CN114549224A - Crop planting guidance method based on big data - Google Patents
Crop planting guidance method based on big data Download PDFInfo
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- CN114549224A CN114549224A CN202210184186.5A CN202210184186A CN114549224A CN 114549224 A CN114549224 A CN 114549224A CN 202210184186 A CN202210184186 A CN 202210184186A CN 114549224 A CN114549224 A CN 114549224A
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- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
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Abstract
The scheme discloses a crop planting guidance method based on big data, which comprises the following steps: s100, selecting crops to be planted and a planting land block based on the crop model and the target planting area; s200, determining sowing time based on planting conditions required by crops to be planted in the crop model; s300, confirming fertilizing and irrigating conditions and insect prevention and weeding time required by crop growth based on the acquired measurement data and in combination with a crop model; s400, predicting the harvest period and yield of the crops based on the acquired environment data and the crop model. The method can be used for wide crop planting, can realize large-scale planting, and has the advantages of low production cost, environmental friendliness, stable crop yield and green and healthy product.
Description
Technical Field
The invention relates to the technical field of crop planting, in particular to a crop planting guidance method based on big data.
Background
Wisdom agriculture is the main trend of current agricultural development, and it has technological leading edge, degree of automation height, the high-efficient management of being convenient for advantage and is extensively promoted. With the introduction of internet, artificial intelligence and big data concepts, those skilled in the art begin to research new directions such as more intensive intelligent management, agricultural interaction, agricultural data sharing statistics, etc. Before 2015, the intelligent agriculture is only limited to automatically collecting parameter data such as land environment and the like so as to achieve the effects of automatic spraying irrigation, automatic matching and mechanized farming and the like, however, in practical application, the non-standardization of basic data collection leads to the difficulty in realizing unified management in regional land or a certain crop planting area, so that agricultural experts are difficult to provide accurate and effective planting and cultivation answers for farmers through background data, and the agricultural experts are still relied on to carry out one-to-one tutoring planting and cultivation for the farmers in the field.
With the expansion of the application range of network communication, the standards between the internet of things and the internet are gradually standardized, the integration degree of the two networks is also continuously improved, the agricultural informatization is an effective means for improving the production efficiency, under the background that the machine plants crops and performs automatic control, the planting equipment can generate a large amount of crop planting data, and the problem that how to guide the planting of crops based on big data is urgently to be solved is solved.
Disclosure of Invention
One object of the present scheme is to provide a big data based crop planting guidance method that can be used for a wide range of crop planting.
In order to achieve the purpose, the scheme is as follows:
a crop planting guidance method based on big data comprises the following steps:
s100, selecting the types of crops to be planted and planting plots based on historical planting data of a certain area;
s200, selecting varieties of crops to be planted based on soil data, meteorological data and pest and disease data of a planting land;
s300, determining sowing time based on the air temperature and relative humidity data;
s400, monitoring the growth stage of the crops to guide crop planting.
Preferably, the historical planting data in step S100 includes soil data, weather data, crop growth data, and pest data.
Preferably, the soil data in step S200 includes soil temperature, soil humidity, soil ph, and soil nitrogen phosphorus potassium content.
Preferably, the meteorological data in step S200 includes air temperature, relative humidity, wind speed, wind direction, daily rainfall, atmospheric pressure, solar radiation and evapotranspiration.
Preferably, the soil data, the meteorological data and the pest data in the step S200 are acquired by a soil moisture content instrument, a water quality instrument, a water level instrument, a pest condition lamp, a meteorological station and a camera.
Preferably, the pest data in step S200 includes an area where the pesticide needs to be sprayed, a spraying time and a spraying amount, and a weeding timing.
In a second aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the preceding claims.
The scheme has the following beneficial effects:
the method can be used for wide crop planting, can realize large-scale planting, and has the advantages of low production cost, environmental friendliness, stable crop yield and green and healthy product.
Drawings
In order to illustrate the implementation of the solution more clearly, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the solution, and that other drawings may be derived from these drawings by a person skilled in the art without inventive effort.
FIG. 1 is a flow chart of a big data based crop planting guidance method;
FIG. 2 is a flow chart of a crop planting guidance method in an embodiment.
