CN118044456A - Intelligent agricultural monitoring system based on Internet of things - Google Patents
Intelligent agricultural monitoring system based on Internet of things Download PDFInfo
- Publication number
- CN118044456A CN118044456A CN202410061940.5A CN202410061940A CN118044456A CN 118044456 A CN118044456 A CN 118044456A CN 202410061940 A CN202410061940 A CN 202410061940A CN 118044456 A CN118044456 A CN 118044456A
- Authority
- CN
- China
- Prior art keywords
- soil
- irrigation
- data
- intelligent
- humidity
- 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 58
- 239000002689 soil Substances 0.000 claims abstract description 176
- 238000003973 irrigation Methods 0.000 claims abstract description 138
- 230000002262 irrigation Effects 0.000 claims abstract description 136
- 238000000034 method Methods 0.000 claims abstract description 34
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 34
- 238000005457 optimization Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000007781 pre-processing Methods 0.000 claims abstract description 6
- 241000196324 Embryophyta Species 0.000 claims description 54
- 230000005068 transpiration Effects 0.000 claims description 38
- 230000006870 function Effects 0.000 claims description 24
- 230000008859 change Effects 0.000 claims description 15
- 230000035699 permeability Effects 0.000 claims description 15
- 238000013178 mathematical model Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 4
- 239000002699 waste material Substances 0.000 claims description 4
- 230000006978 adaptation Effects 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000003306 harvesting Methods 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 230000014759 maintenance of location Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 230000000149 penetrating effect Effects 0.000 claims description 3
- 238000004856 soil analysis Methods 0.000 claims description 3
- 238000009331 sowing Methods 0.000 claims description 3
- 230000001934 delay Effects 0.000 claims description 2
- 238000011065 in-situ storage Methods 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims description 2
- 239000000126 substance Substances 0.000 claims description 2
- 230000006855 networking Effects 0.000 claims 1
- 230000009471 action Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008595 infiltration Effects 0.000 description 2
- 238000001764 infiltration Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
- A01G25/167—Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Water Supply & Treatment (AREA)
- Environmental Sciences (AREA)
- Soil Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent agricultural monitoring system based on the Internet of things, and the operation method of the system comprises the following steps: step one: acquiring soil humidity information through a sensor, and acquiring root system data and weather data, wherein the method comprises the following steps of: and (3) performing hysteresis irrigation optimization through moisture permeation analysis, wherein the step (III) is as follows: the intelligent irrigation of crops is realized by calculating multi-factor data, and the following steps are realized: the intelligent irrigation system is characterized in that the intelligent irrigation system comprises a data acquisition and preprocessing module, an intelligent irrigation algorithm and optimization module, a remote monitoring and manual intervention function, a water resource utilization efficiency control module and a water resource utilization decision control module, wherein the data acquisition and preprocessing module is used for acquiring real-time soil humidity information, crop root systems and weather data and performing data processing, and the intelligent irrigation algorithm and optimization module is used for comprehensively considering soil, plants and weather multi-factor data so as to improve the agricultural irrigation efficiency and ensure that the soil humidity is in a proper range.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to an intelligent agricultural monitoring system based on the Internet of things.
Background
The main problems faced in the current crop irrigation field are low water resource utilization and limitations that rely on manual control. Conventional farm irrigation methods typically rely on manual experience and periodic schedules, which suffer from a number of disadvantages.
Firstly, the traditional manual control mode cannot fully utilize water resources, so that the water resource utilization rate is low. Because of the lack of real-time soil humidity and weather information, farmers are difficult to accurately judge the irrigation demands of farmlands, and therefore, the mode of over-irrigation or under-irrigation is often adopted. This not only wastes water resources, but can also lead to soil salinization and ecological problems.
Secondly, the manual control method has a problem of poor timeliness. Farmers operate according to experience or traditional irrigation schedules and cannot adjust the irrigation schedule in time to accommodate weather changes and different phases of crop growth. This makes irrigation decisions inflexible and difficult to cope with irrigation demands under different environmental conditions. Therefore, it is necessary to design an intelligent agricultural monitoring system based on the internet of things, which improves the water resource utilization efficiency and makes irrigation decisions more flexible.
Disclosure of Invention
The invention aims to provide an intelligent agricultural monitoring system based on the Internet of things, which aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent agricultural monitoring system based on the Internet of things, and an operation method of the system comprises the following steps:
Step one: acquiring soil humidity information through a sensor, and acquiring root system data and weather data;
step two: performing hysteretic irrigation optimization by moisture permeation analysis;
step three: the intelligent irrigation of crops is realized by calculating multi-factor data;
step four: by introducing remote monitoring and manual intervention functions, intelligent control of irrigation quantity of crops is realized.
