CN115326138A - Saline-alkali soil salinity analysis method based on soil surface temperature identification - Google Patents
Saline-alkali soil salinity analysis method based on soil surface temperature identification Download PDFInfo
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- 239000002689 soil Substances 0.000 title claims abstract description 131
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- 230000018109 developmental process Effects 0.000 claims description 40
- 238000011161 development Methods 0.000 claims description 37
- 230000008020 evaporation Effects 0.000 claims description 27
- 238000001704 evaporation Methods 0.000 claims description 27
- 230000003287 optical effect Effects 0.000 claims description 18
- 230000005855 radiation Effects 0.000 claims description 13
- 150000003839 salts Chemical class 0.000 claims description 12
- 238000012544 monitoring process Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 6
- 238000007405 data analysis Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000010835 comparative analysis Methods 0.000 claims description 4
- 238000001931 thermography Methods 0.000 claims description 4
- 238000003709 image segmentation Methods 0.000 claims description 3
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- 230000002708 enhancing effect Effects 0.000 claims description 2
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- 239000011780 sodium chloride Substances 0.000 description 3
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Abstract
The invention discloses a saline-alkali soil salinity analysis method based on soil surface temperature identification.
Description
Technical Field
The invention relates to the technical field of saline-alkali soil salinity analysis, in particular to a saline-alkali soil salinity analysis method based on soil surface temperature identification.
Background
The salinization of the soil causes the solidification and hardening of the shallow layer of the soil, which is not suitable for the cultivation of crops, so that the land can not be effectively developed, and a large amount of land resources are occupied.
The existing research shows that evaporation drives soil pore water and salt to move to the soil surface, so that the salt is concentrated or even precipitated on the soil surface, and the precipitated salt changes the structure of shallow soil and further influences the soil evaporation, therefore, the key for carrying out the soil salinization treatment is to know the process and mechanism of the saline-alkali soil evaporation. In the experiment for exploring the influence of soil evaporation on salt precipitation in the prior art, a cutting ring is mainly adopted to collect a soil sample, and after the soil sample is dissolved by a water dissolving method, the salt concentration of a soil-water mixture is measured, so that the salt content of the soil sample is obtained. The method has the problems of complicated measuring steps, scattered acquired information, discontinuous acquired data and the like in quantification of evaporation factors and soil surface salinity.
Disclosure of Invention
The invention aims to solve the problems of complicated measuring steps, scattered acquired information, discontinuous acquired data and the like in the quantification of evaporation factors and soil surface salinity by the conventional method, and provides a saline-alkali soil salinity analysis method based on soil surface temperature identification.
In order to achieve the purpose, the invention adopts the following technical scheme:
a saline-alkali soil salinity analysis method based on soil surface temperature identification comprises the following steps:
step S1: assembling equipment: preparing required sensors including a temperature and humidity sensor, an air pressure sensor, a wind speed and direction sensor, a solar radiation sensor, a soil moisture content sensor, a soil salinity sensor and an optical rainfall sensor;
the soil salinity sensor and the soil moisture content sensor are embedded at different depths of the saline-alkali soil, the temperature and humidity sensor, the air pressure sensor, the wind speed and wind direction sensor, the solar radiation sensor and the optical rainfall sensor are fixedly arranged on the rigid tripod and fixed at a position 1m away from the surface of the saline-alkali soil, all the sensors are connected to the arduino development board in a serial interface mode, and the storage battery and the solar power supply board are connected to a power management chip arranged in the arduino development board through positive and negative leads; the arduino development board sends a command to the receiving control module to control the switch of the solar power supply board and the storage battery through a detection result of the optical rainfall sensor, the arduino development board sends a command to switch on the solar power supply board and switch off the storage battery when no rain appears in the detection result of the optical rainfall sensor, and the arduino development board sends a command to switch off the solar power supply board and switch on the storage battery when rain appears in the detection result of the optical rainfall sensor;
and a remote control and transmission platform is integrated and set up by adopting multiple sensors, so that multi-source synchronous acquisition, processing and presentation of data are realized. The solar battery is adopted to realize passive power supply, and the storage battery is designed to be used as an energy storage instrument, so that the normal work of the device in rainy days is guaranteed, and the continuous acquisition of field soil condition data and evaporation rule monitoring data is guaranteed.
