Method and system for identifying aerial migrating insects based on insect radar and high altitude light

Abstract

The application provides an aerial migration insect identification method and system based on an insect radar and a high-altitude lamp, the method comprising the following steps: 1, receiving the wing beat frequency, body length, body width and body weight of each aerial migration insect monitored by the insect radar in real time; 2, inputting the four biological parameters into an optimal insect species classification model to obtain the species of each aerial migration insect, wherein the optimal insect species classification model is determined according to the insect species captured by the high-altitude lamp in the latest T3 time. The application can realize real-time accurate identification of the species of the target insect captured by the insect radar, can monitor real insect information in real time according to the received insect data captured by the high-altitude lamp, and can determine the optimal insect species classification model according to the real insect information, so that the optimal insect species classification model is matched with the real insect information, and the application has good expansibility and broad spectrum adaptability.

Description

Technical Field

[0001] This invention relates to the field of insect migration technology, specifically to a method and system for identifying migrating insects in the air based on insect radar and high-altitude lights. Background Technology

[0002] Migratory insect pests are characterized by their sudden onset, rapid spread, and difficulty in control. High-resolution insect radar, with its advantages of wide coverage, all-weather operation, and high spatiotemporal resolution, has become an important means of real-time monitoring of aerial migratory insects. However, the lack of direct species identification capability for radar targets limits its effectiveness in accurate identification and species classification monitoring. Achieving accurate classification of radar-targeted insects is one of the core technological bottlenecks that needs to be overcome in building an efficient monitoring and early warning system for migratory insect pests. In recent years, insect radar has made significant progress in identifying aerial migratory insect species, and various identification methods have been developed.

[0003] Chinese invention patent application No. 202410981019.2 discloses a method and system for identifying target insects based on insect radar. The method includes the following steps: obtaining radar echo signals of insects based on insect radar monitoring technology; trapping insects using insect trapping technology; comparing the number of radar echo signals and the number of trapped insects to obtain the target insects at the radar echo points and the number of target insect species; measuring the flight capability and measurement quality of the target insects; analyzing existing meteorological data and the radar echo signals of the target insects to obtain the target insects' own flight speed and computational quality; performing cluster analysis on the target insects' flight capability and measurement quality with their own flight speed and computational quality; and identifying the target insect species based on the number of target insect species. This invention integrates insect radar monitoring technology and insect trapping technology to identify target insects, solving the problem that it is difficult to identify target insects by relying solely on radar echo point analysis technology. However, it suffers from complex operation and the inability to achieve real-time identification of migratory insect species.

[0004] For example, Chinese invention patent application number 202510104597.2 discloses an insect species identification method based on multidimensional radar electromagnetic scattering feature sequences. This method constructs a multidimensional feature sequence based on a fully polarimetric radar scattering matrix and employs an insect recognition neural network for feature learning. The insect recognition neural network extracts spatial features at different scales through multi-scale convolution and effectively captures the temporal dependencies in the feature sequence through state updates based on temporal input. Finally, it identifies insect species based on multi-scale spatial features and their temporal dependencies. This invention effectively improves the accuracy and robustness of insect species identification and has broad application prospects, particularly suitable for insect migration monitoring, agricultural pest control, and ecological research. However, its ability to identify a limited number of migratory pest species limits its scalability.

[0005] For example, Chinese invention patent application number 202510111754.2 discloses an intelligent identification method for migratory insects based on radar database construction assisted by insect-attracting lamps. First, it integrates insect monitoring data from insect-attracting lamps and radar during the same nighttime period, establishing an insect dataset through matching the two data, effectively overcoming the difficulties in data collection inherent in traditional insect dataset construction methods. Second, it proposes a CL-GC-Swin Transformer network model, introducing a contrastive learning module and a global context block into the Swing Transformer, and training it using the insect's fully polarized time-frequency feature map, significantly enhancing the network's ability to represent the insect's time-frequency feature map, thus effectively improving the accuracy of insect species identification. This invention directly utilizes the insect's flight characteristics for identification, eliminating the need for complex scattering feature calculations and precise radar calibration, greatly reducing the requirements for radar system accuracy and calibration, and improving the adaptability and robustness of identification. However, it is only suitable for large-scale insect swarm identification and cannot achieve individual species identification. Summary of the Invention

[0006] To address at least one of the above technical problems, this invention provides a method and system for identifying migratory insects in the air based on insect radar and high-altitude lights, which can achieve real-time accurate identification of individual species of target insects captured by insect radar.

[0007] The first aspect of the present invention provides a method for identifying migratory insects in the air based on insect radar and high-altitude lights, comprising:

[0008] Step 1: Receive four biological parameters of each migratory insect in the air, namely wingbeat frequency, body length, body width, and weight, as monitored in real time by the insect radar;

[0009] Step 2: Input the received four biological parameters into the optimal insect species classification model to obtain the species of each aerial migratory insect;

[0010] In step 2, the optimal insect species classification model is determined through the following repeated steps:

[0011] Step 201: Receive data on insects captured by the high-altitude light trap according to the set first time interval T1;

[0012] Step 202: Based on the received data of insects trapped by the high-altitude lights, count the types of insects trapped by the high-altitude lights in the most recent time interval T3 according to the set second time interval T2, and compare them with the insect types counted in the previous time. If the insect types have changed, proceed to step 203; if the insect types have not changed, proceed to step 204.