According to the above technical scheme, the step of obtaining soil humidity information through the sensor and obtaining root system data and weather data comprises the following steps:
the system firstly establishes a crop root system database, the database records the root system depth information of various crops in different growth stages in detail, the root system depth change conditions of the various growth stages from a sowing period to a harvesting period are covered, the data are collected by the system through a network, then, when a user logs in a system interface, the type of the crops is input, after the type of the crops is input successfully, the system can utilize corresponding data in the crop root system database, the system retrieves and acquires the root system depth information of the specific crops in the current growth stage through the network, the user carries out sensor installation according to the root system depth information, a multi-layer soil humidity sensor is selected to be used, the sensor is provided with a plurality of detection units, then, the sensor is arranged in different depths according to the differences of the crops, including a root system active area, an intermediate soil layer and a deep soil layer, in addition, the sensor is provided with a temperature compensation function, humidity measurement is adjusted in real time according to the environmental temperature, meanwhile, the system can acquire prediction data in a period of the future through real-time connection with weather service, and the system can acquire weather prediction data including but not limited to rainfall probability, rainfall intensity, temperature and humidity detail prediction data through real-time connection.
According to the technical scheme, the step of performing hysteretic irrigation optimization through moisture penetration analysis comprises the following steps:
Analyzing the humidity of the current soil through the data collected by the soil humidity sensor installed in the first step, wherein the soil humidity sensor provides humidity information of different depths of the soil, the humidity information comprises the humidity of a root system active area H 1, an intermediate soil layer H 2 and a deep soil layer H 3, then, humidity data H i measured by the sensor are converted into relative humidity percentage RH i, then, the overall soil humidity is obtained through integrating the relative humidity of each depth by using a weighted average method, the overall soil humidity is expressed as RH total, and then, according to the characteristics and types of the soil, a mathematical model of the soil water content calculation is established by the system, namely: θ=a×rh total +b, the model converts the relative humidity to a specific moisture content of the soil, θ represents the moisture content of the soil, a and b are coefficients determined according to the type and texture of the soil, and the permeability coefficient K is calculated subsequently, in the following way: by utilizing real-time soil humidity data, a soil moisture change model is established, the permeability coefficient of soil is reversely pushed by a system through analyzing the speed and amplitude information of humidity change, and finally, the moisture permeability model is established on the basis of the soil moisture content theta and the permeability coefficient K, namely Wherein t is time, and v is a gradient operator, and represents the gradient of space coordinates, and the model considers the difficulty of the soil in penetrating moisture, and can better understand the motion law of the moisture in the soil by simulating the propagation process of the moisture in the soil, and forecast the change of the moisture in the soil so as to optimize an irrigation plan and reduce excessive irrigation and water resource waste.
According to the technical scheme, the steps for realizing intelligent irrigation of crops by calculating the multi-factor data comprise the following steps:
considering soil type, transpiration rate and weather forecast factors to improve agricultural irrigation efficiency;
And dynamically adjusting an irrigation plan by setting an irrigation decision rule and a threshold value, and optimizing an objective function.
According to the above technical scheme, the step of considering soil type, transpiration rate and weather forecast factors to improve agricultural irrigation efficiency comprises the following steps:
the system firstly analyzes the soil type, acquires the physical and chemical characteristics of the soil including the water content, the texture and the permeability of the soil through a sensor network or an in-situ sampling means, and can establish a mathematical model of the water retention capacity of the soil through comprehensively considering the characteristics, such as Where F c is soil moisture holding capacity, W c is soil moisture content, a and b are coefficients determined according to soil type, then the system performs adaptive analysis on various plants according to the moisture demand difference of crop varieties, including root depth and transpiration rate factors of the plants, and the system calculates actual transpiration demand of the plants using the following formula: e a＝K*(Ta-Tref), wherein E a is the actual transpiration demand of the plant, T a is the current air temperature, T ref is the reference transpiration temperature of the plant, K is a coefficient determined according to the plant variety, and finally, the system integrates weather forecast data in real time, including rainfall probability, temperature, humidity information, using these data in combination with soil analysis, plant adaptation analysis, the system adjusts the irrigation plan in real time, by optimizing objective functions, such as minimizing water resource usage, maximizing crop yield, the system is able to determine optimal irrigation timing and water quantity, minimizing water resource usage by the following formula: minimizing θ=α×w c+β*Ea+γ*Pr, where α, β, γ are weight coefficients determined according to the optimization objective, W c is soil moisture content, E a is actual transpiration demand of the plant, and P r is rainfall.
