Intelligent agricultural irrigation system and method based on Internet of Things

Abstract

The present invention discloses an intelligent agricultural irrigation system and method based on the Internet of Things, comprising an information collection module, an irrigation analysis module, and an irrigation management module. The information collection module is used to collect and obtain relevant information about the irrigation area, the irrigation analysis module is used to analyze and calculate the appropriate irrigation water volume for the current irrigation area, and the irrigation management module is used to control the real-time irrigation conditions of the irrigation area. The information collection module is electrically connected to the irrigation analysis module, and the irrigation analysis module is electrically connected to the irrigation management module. The present invention can dynamically calculate the most appropriate irrigation water volume based on factors such as soil compaction and crop growth status, ensuring timely water supply and avoiding water waste. This solves the problem that traditional irrigation methods are difficult to ensure uniform water supply and lead to crop yield reduction, improves the efficiency of agricultural water resource utilization, and realizes intelligent and efficient agricultural irrigation management, with intelligent and refined characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural irrigation, and in particular to an intelligent agricultural irrigation system and method based on the Internet of Things. Background Art

[0002] Traditional agricultural irrigation methods mainly use a timed and quantitative method, but due to factors such as weather and terrain slope, it is difficult to achieve uniform and timely water supply to crops, leading to a series of problems such as crop yield reduction, curling, and infertile crops. In addition, soils with different degrees of compaction require different amounts of irrigation water, and for crops with deep roots, irrigation water needs to fully penetrate the roots. However, traditional irrigation methods easily lead to soil compaction, and water remains on the surface and is lost or evaporated, resulting in insufficient actual irrigation water and insufficient penetration. A new type of intelligent agricultural irrigation system and method is urgently needed to address the limitations of traditional irrigation methods. Therefore, it is very necessary to design an intelligent and refined intelligent agricultural irrigation system and method based on the Internet of Things. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent agricultural irrigation system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent agricultural irrigation system and method based on the Internet of Things, including an information collection module, an irrigation analysis module and an irrigation management module, characterized in that: the information collection module is used to collect and obtain relevant information of the irrigation area, the irrigation analysis module is used to analyze and calculate the appropriate irrigation water volume for the current irrigation area, and the irrigation management module is used to control the real-time irrigation conditions of the irrigation area. The information collection module is electrically connected to the irrigation analysis module, and the irrigation analysis module is electrically connected to the irrigation management module.

[0005] According to the above technical solution, the information collection module includes an irrigation information database, a soil collection module, a growth monitoring module and a weather forecast module. The irrigation information database is used to store the irrigation data required for irrigated crops and the basic information of the crops. The soil collection module is used to capture and collect soil image information of the irrigation area through a camera module. The growth monitoring module is used to detect the growth conditions of crops through radar. The weather forecast module is used to obtain weather forecast information for the irrigation area.

[0006] According to the above technical solution, the irrigation analysis module includes a soil compaction analysis module, an irrigation water flow analysis module and an irrigation amount calculation module. The soil compaction analysis module is used to analyze and determine the soil compaction information of the irrigation area, the irrigation water flow analysis module is used to analyze and calculate the maximum irrigation water flow of the current irrigation area, and the irrigation amount calculation module is used to analyze and calculate the irrigation water amount based on the soil compaction, irrigation water flow and weather conditions.

[0007] According to the above technical solution, the irrigation management module includes a soil loosening prompt unit, an irrigation water flow control module and an irrigation water volume management module. The soil loosening prompt unit is used to judge and make a prompt that the irrigation soil needs to be loosened. The irrigation water flow control module is used to control the irrigation water flow in real time according to the analysis results. The irrigation water volume management module is used to manage and control the total amount of single irrigation water of the irrigation equipment.