Step S2: program debugging: the acquisition terminal formed by the step S1 synchronously acquires soil conditions and data influencing the evaporation rate, and carries out debugging of programs and initialization of related parameters;
preferably, in step S2, an arduino-based acquisition system is constructed to realize real-time monitoring, wherein arduino is constructed based on an open source code simple I/O interface version, the arduino programming is realized through an arduino programming language and an arduino development environment, the arduino acquisition system is utilized to realize synchronous acquisition and programming debugging of soil conditions and various big data influencing the evaporation rate, the synchronously acquired data are integrated (the acquired data are organized according to a certain structure, and are convenient to process and share), and are shared, so that continuous monitoring of the multi-source data is realized, wherein the programming debugging needs to be performed on the spot, data acquisition is performed on the saline-alkali soil by professionals, and initialized parameters are calculated through a Penman model and are applied to the program.
And step S3: information acquisition: placing the collection terminal assembled in the step S1 on the saline-alkali soil to be detected and fixing, and collecting information of the saline-alkali soil by each sensor;
specifically, in the step S3, the assembled collecting terminal is placed in the saline-alkali soil to be detected and fixed by using a foot nail, a temperature and humidity sensor detects to obtain surface temperature and humidity, an air pressure sensor detects to obtain surface air pressure, an air speed and wind direction sensor detects to obtain air flow velocity and flow direction, a solar radiation sensor detects to obtain solar radiation intensity, a soil moisture content sensor detects to obtain soil moisture content, a soil salinity sensor detects to obtain soil surface precipitated salt content, and an optical rainfall sensor detects a rainfall value; collecting surface pictures of the saline-alkali soil, and collecting multiple groups of data according to the same information;
and step S4: data analysis and calculation: carrying out data analysis, calculation, processing and parameter correction on the acquired information through an arduino development board, and realizing temperature identification of the soil surface by adopting infrared thermal imaging;
preferably, in step S4, the acquired information is screened through the arduino development board, duplicate data is deleted, the evaporation intensity of the soil surface of the saline-alkali soil is obtained by utilizing a plurality of measured soil condition parameters and meteorological condition parameters, analyzing and calculating by utilizing an evaporation model, and utilizing a soil surface temperature identification algorithm of machine vision, wherein the evaporation model is a Penman model with self-corrected parameters, and the self-correction is realized by means of autoregression of feedforward parameters.
In the formula E 0 For reference, the evaporation capacity (mm. D) of crops -1 ) Namely the evaporation intensity; delta is the change rate of saturated water vapor pressure with temperature (kPa DEG C) at the average temperature -1 )；R n As net radiation (MJ. M) -2 d -1 ) (ii) a G is the soil heat flux (MJ. M) -2 d -1 ) (ii) a r is a humidity table constant (kPa. DEG C.) -1 ) (ii) a T is the thermodynamic temperature (K); u. of 2 Average wind speed (m · s) at 2m height -1 )；e a The saturated water vapor pressure (kPa) when the average air temperature is reached can be obtained by looking up a table; e.g. of the type d The actual water pressure (kPa) at which the average air temperature was reached.
In step S4, the collected image information is processed (converted into a gray-scale image, contrast enhancement, image segmentation threshold search, image black-and-white binarization), and analyzed (white area percentage is counted, and the white area percentage reflects the salt content of the soil surface) by an image recognition technology of an arduino development board. Replacing human beings with computer functions to automatically process and distinguish image physical information, simultaneously realizing temperature identification of the soil surface by adopting infrared thermal imaging and automatically forming a temperature identification picture, integrating the screened information, the calculated information and the surface picture information of the saline-alkali soil by using a unified computer language through an arduino development board, storing the integrated data into a database, extracting all data of the same kind of information by the database, and sending the data to a client;
step S5: data presentation and sharing: establishing a sharing connection through an arduino development board, sending the integrated information data to a database, and obtaining saline-alkali soil information through the database by a client;
step S6: manual control and real-time monitoring: the real-time monitoring is realized through a client development program, a control instruction is manually transmitted to the acquisition equipment by using the client, and the running state of the equipment is controlled by controlling a receiving control module of the acquisition terminal;
preferably, in the step S5, the arduino development board superimposes and separates a temperature identification picture and an original saline-alkali soil picture in a layer form, selective contrastive analysis is performed, a high-temperature area and a low-temperature area are marked out in an isothermal line form for the surface temperature of the saline-alkali soil, extreme values of the temperature are presented, a sharing connection is established between the arduino development board and a client through wireless equipment, integrated data, contrastive analysis data and the saline-alkali soil surface temperature data are shared to the client in real time through sharing equipment, a professional acquires the saline-alkali soil data and environmental data through the client to obtain an initial value of an alpha value, an empirical value of the alpha value is 0.01, the alpha value is a starting reflectivity, the alpha value is self-corrected during program operation, meanwhile, mathematical modeling software is applied to analyze existing multi-element continuous observation data, boundary conditions of the saline-alkali soil are calculated, and an accurate evaporation rate is calculated;