According to the above technical solution, the step of dynamically adjusting the irrigation plan and optimizing the objective function by setting the irrigation decision rule and the threshold value includes:
The irrigation control is realized by comprehensively considering the soil moisture content, the plant transpiration demand and the rainfall of the weather forecast, firstly, an irrigation decision rule is set, judgment is carried out according to the preset rule, under the condition that the soil moisture content is lower than a set threshold value and when no rainfall forecast exists in the weather forecast in the future, the system triggers irrigation, conversely, when the soil moisture content is moderate, but the weather forecast has high rainfall probability, the system correspondingly reduces the irrigation quantity or delays the irrigation time, secondly, the threshold value is set, the demand of plant varieties and the characteristics of the soil types are considered, so that the irrigation can be carried out timely when the soil moisture content is lower than the threshold value, in addition, the system carries out irrigation quantity control, adjustment is carried out according to the rainfall of the plant transpiration demand and the weather forecast, the system can dynamically adjust the irrigation plan according to the soil moisture content change monitored in real time, and feedback information is recorded to optimize the algorithm.
According to the technical scheme, the steps for realizing intelligent control of the irrigation quantity of crops by introducing remote monitoring and manual intervention functions comprise the following steps:
The remote monitoring and manual intervention functions are introduced into the system, firstly, an efficient remote monitoring system is established, key data including soil moisture content, plant transpiration requirements, weather forecast and irrigation plans can be accessed in real time by a user through Internet connection, an intuitive graphical interface is provided by the system, trends and changes of various data are displayed, in addition, the user can adjust system parameters according to actual requirements, such as modifying a soil moisture content threshold value and plant transpiration requirement weight, or optimizing weight coefficients in an objective function, such adjustment can be expressed through a simple formula, such as that a new soil moisture content threshold value is equal to an original threshold value plus an adjustment value set by the user, in the remote monitoring system, the user can directly manually intervene in the irrigation plan of the system, and the irrigation can be started or stopped manually through simple operation selection, so that the system can be adjusted in real time according to local experience and actual conditions, the system is ensured to be well adapted to special conditions, and the manual irrigation quantity can be set by the user.
According to the above technical solution, the system comprises:
The data acquisition and preprocessing module is used for acquiring real-time soil humidity information, crop root systems and weather data so as to provide a reliable data base for an intelligent irrigation algorithm;
The intelligent irrigation algorithm and optimization module is used for comprehensively considering soil, plants and weather multi-factor data, and realizing irrigation decision and optimization through the algorithm so as to improve agricultural irrigation efficiency and ensure that soil humidity is in a proper range;
the remote monitoring and manual intervention module is used for establishing a remote monitoring system, enabling a user to access key data in real time, adjusting system parameters and performing manual irrigation control so as to adapt to different conditions and realize more flexible farmland management.
According to the above technical scheme, the intelligent irrigation algorithm and optimization module comprises:
The soil and plant adaptability analysis module is used for analyzing soil types, establishing a mathematical model of soil moisture retention capacity and carrying out adaptability analysis through moisture demand differences of plant varieties;
the multi-factor data calculation module is used for comprehensively considering the soil water content, the plant transpiration demand and the rainfall of weather forecast to perform irrigation control;
The intelligent irrigation decision rule module is used for setting an irrigation decision rule, judging the water content of soil, weather forecast and rainfall condition, and triggering irrigation or correspondingly adjusting irrigation quantity.
According to the above technical scheme, the remote monitoring and manual intervention module comprises:
The remote monitoring module is used for connecting through the Internet, and a user can access the key data in real time;
The remote parameter adjustment module is used for a user to adjust system parameters according to actual needs, such as modifying a soil water content threshold value and plant transpiration demand weight;
and the manual irrigation control module is used for enabling a user to select to manually start or stop irrigation through simple operation, and timely adjusting according to actual conditions so as to ensure that the system is better suitable for special conditions.