[0013] Step 203: Update the optimal insect species classification model;

[0014] Step 204: Do not update the optimal insect species classification model.

[0015] Preferably, in step 1, the industrial control computer of the insect radar performs real-time inversion calculations of four biological parameters—wingbeat frequency, body length, body width, and weight—for each migratory insect monitored in real time by the insect radar.

[0016] In any of the above schemes, it is preferred that, in step 201, the data on the insects trapped by the high-altitude light includes data on the species of each insect trapped.

[0017] In any of the above schemes, in step 201, the data of the insects trapped by the high-altitude light includes image data of each trapped insect.

[0018] In any of the above schemes, if the data of the insects trapped by the high-altitude light includes image data of each trapped insect, then in step 202, the species of each insect is identified based on the image data.

[0019] Preferably, in any of the above schemes, the value of the second time interval T2 is not less than the value of the first time interval T1.

[0020] Preferably, in any of the above schemes, the value of T3 is not less than 48h.

[0021] In any of the above schemes, step 203, updating the optimal insect species classification model, includes:

[0022] Step 2031: Based on the latest insect species counted in Step 202, read the corresponding insect biological parameter data from the insect biological parameter database to form a sample set;

[0023] Step 2032: Using the sample set, construct three insect species classification models using XGBoost, Random Forest, and BP Neural Network respectively, and select the insect species classification model with the highest F1 score as the optimal insect species classification model.

[0024] In any of the above schemes, in step 2031, each insect biological parameter data in the insect biological parameter database includes the insect's wingbeat frequency, body length, body width, weight, and species.

[0025] In any of the above schemes, in step 2031, after reading the corresponding insect biological parameter data from the insect biological parameter database based on the latest statistical insect species, outlier detection and removal and standardization are performed on the four data of wingbeat frequency, body length, body width and weight in the read insect biological parameter data to form a sample set.

[0026] In any of the above schemes, in step 2032, the sample set is divided into a training set and a test set according to a set ratio.

[0027] In any of the above schemes, if the optimal insect species classification model is updated in step 2, the four biological parameters of each aerial migratory insect monitored in real time by the insect radar received in the past T2 time period, namely wingbeat frequency, body length, body width and weight, are input into the latest optimal insect species classification model to reclassify and obtain the species of each aerial migratory insect.

[0028] A second aspect of the present invention provides an aerial migratory insect identification system based on insect radar and high-altitude light, for performing the aerial migratory insect identification method based on insect radar and high-altitude light.

[0029] Preferably, the aerial migratory insect identification system based on insect radar and high-altitude light is communicatively connected to the high-altitude light and insect radar monitoring station, respectively.

[0030] Preferably, in any of the above embodiments, the high-altitude light is positioned within the L-distance range of the insect radar monitoring station.

[0031] The method and system for identifying migratory insects in the air based on insect radar and high-altitude lights of the present invention have the following beneficial effects:

[0032] 1. Capable of real-time and accurate identification of individual species of target insects captured by insect radar;

[0033] 2. Based on the received data of insects captured by high-altitude lights, monitor real-time insect information and determine the optimal insect species classification model based on the real insect information to ensure that the optimal insect species classification model matches the real insect situation and has good scalability and broad-spectrum adaptability.

[0034] 3. The identified target insect species can be combined with the insect biological parameters and flight parameters calculated by the radar industrial control computer to provide data support for the construction of a monitoring and early warning system for migratory agricultural and forestry pests, which has good prospects for practical application.

[0035] 4. It can provide accurate data support for simulating the migration trajectories of different insect species, monitoring population dynamics, and formulating control strategies, which helps to improve the scientificity and practicality of insect monitoring and early warning. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a preferred embodiment of the aerial migratory insect identification method based on insect radar and high-altitude lights according to the present invention.

[0037] Figure 2 For the aerial migratory insect identification method based on insect radar and high-altitude light according to the present invention, as follows: Figure 1The flowchart shown in the embodiment illustrates the process of determining the optimal insect species classification model.

[0038] Figure 3 For the aerial migratory insect identification method based on insect radar and high-altitude light according to the present invention, as follows: Figure 1 The illustrated embodiment is a flowchart of the process for updating the optimal insect species classification model. Detailed Implementation

[0039] To better understand the present invention, the present invention will be described in detail below with reference to specific embodiments.

[0040] Example 1

[0041] like Figure 1 As shown, a method for identifying migratory insects in the air based on insect radar and high-altitude lights includes:

[0042] Step 1: Receive four biological parameters of each migratory insect in the air, namely wingbeat frequency, body length, body width, and weight, as monitored in real time by the insect radar;

[0043] Step 2: Input the received four biological parameters into the optimal insect species classification model to obtain the species of each aerial migratory insect;

[0044] Among them, such as Figure 2 As shown, in step 2, the optimal insect species classification model is determined through the following repeated steps:

[0045] Step 201: Receive data on insects captured by the high-altitude light trap according to the set first time interval T1;

[0046] Step 202: Based on the received data of insects trapped by the high-altitude lights, count the types of insects trapped by the high-altitude lights in the most recent time interval T3 according to the set second time interval T2, and compare them with the insect types counted in the previous time. If the insect types have changed, proceed to step 203; if the insect types have not changed, proceed to step 204.

