CN115249116A - Agricultural planting management system and method based on Internet of things and artificial intelligence - Google Patents
Agricultural planting management system and method based on Internet of things and artificial intelligence Download PDFInfo
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Abstract
The invention relates to the field of G06Q50/02, in particular to an agricultural planting management system and method based on the Internet of things and artificial intelligence. The system provided by the invention ensures that the intelligent agriculture develops towards high automation and high-efficiency management, and simultaneously creatively realizes linkage of suppliers, crop experts, farmland managers and planters involved in the agricultural planting process, so that the Internet of things and artificial intelligence are fully utilized, the planting technology and the planting experience of the crop experts are artificially intelligentized, and further, an agricultural planting guidance scheme with high accuracy, high efficiency and high demand satisfaction degree and a high-matching-degree planting service list are provided for the planters to the maximum extent through machine learning, the agricultural planting management efficiency is improved, the agricultural planting is ensured to achieve the expected target yield and quality, and the system has very high practical application value and commercial application potential.
Description
Technical Field
The invention relates to the field of G06Q50/02, in particular to an agricultural planting management system and method based on the Internet of things and artificial intelligence.
Background
Agricultural planting includes the planting cultivation of plants such as various crops, woods, fruit trees, flowers and plants, medicine and sight and admire, but the operation of the whole planting process of traditional agricultural planting all goes on based on the experience of planting personnel, and agricultural planting guidance scheme needs technical teacher to provide corresponding agricultural planting guidance scheme after gathering relevant data, consumes the manpower, and the time is longer, and efficiency is lower, and planting application technique and planting inspection are all carried out input, conversion, storage and output by people's brain, can't provide effectual reference experience to subsequent planting.
With the wide popularization and application of the internet, artificial intelligence and big data, smart agriculture gradually becomes the main direction and trend of current agricultural development, and the chinese patent CN109766380B discloses an agricultural planting pesticide prescription method and system based on the internet and artificial intelligence. Chinese patent CN109767347B discloses an internet and intelligent agricultural planting management system, which mainly aims at crop planting areas, crop planting monitoring and crop planting equipment, and obtains corresponding information of crops through monitoring setting to further realize automatic monitoring management, however, growth trend of crops or plants in the planting process or various problems cannot be accurately identified and judged through video monitoring, which easily causes loss of characteristics and neglect of problems, and seriously affects accuracy and matching degree of subsequent planting schemes.
Therefore, the agricultural planting management system and method based on the Internet of things and artificial intelligence are provided, intelligent agriculture is enabled to develop towards high automation and high-efficiency management, linkage of suppliers, crop experts, farmland managers and growers involved in the agricultural planting process is creatively realized, the Internet of things and the artificial intelligence are fully utilized, planting technologies and planting experiences of the crop experts are artificially intelligentized, further, through machine learning, an agricultural planting guidance scheme with high accuracy, high efficiency and high demand satisfaction degree and a planting service list with high matching degree are provided for the growers to the maximum extent, agricultural planting management efficiency is improved, the agricultural planting is enabled to achieve expected target yield and quality, and the system and method have very high practical application value and commercial application potential.
Disclosure of Invention
In order to solve the problems, the invention provides an agricultural planting management system and method based on the Internet of things and artificial intelligence.
The invention provides an agricultural planting management system and method based on the Internet of things and artificial intelligence, which at least comprises the following steps:
(1) Accurately collecting crop planting demand data by matching a farmland manager terminal with a planter terminal;
(2) Carrying out digital standard definition on crop growth basic data through a crop expert terminal;
(3) Establishing a crop growth model by using the crop planting demand data collected in the step (1) and the crop growth basic data collected in the step (2) through an artificial intelligent terminal;
(4) Continuously collecting historical planting sample data of the region through an artificial intelligent terminal to establish a historical planting sample database of the region;
(5) The artificial intelligent terminal intelligently makes a prescription according to a crop growth model, a regional historical planting sample database and supply chain goods and agricultural and living service data provided by a supplier terminal, and provides a block-level crop planting technical service scheme and a service list;
(6) A grower obtains an intelligent planting management service and a service list through a grower terminal, and related data in the intelligent management service process are reserved and continuously collected to obtain a block-level traceable agricultural product planting record.
