CN113723690A - Regional prediction method for citrus variety suitability - Google Patents
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- 241000207199 Citrus Species 0.000 title claims abstract description 34
- 235000020971 citrus fruits Nutrition 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims description 8
- 238000003825 pressing Methods 0.000 claims abstract description 7
- 241000512668 Eunectes Species 0.000 claims description 7
- KRKNYBCHXYNGOX-UHFFFAOYSA-K Citrate Chemical compound [O-]C(=O)CC(O)(CC([O-])=O)C([O-])=O KRKNYBCHXYNGOX-UHFFFAOYSA-K 0.000 claims description 3
- 208000025174 PANDAS Diseases 0.000 claims description 3
- 208000021155 Paediatric autoimmune neuropsychiatric disorders associated with streptococcal infection Diseases 0.000 claims description 3
- 240000000220 Panda oleosa Species 0.000 claims description 3
- 235000016496 Panda oleosa Nutrition 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000008676 import Effects 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 238000013277 forecasting method Methods 0.000 abstract description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 5
- 238000001556 precipitation Methods 0.000 description 14
- 230000018109 developmental process Effects 0.000 description 10
- 238000013528 artificial neural network Methods 0.000 description 6
- 235000013399 edible fruits Nutrition 0.000 description 6
- 244000276331 Citrus maxima Species 0.000 description 5
- 240000002319 Citrus sinensis Species 0.000 description 4
- 235000005976 Citrus sinensis Nutrition 0.000 description 4
- 244000183685 Citrus aurantium Species 0.000 description 3
- 235000007716 Citrus aurantium Nutrition 0.000 description 3
- 235000000228 Citrus myrtifolia Nutrition 0.000 description 3
- 235000016646 Citrus taiwanica Nutrition 0.000 description 3
- 235000001759 Citrus maxima Nutrition 0.000 description 2
- 241001672694 Citrus reticulata Species 0.000 description 2
- 241001093501 Rutaceae Species 0.000 description 2
- 241001001777 Citrus limetta Species 0.000 description 1
- 235000005979 Citrus limon Nutrition 0.000 description 1
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 1
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Abstract
The invention discloses a regional forecasting method for citrus variety suitability in the field of citrus planting, which inputs a command line under a python virtual environment: carrying out model training and development, and building a website by utilizing a Django Web application framework; and simultaneously opening a browser, inputting localhost of 8000/st, displaying a web initial page, pressing the page, displaying a form page waiting for data input, sequentially inputting 23 values of climate data, verifying the input data by the form of the web page, submitting the input data after no errors exist, entering a prediction result web page, and obtaining a prediction result. The invention provides a regional forecasting method for citrus variety suitability, which is characterized in that according to the layout and quality characteristics of citrus varieties, a prediction model is established by using national climate characteristics to predict the performances of different citrus varieties under different ecological climates, and after a user inputs local climate condition data, the type of the citrus variety which is most developed locally can be obtained, so that reference is provided for variety production layout.
Description
Technical Field
The invention relates to the field of citrus planting, in particular to a regional prediction method for citrus varieties.
Background
Citrus belongs to the genus Citrus of the family Rutaceae, and has leaves with single body and multiple leaves, usually narrow leaves, and leaves with needle shape, ellipse or oval shape; the flowers grow singly or 2-3 flowers grow in clusters, calyces are irregular and 5-3 shallow cracks, and flower columns are slender; the fruit shape is generally oblate to near spherical, the peel is very thin and smooth, or thick and coarse, light yellow, vermilion or dark red; the fruit flesh is sour or sweet, the number of seeds is more or less, the seeds are generally oval, the flowering phase is 4-5 months, and the fruit phase is 10-12 months. The Citrus subfamily of Rutaceae is distributed between 16-37 ° north latitude, and is a tropical and subtropical evergreen fruit tree. The producing areas of the citrus aurantium are mainly distributed in the south areas of the Yangtze river in China, the citrus aurantium is fond of warm and humid climate, the citrus aurantium is in various types, such as citrus reticulata, pomelos, sweet oranges, lemons and the like, the cold resistance of different types of citrus is different, and the most cold-resistant fructus aurantii and Yichang oranges can be planted in the areas with the altitude of more than 1900 m. However, pomelos and sweet oranges have weak cold resistance and can be frozen when the lowest temperature is lower than-3 ℃.
