A (en) * | 2019-12-05 | 2020-04-03 | 河南工业大学 | Wisdom agricultural information processing system based on thing networking |
| CN111507857A (en) * | 2020-04-22 | 2020-08-07 | 青岛逸景数字科技有限公司 | Digital agricultural planting system and method based on Internet of things technology |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113295208A (en) * | 2021-05-19 | 2021-08-24 | 天津现代职业技术学院 | Intelligent agricultural planting data monitoring method |
| CN113807750A (en) * | 2021-11-19 | 2021-12-17 | 中国气象局公共气象服务中心(国家预警信息发布中心) | Service decision device and method based on environment elements |
| CN114041408A (en) * | 2021-12-20 | 2022-02-15 | 巫溪县八步云核桃种植有限公司 | Walnut seedling is grown seedlings and is used self-adaptation intelligent irrigation system |
| CN114545833A (en) * | 2022-03-02 | 2022-05-27 | 腾圣福(广州)农业科技有限公司 | Facility agriculture intelligent interactive processing system based on internet of things technology |
| CN114545833B (en) * | 2022-03-02 | 2023-05-02 | 深圳市丰农数智农业科技有限公司 | Intelligent interactive processing system of facility agriculture based on internet of things |
| CN115984028A (en) * | 2023-03-21 | 2023-04-18 | 山东科翔智能科技有限公司 | AI technology-based intelligent agricultural production data decision management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112561367A (en) | Intelligent planting decision control system | |
| US10028426B2 (en) | Agronomic systems, methods and apparatuses | |
| US20180014452A1 (en) | Agronomic systems, methods and apparatuses | |
| CN110347127A (en) | Crop planting mandatory system and method based on cloud service | |
| US20160309646A1 (en) | Agronomic systems, methods and apparatuses | |
| US20180349520A1 (en) | Methods for agricultural land improvement | |
| CN113409023A (en) | Wisdom gardens integrated service management platform | |
| CN114202438A (en) | Sky and ground integrated agricultural remote sensing big data system based on wisdom agriculture | |
| Gawande et al. | Potential of precision farming technologies for eco-friendly agriculture | |
| Ahmad et al. | A review of best management practices for potato crop using precision agricultural technologies | |
| CN105678629A (en) | Planting industry problem solution system based on internet of things | |
| CN113920288A (en) | Greenhouse cucumber planting management system based on Internet of things | |
| CN111435260A (en) | Plant growth environment parameter monitoring and environment simulation system | |
| CN115943414A (en) | Adaptive planting information providing system based on crop planting application | |
| CN116843164A (en) | Agricultural machinery intelligent control system based on image analysis | |
| CN114578881B (en) | Intelligent maintenance management system | |
| Huang et al. | Digital image analysis technologies for decision support systems in agricultural | |
| CN115486358A (en) | Perennial forage grass drip irrigation automatic irrigation management decision control system | |
| CN213848015U (en) | Crop water-saving irrigation measurement and control system based on multi-source information fusion | |
| Kumar et al. | Precision agriculture–a vital approach towards modernizing the smart farming in India | |
| Elbashir | Agricultural Mechanization and Food Security in Saudi Arabia | |
| RU227483U1 (en) | Device for monitoring plant growing conditions | |
| Kumari et al. | Leveraging Internet of Things (IoT) for Sustainable Agriculture: A Comprehensive Review and Future Perspectives | |
| CN116307588A (en) | Big data agricultural management system | |
| Jha et al. | Smart and Reliable Agriculture Application Using IoT-Enabled Fog-Cloud Platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210326 |