[0008] An intelligent agricultural irrigation method based on the Internet of Things, the method comprising the following steps:

[0009] Step S1: Establish an irrigation information database to register and store crop information in the irrigation area, including crop types, planting time, and irrigation water requirements;

[0010] Step S2: Using the soil acquisition module to access the camera module, collect soil images of the irrigation area;

[0011] Step S3: In the agricultural irrigation area, the growth of crops is monitored by a growth monitoring module;

[0012] Step S4: Obtain local weather and precipitation conditions through the Internet;

[0013] Step S5: analyzing the soil compaction degree based on the soil image acquired by the soil collection module;

[0014] Step S6: Analyze and calculate the maximum flow rate of the current irrigation based on the soil compaction degree and the information of the crops currently being irrigated;

[0015] Step S7: Then, the irrigation water volume is calculated based on the irrigation water flow and weather conditions;

[0016] Step S8: Finally, the system updates the irrigation analysis results in real time and manages and controls the irrigation process.

[0017] According to the above technical solution, in step S3, the method for monitoring the growth of crops is:

[0018] Step S31: Setting up a growth monitoring point directly above the monitored crop;

[0019] Step S32: The growth monitoring module triggers a signal to control the signal transmitting unit at the growth monitoring point to transmit a radar signal;

[0020] Step S33: Feedback a signal when the crop reaches its apex, and the growth monitoring module obtains the distance l from the monitoring point to the crop apex based on signal attenuation and a preset distance conversion coefficient;

[0021] Step S34: Calculate the height h of the crop according to the height H of the monitoring point and the distance l using the formula h=Hl;

[0022] Step S35: The growth monitoring module outputs a growth signal of the crop using the crop height h.

[0023] According to the above technical solution, step S5 further includes the following steps:

[0024] Step S51: Obtain a soil image taken at an oblique angle, identify the boundary contour between the soil surface and the background from the cross section, and enlarge it proportionally;

[0025] Step S52: fitting a contour line to the surface features of the boundary contour;

[0026] Step S53: Select the starting point and end point of the contour line, connect the line segment L, and establish a plane rectangular coordinate system with the starting point of the contour line as the origin, the line where the line segment L is located as the X-axis, and the line perpendicular to the line segment L and passing through the origin as the Y-axis;

[0027] Step S54: Align the start and end points of the contour line with the start and end points of the line segment L, and place the contour line on the coordinate system;

[0028] Step S55: Mark the points on the contour line at a fixed distance c on the X axis, and obtain the value b of the point coordinates on the Y axis, b={ b 1, b2, …, bn};

[0029] Step S56: By formula Calculate the soil compaction value B, where k is the conversion coefficient between the fluctuation degree of the calibration data on the contour line and the soil surface compaction degree of the current irrigation area, which is a constant greater than 0.

[0030] According to the above technical solution, step S6 further includes the following steps:

[0031] Step S61: obtaining the soil compaction value B of the irrigation area;

[0032] Step S62: Obtain the type and planting time of the currently irrigated crop from the irrigation information database, retrieve the normal growth interval value of the crop from the irrigation information database based on the planting time of the crop type, and then make a judgment based on the crop height h output by the growth monitoring module;

[0033] Step S63: If the crop is not within the normal growth range, the system is triggered to issue a growth abnormality prompt and the subsequent irrigation management authority is converted to manual setting management; if the crop is within the normal growth range, the irrigation information database is continuously retrieved to obtain the rooting depth value r of the crop under the current growth condition;

[0034] Step S64: By formula: The maximum irrigation flow U is calculated, where α is the control parameter.

[0035] According to the above technical solution, step S7 further includes the following steps:

[0036] Step S71: Retrieving the ideal irrigation water demand q of the current growth state of the irrigated crops from the irrigation information database;

[0037] Step S72: The calculation formula for irrigation water volume is:

[0038] Q=β·U·(qj)

[0039] Where β is the conversion coefficient between irrigation water flow and irrigation water volume, which is a constant greater than 0, j is the rainfall value within the irrigation cycle predicted by the weather, and Q is the actual required irrigation water volume.

[0040] According to the above technical solution, step S8 further includes:

[0041] Step S81: When the soil compaction degree B is greater than the maximum compaction degree requirement under the current growth of the irrigated crops in the irrigation information database, the soil loosening prompt unit triggers and outputs a soil loosening prompt signal;

[0042] Step S82: When the irrigation time comes, the system controls the irrigation of crops with the irrigation water flow rate U and the irrigation water volume Q in real time according to the analysis output of the irrigation analysis module.