The citrus is an important fruit tree in the south of China, but the climate suitability of citrus varieties is not known when citrus is grown in various places, so that the variety selection in production is very blind in development, the phenomenon of planting with wind is serious, the variety selection and planting are often carried out according to local climate conditions, the quality of produced fruits is poor, the benefit is low, and severe frost can be caused in some years, so that the trees are reduced in yield and even die. Therefore, a prediction model for citrus variety selection is urgently needed to be provided for production, and guidance is provided for citrus production layout development. Therefore, we propose a regional prediction method for the suitability of citrus varieties.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a regional prediction method for citrus variety suitability, so as to solve the problems in the background art.
The invention provides the following technical scheme:
the regional forecasting method for the suitability of citrus varieties comprises the following steps:
(1) performing bash Anaconda3-2019.10-linux-x86_64.sh, and installing Anaconda according to the prompt;
(2) after Anaconda is installed, Python3.6 is installed, and source-/. bashrc is executed to enable configuration to take effect;
(3) creating a virtual Python environment by executing a conda create-name Python 3.6;
(4) in a Python virtual environment, using pip install to install software packages and modules such as tensierflow, Keras, numpy, pandas, Django and the like;
(5) establishing/user/close/directory, copying software and data to the directory for model prediction and development, wherein the development software for model prediction comprises close _ runpe.py, etconfig.py, configuration file config.txt, data file close _ data.csv, and the result of model prediction is stored by a network weight file close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001;
(6) the development and training of the predictive model can be performed in a python interactive virtual environment. Open the terminal under the click directory, type: [ root @ localhost citrate ] # actuation python;
(7) and entering a python virtual environment, verifying whether the TensorFlow is successfully installed, and inputting:
>>>import tensorflow as tf
>>>print(tf.__version__)
returning to tensorflow;
(8) in the python virtual environment, the input command line: and > Python close _ vernier. The weight and the structure of the trained model are stored in two files, namely, close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001;
(9) building a website by using a Django Web application framework; and simultaneously opening a browser, inputting localhost of 8000/st, displaying a web initial page, pressing the page, displaying a form page waiting for data input, sequentially inputting 23 values of climate data, verifying the input data by the form of the web page, submitting the input data after no errors exist, entering a prediction result web page, and obtaining a prediction result.
Preferably, in the step (9), when the website is built, the Web server is deployed by using Django, and the Web application framework includes view file view.py, reusable form.py, settings.py, urls.py, getconfig.py, network weight files of the prediction model, close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001, data files close _ data.csv, and configuration files config.txt, where the Web page files are stored in a schedule directory and include three files of index.html, close _ form.html, and close _ result.html, and all the files must be complete.
Preferably, the 23 pieces of climate characteristic data input in step (9) are respectively: the average air temperature of a month, an average maximum air temperature of a year, an average minimum air temperature of a year, a maximum air temperature of a year, a minimum air temperature of a year, a day of a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a.