preferably, in step S6, the manual work is acquireed through the client side the data of saline and alkaline land to send control command to arduino development board through the client side, arduino development board receives the language that the command turned into the adaptation with control command and sends for receiving control module, and it is right to accept control module through gathering different position information data and with data transmission to arduino development board, and it is right to obtain saline and alkaline land soil surface temperature and soil condition and soil evaporation rate, the realization of the correlation between the salt deposit precipitation rate through algorithm model analysis by arduino development board saline and alkaline land carry out real time monitoring.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the data are obtained through the arranged sensors, the accurate evaporation rate is obtained through calculation, the time for later data processing is greatly shortened, and the salinity of the saline-alkali soil is analyzed through identifying the soil surface temperature based on a machine vision technology and a saline-alkali soil surface temperature identification algorithm.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
A saline-alkali soil salinity analysis method based on soil surface temperature identification comprises the following steps:
step S1: assembling equipment: preparing required sensors, including a T118 temperature and humidity sensor, a Ginshi AP-C30 digital air pressure sensor, a 70cm plug-in anemoscope for eight-direction measurement of Jiandalong Ke, a sunshine meteorology TBQ-2 solar radiation sensor, an EC-5 soil moisture content sensor of Beijing Huayirui science and technology Limited, a Bell analysis instrument ECS-5020 digital salinity sensor and an optical rainfall sensor with smooth essence;
the soil salinity sensor and the soil moisture content sensor are embedded at different depths of the saline-alkali soil, the temperature and humidity sensor, the air pressure sensor, the wind speed and wind direction sensor, the solar radiation sensor and the optical rainfall sensor are fixedly arranged on the rigid tripod and fixed at a position 1m away from the surface of the saline-alkali soil, all the sensors are connected to the arduino development board in a serial interface mode, and the storage battery and the solar power supply board are connected to a power management chip arranged in the arduino development board through positive and negative leads; the arduino development board sends a command to the control receiving module to control the switch of the solar power supply board and the storage battery through a detection result of the optical rainfall sensor, the arduino development board sends a command to switch on the solar power supply board and switch off the storage battery when the detection result of the optical rainfall sensor shows that no rain exists, and the arduino development board sends a command to switch off the solar power supply board and switch on the storage battery when the detection result of the optical rainfall sensor shows that rain exists;
step S2: program debugging: the acquisition terminal formed by the step S1 synchronously acquires soil conditions and data influencing the evaporation rate, and carries out debugging of programs and initialization of related parameters;
and step S3: information acquisition: placing the collection terminal assembled in the step S1 on the saline-alkali soil to be detected and fixing, and collecting information of the saline-alkali soil by each sensor;
placing the assembled acquisition terminal in a saline-alkali soil to be detected and fixing the acquisition terminal by using a foot nail, detecting by a temperature and humidity sensor to obtain surface temperature and humidity, detecting by an air pressure sensor to obtain surface air pressure, detecting by a wind speed and direction sensor to obtain air flow velocity and flow direction, detecting by a solar radiation sensor to obtain solar radiation intensity, detecting by a soil moisture content sensor to obtain soil moisture content, detecting by a soil salinity sensor to obtain soil surface precipitated salt content, and detecting by an optical rainfall sensor to obtain a rainfall value; and collecting the earth surface pictures of the saline-alkali soil, and collecting multiple groups of data by the same information.
And step S4: data analysis and calculation: carrying out data processing, analysis, calculation and parameter correction on the acquired information through an arduino development board, and realizing temperature identification of the soil surface by adopting infrared thermal imaging;
the collected image information is processed by converting into a gray-scale image, enhancing contrast, searching for an image segmentation threshold value and performing black-and-white binarization operation on the image, and the data analysis is to count the percentage of a white area, wherein the percentage of the white area reflects the salt content of the soil surface.
The method comprises the steps of screening collected information through an arduino development board, deleting repeated data, utilizing a plurality of measured soil condition parameters and meteorological condition parameters, utilizing an evaporation model for analysis and calculation, and utilizing a soil surface temperature identification algorithm of machine vision to obtain the evaporation intensity of a soil surface of the saline-alkali soil, wherein the evaporation model is a Penman model with self-corrected parameters, and self-correction is realized by means of autoregression of feedforward parameters.