Compared with the prior art, the invention has the following beneficial effects: according to the intelligent irrigation system, firstly, soil humidity information is obtained through a sensor, a crop root system database and real-time weather data are combined, a comprehensive soil humidity, weather and crop root system information database is built by the system, secondly, the infiltration behavior of moisture can be predicted more accurately through real-time monitoring of the soil humidity and application of a infiltration model, so that the situation of excessive moisture hysteresis in irrigation is avoided, then, intelligent irrigation decision is realized through comprehensive consideration of factors such as soil type, transpiration rate and weather forecast by the system through a mathematical model and an optimization algorithm, finally, remote monitoring and manual intervention functions are introduced, so that a user can access key data in real time, adjust system parameters and manually intervene in an irrigation plan when needed, flexibility and adaptability of the system are improved, and the comprehensive design aims at optimizing agricultural irrigation efficiency, improving water resource utilization and providing an intelligent and real-time farmland management scheme for farmers.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
Fig. 1 is a flowchart of an intelligent agricultural monitoring method based on internet of things according to an embodiment of the present invention;
fig. 2 is a schematic diagram of module composition of an intelligent agricultural monitoring system based on internet of things according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: fig. 1 is a flowchart of an intelligent agricultural monitoring method based on the internet of things, where the present embodiment may be applied to a scenario of crop irrigation control, and the method may be executed by an intelligent agricultural monitoring system based on the internet of things, as shown in fig. 1, and the method specifically includes the following steps:
Step one: acquiring soil humidity information through a sensor, and acquiring root system data and weather data;
in the embodiment of the invention, the system acquires soil humidity information through the soil humidity sensor, and acquires weather data in real time, so as to lay a foundation for subsequent intelligent irrigation;
The system firstly establishes a crop root system database, the database records the root system depth information of various crops in different growth stages in detail, the change condition of the root system depth of each growth stage from a sowing period to a harvesting period is covered, the data are collected by the system through a network, then, when a user logs in a system interface, the type of the crops is input, after the type of the crops is successfully input, the system can utilize corresponding data in the crop root system database, the user carries out sensor installation according to the root system depth information, a multi-level soil humidity sensor is selected to be used, the sensor is provided with a plurality of detection units, then, the sensor is arranged at different depths according to different crops, comprising a root system active area, an intermediate soil layer and a deep soil layer, in addition, the sensor is provided with a temperature compensation function, humidity measurement is adjusted in real time according to the environmental temperature, meanwhile, the system acquires prediction data in a period in the future through real time connection with weather prediction service, and can acquire detailed prediction data of multiple weather parameters including but not limited by rainfall probability, rainfall intensity, temperature, humidity and the like, the system can acquire complete soil and provide weather information for the following agriculture foundation information for irrigation.
Step two: performing hysteretic irrigation optimization by moisture permeation analysis;
In the embodiment of the invention, the current soil moisture condition is analyzed in detail by utilizing real-time data acquired by a soil humidity sensor;
Exemplary, the humidity of the current soil is analyzed by the data collected by the soil humidity sensor installed in the first step, the soil humidity sensor provides humidity information of different depths of the soil, including the humidity of the root system active area H 1, the middle soil layer H 2 and the deep soil layer H 3, then, the humidity data H i measured by the sensor is converted into relative humidity percentage RH i, then, by integrating the relative humidity of each depth, the overall soil humidity is obtained by using a weighted average method, which is represented as RH total, and then, according to the characteristics and types of the soil, the system builds a mathematical model of the soil water content calculation, namely: θ=a×rh total +b, the model converts the relative humidity to a specific moisture content of the soil, θ represents the moisture content of the soil, a and b are coefficients determined according to the type and texture of the soil, and the permeability coefficient K is calculated subsequently, in the following way: by utilizing real-time soil humidity data, a soil moisture change model is established, the permeability coefficient of soil is reversely pushed by a system through analyzing the speed and amplitude information of humidity change, and finally, the moisture permeability model is established on the basis of the soil moisture content theta and the permeability coefficient K, namely Wherein t is time, and v is a gradient operator, and represents the gradient of space coordinates, and the model considers the difficulty of the soil in penetrating moisture, and can better understand the motion law of the moisture in the soil by simulating the propagation process of the moisture in the soil, and forecast the change of the moisture in the soil so as to optimize an irrigation plan and reduce excessive irrigation and water resource waste.