The invention provides a regional forecasting method for citrus variety suitability, which is characterized in that according to the layout and quality characteristics of citrus varieties, a prediction model is established by using national climate characteristics to predict the performances of different citrus varieties under different ecological climates, and after a user inputs local climate condition data, the type of the citrus variety which is most developed locally can be obtained, so that reference is provided for variety production layout.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that:
the regional forecasting method for the suitability of citrus varieties comprises the following steps:
the development and training of the artificial neural network prediction model requires building an operation environment, firstly downloading an Anaconda installation file from an official website, and selecting Python3.6 version under linux;
(1) performing bash Anaconda3-2019.10-linux-x86_64.sh, and installing Anaconda according to the prompt;
(2) after Anaconda is installed, Python3.6 is installed, and source-/. bashrc is executed to enable configuration to take effect;
(3) creating a virtual Python environment by executing a conda create-name Python 3.6;
(4) in a Python virtual environment, using pip install to install software packages and modules such as tensierflow, Keras, numpy, pandas, Django and the like;
(5) establishing/user/close/directory, copying software and data to the directory for model prediction and development, wherein the development software for model prediction comprises close _ runpe.py, etconfig.py, configuration file config.txt, data file close _ data.csv, and the result of model prediction is stored by a network weight file close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001;
(6) the development and training of the predictive model can be performed in a python interactive virtual environment. Open the terminal under the click directory, type: [ root @ localhost citrate ] # actuation python;
(7) and entering a python virtual environment, verifying whether the TensorFlow is successfully installed, and inputting:
>>>import tensorflow as tf
>>>print(tf.__version__)
returning to tensorflow;
(8) in the python virtual environment, the input command line: and > Python close _ vernier. The weight and the structure of the trained model are stored in two files, namely, close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001;
(9) building a website by using a Django Web application framework; and simultaneously opening a browser, inputting localhost of 8000/st, displaying a web initial page, pressing the page, displaying a form page waiting for data input, sequentially inputting 23 values of climate data, verifying the input data by the form of the web page, submitting the input data after no errors exist, entering a prediction result web page, and obtaining a prediction result.
Further, in the step (9), when the website is built, a Web server is deployed by using Django, and a Web application framework of the Web server includes view file view, and also includes reusable form, settings, urls, getconfig, and network weight files of the prediction model, namely, close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001, data file close _ data.csv, and configuration file match.txt, where the Web page files are stored in a templates directory, and include three files, namely, index.html, close _ form.html, and close _ restore.html, and all the files must be complete.
Further, the 23 pieces of climate characteristic data input in the step (9) are respectively: month, average air temperature of year-round month, average maximum air temperature of year-round month, average minimum air temperature of year-round month, maximum air temperature of year-round month, minimum air temperature of year-round month, maximum air temperature of year-round month, minimum air temperature of year-round, average relative humidity of year-round, average monthly precipitation at year-round 20-20, average monthly precipitation at year-round 08-round, maximum precipitation at year-round, minimum precipitation at year-round month, average daily precipitation at year-round month is not less than 10.0mm, average daily precipitation at year-round month is not less than 25.0mm, maximum continuous precipitation at year-round, average precipitation difference at year-round;
data format:
except for the integers with the month of 1-12, the input climate data are floating point numbers, and can be accurate to 2 bits behind the decimal point. All 23 data need to be entered in their entirety and cannot be missing. The size range of the data is limited by the web page form, and data beyond the limited range cannot be received and submitted.
Examples of inputs are:
the 23 climate characteristic data entered are, for example, as follows:
month: 8
Average temperature in successive months: 21
Average maximum air temperature in the month of the year: 26.9
Average minimum air temperature in the month of the year: 17
The highest temperature at the extreme of the year: 34.4
The lowest temperature at the extreme end of the year-round moon is 8.9
The average temperature day of the year is 9.9
19.5 percent of maximum daily temperature difference in successive months
The minimum temperature day is 1.9
The number of days with the highest temperature more than or equal to 35.0 ℃ in the successive months and days is 0
The lowest temperature of the year, month and day is less than or equal to 0.0℃ and the number of days is 0
The lowest temperature of the year, month and day is less than or equal to-2.0 deg.C day number is 0
Average difference of temperature of 0.7 in successive months
Average relative humidity of 80 in the year and month
Mean difference in relative humidity of successive months 2
161.9 average monthly rainfall at 20-20 years
Average monthly rainfall of 162.9 when the year is 08-08
Maximum precipitation of 291.2 percent in the year and month
Minimum precipitation of 72 in the year and month
The daily precipitation of more than or equal to 10.0mm in the successive months is 4.9
The daily precipitation of more than or equal to 25.0mm in the successive months is 1.8
Maximum continuous precipitation of 172.3 in successive months
Average difference of precipitation in the year and month of the year is 50.9
And after submission, outputting a result and returning the result to the user in a webpage close _ result.html form, wherein the result is the suitable planting ratio scores of the citrus reticulata, the sweet orange and the pomelo respectively, the scores are between 0 and 1, and the larger the ratio value is, the better the climate suitability of the species in the local area is shown.
