CN112418470A - Method for establishing yield monitoring and early warning model based on northern japonica rice - Google Patents
Method for establishing yield monitoring and early warning model based on northern japonica rice Download PDFInfo
- Publication number
- CN112418470A CN112418470A CN202011552506.5A CN202011552506A CN112418470A CN 112418470 A CN112418470 A CN 112418470A CN 202011552506 A CN202011552506 A CN 202011552506A CN 112418470 A CN112418470 A CN 112418470A
- Authority
- CN
- China
- Prior art keywords
- data
- yield
- japonica rice
- early warning
- monitoring
- 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 title claims abstract description 49
- 240000008467 Oryza sativa Japonica Group Species 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 8
- 241000196324 Embryophyta Species 0.000 claims abstract description 19
- 241000238631 Hexapoda Species 0.000 claims abstract description 19
- 241000607479 Yersinia pestis Species 0.000 claims abstract description 19
- 201000010099 disease Diseases 0.000 claims abstract description 19
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 19
- 235000007164 Oryza sativa Nutrition 0.000 claims abstract description 11
- 235000009566 rice Nutrition 0.000 claims abstract description 11
- 238000001514 detection method Methods 0.000 claims abstract description 10
- 239000002689 soil Substances 0.000 claims description 16
- 240000007594 Oryza sativa Species 0.000 claims description 9
- 239000003337 fertilizer Substances 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 241000209094 Oryza Species 0.000 abstract 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004720 fertilization Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 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
- G06Q10/02—Reservations, e.g. for tickets, services or events
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2471—Distributed queries
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Agronomy & Crop Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Marine Sciences & Fisheries (AREA)
- Health & Medical Sciences (AREA)
- Animal Husbandry (AREA)
- Mining & Mineral Resources (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Catching Or Destruction (AREA)
Abstract
The invention relates to the technical field of japonica rice yield monitoring and early warning, in particular to a method for establishing a yield monitoring and early warning model based on northern japonica rice, which comprises a yield monitoring system; the yield monitoring system is used for acquiring image information of japonica rice within monitoring time and sending color images of mature japonica rice as distinguishing image data to the data processing center; the yield monitoring system is used for sending average meteorological processing data in each time period monitoring area to a data processing center as meteorological data, the yield monitoring early warning model determines the prediction result of plant diseases and insect pests in the detection area through meteorological data conditions corresponding to the meteorological data and a predetermined plant disease and insect pest model, a second database is established according to yield statistical data and real-time unit price, thereby determining the prediction value of the total yield price in the detection area through the product of the average value of the statistical data and the real-time unit price, the yield of the rice field can be effectively detected and early warned, and rice planting is facilitated.
Description
Technical Field
The invention relates to the field of japonica rice yield monitoring and early warning, in particular to a northern japonica rice yield monitoring and early warning model building method.
Background
Japonica rice is a variety of rice, needs short sunshine time, but has long growth period, is cold-resistant, has strong viscosity, short and round rice grains, higher protein content and good taste. The milled rice is called as japonica rice, and some places refer to the japonica rice as rice, and the japonica rice is only one variety of rice.
However, the yield of the existing japonica rice can be determined only when the japonica rice is harvested, the automation degree of the japonica rice monitoring and early warning equipment is not high, and the japonica rice yield cannot be estimated according to the meteorological environment and the environment in the rice field.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method for establishing a yield monitoring and early warning model based on northern japonica rice.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows: the utility model provides a yield monitoring early warning model based on northern japonica rice includes output monitoring system, and output monitoring system is used for gathering the image information of japonica rice in the monitoring time, carries out real time monitoring to the paddy field through the camera to send the colour image of ripe japonica rice for data processing center as distinguishing image data, distinguish the colour image of ripe japonica rice through data processing center, carry out preliminary calculation to japonica rice quantity.
And the yield monitoring system is used for sending the average meteorological processing data in the monitoring area of each time period to the data processing center as meteorological data.
And the data processing center is used for determining a prediction result of the plant diseases and insect pests in the detection area according to the meteorological data and meteorological data conditions corresponding to the plant disease and insect pest model which are determined in advance, and sending the prediction result to the early warning system.
And the data processing center is also used for carrying out japonica rice yield statistics according to the distinguishing image data and sending the yield statistical data to the total price processing module.
