Crop growth situation monitoring system and method

Abstract

The invention belongs to the technical field of crop growth situation monitoring, and discloses a crop growth situation monitoring system and method.The method comprises the steps that a monitoring area is divided into a plurality of areas, a remote sensing image of each monitoring area is obtained based on an unmanned aerial vehicle, and crop growth situation related parameters of each monitoring area are obtained based on the remote sensing images; the crop growth situation coefficient of each monitoring area is calculated based on the parameters, and whether the crop growth situation of each area is normal or not is judged according to the crop growth situation coefficient of each monitoring area; the physical parameters of the soil environment of an area with an abnormal crop growth situation are obtained through a ground Internet of Things sensor, the physical parameters which do not meet the standard are rectified, and all-around and real-time monitoring of the crop growth environment is realized through a mode of combining unmanned aerial vehicle remote sensing and ground Internet of Things sensing; and the accuracy and timeliness of the monitoring data are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of crop growth situation monitoring, and in particular relates to a crop growth situation monitoring system and method. Background Art

[0002] With the advancement of agricultural modernization, the demand for real-time monitoring of crop growth trends is becoming increasingly urgent. In the existing technology, although there are monitoring methods based on remote sensing technology, they are mostly limited to satellite remote sensing, with shortcomings such as long data update cycle and limited resolution. UAV remote sensing technology has gradually become an important tool for crop growth monitoring due to its advantages such as strong mobility, high operating efficiency and low cost.

[0003] Traditional crop growth monitoring methods mainly rely on manual field surveys, which have problems such as small observation area, low efficiency, and difficulty in ensuring accuracy and consistency. In addition, they cannot monitor physiological changes during crop growth. Therefore, the present invention proposes a crop growth status monitoring system and method. Summary of the invention

[0004] The purpose of the present invention is to provide a crop growth status monitoring system and method to solve the problems faced by the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A crop growth situation monitoring system, the monitoring system comprising: an unmanned aerial vehicle dispatching module, a remote sensing data acquisition module, a ground Internet of Things sensor monitoring module, a data analysis module and a crop status assessment module;

[0007] The drone dispatching module is used to monitor the operating status of the drone group used for monitoring. If a drone with an abnormal status appears, a standby drone is immediately dispatched to replace the drone with the abnormal status to work;

[0008] The remote sensing data acquisition module is used to monitor the monitoring area through the drone, obtain the remote sensing image data of the monitoring area, and transmit the remote sensing image data of the monitoring area to the data analysis module through the Internet of Things technology;

[0009] The ground IoT sensor monitoring module is used to monitor the physical parameter data of the soil in real time based on sensors, and transmit the physical parameter data of the soil to the data analysis module through the IoT technology;

[0010] The data analysis module is used to analyze the remote sensing image data collected by the remote sensing data collection module and the physical parameter data of the soil collected by the ground Internet of Things sensor monitoring module;

[0011] The crop status assessment module is used to assess the growth status of crops in the monitoring area according to the analysis results of the data analysis module.

[0012] As a further description of the solution of the present invention, the working process of the drone scheduling module includes:

[0013] All drone groups used for monitoring are numbered 1, 2, ..., n;

[0014] Obtain the time-varying curve f of the jth working parameter of each drone in real time ij (t), where i is the corresponding drone number, i belongs to n;

[0015] The operating state coefficient ρ of the i-th UAV is calculated according to the following formula: i

[0016]

[0017] In the formula, m is the number of parameters monitored during the operation of the UAV, j belongs to m, t1 is the initial time of monitoring the operation of the UAV, t2 is the current time of monitoring the operation of the UAV, and f ij0 (t) is the standard curve of the jth working parameter of the i-th UAV preset by the system over time, x j is the weight coefficient corresponding to the jth parameter;

[0018] The operating state coefficient ρ of the i-th UAV i Compared with the preset threshold of the operating state coefficient of the i-th UAV, when the operating state coefficient ρ of the i-th UAV i When the operating status coefficient of the ith UAV is greater than the preset threshold value, the operating status of the ith UAV is determined to be abnormal, and a backup UAV is immediately dispatched to replace the ith UAV for monitoring.