Background of data
The model does not store the data input by the client side in a database, so that the data has safety, and the output result is invalid after being returned to the client side, and the next prediction can be carried out unless the data is submitted again.
Data format
The format of the output data is presented as a list, which includes scores for 3 citrus varieties under the input climate conditions;
and (4) outputting a result table:
| citrus with wide peel | 0.4679506 |
| Sweet orange | 0.3063717 |
| Shaddock | 0.2256777 |
The above table indicates that wide citrus has the highest score, indicating that wide citrus is more suitable for planting and shaddock is least suitable for planting in this climate pattern.
Error and recovery
All 23 climate characteristic input data need to be completely filled, wherein a month is an integer from 1 to 12, and the rest are integers or decimal numbers, before submission, if errors occur, the form is verified, a user is prompted to re-input data or modify the data, a cancel button is pressed, the data can be refilled, after the errors do not occur, the submit button is pressed to submit the data, and a back-end program utilizes a built artificial network model to perform data calculation.
Operating procedure
(1) Development and training of artificial neural network models
The invention can develop and train the artificial neural network model without user participation, the model construction and optimization are carried out by programmers, and the constructed artificial neural network model is only deployed on the network and used for predicting the input data of the user.
Constructing and optimizing an artificial neural network model locally, firstly modifying a configuration file Config. txt, and inputting a correct file path and a file name of training data:
dataset _ path ═/usr/click/# software and directory where data files are located
file _ name ═ name _ data. csv # training file name
rate 0.5# learning rate
(2) Under the/usr/clicate directory, the terminal is opened, and the input > > > conda activate python
(3) Inputting python./close _ run. py, training and learning the climate data close _ data. csv, generating a prediction model, and storing the weight parameters of the model in a catalogue.
(4) Opening a browser, inputting http:// localhost:8000/st, displaying an initial page, pressing one step, entering a form webpage for data input, and waiting for a user to input climate data. After entering climate data with reference to 4.2.3, submit is submitted.
Example of user operation
(1) Firstly, constructing an artificial neural network model and optimizing parameters. Before a network model is built, a configuration file config.txt is modified, a correct file copy path and a file name of training data are input, for example, the file is copied to dataset _ path ═ usr/close/, and the name of the training file is file _ name ═ close _ data.csv;
(2) opening the terminal under the/usr/climate directory, and inputting a conda activate python;
(3) inputting python-/click-kind, training and learning climate data, continuously optimizing parameters, generating a prediction model, and storing weight parameters of the model in a Django application directory;
(4) a Web application frame is built by using Django, a browser is opened, http:// localhost:8000/st is input into the browser, an initial page appears, the page enters a form webpage after the initial page is pressed, and a user waits for inputting climate data. And 4.2.3, submitting the weather data according to the determination, if the data is wrong or incomplete, presenting prompt information on the form, waiting for the user to input or modify again, and determining the weather data after no mistake is made.
(5) And waiting for the page to return the result. And (4) according to a return button, re-entering a form webpage to input new climate data, and performing new prediction. If the cancel button is pressed, the program exits and returns to the initial page.