Step three: the intelligent irrigation of crops is realized by calculating multi-factor data;
In the embodiment of the invention, a plurality of factors such as soil type, transpiration rate, weather forecast and the like are considered so as to improve the efficiency of agricultural irrigation and ensure that the soil humidity is in a proper range;
By way of example, the system first performs an analysis of the soil type, including soil moisture content, texture, permeability, and by taking into account a combination of these characteristics, the system can build a mathematical model of the soil moisture holding capacity, such as Where F c is soil moisture holding capacity, W c is soil moisture content, a and b are coefficients determined according to soil type, then the system performs adaptive analysis on various plants according to the moisture demand difference of crop varieties, including root depth and transpiration rate factors of the plants, and the system calculates actual transpiration demand of the plants using the following formula: e a＝K*(Ta-Tref), wherein E a is the actual transpiration demand of the plant, T a is the current air temperature, T ref is the reference transpiration temperature of the plant, K is a coefficient determined according to the plant variety, and finally, the system integrates weather forecast data in real time, including rainfall probability, temperature, humidity information, using these data in combination with soil analysis, plant adaptation analysis, the system adjusts the irrigation plan in real time, by optimizing objective functions, such as minimizing water resource usage, maximizing crop yield, etc., the system is able to determine the optimal irrigation opportunity and water quantity, minimizing water resource usage by the following formula: minimizing θ=α×w c+β*Ea+γ*Pr, where α, β, γ are weight coefficients determined according to the optimization objective, W c is soil moisture content, E a is actual transpiration demand of the plant, and P r is rainfall;
The system can correspondingly reduce the irrigation amount or delay the irrigation time when the soil moisture content is moderate but the weather forecast has high rainfall probability, secondly, the threshold value is set, the requirements of plant varieties and the characteristics of soil types are considered to ensure that irrigation can be timely carried out when the soil moisture content is lower than the threshold value, in addition, the system carries out irrigation amount control, adjusts according to the rainfall of the plant transpiration requirements and the weather forecast, dynamically adjusts an irrigation plan according to the change of the soil moisture content monitored in real time, records feedback information to optimize an algorithm, and can timely know the current soil humidity condition through real-time monitoring and feedback so as to realize intelligent and real-time irrigation control.
Step four: by introducing remote monitoring and manual intervention functions, intelligent control of irrigation quantity of crops is realized.
In the embodiment of the invention, a remote monitoring and manual intervention function is introduced into a system, firstly, a high-efficiency remote monitoring system is established, key data including soil moisture content, plant transpiration requirements, weather forecast, irrigation plans and the like can be accessed in real time by a user through Internet connection, an intuitive graphical interface is provided by the system, trends and changes of various data are displayed, in addition, the user can adjust system parameters according to actual requirements, such as modifying a soil moisture content threshold value and plant transpiration requirement weight, or optimizing weight coefficients in an objective function, such adjustment can be expressed through a simple formula, for example, a new soil moisture content threshold value is equal to an original threshold value plus an adjustment value set by the user, in the remote monitoring system, the user can directly manually intervene in the irrigation plan of the system, and manually start or stop irrigation is selected through simple operation, the function enables the user to adjust in real time according to local experience and actual conditions, so that the system can be well adapted to special conditions, the manual irrigation quantity can be set by the user, and unexpected rainfall or other emergency conditions can be more flexibly handled by the user through the manual function.
Embodiment two: the second embodiment of the present invention provides an intelligent agricultural monitoring system based on the internet of things, and fig. 2 is a schematic diagram of module composition of the intelligent agricultural monitoring system based on the internet of things, as shown in fig. 2, where the system includes:
The data acquisition and preprocessing module is used for acquiring real-time soil humidity information, crop root systems and weather data so as to provide a reliable data base for an intelligent irrigation algorithm;
The intelligent irrigation algorithm and optimization module is used for comprehensively considering soil, plants and weather multi-factor data, and realizing irrigation decision and optimization through the algorithm so as to improve agricultural irrigation efficiency and ensure that soil humidity is in a proper range;
The remote monitoring and manual intervention module is used for establishing a remote monitoring system, enabling a user to access key data in real time, adjusting system parameters and performing manual irrigation control so as to adapt to different conditions and realize more flexible farmland management;
In some embodiments of the present invention, the data acquisition and preprocessing module comprises:
The soil humidity information acquisition module is used for acquiring real-time soil humidity information through the soil humidity sensor;
The system records root depth information of different crops in each growth stage in detail by establishing a crop root database, and simultaneously acquires detailed weather prediction data in a future period by connecting weather prediction service in real time;
the growth cycle recognition module is used for introducing a growth cycle recognition algorithm and analyzing the growth state of the plant in real time through a computer vision technology and a machine learning model;
in some embodiments of the invention, the intelligent irrigation algorithm and optimization module comprises:
The soil and plant adaptability analysis module is used for analyzing soil types, establishing a mathematical model of soil moisture retention capacity and carrying out adaptability analysis through moisture demand differences of plant varieties;
the multi-factor data calculation module is used for comprehensively considering the soil water content, the plant transpiration demand and the rainfall of weather forecast to perform irrigation control;
the intelligent irrigation decision rule module is used for setting an irrigation decision rule, judging the water content of soil, weather forecast and rainfall condition, and triggering irrigation or correspondingly adjusting irrigation quantity;
in some embodiments of the invention, the remote monitoring and manual intervention module comprises:
The remote monitoring module is used for connecting through the Internet, and a user can access the key data in real time;
The remote parameter adjustment module is used for a user to adjust system parameters according to actual needs, such as modifying a soil water content threshold value, plant transpiration demand weight and the like;
and the manual irrigation control module is used for enabling a user to select to manually start or stop irrigation through simple operation, and timely adjusting according to actual conditions so as to ensure that the system is better suitable for special conditions.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent agricultural monitoring method based on the Internet of things is characterized by comprising the following steps of: the method comprises the following steps:
Step one: acquiring soil humidity information through a sensor, and acquiring root system data and weather data;
step two: performing hysteretic irrigation optimization by moisture permeation analysis;
step three: the intelligent irrigation of crops is realized by calculating multi-factor data;
step four: by introducing remote monitoring and manual intervention functions, intelligent control of irrigation quantity of crops is realized.