Detailed Description
Embodiments of the present solution are described in further detail below. It is clear that the described embodiments are only a part of the embodiments of the present solution, and not an exhaustive list of all embodiments. It should be noted that, in the present embodiment, features of the embodiment and the embodiment may be combined with each other without conflict.
The terms first, second and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Part of the current agricultural production still exists: 1) the scale of a producer is small, and a uniform planting standard is lacked; 2) the field patrol efficiency is low; 3) the pesticide is used in an excessive amount to cause serious residue; 4) the environmental pollution is serious due to the excessive use of the fertilizer; 5) the irrigation is unreasonable, so that the waste of water resources is serious; 6) the planting type of crops is single, and reasonable sowing can not be carried out according to the storage amount of local resources; 7) the planting pattern has poor replication ability.
Aiming at the problems, the internet and agriculture provide a good solution, and the new product, the new mode and the new state of modern agriculture based on an internet platform are created by fusing agricultural big data, emerging information technologies such as the internet of things and the like. The method is driven by 'Internet + agriculture' and strives to create 'information support and management cooperation, and the method has the advantages of high yield, high product safety, resource saving and environment-friendly' modern agriculture development upgrade edition and has a profound influence on agriculture modernization in China.
As shown in fig. 1 and 2, the inventors of the present application propose a big data-based crop planting guidance method, which includes the steps of:
s100, selecting the types of crops to be planted and planting plots based on historical planting data of a certain area;
selecting the crop species suitable for planting according to the corresponding environmental conditions, and circling out the target plot, wherein the specific process is as follows:
and S101, collecting historical planting big data, and obtaining the types of crops suitable for planting in various environments through data mining analysis. Taking rice as an example, the suitable planting environment of rice such as Meixiangzhan No. 2, Nanjing 46 and the like is obtained at present;
s102: determining the types of crops suitable for planting according to basic conditions such as local climate, moisture, illumination and the like, wherein rice is selected as a target crop;
s103: a target land block to be planted is circled on a satellite map by using a terminal, wherein the terminal comprises a personal computer, a tablet and a mobile phone, and the field is circled for 146.02 mu.
S200, selecting varieties of crops to be planted based on soil data, meteorological data and pest and disease damage data of a planting land;
installing a sensor capable of sensing the environment according to the land type, measuring the soil environment condition of a target land, and selecting the best crop variety suitable for planting, wherein the specific process is as follows:
s201: different sensors can be used according to paddy fields and dry lands, wherein the sensors are provided with soil moisture content instruments capable of sensing information such as temperature, moisture content, PH value, conductivity and the like of soil; the weather station can sense data such as air temperature, relative humidity, wind speed, wind direction, daily rainfall, atmospheric pressure, solar radiation, evapotranspiration and the like; and insect condition lamps and the like capable of monitoring the existence condition and the quantity of pests in the planting area, and installing corresponding sensors according to a planting guidance method. Selecting a paddy field of a certain base as an example, and installing a water level instrument, a soil moisture content instrument, a weather station, insect pest information lamps and cameras in the base according to requirements;
s202: performing data analysis according to soil data transmitted back by a soil sensor, wherein the soil data comprises data such as soil pH value, soil nitrogen phosphorus potassium content, soil temperature and the like, and the big data background analysis recommends planting of No. 2 variety Meixiangzhan of rice;
s300, determining sowing time based on the air temperature and relative humidity data;
the method comprises the following steps of analyzing according to data such as air temperature and relative humidity measured by a weather station and a big data mining algorithm to obtain a recommended sowing date and predict an actual sowing date, and sending the recommended sowing date and the predicted actual sowing date to a terminal to remind a planter of preparing sowing, wherein the specific process comprises the following steps:
s301: the meteorological station transmits back data such as air temperature, relative humidity and the like in real time, and statistics are well carried out and stored in a data warehouse;
s302: according to the comparison of the statistical data and the big data analysis result, the sowing time of crops is estimated by combining a planting guidance method, and for the first-season late rice Meixiang account 2, the big data recommend transplanting time to be 6 months and 25 days, so that the corresponding seedling raising time can be obtained;
s400, monitoring the growth stage of the crops to guide the crop planting;
the growth stages of crops are distinguished through camera monitoring and remote sensing data, a planting guidance method of the growth stages of the crops is formed by combining sensor data, and the specific process of generating the planting guidance method is as follows:
wherein the data of the crop growth stage comprises: global growth phase data and region growth phase data. Overall growth stage data include, but are not limited to, crop unit area plant number, crop coverage, leaf area index; region growing phase data includes, but is not limited to, crop plant height, number of leaves, leaf color of the region.