And the total price processing module is used for establishing a second database according to the yield statistical data and the real-time unit price, so that the predicted value of the yield total price in the detection area is determined according to the product of the average value of the statistical data and the real-time unit price, and the predicted value 3 of the total price is sent to the early warning system.
The data processing center adopts a Hadoop architecture, the Hadoop architecture is an open-source Java-based programming framework, and the Hadoop architecture has two main components: the distributed file system is a Hadoop Distributed File System (HDFS), a program is stored in the Hadoop distributed file system, the MapReduce engine is a framework for executing the program, the distributed data calculation model 202 is used for determining a prediction result of plant diseases and insect pests in a monitored area according to first meteorological data and a first database 201 established according to meteorological conditions corresponding to a pre-established plant disease and insect pest model, and large-scale data processing can be rapidly completed by adopting distributed data calculation.
The early warning system is used for respectively displaying the areas and the severity of the plant diseases and insect pests in the monitored areas on a map; the early warning system is also used for displaying the prediction result, and the prediction result can be displayed through a display board, a webpage, an APP and the like.
First weather data includes the temperature in the monitoring area, humidity, rainfall and soil parameter, wherein output monitoring system specifically includes temperature-sensing ware, humidity transducer, the soil tester, rain gauge and camera, with temperature-sensing ware, humidity transducer, the soil tester, rain gauge and camera are installed in the monitoring area, wherein gather temperature data through temperature-sensing ware, gather humidity data through humidity transducer, gather the information of fertilizer in the soil through soil tester 1, be convenient for in time fertilize, gather rainfall through the rain gauge, be convenient for control the water yield in field according to the rainfall, be convenient for japonica rice to grow, gather image information through the camera.
Compared with the prior art, the invention has the following beneficial effects:
one, this output monitoring early warning model is through the meteorological data condition according to meteorological data and the plant diseases and insect pests model correspondence of predetermined, confirm the prediction result of plant diseases and insect pests in the detection zone, according to output statistical data and real-time unit price, establish the second database, thereby confirm the prediction value of output total price in the detection zone through the product of statistical data's average value and real-time unit price, and pass through the show board with prediction value and prediction result, the webpage, APP etc. show, the degree of automation is high, can effectually detect and the early warning to paddy field output, be convenient for rice planting.
Drawings
FIG. 1 is a first schematic view of an overall flow diagram of the present invention;
FIG. 2 is a second schematic view of the overall flow chart of the present invention.
In the figure: the system comprises a yield monitoring system 1, a temperature sensor 101, a humidity sensor 102, a soil tester 103, a rain gauge 104 and a camera 105; the system comprises a data processing center 2, a first database 201 and a distributed data calculation model 202; a total price processing module 3, a second database 301 and a predicted value 302; an early warning system 4 and a prediction result 401.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art.
Example 1
The yield monitoring and early warning model based on northern japonica rice as shown in fig. 1-2 comprises a yield monitoring system 1.
The yield monitoring system 1 is used for collecting image information of japonica rice within monitoring time, monitoring the rice field in real time through the camera 105, sending color images of mature japonica rice serving as distinguishing image data to the data processing center 2, distinguishing the color images of the mature japonica rice through the data processing center 2, and preliminarily calculating the quantity of the japonica rice.
The yield monitoring system 1 is used for sending the average meteorological processing data in the monitoring area of each time period to the data processing center 2 as meteorological data.
And the data processing center 2 is used for determining a prediction result 401 of the plant diseases and insect pests in the detection area according to the meteorological data and meteorological data conditions corresponding to the plant disease and insect pest model determined in advance, and sending the prediction result 401 to the early warning system 4.
The data processing center 2 is further configured to perform japonica rice yield statistics according to the difference image data, and send the yield statistics data to the total price processing module 3.
And the total price processing module 3 is configured to establish a second database 301 according to the yield statistical data and the real-time unit price, so as to determine a predicted value 302 of the yield total price in the detection area according to a product of an average value of the statistical data and the real-time unit price, and send the predicted value 302 of the total price to the early warning system 4.
The data processing center adopts a Hadoop architecture, the Hadoop architecture is an open-source Java-based programming framework, and the Hadoop architecture has two main components: the distributed file system is mainly a Hadoop Distributed File System (HDFS) which stores programs, and the MapReduce engine is a framework for executing the programs.