[0019] As a further description of the solution of the present invention, the working process of the remote sensing data acquisition module includes:

[0020] Obtain images of the monitoring area collected by the drone, extract edge features from all images, and splice all images according to the edge features to form a complete surface image of the monitoring area;

[0021] Divide the complete image into regions and number each region, which are 1, 2, ..., x;

[0022] The drone collects images of the monitoring area at regular intervals, merges the images into a complete image and stores them in the system database.

[0023] As a further description of the solution of the present invention, the working process of the data analysis module includes:

[0024] Based on the image data, obtain the crop area S of the yth region y , obtain the crop leaf area change data S of the yth region over time μ (t), obtain the time-varying data of the normalized vegetation index of the yth area N y (t), obtain the chlorophyll content index of the yth area over time data C y (t);

[0025] The crop growth trend coefficient σ in the yth region is calculated by the following formula y :

[0026]

[0027] In the formula, α, β and γ are weight coefficients, and y belongs to x;

[0028] Calculate the crop growth situation coefficients of x regions in turn, and judge the crop growth situation of all regions based on the crop growth situation coefficients.

[0029] As a further description of the solution of the present invention, the working process of the crop status assessment module includes:

[0030] The crop growth trend coefficient σ in the yth region y and the preset crop growth coefficient threshold σ yth Compare;

[0031] If the crop growth trend coefficient σ in the yth region y Less than or equal to the preset crop growth coefficient threshold σ yth , it means that the growth trend of crops in the yth area is abnormal;

[0032] If the crop growth trend coefficient σ in the yth region y Greater than the preset crop growth coefficient threshold σ yth , it means that the growth situation of crops in the yth area is normal, and further analysis is carried out.

[0033] As a further description of the solution of the present invention, the growth situation of the crops in the yth area is normal, and the process of further analysis includes:

[0034] Obtain the crop growth status coefficients of x regions in turn, and count the number of crops with normal growth status as z;

[0035] Construct a calculation model for the difference in crop growth trends, and the expression is:

[0036]

[0037] In the formula, ρ y is the coefficient of crop growth trend difference in the yth region;

[0038] The coefficient of difference in crop growth trend in the yth region ρ y The difference coefficient threshold ρ from the preset crop growth trend yth Compare;

[0039] If the coefficient of crop growth trend difference in the yth region is ρ y Greater than or equal to the preset crop growth trend difference coefficient threshold ρ yth , it means that the growth trend of crops in the yth region is quite different from that in the entire monitoring area, and the growth trend of crops in the yth region is abnormal;

[0040] Otherwise, it means that the growth of crops in the yth area is normal.

[0041] As a further description of the solution of the present invention, the working process of the crop status assessment module also includes:

[0042] Obtain the numbers of all areas where crop growth is abnormal, and obtain the physical parameters of the soil in all areas where crop growth is abnormal through the ground IoT sensor monitoring module;

[0043] The physical parameters of the soil in the area with abnormal growth are compared with the preset standard values. If any physical parameters do not meet the requirements, the corresponding physical parameters are adjusted.

[0044] A method for monitoring the growth status of crops, wherein the monitoring method is applicable to the crop growth status monitoring system.

[0045] Beneficial effects of the present invention:

[0046] The present invention divides the monitoring area into several areas, obtains remote sensing images of each monitoring area based on a drone, obtains parameters related to crop growth status in each monitoring area based on the remote sensing images, and obtains a crop growth status coefficient of each monitoring area based on the parameter calculation, and judges whether the crop growth status of each area is normal according to the crop growth status coefficient of each monitoring area, and obtains physical parameters of the soil environment of the area with abnormal crop growth status through ground Internet of Things sensors, and rectifies physical parameters that do not meet the standards, and realizes all-round and real-time monitoring of the crop growth environment by combining drone remote sensing with ground Internet of Things sensing, thereby improving the accuracy and timeliness of monitoring data.

[0047] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 It is a schematic diagram of the structure of the crop growth situation monitoring system of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 As shown, a crop growth situation monitoring system is disclosed, the monitoring system includes: a drone scheduling module, a remote sensing data acquisition module, a ground Internet of Things sensor monitoring module, a data analysis module and a crop status assessment module;

[0052] The drone dispatching module is used to monitor the operating status of the drone group used for monitoring. If a drone with an abnormal status appears, a standby drone is immediately dispatched to replace the drone with the abnormal status to work;

[0053] The remote sensing data acquisition module is used to monitor the monitoring area through the drone, obtain the remote sensing image data of the monitoring area, and transmit the remote sensing image data of the monitoring area to the data analysis module through the Internet of Things technology;

[0054] The ground IoT sensor monitoring module is used to monitor the physical parameter data of the soil in real time based on sensors, and transmit the physical parameter data of the soil to the data analysis module through the IoT technology;

[0055] The data analysis module is used to analyze the remote sensing image data collected by the remote sensing data collection module and the physical parameter data of the soil collected by the ground Internet of Things sensor monitoring module;

[0056] The crop status assessment module is used to assess the growth status of crops in the monitoring area according to the analysis results of the data analysis module.