Preferably, the crop planting demand data in the step (1) at least comprises planter habit data, plot related data, planted crop variety data and planted crop target data; preferably, the collection of the data required by the planted crops in the step (1) is to collect data such as land parcels, terrains, weather, soil, planted crops and varieties, planting habits, historical input and output, diseases, insect pests, weeds, crop growth vigor and the like based on GPS coordinates; further preferably, the demand data of the planted crops in the step (1) further comprises block-level traceable agricultural product planting records, the end of each crop season is based on farmer blocks, the planted crops are automatically summarized and compared to form data samples for memory storage and machine learning, the sample database is continuously updated and expanded, the next season scheme is finely optimized, and an agricultural planting technology guidance service scheme and a service list with high accuracy, high efficiency and high demand satisfaction degree are provided for growers to the maximum extent.
Preferably, the basic data of crop growth in step (2) is obtained by defining indexes related to the growth of crop varieties in different areas (minimum to village) and indexes related to the scientific crop management by combining a crop base knowledge base and a crop area knowledge base according to self experiences of crop experts, and the definition at least comprises basic crop and growth index definitions and plant management node and index definitions;
preferably, the basic crop definition at least comprises crop and crop variety definition, crop planting area definition, crop planting mode definition and crop planting target definition; the definition of the crops and the crop varieties specifically comprises the steps of coding the crops and the crop varieties, digitalizing variety characteristic indexes, facilitating system identification for subsequent processes, and for clearer explanation, taking strawberries as an example, defining the number of the strawberries as 001; the strawberry varieties at least comprise Shuofeng, mingming, mingxi and Chunshi, the serial numbers of Shuofeng, mingming, mingxi and Chunshi are defined as 001, 002, 003 and 004 in sequence, and the identification codes of Shuofeng strawberry, mingming strawberry, mingxi strawberry and Chunshi strawberry in the system are 001001, 001002, 001003 and 001004 in sequence. Preferably, the contents defined by the crop planting area at least comprise regions, altitudes, climates and accumulated temperatures; preferably, the contents of the crop planting target definition at least comprise planting input, planting output and planting quality.
Preferably, the planting management node definition at least comprises a farm guidance rule definition, a farm reminding rule definition, a field patrol rule definition, an easily-caused disease, pest and weed control rule definition, a required production material rule definition and a required agricultural labor service rule definition; in the early stage, areas where crops are planted, the types of the crops, the planting modes of the crops and the planting targets needing to be achieved are determined, and then planting management nodes are defined so as to achieve the planting targets. The content defined by the farming guidance rule at least comprises weather, diseases, insect pests, weeds, farming operation, medication and fertilizer application, and aims to provide farming education guidance for growers through the farming guidance rule in an article or video mode by crop experts. The purpose of the definition of the farming reminding rule is to timely and accurately provide farming reminding for a grower, and the grower performs related farming operation according to the farming guidance rule after receiving the intelligent farming reminding automatically sent by the system. The content defined by the field patrol rule comprises a crop growth index measurement standard, the accurate crop growth condition and the accurate planting state are obtained by patrolling the field by the farmer, and the information is fed back to crop experts. The content defined by the easy-to-develop pest and weed control rule at least comprises a pest and weed control method and a material index measurement standard, and aims to provide a corresponding pest and weed control means for a grower in time, help the grower to solve the pest and weed and avoid causing serious economic loss; the required production substance rule defines index measurement standards for providing required production substances, and at least comprises index measurement standards of various nutrient element components (such as nitrogen, phosphorus and potassium) in the fertilizer, fertilizer dosage index measurement standards, effective substance content index measurement standards in the pesticide, pesticide dosage index measurement standards and planting equipment index measurement standards; the required agricultural labor service rule defines an index measurement standard for providing agricultural labor service required by a grower in a planting process, and the required agricultural labor service at least comprises land preparation service, pesticide spraying service, watering service and harvesting service;
the variety, planting area, planting mode and planting target of the crop are different, the finally provided guidance scheme is different, the corresponding subsequent management is also different, and the basic definition is matched with the planting management node definition, so that the accuracy can be more accurately provided for the grower, and the agricultural planting guidance scheme and the planting service list with high matching degree required by the grower are met.