A method for establishing a yield monitoring and early warning model based on northern japonica rice comprises the following steps: the distributed data calculation model 202 is used for determining the prediction result of the plant diseases and insect pests in the monitored area according to the first meteorological data and the first database 201 established according to the meteorological conditions corresponding to the pre-established plant disease and insect pest model, and adopts distributed data calculation, so that the large-scale data processing can be rapidly completed, the calculation time is reduced, the current growth condition of crops can be represented more accurately, the prevention and treatment work can be carried out in time, and the normal growth of the crops is further ensured.
The early warning system 4 is used for respectively displaying the areas and the severity of the plant diseases and insect pests in the monitored areas on a map; the early warning system 4 is also used for displaying the prediction result 401, and the prediction result 401 can be displayed through a display board, a webpage, an APP and the like, so that people can observe the prediction result conveniently.
The first weather data comprise temperature in a monitoring area, humidity, rainfall and soil parameters, wherein the yield monitoring system specifically comprises a temperature sensor 101, a humidity sensor 102, a soil tester 103, a rain gauge 104 and a camera 105, the temperature sensor 101, the humidity sensor 102, the soil tester 103, the rain gauge 104 and the camera 105 are installed in the monitoring area, temperature data are collected through the temperature sensor 101, humidity data are collected through the humidity sensor 102, information of fertilizer in soil is collected through the soil tester 103, timely fertilization is facilitated, the situation that the fertilizer is used too much is avoided, rainfall is collected through the rain gauge 104, water quantity in the field is conveniently controlled according to the rainfall, japonica rice growth is facilitated, and image information is collected through the camera 105.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. The utility model provides a yield monitoring early warning model based on northern japonica rice which characterized in that: the method comprises a yield monitoring system, wherein the yield monitoring system is used for acquiring image information of japonica rice within monitoring time, monitoring a rice field in real time through a camera, sending color images of mature japonica rice serving as distinguishing image data to a data processing center, distinguishing the color images of the mature japonica rice through the data processing center, and preliminarily calculating the quantity of the japonica rice;
the yield monitoring system is used for sending the average meteorological processing data in the monitoring area of each time period to the data processing center as meteorological data;
the data processing center is used for determining a prediction result of the plant diseases and insect pests in the detection area according to the meteorological data and meteorological data conditions corresponding to a predetermined plant disease and insect pest model, and sending the prediction result to the early warning system;
the data processing center is also used for carrying out japonica rice yield statistics according to the distinguishing image data and sending the yield statistical data to the total price processing module;
and the total price processing module is used for establishing a second database according to the yield statistical data and the real-time unit price, so that a predicted value of the yield total price in the detection area is determined according to the product of the average value of the statistical data and the real-time unit price, and the predicted value of the total price is sent to the early warning system.
2. The northern japonica rice-based yield monitoring and early warning model as claimed in claim 1, characterized in that: the data processing center adopts a Hadoop architecture, and the Hadoop architecture is an open-source Java-based programming framework.
3. The northern japonica rice-based yield monitoring and early warning model as claimed in claim 1, characterized in that: the Hadoop architecture has two main components: the distributed file system is mainly a Hadoop Distributed File System (HDFS) which stores programs, and the MapReduce engine is a framework for executing the programs.