[0043] Compared with existing technologies, the present invention achieves the following beneficial effects: through the collaborative operation of information collection, irrigation analysis, and irrigation management modules, it achieves refined control of the irrigation process. It can also dynamically calculate the optimal irrigation water volume based on factors such as soil compaction and crop growth status, ensuring timely water supply and avoiding water waste. This solves the problem of traditional irrigation methods' difficulty in ensuring uniform water supply and resulting in reduced crop yields, improves agricultural water resource utilization efficiency, and achieves intelligent and efficient agricultural irrigation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] See also Figure 1 The present invention provides a technical solution: an intelligent agricultural irrigation system based on the Internet of Things, including an information collection module, an irrigation analysis module and an irrigation management module. The information collection module is used to collect and obtain relevant information of the irrigation area, the irrigation analysis module is used to analyze and calculate the appropriate irrigation water volume in the current irrigation area, and the irrigation management module is used to control the real-time irrigation situation of the irrigation area. The information collection module is electrically connected to the irrigation analysis module, and the irrigation analysis module is electrically connected to the irrigation management module.

[0048] The information collection module includes an irrigation information database, a soil collection module, a growth monitoring module and a weather forecast module. The irrigation information database is used to store the irrigation data required for irrigated crops and the basic information of the crops. The soil collection module is used to capture and collect soil image information of the irrigation area through a camera module. The growth monitoring module is used to detect the growth of crops through radar. The weather forecast module is used to obtain weather forecast information for the irrigation area.

[0049] The irrigation analysis module includes a soil compaction analysis module, an irrigation water flow analysis module and an irrigation quantity calculation module. The soil compaction analysis module is used to analyze and determine the soil compaction information of the irrigation area. The irrigation water flow analysis module is used to analyze and calculate the maximum irrigation water flow of the current irrigation area. The irrigation quantity calculation module is used to analyze and calculate the irrigation water quantity based on the soil compaction, irrigation water flow and weather conditions.

[0050] The irrigation management module includes a soil loosening prompt unit, an irrigation water flow control module and an irrigation water volume management module. The soil loosening prompt unit is used to judge and give a prompt that the irrigation soil needs to be loosened. The irrigation water flow control module is used to control the irrigation water flow in real time according to the analysis results. The irrigation water volume management module is used to manage and control the total amount of single irrigation water of the irrigation equipment.

[0051] An intelligent agricultural irrigation method based on the Internet of Things, the method comprising the following steps:

[0052] Step S1: Establish an irrigation information database to register and store crop information in the irrigation area, including crop types, planting time, and irrigation water requirements;

[0053] Step S2: Using the soil acquisition module to access the camera module, collect soil images of the irrigation area;

[0054] Step S3: In the agricultural irrigation area, the growth of crops is monitored by a growth monitoring module;

[0055] Step S4: Obtain local weather and precipitation conditions through the Internet;

[0056] Step S5: analyzing the soil compaction degree based on the soil image acquired by the soil collection module;

[0057] Step S6: Analyze and calculate the maximum flow rate of the current irrigation based on the soil compaction degree and the information of the crops currently being irrigated;

[0058] Step S7: Then, the irrigation water volume is calculated based on the irrigation water flow and weather conditions;

[0059] Step S8: Finally, the system updates the irrigation analysis results in real time and manages and controls the irrigation process.

[0060] In step S3, the method for monitoring the growth of crops is:

[0061] Step S31: Setting up a growth monitoring point directly above the monitored crop;

[0062] Step S32: The growth monitoring module triggers a signal to control the signal transmitting unit at the growth monitoring point to transmit a radar signal;

[0063] Step S33: Feedback a signal when the crop reaches its apex, and the growth monitoring module obtains the distance l from the monitoring point to the crop apex based on signal attenuation and a preset distance conversion coefficient;

[0064] Step S34: Calculate the height h of the crop according to the height H of the monitoring point and the distance l using the formula h=Hl;

[0065] Step S35: The growth monitoring module outputs a growth signal of the crop using the crop height h.