Preferably, the historical planting sample data in the step (4) at least comprises plots, terrains, weather, soil, planting crops and varieties, planting habits, historical input and output, and growth vigor of the crops with diseases, pests and weeds;
preferably, the supply chain commodity and agricultural service data in the step (5) at least include production material commodity release data, agricultural labor service commodity release data and financial insurance service commodity release data.
Preferably, the commodity release data of the produced materials is obtained by releasing the produced materials required in the planting process by a supplier, and at least comprises pesticides, fertilizers, planting equipment, pesticide applying equipment and fertilizer applying equipment, so that the produced materials required in the planting process are provided for a planter.
Preferably, the releasing data of the agricultural and labor service commodity is obtained by releasing agricultural and labor service information by labor personnel, the agricultural and labor service information at least comprises the number of the labor personnel, the working type of the labor personnel, the working content of the labor personnel, the charging standard of the labor personnel, the working time of the labor personnel and the working capacity of the labor personnel, the agricultural and labor service required in different planting stage processes is provided for a planter, and the planter is helped to complete various agricultural and labor work in the planting process.
Preferably, the financial insurance service commodity issuing data is obtained by issuing financial insurance service commodities by a financial institution, the financial insurance service commodities are financial credit products, are evaluated according to the information of the grower and the planting order, provide corresponding credit and loan standards, provide fund assistance required by the grower in the planting process, and help the grower purchase production materials required by planting or labor service of agricultural activities.
Preferably, the intelligent management service in the step (6) at least comprises an intelligent farm work operation guide, a field patrol guide, an intelligent farm work reminding, an inquiry service, a consultation service and an intelligent mall.
Preferably, data in the efficient agricultural planting management system based on the Internet of things and artificial intelligence are loaded into the model base through interactive collection, cleaning, conversion and calculation of the data by using the Internet of things technology (sensor), the mobile internet technology (mobile phone APP) and the data warehouse technology according to the crop growth model through information technology experts.
The artificial intelligent terminal is used for converting crop planting technology and experience of crop experts into digitalization and intellectualization, intelligently matching the crop planting technology and experience with a supply chain through a near principle and a lowest cost principle for providing a crop planting technical service scheme and a service list, matching the demand of a planter with supply commodity services of a supplier, creatively realizing linkage of the supplier, the crop experts, a farmland manager and the planter involved in the agricultural planting process, improving the agricultural planting management efficiency and ensuring that the agricultural planting achieves the expected target yield and quality.
Advantageous effects
1. The invention provides an agricultural planting management system and method based on the Internet of things and artificial intelligence, which can ensure that intelligent agriculture develops towards high automation and high-efficiency management, and meanwhile, linkage of suppliers, crop experts, farmland managers and growers involved in an agricultural planting process is creatively realized, so that the Internet of things and the artificial intelligence are fully utilized, the planting technology and the planting experience of the crop experts are artificially intelligentized, and further, an agricultural planting guidance scheme with high accuracy, high efficiency and high demand satisfaction degree and a high matching degree planting service list are provided for the growers to the maximum extent through machine learning, the agricultural planting management efficiency is improved, the agricultural planting is ensured to reach the expected target yield and quality, and the system and method have very high practical application value and commercial application potential.
2. According to the system, the block-level traceable agricultural product planting record is formed by finishing each crop season, automatically summarizing and comparing the planted crops based on the planted blocks and forming sample data through memory storage and machine learning, the sample database is continuously updated and expanded, the lower season scheme is refined and optimized, and the agricultural planting guidance scheme and the service list which are high in accuracy, efficiency and demand satisfaction degree are provided for growers to the greatest extent.
3. The method has the advantages that the guiding scheme and the service list provided finally are different corresponding to the difference of subsequent management due to the difference of crops, varieties, planting areas, planting modes and planting targets, and the accuracy can be more accurately provided for the grower by matching with the definition of the planting management nodes through the basic definition, so that the agricultural planting guiding scheme and the planting service list with high matching degree required by the grower are met.