S401: by collecting crop growth data and comparing the crop growth data with the same crop growth data in the crop model, analyzing the soil environment data in the area to obtain the water and fertilizer data required by the corresponding growth stage, obtaining the quantity and time of fertilizer application and the corresponding area, and simultaneously obtaining the quantity and time of irrigation water required by the area;
s402: and generating a planting guidance method of a corresponding stage according to the obtained water and fertilizer application data, and reminding a planting user to perform corresponding farm work operation through a terminal, wherein the water and fertilizer application amount can give a suggested usage amount according to each area. For all the land parcels, the tillering fertilizer is recommended to be applied for 7 months and 5 days to 9 days, and the dosage of urea per mu is as follows: 10 kg/mu, the spike fertilizer is applied between 7 months and 29 days to 8 months and 8 days, and the fertilizer dosage is determined according to the growth condition of the rice according to the farm work instruction;
insect pest situations of crops in corresponding areas are obtained through combination of the insect pest situation lamps and remote sensing data, and an insect pest control planting guidance method is generated, and the specific process is as follows:
s403: acquiring insect condition data in the area through an insect condition lamp;
s404: identifying leaf colors and plant heights of crop plants through remote sensing monitoring data and camera monitoring data to obtain severity of diseases and insect pests of regional crops, and predicting development trend of the diseases and insect pests of the crops by combining data of a crop model;
s405: by analyzing the situation of the insect pests, the types and the use amounts of the applied pesticides in the area are obtained, a method for guiding the operation of the insect pest control agriculture is generated, and a grower is prompted to perform corresponding agriculture operation at a terminal, in the embodiment, the application of the pesticide is recommended to be performed 5-7 days before the break in the rice false smut control from 16 days at 8 months to 20 days at 8 months.
Through the growth vigor and the weeds growth condition of camera monitoring farming, remind the grower to clear up the weeds in planting the region through the terminal, specific process is as follows:
s406: a grower can monitor the farmland scene through a camera and know the growth conditions of crops and weeds;
s407: the planting guidance method provides weeding suggestions through the growth stage of crops for the grower to choose to use. In the embodiment, closed weeding is carried out 3-7 days before rice transplanting and between 18 days in 6 months and 22 days in 6 months, 35% of propyzamide EW is recommended to be used, and the dosage per mu is as follows: 100-. The closed weeding is carried out for 5 to 10 days in 7 months, and the application amount is determined according to the growth vigor of weeds.
The harvest period and yield of crops are predicted by combining the crop model with local environmental data, and a grower is reminded to prepare for harvesting in advance, and the specific process is as follows:
s408: collecting crop growth data by a camera and remote sensing;
s409: the method comprises the steps that a weather station collects local weather data;
s410: the harvest period of the crops is presumed according to the collected crop growth data and historical data analysis, and the grower is reminded to prepare before harvesting at the terminal, wherein the harvest period is predicted to be 10 months and 15 days to 10 months and 20 days, the yield per mu is predicted to be 436.37kg, and the actual yield is 473 kg.
The method can establish a crop model according to data such as soil environment, weather environment, crop growth, plant diseases and insect pests of the historical planted crops, is used for solving the problem of variety selection of farmers for crop planting, predicts and assists to generate a farming guidance method, and stabilizes the crop yield.
By installing the sensors in the land, collecting the environmental data and generating a farming operation guidance method by matching with a crop model, the application amount of water, fertilizer and pesticide of crops can be accurately predicted, the production cost of the crops is reduced, the labor intensity of growers and the damage to the ecological environment are reduced, and the big data is used as a crop planting basis, so that the standardization and the popularization of crop planting are facilitated.
The present solution further provides a computer-readable storage medium. The computer-readable storage medium is a program product for implementing the above-described data acquisition method, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as JAvA, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (7)
1. A crop planting guidance method based on big data is characterized by comprising the following steps:
s100, selecting the types of crops to be planted and planting plots based on historical planting data of a certain area;
s200, selecting varieties of crops to be planted based on soil data, meteorological data and pest and disease data of a planting land;
s300, determining sowing time based on the air temperature and relative humidity data;
s400, monitoring the growth stage of the crops to guide crop planting.