[0066] Step S5 further includes the following steps:

[0067] Step S51: Obtain a soil image taken at an oblique angle, identify the boundary contour between the soil surface and the background from the cross section, and enlarge it proportionally;

[0068] Step S52: fitting a contour line to the surface features of the boundary contour;

[0069] Step S53: Select the starting point and end point of the contour line, connect the line segment L, and establish a plane rectangular coordinate system with the starting point of the contour line as the origin, the line where the line segment L is located as the X-axis, and the line perpendicular to the line segment L and passing through the origin as the Y-axis;

[0070] Step S54: Align the start and end points of the contour line with the start and end points of the line segment L, and place the contour line on the coordinate system;

[0071] Step S55: Mark the points on the contour line at a fixed distance c on the X axis, and obtain the value b of the point coordinates on the Y axis, b={ b 1, b2, …, bn};

[0072] Step S56: By formula Calculate the soil compaction value B, where k is the conversion coefficient between the fluctuation degree of the calibration data on the contour line and the soil surface compaction degree of the current irrigation area, which is a constant greater than 0. The greater the distance between the calibration point and the X-axis, the greater the dispersion of the calibration point, indicating that the soil surface is rugged and relatively loose. Conversely, the smaller the distance between the calibration point and the X-axis, the smaller the dispersion of the calibration point, indicating that the soil surface is flat and the soil is more compacted, so the compaction value B is larger.

[0073] Step S6 further includes the following steps:

[0074] Step S61: obtaining the soil compaction value B of the irrigation area;

[0075] Step S62: Obtain the type and planting time of the currently irrigated crop from the irrigation information database, retrieve the normal growth interval value of the crop from the irrigation information database based on the planting time of the crop type, and then make a judgment based on the crop height h output by the growth monitoring module;

[0076] Step S63: If the crop is not within the normal growth range, the system is triggered to issue a growth abnormality prompt and the subsequent irrigation management authority is converted to manual setting management; if the crop is within the normal growth range, the irrigation information database is continuously retrieved to obtain the rooting depth value r of the crop under the current growth condition;

[0077] Step S64: By formula: The maximum irrigation flow rate U is calculated, where α is the control parameter. It can be seen from the formula that the maximum irrigation water flow rate U is inversely proportional to the soil compaction degree and the rooting depth of crops. The more compacted the soil is, the more difficult it is for the irrigation water to be absorbed by the soil in time, which can easily lead to more water remaining on the soil surface and being lost, resulting in insufficient actual irrigation water. Increasing the irrigation amount will also cause a waste of water resources. Similarly, the deeper the plant roots are, the longer the soil needs to maintain water intake before it can fully penetrate the soil and reach all the roots of the crops. In order to further avoid the loss of water caused by rapid irrigation water flow, the rooting depth parameter in the formula can further realize the fine control of irrigation water according to the rooting depth of crops, ensuring the irrigation effect while avoiding the possibility of water accumulation.

[0078] Step S7 further includes the following steps:

[0079] Step S71: Retrieving the ideal irrigation water demand q of the current growth state of the irrigated crops from the irrigation information database;

[0080] Step S72: The calculation formula for irrigation water volume is:

[0081] Q=β·U·(qj)

[0082] Where β is the conversion coefficient between irrigation water flow and irrigation water volume, which is a constant greater than 0, j is the rainfall value within the irrigation cycle predicted by the weather, and Q is the actual required irrigation water volume.

[0083] Step S8 further includes:

[0084] Step S81: When the soil compaction degree B is greater than the maximum compaction degree requirement under the current growth of the irrigated crops in the irrigation information database, the soil loosening prompt unit triggers and outputs a soil loosening prompt signal;

[0085] Step S82: When the irrigation opportunity arrives, the system uses the analysis output from the irrigation analysis module to control the irrigation of crops in real time, using irrigation water flow rate U and irrigation water volume Q. Through the collaborative operation of information collection, irrigation analysis, and irrigation management modules, refined control of the irrigation process is achieved. Furthermore, the optimal irrigation water volume is dynamically calculated based on factors such as soil compaction and crop growth status, ensuring timely water supply and avoiding water waste. This solves the problem of traditional irrigation methods struggling to ensure uniform water supply, which can lead to reduced crop yields. It improves the efficiency of agricultural water resource utilization and enables intelligent and efficient agricultural irrigation management.