4. The crop planting technology and experience of crop experts are intelligently and digitally converted through the artificial intelligent terminal, in order to provide a crop planting technical service scheme and a service list, the intelligent matching is carried out on the crop planting technical service scheme and the service list through a near principle and a lowest cost principle, the demand of a grower is matched with the supply commodity of a supplier and the agricultural and domestic labor service, the linkage of the supplier, the crop experts, a farmland manager and the grower involved in the agricultural planting process is creatively realized, the agricultural planting management efficiency is improved, and the agricultural planting is ensured to achieve the expected target yield and quality.
Drawings
Fig. 1 is a flow chart of an agricultural planting management method based on the internet of things and artificial intelligence.
Fig. 2 is a data structure diagram of a crop growth model in an agricultural planting management system based on the internet of things and artificial intelligence.
Detailed Description
The embodiment of the invention provides an agricultural planting management system and method based on the Internet of things and artificial intelligence.
In another aspect, the embodiment of the invention provides an efficient agricultural planting management method based on the internet of things and artificial intelligence, which comprises the following steps:
(1) Accurately collecting crop planting demand data by matching a farmland manager terminal with a planter terminal;
(2) Carrying out digital standard definition on crop growth basic data through a crop expert terminal;
(3) Establishing a crop growth model by using the crop planting demand data collected in the step (1) and the crop growth basic data collected in the step (2) through an artificial intelligent terminal;
(4) Continuously collecting historical planting sample data of the region through an artificial intelligent terminal to establish a historical planting sample database of the region;
(5) The artificial intelligent terminal makes an intelligent evolution according to the crop growth model, the regional historical planting sample database and the supply chain commodity and agricultural and living service data provided by the supplier terminal, and provides a block-level crop planting technical service scheme and a service list;
(6) A grower obtains an intelligent planting management service and a service list through a grower terminal, and related data in the intelligent management service process are reserved and continuously collected to obtain a block-level traceable agricultural product planting record.
The crop planting demand data in the step (1) comprises planter habit data, land parcel related data, planted crop variety data and planted crop target data; the collection of the crop planting demand data in the step (1) is to collect data such as land parcels, terrains, weather, soil, planted crops and varieties, planting habits, historical input and output, diseases, insect pests, weeds, crop growth vigor and the like based on GPS coordinates; the crop planting demand data in the step (1) further comprises block-level traceable agricultural product planting records, each crop season is finished based on farmer blocks, planted crops are automatically summarized and compared to form data samples for memory storage and machine learning, the sample database is continuously updated and expanded, the next season scheme is refined and optimized, and an agricultural planting technical guidance service scheme and a service list which are high in accuracy, efficiency and demand satisfaction degree are provided for growers to the greatest extent.
The basic data of the crop growth in the step (2) is obtained by defining indexes related to the crop variety growth and the indexes related to the scientific crop planting management in different areas (minimum to village) by combining a crop basic knowledge base and a crop area knowledge base according to self experiences of crop experts, and the definition comprises basic crop definition and planting management node definition;
the basic crop definition comprises crop and crop variety definition, crop planting area definition, crop planting mode definition and crop planting target definition; the definition of the crops and the crop varieties specifically comprises the steps of coding the crops and the crop varieties, digitalizing variety characteristic indexes, facilitating system identification for subsequent processes, and for clearer explanation, taking strawberries as an example, defining the number of the strawberries as 001; the strawberry variety comprises Shuofeng, mingxi, mingxia and Chuxu, the serial numbers of Shuofeng, mingxi, mingxia and Chuxu are defined as 001, 002, 003 and 004 in sequence, and the identification codes of Shuofeng, mingxi and Chuxu strawberries in the system are 001001, 001002, 001003 and 001004 in sequence. The contents defined by the crop planting area comprise an area, an altitude, a climate and a temperature; the content of the crop planting target definition comprises planting input, planting output and planting quality.