2. The intelligent agricultural monitoring method based on the internet of things according to claim 1, wherein the intelligent agricultural monitoring method based on the internet of things is characterized in that: the step of acquiring soil humidity information through the sensor and root system data and weather data comprises the following steps:
the system firstly establishes a crop root system database, the database records the root system depth information of various crops in different growth stages in detail, the root system depth change conditions of the various growth stages from a sowing period to a harvesting period are covered, the data are collected by the system through a network, then, when a user logs in a system interface, the type of the crops is input, after the type of the crops is input successfully, the system can utilize corresponding data in the crop root system database, the system retrieves and acquires the root system depth information of the specific crops in the current growth stage through the network, the user carries out sensor installation according to the root system depth information, a multi-layer soil humidity sensor is selected to be used, the sensor is provided with a plurality of detection units, then, the sensor is arranged in different depths according to the differences of the crops, including a root system active area, an intermediate soil layer and a deep soil layer, in addition, the sensor is provided with a temperature compensation function, humidity measurement is adjusted in real time according to the environmental temperature, meanwhile, the system can acquire prediction data in a period of the future through real-time connection with weather service, and the system can acquire weather prediction data including but not limited to rainfall probability, rainfall intensity, temperature and humidity detail prediction data through real-time connection.
3. The intelligent agricultural monitoring method based on the internet of things according to claim 2, wherein the intelligent agricultural monitoring method based on the internet of things is characterized in that: the step of performing a hysteretic irrigation optimization by moisture permeation analysis comprises:
Analyzing the humidity of the current soil through the data collected by the soil humidity sensor installed in the first step, wherein the soil humidity sensor provides humidity information of different depths of the soil, the humidity information comprises the humidity of a root system active area H 1, an intermediate soil layer H 2 and a deep soil layer H 3, then, humidity data H i measured by the sensor are converted into relative humidity percentage RH i, then, the overall soil humidity is obtained through integrating the relative humidity of each depth by using a weighted average method, the overall soil humidity is expressed as RH total, and then, according to the characteristics and types of the soil, a mathematical model of the soil water content calculation is established by the system, namely: θ=a×rh total +b, the model converts the relative humidity to a specific moisture content of the soil, θ represents the moisture content of the soil, a and b are coefficients determined according to the type and texture of the soil, and the permeability coefficient K is calculated subsequently, in the following way: by utilizing real-time soil humidity data, a soil moisture change model is established, the permeability coefficient of soil is reversely pushed by a system through analyzing the speed and amplitude information of humidity change, and finally, the moisture permeability model is established on the basis of the soil moisture content theta and the permeability coefficient K, namely Wherein t is time, and v is a gradient operator, and represents the gradient of space coordinates, and the model considers the difficulty of the soil in penetrating moisture, and can better understand the motion law of the moisture in the soil by simulating the propagation process of the moisture in the soil, and forecast the change of the moisture in the soil so as to optimize an irrigation plan and reduce excessive irrigation and water resource waste.
4. The intelligent agricultural monitoring method based on the internet of things according to claim 3, wherein: the method for realizing intelligent irrigation of crops by calculating multi-factor data comprises the following steps:
considering soil type, transpiration rate and weather forecast factors to improve agricultural irrigation efficiency;
And dynamically adjusting an irrigation plan by setting an irrigation decision rule and a threshold value, and optimizing an objective function.