[0057] Through the above technical scheme, the present invention divides the monitoring area into several areas, obtains remote sensing images of each monitoring area based on drones, obtains crop growth situation related parameters of each monitoring area based on the remote sensing images, and obtains the crop growth situation coefficient of each monitoring area based on the parameter calculation, and judges whether the crop growth situation of each area is normal according to the crop growth situation coefficient of each monitoring area, and obtains the physical parameters of the soil environment of the area with abnormal crop growth situation through ground Internet of Things sensors, and rectifies the physical parameters that do not meet the standards, and realizes all-round and real-time monitoring of the crop growth environment by combining drone remote sensing with ground Internet of Things sensing, thereby improving the accuracy and timeliness of monitoring data.

[0058] The working process of the drone scheduling module includes:

[0059] All drone groups used for monitoring are numbered 1, 2, ..., n;

[0060] Obtain the time-varying curve f of the jth working parameter of each drone in real time ij (t), where i is the corresponding drone number, i belongs to n;

[0061] The operating state coefficient ρ of the i-th UAV is calculated according to the following formula: i

[0062]

[0063] In the formula, m is the number of parameters monitored during the operation of the UAV, j belongs to m, t1 is the initial time of monitoring the operation of the UAV, t2 is the current time of monitoring the operation of the UAV, and f ij0 (t) is the standard curve of the jth working parameter of the i-th UAV preset by the system over time, x j is the weight coefficient corresponding to the jth parameter;

[0064] The operating state coefficient ρ of the i-th UAV i Compared with the preset threshold of the operating state coefficient of the i-th UAV, when the operating state coefficient ρ of the i-th UAV i When the operating status coefficient of the ith UAV is greater than the preset threshold value, the operating status of the ith UAV is determined to be abnormal, and a backup UAV is immediately dispatched to replace the ith UAV for monitoring.

[0065] Through the above technical solution, this embodiment monitors the actual cumulative changes of various parameters within a time period during the operation of the drone in real time, and then compares them with the standard cumulative changes within a preset time period. The operating status coefficient of each drone is obtained by combining the relevant weights of different parameters. The drone status is measured by the cumulative value to avoid external factors causing instantaneous value changes, thereby preventing misjudgment and affecting the monitoring progress. The operating status coefficient of each drone is compared with the preset threshold value of the drone's operating status coefficient. If the operating status coefficient of the drone is greater than the preset threshold value of the drone's operating status coefficient, a standby drone is immediately dispatched to replace the i-th drone for monitoring.

[0066] The working process of the remote sensing data acquisition module includes:

[0067] Obtain images of the monitoring area collected by the drone, extract edge features from all images, and splice all images according to the edge features to form a complete surface image of the monitoring area;

[0068] Divide the complete image into regions and number each region, which are 1, 2, ..., x;

[0069] The drone collects images of the monitoring area at regular intervals, merges the images into a complete image and stores them in the system database.

[0070] The working process of the data analysis module includes:

[0071] Based on the image data, obtain the crop area S of the yth region y , obtain the crop leaf area change data S of the yth region over time μ (t), obtain the time-varying data of the normalized vegetation index of the yth area N y (t), obtain the chlorophyll content index of the yth area over time data C y (t);

[0072] The crop growth trend coefficient σ in the yth region is calculated by the following formula y :

[0073]

[0074] In the formula, α, β and γ are weight coefficients, and y belongs to x;

[0075] Calculate the crop growth situation coefficients of x regions in turn, and judge the crop growth situation of all regions based on the crop growth situation coefficients.

[0076] The working process of the crop status assessment module includes:

[0077] The crop growth trend coefficient σ in the yth region y and the preset crop growth coefficient threshold σ yth Compare;

[0078] If the crop growth trend coefficient σ in the yth region y Less than or equal to the preset crop growth coefficient threshold σ yth , it means that the growth trend of crops in the yth area is abnormal;

[0079] If the crop growth trend coefficient σ in the yth region y Greater than the preset crop growth coefficient threshold σ yth , it means that the growth situation of crops in the yth area is normal, and further analysis is carried out.