[0086] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0087] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An intelligent agricultural irrigation method based on the Internet of Things, characterized by: The method comprises the following steps: Step S1: Establish an irrigation information database to register and store crop information in the irrigation area, including crop types, planting time, and irrigation water requirements; Step S2: Using the soil acquisition module to access the camera module, collect soil images of the irrigation area; Step S3: In the agricultural irrigation area, the growth of crops is monitored by a growth monitoring module; Step S4: Obtain local weather and precipitation conditions through the Internet; Step S5: analyzing the soil compaction degree based on the soil image acquired by the soil collection module; Step S6: Analyze and calculate the maximum flow rate of the current irrigation based on the soil compaction degree and the information of the crops currently being irrigated; Step S7: Then, the irrigation water volume is calculated based on the irrigation water flow and weather conditions; Step S8: Finally, the system updates the irrigation analysis results in real time and manages and controls the irrigation process; In step S3, the method for monitoring the growth of crops is as follows: Step S31: Setting up a growth monitoring point directly above the monitored crop; Step S32: The growth monitoring module triggers a signal to control the signal transmitting unit at the growth monitoring point to transmit a radar signal; Step S33: Feedback a signal when the crop reaches its apex, and the growth monitoring module obtains the distance l from the monitoring point to the crop apex based on signal attenuation and a preset distance conversion coefficient; Step S34: According to the height H and distance l of the monitoring point, the formula , calculate the height h of the crop; Step S35: The growth monitoring module outputs a growth signal of the crop using the crop height h; The step S5 further comprises the following steps: Step S51: Obtain a soil image taken at an oblique angle, identify the boundary contour between the soil surface and the background from the cross section, and enlarge it proportionally; Step S52: fitting a contour line to the surface features of the boundary contour; Step S53: Select the starting point and end point of the contour line, connect the line segment L, and establish a plane rectangular coordinate system with the starting point of the contour line as the origin, the line where the line segment L is located as the X-axis, and the line perpendicular to the line segment L and passing through the origin as the Y-axis; Step S54: Align the start and end points of the contour line with the start and end points of the line segment L, and place the contour line on the coordinate system; Step S55: Mark the points on the contour line at a fixed distance c on the X axis, and obtain the value b of the point coordinates on the Y axis respectively. ; Step S56: By formula Calculate the soil compaction value B, where k is the conversion coefficient between the fluctuation degree of the calibration data on the contour line and the soil surface compaction degree of the current irrigation area, which is a constant greater than 0; The step S6 further comprises the following steps: Step S61: obtaining the soil compaction value B of the irrigation area; Step S62: Obtain the type and planting time of the currently irrigated crop from the irrigation information database, retrieve the normal growth interval value of the crop from the irrigation information database based on the planting time of the crop type, and then make a judgment based on the crop height h output by the growth monitoring module; Step S63: If the crop is not within the normal growth range, the system is triggered to issue a growth abnormality prompt and the subsequent irrigation management authority is converted to manual setting management; if the crop is within the normal growth range, the irrigation information database is continuously retrieved to obtain the rooting depth value r of the crop under the current growth condition; Step S64: by formula: The maximum irrigation flow U is calculated, where is the control parameter.

2. The smart agricultural irrigation method based on the Internet of Things according to claim 1, characterized in that: The step S7 further comprises the following steps: Step S71: Retrieving the ideal irrigation water demand q of the current growth state of the irrigated crops from the irrigation information database; Step S72: The calculation formula for irrigation water volume is: Where, is the conversion coefficient between irrigation water flow and irrigation water volume, which is a constant greater than 0, j is the rainfall value within the weather forecast irrigation cycle, and Q is the actual required irrigation water volume.

3. The smart agricultural irrigation method based on the Internet of Things according to claim 2, characterized in that: The step S8 further comprises: Step S81: When the soil compaction degree B is greater than the maximum compaction degree requirement under the current growth of the irrigated crops in the irrigation information database, the soil loosening prompt unit triggers and outputs a soil loosening prompt signal; Step S82: When the irrigation time comes, the system controls the irrigation of crops with the irrigation water flow rate U and the irrigation water volume Q in real time according to the analysis output of the irrigation analysis module.

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