Step S5: data presentation and sharing: establishing a sharing connection through an arduino development board, sending the integrated information data to a database, and obtaining saline-alkali soil information through the database by a client; superposing and separating the temperature identification picture and the original saline-alkali soil picture in a layer form by an arduino development board, and carrying out selective comparative analysis; marking the saline-alkali soil surface temperature out of a high-temperature area and a low-temperature area in an isotherm mode, presenting extreme values of the temperature, establishing sharing connection between an arduino development board and a client through wireless equipment, sharing the integrated data, comparative analysis data and the saline-alkali soil surface temperature data to the client through sharing equipment in real time, obtaining saline-alkali soil data and environment data through the client by a professional, obtaining an alpha value initial value, obtaining an alpha value empirical value of 0.01, taking alpha as a launch reflectivity, self-correcting the alpha value during program operation, analyzing the existing multi-element continuous observation data by using mathematical modeling software, calculating boundary conditions of the saline-alkali soil, and calculating accurate evaporation rate.
Step S6: manual control and real-time monitoring: the real-time monitoring is realized through a client development program, a control instruction is manually transmitted to the acquisition equipment by using the client, and the running state of the equipment is controlled by controlling a control module of the acquisition terminal.
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 (5)
1. A saline-alkali soil salinity analysis method based on soil surface temperature identification is characterized by comprising the following steps:
step S1: assembling equipment: preparing required sensors including a temperature and humidity sensor, an air pressure sensor, a wind speed and direction sensor, a solar radiation sensor, a soil moisture content sensor, a soil salinity sensor and an optical rainfall sensor;
the soil salinity sensor and the soil moisture content sensor are buried at different depths of the saline-alkali soil, the temperature and humidity sensor, the air pressure sensor, the wind speed and direction sensor, the solar radiation sensor and the optical rainfall sensor are fixedly arranged on the rigid tripod and fixed at a position 1m higher than the surface of the saline-alkali soil, all the sensors are connected to the arduino development board in a serial interface mode, and the storage battery and the solar power supply board are connected to a power management chip arranged in the arduino development board through positive and negative leads; the arduino development board sends a command to the receiving control module to control the switch of the solar power supply board and the storage battery through a detection result of the optical rainfall sensor, the arduino development board sends a command to switch on the solar power supply board and switch off the storage battery when no rain appears in the detection result of the optical rainfall sensor, and the arduino development board sends a command to switch off the solar power supply board and switch on the storage battery when rain appears in the detection result of the optical rainfall sensor;
step S2: program debugging: the acquisition terminal formed by the step S1 synchronously acquires soil conditions and data influencing the evaporation rate, and carries out debugging of programs and initialization of related parameters;
and step S3: information acquisition: placing the assembled acquisition terminal in the step S1 in a saline-alkali soil to be detected and fixing, and acquiring information of the saline-alkali soil by each sensor;
and step S4: data analysis and calculation: carrying out data processing, analysis, calculation and parameter correction on the acquired information through an arduino development board, and realizing temperature identification of the soil surface by adopting infrared thermal imaging;
step S5: data presentation and sharing: establishing a sharing connection through an arduino development board, sending the integrated information data to a database, and obtaining saline-alkali soil information through the database by a client;
step S6: manual control and real-time monitoring: the real-time monitoring is realized through a client development program, a control instruction is manually transmitted to the acquisition equipment by using the client, and the running state of the equipment is controlled by controlling a control module of the acquisition terminal.
2. The saline-alkali soil salinity analysis method based on soil surface temperature identification according to claim 1, characterized in that in step S3, the assembled collection terminal is placed on the saline-alkali soil to be detected and fixed by foot nails, the temperature and humidity sensor detects the surface temperature and humidity, the air pressure sensor detects the surface air pressure, the wind speed and direction sensor detects the air flow velocity and flow direction, the solar radiation sensor detects the solar radiation intensity, the soil water content sensor detects the soil water content, the soil salinity sensor detects the soil surface precipitated salt content, and the optical rain sensor detects the rain amount value; and collecting the earth surface pictures of the saline-alkali soil, and collecting multiple groups of data by the same information.
3. The saline-alkali soil surface temperature identification-based saline-alkali soil surface salinity analysis method according to claim 2, characterized in that in step S4, the acquired information is screened by an arduino development board, duplicate data is deleted, a plurality of measured soil condition parameters and meteorological condition parameters are utilized, an evaporation model is utilized for analysis and calculation, and a machine vision soil surface temperature identification algorithm is utilized to obtain the evaporation intensity of the saline-alkali soil surface, wherein the evaporation model is a Penman model with self-corrected parameters, and the self-correction is realized by means of auto-regression of feedforward parameters.