5. The intelligent agricultural monitoring method based on the internet of things according to claim 4, wherein the intelligent agricultural monitoring method based on the internet of things is characterized in that: the step of considering soil type, transpiration rate and weather forecast factors to improve agricultural irrigation efficiency comprises the following steps:
the system firstly analyzes the soil type, acquires the physical and chemical characteristics of the soil including the water content, the texture and the permeability of the soil through a sensor network or an in-situ sampling means, and can establish a mathematical model of the water retention capacity of the soil through comprehensively considering the characteristics, such as Where F c is soil moisture holding capacity, W c is soil moisture content, a and b are coefficients determined according to soil type, then the system performs adaptive analysis on various plants according to the moisture demand difference of crop varieties, including root depth and transpiration rate factors of the plants, and the system calculates actual transpiration demand of the plants using the following formula: e a＝K*(Ta-Tref), wherein E a is the actual transpiration demand of the plant, T a is the current air temperature, T ref is the reference transpiration temperature of the plant, K is a coefficient determined according to the plant variety, and finally, the system integrates weather forecast data in real time, including rainfall probability, temperature, humidity information, using these data in combination with soil analysis, plant adaptation analysis, the system adjusts the irrigation plan in real time, by optimizing objective functions, such as minimizing water resource usage, maximizing crop yield, the system is able to determine optimal irrigation timing and water quantity, minimizing water resource usage by the following formula: minimizing θ=α×w c+β*Ea+γ*Pr, where α, β, γ are weight coefficients determined according to the optimization objective, W c is soil moisture content, E a is actual transpiration demand of the plant, and P r is rainfall.
6. The intelligent agricultural monitoring method based on the internet of things according to claim 5, wherein the intelligent agricultural monitoring method based on the internet of things is characterized in that: the step of dynamically adjusting the irrigation plan and optimizing the objective function by setting the irrigation decision rule and the threshold value comprises the following steps:
The irrigation control is realized by comprehensively considering the soil moisture content, the plant transpiration demand and the rainfall of the weather forecast, firstly, an irrigation decision rule is set, judgment is carried out according to the preset rule, under the condition that the soil moisture content is lower than a set threshold value and when no rainfall forecast exists in the weather forecast in the future, the system triggers irrigation, conversely, when the soil moisture content is moderate, but the weather forecast has high rainfall probability, the system correspondingly reduces the irrigation quantity or delays the irrigation time, secondly, the threshold value is set, the demand of plant varieties and the characteristics of the soil types are considered, so that the irrigation can be carried out timely when the soil moisture content is lower than the threshold value, in addition, the system carries out irrigation quantity control, adjustment is carried out according to the rainfall of the plant transpiration demand and the weather forecast, the system can dynamically adjust the irrigation plan according to the soil moisture content change monitored in real time, and feedback information is recorded to optimize the algorithm.
7. The intelligent agricultural monitoring method based on the internet of things according to claim 6, wherein the intelligent agricultural monitoring method based on the internet of things is characterized in that: the intelligent control of the irrigation quantity of crops is realized by introducing remote monitoring and manual intervention functions, and the intelligent control method comprises the following steps:
The remote monitoring and manual intervention functions are introduced into the system, firstly, an efficient remote monitoring system is established, key data including soil moisture content, plant transpiration requirements, weather forecast and irrigation plans can be accessed in real time by a user through Internet connection, an intuitive graphical interface is provided by the system, trends and changes of various data are displayed, in addition, the user can adjust system parameters according to actual requirements, such as modifying a soil moisture content threshold value and plant transpiration requirement weight, or optimizing weight coefficients in an objective function, such adjustment can be expressed through a simple formula, such as that a new soil moisture content threshold value is equal to an original threshold value plus an adjustment value set by the user, in the remote monitoring system, the user can directly manually intervene in the irrigation plan of the system, and the irrigation can be started or stopped manually through simple operation selection, so that the system can be adjusted in real time according to local experience and actual conditions, the system is ensured to be well adapted to special conditions, and the manual irrigation quantity can be set by the user.
8. Intelligent agriculture monitoring system based on thing networking, its characterized in that: the system comprises:
The data acquisition and preprocessing module is used for acquiring real-time soil humidity information, crop root systems and weather data so as to provide a reliable data base for an intelligent irrigation algorithm;
The intelligent irrigation algorithm and optimization module is used for comprehensively considering soil, plants and weather multi-factor data, and realizing irrigation decision and optimization through the algorithm so as to improve agricultural irrigation efficiency and ensure that soil humidity is in a proper range;
the remote monitoring and manual intervention module is used for establishing a remote monitoring system, enabling a user to access key data in real time, adjusting system parameters and performing manual irrigation control so as to adapt to different conditions and realize more flexible farmland management.