And (4) program exit operation:
(1) the user opens the browser, inputs http:// localhost:8000/st, and an initial page appears;
(2) pressing the next step, entering a form webpage, inputting climate data by a user, and submitting according to submit;
(3) and waiting for the page to return the result. If the forecast is needed again, a return button is pressed, new climate data are input into a form webpage, and the forecast is carried out for the next time. Pressing a cancel button, exiting the program and returning to the initial page;
(4) if the cancel button is pressed, the page jumps to the initial page and enters the next prediction;
(5) the browser exit program may be closed.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (3)
1. The citrus variety suitability regional prediction method is characterized by comprising the following steps of:
(1) performing bash Anaconda3-2019.10-linux-x86_64.sh, and installing Anaconda according to the prompt;
(2) after Anaconda is installed, Python3.6 is installed, and source-/. bashrc is executed to enable configuration to take effect;
(3) creating a virtual Python environment by executing a conda create-name Python 3.6;
(4) in a Python virtual environment, using pip install to install software packages and modules such as tensierflow, Keras, numpy, pandas, Django and the like;
(5) establishing/user/close/directory, copying software and data to the directory for model prediction and development, wherein the development software for model prediction comprises close _ runpe.py, etconfig.py, configuration file config.txt, data file close _ data.csv, and the result of model prediction is stored by a network weight file close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001;
(6) the development and training of a prediction model can be carried out under a python interactive virtual environment, a terminal is opened under a click directory, and the following steps are entered: [ root @ localhost citrate ] # actuation python;
(7) and entering a python virtual environment, verifying whether the TensorFlow is successfully installed, and inputting:
>>>import tensorflow as tf
>>>print(tf.__version__)
returning to tensorflow;
(8) in the python virtual environment, the input command line: carrying out model training and development, wherein the weight and the structure of the trained model are stored in two files, namely close _ pre _ weights.index and close _ pre _ weights.data-00000-of-00001;
(9) building a website by using a Django Web application framework; and simultaneously opening a browser, inputting localhost of 8000/st, displaying a web initial page, pressing the page, displaying a form page waiting for data input, sequentially inputting 23 values of climate data, verifying the input data by the form of the web page, submitting the input data after no errors exist, entering a prediction result web page, and obtaining a prediction result.
2. A citrus variety suitability regional prediction method according to claim 1, characterized in that: in the step (9), when a website is built, Django is used for deploying a webpage server side, a Web application framework of the website includes a view file view, a reusable form, a settings, a url, a getConfig, a network weight file of a prediction model, a value _ pre _ weights.index and a value _ pre _ weights.data-00000-of-00001, a data file value _ data.csv, a configuration file config.txt, and a webpage file is stored in a templates list and includes three files of index.html, value _ form.html and value _ result.html, and all files must be complete.
3. A citrus variety suitability regional prediction method according to claim 1, characterized in that: the 23 pieces of climate characteristic data input in the step (9) are respectively: the average air temperature of a month, an average maximum air temperature of a year, an average minimum air temperature of a year, a maximum air temperature of a year, a minimum air temperature of a year, a day of a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a month, a year, a.
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| KR102099655B1 (en) * | 2019-04-02 | 2020-05-26 | 부경대학교 산학협력단 | Method of Long-term prediction using determininistic prediction and probabilistic prediction |
| CN111667889A (en) * | 2020-07-20 | 2020-09-15 | 山东中医药大学 | Method for predicting content of quality marker in salvia miltiorrhiza |
| CN113095629A (en) * | 2021-03-18 | 2021-07-09 | 中国农业科学院作物科学研究所 | Evaluation method for suitability of high-quality rice planting region and application thereof |
| CN113159439A (en) * | 2021-04-30 | 2021-07-23 | 兰州里丰正维智能科技有限公司 | Crop yield prediction method and system, storage medium and electronic equipment |
-
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- 2021-09-02 CN CN202111026068.3A patent/CN113723690A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106845699A (en) * | 2017-01-05 | 2017-06-13 | 南昌大学 | A kind of method for predicting oil tea normal region |
| KR102099655B1 (en) * | 2019-04-02 | 2020-05-26 | 부경대학교 산학협력단 | Method of Long-term prediction using determininistic prediction and probabilistic prediction |
| CN111667889A (en) * | 2020-07-20 | 2020-09-15 | 山东中医药大学 | Method for predicting content of quality marker in salvia miltiorrhiza |
| CN113095629A (en) * | 2021-03-18 | 2021-07-09 | 中国农业科学院作物科学研究所 | Evaluation method for suitability of high-quality rice planting region and application thereof |
| CN113159439A (en) * | 2021-04-30 | 2021-07-23 | 兰州里丰正维智能科技有限公司 | Crop yield prediction method and system, storage medium and electronic equipment |
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