4. The saline-alkali soil salinity analysis method based on soil surface temperature identification as claimed in claim 3, characterized in that the processing of the collected image information in step S4 comprises converting into a gray scale, enhancing contrast, finding an image segmentation threshold and image black and white binarization, and the data analysis is statistical white area percentage, which reflects the soil surface salinity.
5. The saline-alkali soil salinity analysis method based on soil surface temperature identification as claimed in claim 4, characterized in that in step S5, the arduino development board superimposes and separates the temperature identification picture and the original saline-alkali soil picture in the form of image layers, and performs selective comparative analysis; marking the saline-alkali soil surface temperature out of a high-temperature area and a low-temperature area in an isotherm mode, presenting extreme values of the temperature, establishing sharing connection between an arduino development board and a client through wireless equipment, sharing the integrated data, comparative analysis data and the saline-alkali soil surface temperature data to the client through sharing equipment in real time, obtaining saline-alkali soil data and environment data through the client by a professional, obtaining an alpha value initial value, obtaining an alpha value empirical value of 0.01, taking alpha as a launch reflectivity, self-correcting the alpha value during program operation, analyzing the existing multi-element continuous observation data by using mathematical modeling software, calculating boundary conditions of the saline-alkali soil, and calculating accurate evaporation rate.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117074403A (en) * | 2023-08-17 | 2023-11-17 | 宁夏大学 | Automatic extraction element of soil moisture salinity information |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105953830A (en) * | 2015-10-14 | 2016-09-21 | 山东农业大学 | Saline-alkali soil crop growth key factor internet of things synchronization automatic detection system |
| US20170241973A1 (en) * | 2016-02-23 | 2017-08-24 | Stevens Water Monitoring Systems, Inc. | System and method for instantaneously determining uniform distribution of water, salinity, conductivity, temperature and other conditions in soil |
| CN109342337A (en) * | 2018-12-19 | 2019-02-15 | 山东农业大学 | A kind of severe Soluble Salts In Salt-affected Soil acquisition methods, system and device |
| CN109653185A (en) * | 2018-11-27 | 2019-04-19 | 兰州大学 | A kind of multi-data source subgrade in salty soil zone monitoring system and its monitoring and evaluation method |
| KR20200128958A (en) * | 2019-05-07 | 2020-11-17 | 주식회사 어니언커뮤니케이션즈 | Smart Wireless measuring system for soil information using wireless network commnunication |
-
2022
- 2022-08-18 CN CN202210993790.2A patent/CN115326138B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105953830A (en) * | 2015-10-14 | 2016-09-21 | 山东农业大学 | Saline-alkali soil crop growth key factor internet of things synchronization automatic detection system |
| US20170241973A1 (en) * | 2016-02-23 | 2017-08-24 | Stevens Water Monitoring Systems, Inc. | System and method for instantaneously determining uniform distribution of water, salinity, conductivity, temperature and other conditions in soil |
| CN109653185A (en) * | 2018-11-27 | 2019-04-19 | 兰州大学 | A kind of multi-data source subgrade in salty soil zone monitoring system and its monitoring and evaluation method |
| CN109342337A (en) * | 2018-12-19 | 2019-02-15 | 山东农业大学 | A kind of severe Soluble Salts In Salt-affected Soil acquisition methods, system and device |
| KR20200128958A (en) * | 2019-05-07 | 2020-11-17 | 주식회사 어니언커뮤니케이션즈 | Smart Wireless measuring system for soil information using wireless network commnunication |
Non-Patent Citations (2)
| Title |
|---|
| 刘倩: "盐渍土蒸发积盐特征及其对土壤水力参数的影响", 中国优秀博士论文电子期刊, 15 February 2022 (2022-02-15) * |
| 王丽娜;朱西存;刘庆;赵庚星;李海燕;王凌;张圣武;: "黄河三角洲盐碱土盐分的高光谱定量估测研究", 土壤通报, no. 05, 6 October 2013 (2013-10-06) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117074403A (en) * | 2023-08-17 | 2023-11-17 | 宁夏大学 | Automatic extraction element of soil moisture salinity information |
| CN117074403B (en) * | 2023-08-17 | 2024-05-28 | 宁夏大学 | Automatic extraction element of soil moisture salinity information |
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