The planting management node definition comprises a farming guidance rule definition, a farming reminding rule definition, a field inspection rule definition, an easily-occurring disease, pest and weed prevention rule definition, a required production material rule definition and a required farming labor service rule definition; in the early stage, areas where crops are planted, the types of the crops, the planting modes of the crops and the planting targets needing to be achieved are determined, and then planting management nodes are defined so as to achieve the planting targets. The content defined by the farming guidance rule comprises weather, diseases, insect pests, weeds, farming operation, medication and fertilizer application, and aims to provide farming education guidance for growers through the farming guidance rule in an article or video mode by crop experts. The purpose of the definition of the farming reminding rule is to timely and accurately provide farming reminding for a grower, and the grower performs related farming operation according to the farming guidance rule after receiving the intelligent farming reminding automatically sent by the system. The content defined by the field patrol rule comprises a crop growth index measurement standard, and the accurate crop growth condition and the planting state are obtained by patrolling the field by the farmland manager and the information is fed back to crop experts. The content defined by the easy-to-develop pest and weed control rule comprises a pest and weed control method and a material index measurement standard, and aims to provide a corresponding pest and weed control means for a grower in time, help the grower to solve the pest and weed and avoid causing serious economic loss; the required production substance rule defines index measurement standards for providing required production substances, including index measurement standards of various nutrient element components (such as nitrogen, phosphorus and potassium) in the fertilizer, fertilizer dosage index measurement standards, effective substance content index measurement standards in the pesticide, pesticide dosage index measurement standards and planting equipment index measurement standards; the required agricultural labor service rule defines an index measurement standard for providing agricultural labor service required by a grower in a planting process, and the required agricultural labor service comprises land preparation service, pesticide spraying service, watering service and harvesting service;
the variety, planting area, planting mode and planting target of the crop are different, the finally provided guidance scheme is different, the corresponding subsequent management is also different, and the basic definition is matched with the planting management node definition, so that the accuracy can be more accurately provided for the grower, and the agricultural planting guidance scheme and the planting service list with high matching degree required by the grower are met.
The historical planting sample data in the step (4) comprises land parcels, terrains, weather, soil, planting crops and varieties, planting habits, historical input and output and growth vigor of the crops with diseases, pests and weeds;
the supply chain commodity and agricultural and domestic service data in the step (5) comprise production substance commodity release data, agricultural and domestic labor service commodity release data and financial insurance service commodity release data.
The commodity release data of the produced materials are obtained by releasing the produced materials required in the planting process by a supplier, and the commodity release data of the produced materials comprise pesticides, fertilizers, planting equipment, pesticide applying equipment and fertilizer applying equipment, and provide the produced materials required in the planting process for a planter.
The agricultural and labor service commodity publishing data is obtained by publishing agricultural and labor service information by labor personnel, wherein the agricultural and labor service information comprises the number of labor personnel, the work type of the labor personnel, the work content of the labor personnel, the charging standard of the labor personnel, the work time of the labor personnel and the work capacity of the labor personnel, provides agricultural and labor service required in different planting stage processes for a planter, and helps the planter to complete various agricultural and labor work in the planting process.
The financial insurance service commodity issuing data is obtained by issuing financial insurance service commodities by a financial institution, the financial insurance service commodities are financial credit products, are evaluated according to the information of the grower and the planting order, provide corresponding credit and loan standards, provide fund assistance required in the planting process for the grower, and help the grower to purchase production materials required for planting or agricultural labor service.
The intelligent management service in the step (6) comprises intelligent farm work operation guidance, field patrol guidance, intelligent farm work reminding, inquiry service, consultation service and an intelligent mall.
Data in the efficient agricultural planting management system based on the Internet of things and artificial intelligence are loaded into a model base through an information technology expert by using the Internet of things technology (sensor), the mobile internet technology (mobile phone APP) and the data warehouse technology according to a crop growth model in an interactive mode for collecting, cleaning, converting and calculating.
The artificial intelligent terminal is used for converting crop planting technology and experience of crop experts into digitalization and intellectualization, intelligently matching the crop planting technology and experience with a supply chain through a near principle and a lowest cost principle for providing a crop planting technical service scheme and a service list, matching the demand of a planter with supply commodity services of a supplier, creatively realizing linkage of the supplier, the crop experts, a farmland manager and the planter involved in the agricultural planting process, improving the agricultural planting management efficiency and ensuring that the agricultural planting achieves the expected target yield and quality.