[0080] Through the above technical solution, this embodiment obtains the crop area S of the yth region based on the image data. y , obtain the crop leaf area change data S of the yth region over time μ (t), obtain the time-varying data of the normalized vegetation index of the yth area N y (t), obtain the chlorophyll content index of the yth area over time data C y (t), and then calculate the crop growth trend coefficient σ in the yth region by the formula y , where the three polynomials represent the cumulative changes of crop leaf area, normalized vegetation index and chlorophyll content index over time. Then, the crop growth trend coefficient σ in the yth region is y and the preset crop growth coefficient threshold σ yth Compare; if the crop growth trend coefficient σ in the yth region y Less than or equal to the preset crop growth coefficient threshold σ yth , it means that the growth trend of crops in the yth region is abnormal; if the growth trend coefficient of crops in the yth region σ y Greater than the preset crop growth coefficient threshold σ yth , it means that the growth situation of crops in the yth area is normal, and further analysis is carried out.

[0081] The growth situation of crops in the yth area is normal, and the process of further analysis includes:

[0082] Obtain the crop growth status coefficients of x regions in turn, and count the number of crops with normal growth status as z;

[0083] Construct a calculation model for the difference in crop growth trends, and the expression is:

[0084]

[0085] In the formula, ρ y is the coefficient of crop growth trend difference in the yth region;

[0086] The coefficient of difference in crop growth trend in the yth region ρ y The difference coefficient threshold ρ from the preset crop growth trend yth Compare;

[0087] If the coefficient of crop growth trend difference in the yth region is ρ y Greater than or equal to the preset crop growth trend difference coefficient threshold ρ yth , it means that the growth trend of crops in the yth region is quite different from that in the entire monitoring area, and the growth trend of crops in the yth region is abnormal;

[0088] Otherwise, it means that the growth of crops in the yth area is normal.

[0089] Through the above technical scheme, in order to avoid errors in the monitoring results, the present invention further analyzes the areas with normal crop growth conditions, and uses formulas to perform difference analysis on each area with normal crop growth conditions. If an area with large differences appears, it means that the growth condition of the area is abnormal.

[0090] The working process of the crop status assessment module also includes:

[0091] Obtain the numbers of all areas where crop growth is abnormal, and obtain the physical parameters of the soil in all areas where crop growth is abnormal through the ground IoT sensor monitoring module;

[0092] The physical parameters of the soil in the area with abnormal growth are compared with the preset standard values. If any physical parameters do not meet the requirements, the corresponding physical parameters are adjusted.

[0093] Through the above technical scheme, this embodiment combines the ground Internet of Things sensor monitoring module to monitor the physical parameters of the soil in the abnormal growth area, monitors the soil temperature, humidity, pH value, nutrient content in real time and analyzes it, and compares the acquired physical parameters of the soil in the abnormal growth area with the preset standard values. If there are physical parameters that do not meet the requirements, the corresponding physical parameters are adjusted. If there are no physical parameters that do not meet the requirements, analysis is performed from other aspects, such as: air temperature, humidity, light intensity and other environmental parameters or human factors, to improve the abnormal growth area.

[0094] A method for monitoring the growth status of crops, wherein the monitoring method is applicable to the crop growth status monitoring system.

[0095] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A crop growth status monitoring system, It is characterized in that The monitoring system includes: a drone dispatching module, a remote sensing data acquisition module, a ground IoT sensor monitoring module, a data analysis module and a crop status assessment module; The drone dispatching module is used to monitor the operating status of the drone group used for monitoring. If a drone with an abnormal status appears, a standby drone is immediately dispatched to replace the drone with the abnormal status to work; The remote sensing data acquisition module is used to monitor the monitoring area through the drone, obtain the remote sensing image data of the monitoring area, and transmit the remote sensing image data of the monitoring area to the data analysis module through the Internet of Things technology; The ground IoT sensor monitoring module is used to monitor the physical parameter data of the soil in real time based on sensors, and transmit the physical parameter data of the soil to the data analysis module through the IoT technology; The data analysis module is used to analyze the remote sensing image data collected by the remote sensing data collection module and the physical parameter data of the soil collected by the ground Internet of Things sensor monitoring module; The crop status assessment module is used to assess the growth status of crops in the monitoring area according to the analysis results of the data analysis module.