4. The method for establishing the northern japonica rice yield monitoring and early warning model based on claim 1 is characterized by comprising the following steps of: the distributed data calculation model is used for determining the prediction result of the plant diseases and insect pests in the monitored area according to the first meteorological data and a first database established according to meteorological conditions corresponding to a pre-established plant disease and insect pest model, and large-scale data processing can be rapidly completed by adopting distributed data calculation;
the early warning system is used for respectively displaying the areas and the severity of the plant diseases and insect pests in the monitored areas on a map; the early warning system is also used for displaying the prediction result, and the prediction result can be displayed through a display board, a webpage and an APP;
the first meteorological data comprise temperature, humidity, rainfall and soil parameters in a monitored area;
output monitoring system specifically include temperature-sensing ware, humidity transducer, soil tester, rain gauge and camera, install temperature-sensing ware, humidity transducer, soil tester, rain gauge and camera in the monitoring area, wherein gather temperature data through temperature-sensing ware, gather humidity data through humidity transducer, gather the information of fertilizer in the soil through soil tester, be convenient for in time fertilize, gather rainfall through the rain gauge, the water yield in the control field of being convenient for according to rainfall, the japonica rice of being convenient for grows, gather image information through the camera.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011552506.5A CN112418470A (en) | 2020-12-24 | 2020-12-24 | Method for establishing yield monitoring and early warning model based on northern japonica rice |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011552506.5A CN112418470A (en) | 2020-12-24 | 2020-12-24 | Method for establishing yield monitoring and early warning model based on northern japonica rice |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112418470A true CN112418470A (en) | 2021-02-26 |
Family
ID=74782552
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011552506.5A Pending CN112418470A (en) | 2020-12-24 | 2020-12-24 | Method for establishing yield monitoring and early warning model based on northern japonica rice |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112418470A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN206057612U (en) * | 2016-10-13 | 2017-03-29 | 新疆天翔精准农业装备有限公司 | A kind of weather monitoring based on big-dipper satellite and pest and disease damage early-warning and predicting system |
| CN106768081A (en) * | 2017-02-28 | 2017-05-31 | 河源弘稼农业科技有限公司 | A kind of method and system for judging fruits and vegetables growth conditions |
| CN107944596A (en) * | 2017-10-20 | 2018-04-20 | 上海交通大学 | A kind of muskmelon growth management expert system based on Internet of Things |
| CN108304953A (en) * | 2017-01-13 | 2018-07-20 | 北京金禾天成科技有限公司 | The method for early warning and system of diseases and pests of agronomic crop |
-
2020
- 2020-12-24 CN CN202011552506.5A patent/CN112418470A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN206057612U (en) * | 2016-10-13 | 2017-03-29 | 新疆天翔精准农业装备有限公司 | A kind of weather monitoring based on big-dipper satellite and pest and disease damage early-warning and predicting system |
| CN108304953A (en) * | 2017-01-13 | 2018-07-20 | 北京金禾天成科技有限公司 | The method for early warning and system of diseases and pests of agronomic crop |
| CN106768081A (en) * | 2017-02-28 | 2017-05-31 | 河源弘稼农业科技有限公司 | A kind of method and system for judging fruits and vegetables growth conditions |
| CN107944596A (en) * | 2017-10-20 | 2018-04-20 | 上海交通大学 | A kind of muskmelon growth management expert system based on Internet of Things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111507857A (en) | Digital agricultural planting system and method based on Internet of things technology | |
| CN109781963A (en) | A kind of field planting environmental monitoring system | |
| CN102954816B (en) | Crop growth monitoring method | |
| CN116738766B (en) | Intelligent agriculture online industrialization service system based on digital twinning | |
| CN107844089A (en) | A kind of method, system and Cultivate administration system for planting early warning | |
| CN203537985U (en) | System for green environment monitoring, crop diseases diagnosing and applying pesticides to control according to conditions based on wireless sensor network | |
| CN111460990A (en) | Big data-based alpine pastoral area grassland insect pest monitoring and early warning system and method | |
| CN117057946A (en) | Intelligent agricultural park data monitoring device and method | |
| CN112070297A (en) | Weather index prediction method, device, equipment and storage medium for farming activities | |
| CN204833020U (en) | Intelligence agricultural production system based on thing networking | |
| CN210570820U (en) | Agricultural detection early warning system | |
| CN110825058A (en) | Crop real-time monitoring system | |
| CN110514248A (en) | A kind of intelligence based on big data, automation agricultural data acquisition system | |
| CN115685862B (en) | Intelligent agriculture detecting system based on edge calculation | |
| CN112116206A (en) | Intelligent agricultural system based on big data | |
| CN117094532B (en) | Orchard intelligent monitoring system | |
| CN116300608A (en) | Intelligent agriculture remote monitoring system based on big data | |
| JP2021128756A (en) | Futures Trading Information Display Program | |
| CN109087211A (en) | A kind of fertile mandarin orange wisdom implant system | |
| CN118071194A (en) | System and method for monitoring meteorological disasters of strawberry tea | |
| CN117315915A (en) | Crop planting supervision system based on remote sensing data monitoring | |
| CN113377141A (en) | Artificial intelligence agricultural automatic management system | |
| CN112418470A (en) | Method for establishing yield monitoring and early warning model based on northern japonica rice | |
| CN112019607A (en) | Intelligent farm comprehensive control method and system based on visual cloud platform | |
| CN111207789A (en) | Wireless data acquisition terminal and method suitable for fruit and vegetable planting environment |
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 |