Claims (10)
1. The agricultural planting management system based on the Internet of things and artificial intelligence is characterized by at least comprising a supplier terminal, a crop expert terminal, an artificial intelligence terminal, a farmland housekeeper terminal and a planter terminal.
2. An efficient agricultural planting management method based on the Internet of things and artificial intelligence is characterized by at least comprising the following steps:
(1) Accurately collecting crop planting demand data by matching a farmland manager terminal with a planter terminal;
(2) Carrying out digital standard definition on crop growth basic data through a crop expert terminal;
(3) Establishing a crop growth model by using the crop planting demand data collected in the step (1) and the crop growth basic data collected in the step (2) through an artificial intelligent terminal;
(4) Continuously collecting regional historical planting sample data through an artificial intelligent terminal to establish a regional historical planting sample database;
(5) The artificial intelligent terminal makes an intelligent evolution according to the crop growth model, the regional historical planting data sample base and the supply chain commodity and agricultural and living service data provided by the supplier terminal, and provides a block-level crop planting technical service scheme and a service list;
(6) A grower obtains an intelligent planting management service and a service list through a grower terminal, and related data in the intelligent management service process are reserved and continuously collected to obtain a block-level traceable agricultural product planting record.
3. The method for managing agricultural planting based on the internet of things and artificial intelligence of claim 2, wherein the crop demand data in step (1) at least comprises grower habit data, plot related data, planted crop variety data and planted crop target data.
4. The method for managing high-efficiency agricultural planting based on the internet of things and artificial intelligence as claimed in claim 2, wherein the collection of the demand data of the planted crops in the step (1) is based on GPS coordinates to collect data of land parcels, terrains, weather, soil, planted crops and varieties, planting habits, historical input and output, diseases, insect pests, growth vigor of the crops and the like.
5. The Internet of things and artificial intelligence based high-efficiency agricultural planting management method according to claim 4, wherein the planting crop demand data in the step (1) further comprises a plot-level traceable agricultural product planting record.
6. The method for managing agricultural planting based on the internet of things and artificial intelligence of claim 2, wherein the basic data of crop growth in step (2) is obtained by a crop expert according to their own experience, and by combining with a crop basic knowledge base and a crop regional knowledge base, defining indexes related to the growth of different regional crop varieties and indexes related to crop scientific planting management, and the definition at least comprises a basic crop and growth index definition and a planting management node and index definition.
7. The method for efficient agricultural planting management based on the internet of things and artificial intelligence of claim 6, wherein the basic crop definition at least comprises a crop and crop variety definition, a crop planting area definition, a crop planting mode definition and a crop planting target definition.
8. The method for managing the agricultural planting based on the internet of things and artificial intelligence of claim 6, wherein the planting management node definition at least comprises a farming guidance rule definition, a farming reminding rule definition, a field patrol rule definition, an easily-occurring pest and weed control rule definition, a required production material rule definition and a required farming labor service rule definition.
9. The efficient agricultural planting management method based on the internet of things and artificial intelligence of claim 2, wherein the supply chain commodity and agricultural service data in the step (5) at least comprise production material commodity release data, agricultural labor service commodity release data and financial insurance service commodity release data.
10. The efficient agricultural planting management method based on the internet of things and artificial intelligence of claim 2, wherein the intelligent management services in the step (6) at least comprise intelligent farm work operation guidance, field patrol guidance, intelligent farm work reminding, inquiry service, consultation service and intelligent shopping mall.
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| CN202210250555.6A CN115249116A (en) | 2022-03-15 | 2022-03-15 | Agricultural planting management system and method based on Internet of things and artificial intelligence |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117474294A (en) * | 2023-12-26 | 2024-01-30 | 山东麦港数据系统有限公司 | Planting auxiliary decision-making system based on crop cultivation management model |
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2022
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117474294A (en) * | 2023-12-26 | 2024-01-30 | 山东麦港数据系统有限公司 | Planting auxiliary decision-making system based on crop cultivation management model |
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