2. The big data based crop planting guidance method of claim 1, wherein the historical planting data in step S100 comprises soil data, weather data, crop growth data and pest data.
3. The big data based crop planting guidance method of claim 1, wherein the soil data in step S200 comprises soil temperature, soil humidity, soil ph, and soil nitrogen phosphorus and potassium content.
4. The big data based crop planting guidance method of claim 1, wherein the meteorological data in step S200 comprises air temperature, relative humidity, wind speed, wind direction, daily rainfall, atmospheric pressure, solar radiation and evapotranspiration.
5. The big data based crop planting guidance method of claim 1, wherein the soil data, meteorological data and pest data in step S200 are obtained from a soil moisture content meter, a water quality meter, a water level meter, a pest status light, a meteorological station and a camera.
6. The big data-based crop planting guidance method of claim 1, wherein the pest data in step S200 includes an area where the pesticide needs to be sprayed, a spraying time and amount, and a weeding timing.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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| CN115293557A (en) * | 2022-08-01 | 2022-11-04 | 云南农业大学 | Crop data processing method based on block chain |
| CN115486247A (en) * | 2022-08-30 | 2022-12-20 | 中联智慧农业股份有限公司 | Method, storage medium, and processor for determining fertilizer proportioning |
| CN115511158A (en) * | 2022-09-01 | 2022-12-23 | 珠海市现代农业发展中心(珠海市金湾区台湾农民创业园管理委员会、珠海市农渔业科研与推广中心) | Big data-based intelligent crop breeding analysis method and system |
| CN116090603A (en) * | 2022-12-02 | 2023-05-09 | 浙江天演维真网络科技股份有限公司 | Multi-period crop planting prediction method, device, equipment and medium |
| CN116307403A (en) * | 2023-05-12 | 2023-06-23 | 湖北泰跃卫星技术发展股份有限公司 | Planting process recommendation method and system based on digital village |
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| CN117033810A (en) * | 2023-05-23 | 2023-11-10 | 上海华维可控农业科技集团股份有限公司 | Agricultural data analysis management system and method based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115293557A (en) * | 2022-08-01 | 2022-11-04 | 云南农业大学 | Crop data processing method based on block chain |
| CN115486247A (en) * | 2022-08-30 | 2022-12-20 | 中联智慧农业股份有限公司 | Method, storage medium, and processor for determining fertilizer proportioning |
| CN115486247B (en) * | 2022-08-30 | 2023-08-22 | 中联智慧农业股份有限公司 | Method, storage medium and processor for determining fertilizer proportions |
| CN115511158A (en) * | 2022-09-01 | 2022-12-23 | 珠海市现代农业发展中心(珠海市金湾区台湾农民创业园管理委员会、珠海市农渔业科研与推广中心) | Big data-based intelligent crop breeding analysis method and system |
| CN115511158B (en) * | 2022-09-01 | 2023-08-29 | 珠海市现代农业发展中心(珠海市金湾区台湾农民创业园管理委员会、珠海市农渔业科研与推广中心) | Intelligent crop breeding analysis method and system based on big data |
| CN116090603A (en) * | 2022-12-02 | 2023-05-09 | 浙江天演维真网络科技股份有限公司 | Multi-period crop planting prediction method, device, equipment and medium |
| CN116307403A (en) * | 2023-05-12 | 2023-06-23 | 湖北泰跃卫星技术发展股份有限公司 | Planting process recommendation method and system based on digital village |
| CN117033810A (en) * | 2023-05-23 | 2023-11-10 | 上海华维可控农业科技集团股份有限公司 | Agricultural data analysis management system and method based on big data |
| CN117033810B (en) * | 2023-05-23 | 2024-04-23 | 上海华维可控农业科技集团股份有限公司 | Agricultural data analysis management system and method based on big data |
| CN116930459A (en) * | 2023-07-25 | 2023-10-24 | 江苏龙环环境科技有限公司 | Soil in-situ detection device and detection method thereof |
| CN116930459B (en) * | 2023-07-25 | 2024-03-12 | 江苏龙环环境科技有限公司 | Soil in-situ detection device and detection method thereof |
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