2. A crop growth situation monitoring system according to claim 2, It is characterized in that The working process of the drone scheduling module includes: All drone groups used for monitoring are numbered 1, 2, ..., n; Obtain the time-varying curve f of the jth working parameter of each drone in real time ij (t), where i is the corresponding drone number, i belongs to n; The operating state coefficient ρ of the i-th UAV is calculated according to the following formula: i In the formula, m is the number of parameters monitored during the operation of the UAV, j belongs to m, t 1 is the initial time of monitoring the UAV operation, t 2 To monitor the current moment of the UAV operation, f ij0 (t) is the standard curve of the jth working parameter of the i-th UAV preset by the system over time, x j is the weight coefficient corresponding to the jth parameter; The operating state coefficient ρ of the i-th UAV i Compared with the preset threshold of the operating state coefficient of the i-th UAV, when the operating state coefficient ρ of the i-th UAV i When the operating status coefficient of the ith UAV is greater than the preset threshold value, the operating status of the ith UAV is determined to be abnormal, and a backup UAV is immediately dispatched to replace the ith UAV for monitoring.

3. A crop growth status monitoring system according to claim 1, It is characterized in that The working process of the remote sensing data acquisition module includes: Obtain images of the monitoring area collected by the drone, extract edge features from all images, and splice all images according to the edge features to form a complete surface image of the monitoring area; Divide the complete image into regions and number each region, which are 1, 2, ..., x; The drone collects images of the monitoring area at regular intervals, merges the images into a complete image and stores them in the system database.

4. A crop growth status monitoring system according to claim 1, It is characterized in that The working process of the data analysis module includes: Based on the image data, obtain the crop area S of the yth region y , obtain the crop leaf area change data S of the yth region over time μ (t), obtain the time-varying data of the normalized vegetation index of the yth area N y (t), obtain the chlorophyll content index of the yth area over time data C y (t); The crop growth trend coefficient σ in the yth region is calculated by the following formula y : In the formula, α, β and γ are weight coefficients, and y belongs to x; Calculate the crop growth situation coefficients of x regions in turn, and judge the crop growth situation of all regions based on the crop growth situation coefficients.

5. A crop growth situation monitoring system according to claim 4, It is characterized in that The working process of the crop status assessment module includes: The crop growth trend coefficient σ in the yth region y and the preset crop growth coefficient threshold σ yth Compare; If the crop growth trend coefficient σ in the yth region y Less than or equal to the preset crop growth coefficient threshold σ yth , it means that the growth trend of crops in the yth area is abnormal; If the crop growth trend coefficient σ in the yth region y Greater than the preset crop growth coefficient threshold σ yth , it means that the growth situation of crops in the yth area is normal, and further analysis is carried out.

6. A crop growth status monitoring system and method according to claim 5, It is characterized in that The growth situation of crops in the yth area is normal, and the process of further analysis includes: Obtain the crop growth status coefficients of x regions in turn, and count the number of crops with normal growth status as z; Construct a calculation model for the difference in crop growth trends, and the expression is: In the formula, ρ y is the coefficient of crop growth trend difference in the yth region; The coefficient of difference in crop growth trend in the yth region ρ y The difference coefficient threshold ρ from the preset crop growth trend yth Compare; If the coefficient of crop growth trend difference in the yth region is ρ y Greater than or equal to the preset crop growth trend difference coefficient threshold ρ yth , it means that the growth trend of crops in the yth region is quite different from that in the entire monitoring area, and the growth trend of crops in the yth region is abnormal; Otherwise, it means that the growth of crops in the yth area is normal.

7. A crop growth status monitoring system according to claim 6, It is characterized in that The working process of the crop status assessment module also includes: Obtain the numbers of all areas where crop growth is abnormal, and obtain the physical parameters of the soil in all areas where crop growth is abnormal through the ground IoT sensor monitoring module; The physical parameters of the soil in the area with abnormal growth are compared with the preset standard values. If any physical parameters do not meet the requirements, the corresponding physical parameters are adjusted.

8. A method for monitoring the growth status of crops, It is characterized in that The monitoring method is applicable to the crop growth status monitoring system described in any one of claims 1-7.

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