9. The intelligent agricultural monitoring system based on the internet of things of claim 8, wherein: the intelligent irrigation algorithm and optimization module comprises:
The soil and plant adaptability analysis module is used for analyzing soil types, establishing a mathematical model of soil moisture retention capacity and carrying out adaptability analysis through moisture demand differences of plant varieties;
the multi-factor data calculation module is used for comprehensively considering the soil water content, the plant transpiration demand and the rainfall of weather forecast to perform irrigation control;
The intelligent irrigation decision rule module is used for setting an irrigation decision rule, judging the water content of soil, weather forecast and rainfall condition, and triggering irrigation or correspondingly adjusting irrigation quantity.
10. The intelligent agricultural monitoring system based on the internet of things of claim 9, wherein: the remote monitoring and manual intervention module comprises:
The remote monitoring module is used for connecting through the Internet, and a user can access the key data in real time;
The remote parameter adjustment module is used for a user to adjust system parameters according to actual needs, such as modifying a soil water content threshold value and plant transpiration demand weight;
and the manual irrigation control module is used for enabling a user to select to manually start or stop irrigation through simple operation, and timely adjusting according to actual conditions so as to ensure that the system is better suitable for special conditions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410061940.5A CN118044456A (en) | 2024-01-16 | 2024-01-16 | Intelligent agricultural monitoring system based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410061940.5A CN118044456A (en) | 2024-01-16 | 2024-01-16 | Intelligent agricultural monitoring system based on Internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118044456A true CN118044456A (en) | 2024-05-17 |
Family
ID=91052983
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410061940.5A Pending CN118044456A (en) | 2024-01-16 | 2024-01-16 | Intelligent agricultural monitoring system based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118044456A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118261335A (en) * | 2024-05-23 | 2024-06-28 | 山东祥辰科技集团有限公司 | Ancient tree ecological index monitoring system |
| CN118435855A (en) * | 2024-07-04 | 2024-08-06 | 苏州朗禾农业科技有限公司 | Irrigation monitoring method and system based on optical radiation and drainage amount analysis |
-
2024
- 2024-01-16 CN CN202410061940.5A patent/CN118044456A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118261335A (en) * | 2024-05-23 | 2024-06-28 | 山东祥辰科技集团有限公司 | Ancient tree ecological index monitoring system |
| CN118435855A (en) * | 2024-07-04 | 2024-08-06 | 苏州朗禾农业科技有限公司 | Irrigation monitoring method and system based on optical radiation and drainage amount analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112602563B (en) | Water-saving irrigation system and accurate irrigation method | |
| CN118044456A (en) | Intelligent agricultural monitoring system based on Internet of things | |
| CN107219759B (en) | Greenhouse environment control method and device | |
| CN112772384B (en) | Agricultural water irrigation system and method based on convolutional neural network | |
| CN110579961B (en) | Three-dimensional planting-oriented garden intelligent water supply method and system | |
| CN117652386A (en) | Water-saving irrigation control system | |
| CN114429592A (en) | Automatic irrigation method and equipment based on artificial intelligence | |
| CN117455062A (en) | Crop yield prediction algorithm based on multi-source heterogeneous agricultural data | |
| CN115088595A (en) | Automatic irrigation method, device and system for crops and processor | |
| KR20210083708A (en) | A system forecasting water content of soil using weather information | |
| CN118058178B (en) | Paddy field intelligent irrigation control method and system based on cloud service platform | |
| CN115511219A (en) | Intelligent prediction method and system for growth state of wheat crop | |
| CN117911187A (en) | Intelligent supervision system based on agricultural Internet of things | |
| CN116578047A (en) | Fine intelligent control method and system for chilli production | |
| CN117296538B (en) | Green plant maintenance method, device and system based on vegetation soil component detection | |
| KR20210114751A (en) | Method and apparatus for estimating crop growth quantity | |
| CN117634738B (en) | Internet of things agricultural environment-friendly integrated platform based on artificial intelligence | |
| KR102471743B1 (en) | Method for forecasting future production of smart farms | |
| CN109934400B (en) | Rain collecting, regulating and deficiency crop water demand prediction method based on improved neural network | |
| CN211908856U (en) | Plant data acquisition system based on Internet of things | |
| KR102471742B1 (en) | Method for building an optimal linear model of production and environment of smart farm | |
| CN111262950A (en) | Plant data acquisition system based on Internet of things | |
| CN118733696B (en) | Land information mapping system based on Internet of things | |
| CN118014757B (en) | Intelligent planning method for intelligent agricultural park | |
| CN117952275A (en) | Agricultural water estimation system based on cloud service |
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 |