[0047] Step 203: Update the optimal insect species classification model;

[0048] Step 204: Do not update the optimal insect species classification model.

[0049] Preferably, in step 1, the received data is the four biological parameters of each migratory insect in the air, namely wing beat frequency, body length, body width, and weight, which are calculated in real time by the industrial control computer of the insect radar and monitored in real time by the insect radar.

[0050] More preferably, in step 201, the data of insects trapped by the high-altitude light includes data on the species of each trapped insect; or image data of each insect. When the data of insects trapped by the high-altitude light includes image data of each trapped insect, in step 202, the species of each insect is identified based on the image data. It should be noted that determining the species data of each trapped insect after being trapped by the high-altitude light can be achieved using existing technology, and identifying the species of each insect based on the image data of each trapped insect can also be achieved using existing technology, which will not be described in detail in this application.

[0051] In this embodiment, preferably, the second time interval T2 is not less than the value of the first time interval T1. This ensures that the latest insect situation information is used each time the types of insects captured are counted and it is determined whether the insect types have changed. The value of T3 can be set as needed, but it is generally not less than 48 hours.

[0052] like Figure 3 As shown, in step 203, the optimal insect species classification model is updated, including:

[0053] Step 2031: Based on the latest insect species counted in Step 202, read the corresponding insect biological parameter data from the insect biological parameter database to form a sample set;

[0054] Step 2032: Using the sample set, construct three insect species classification models using XGBoost, Random Forest, and BP Neural Network respectively, and select the insect species classification model with the highest F1 score as the optimal insect species classification model.

[0055] In step 2031, each insect biological parameter data in the insect biological parameter database includes the insect's wingbeat frequency, body length, body width, weight, and species. Based on the latest statistical count of insect species, the corresponding insect biological parameter data is read from the insect biological parameter database. After outlier detection and removal and standardization processing are performed on the four data items of wingbeat frequency, body length, body width, and weight in the read insect biological parameter data, a sample set is formed.

[0056] In step 2032, the sample set is divided into a training set and a test set according to a set ratio. It should be noted that the application of XGBoost, Random Forest, and BP Neural Network to construct classification models for the three insect species can be achieved using existing technologies, and will not be described in detail in this application.

[0057] In step 2, if the optimal insect species classification model is updated, the four biological parameters of each migratory insect in the air, namely wingbeat frequency, body length, body width and weight, which were received by the insect radar in real time during the past T2 time period, are input into the latest optimal insect species classification model to reclassify and obtain the species of each migratory insect in the air.

[0058] It should be understood that the process of counting insect species and updating the optimal insect species classification model according to the T2 time interval will not affect the process of receiving data on insects trapped by high-altitude lights according to the T1 time interval; the process of receiving the four biological parameters of wingbeat frequency, body length, body width and weight of each migratory insect monitored in real time by insect radar will also not be affected by other processes.

[0059] It should be noted that the insect biological parameter database covers all known migratory insect species, including some non-migratory insect species. While high-altitude lights trap migratory insects, non-migratory insects may inevitably fall into the lights as well. Considering that updating the optimal insect species classification model in step 203 relies on data from the insect biological parameter database, the counting of insect species trapped by high-altitude lights in step 202 is limited to those recorded in the database. This will not affect the optimal insect species classification model.

[0060] It should be further noted that the initial optimal insect species classification model can be determined by the insect species counted in the initial T3 time period, or it can be determined directly based on all data in the insect biological parameter database.

[0061] Example 2

[0062] An aerial migratory insect identification system based on insect radar and high-altitude light is disclosed, used to execute the aforementioned aerial migratory insect identification method based on insect radar and high-altitude light. The system is communicatively connected to both the high-altitude light and an insect radar monitoring station. The high-altitude light is positioned within a distance L of the insect radar monitoring station to ensure consistency between the insect species captured by the high-altitude light and those detected by the insect radar, thereby ensuring the accuracy of species identification for each aerial migratory insect detected by the insect radar.

[0063] Example 3

[0064] Prior to this embodiment, the insect biological parameter database already contained nearly 15,000 data entries for 54 migratory insect species. Each data entry included wingbeat frequency, body length, body width, weight, and species information. The data in this insect biological parameter database was collected daily from multiple insect trapping devices (such as high-altitude lights and traps) set up in region A, starting in 2023. Healthy and active insect individuals were selected, their species were identified, and their wingbeat frequency, body length, body width, and weight were measured in a laboratory. These parameters, along with the species information, were then entered into the insect biological parameter database. It should be noted that the data source in the insect biological parameter database described in this embodiment is not restrictive. The database can also include data from insects trapped by intelligent high-altitude lights set up in other regions, or data from insects identified through other methods. Furthermore, the database supports continuous data entry to enrich the data volume.

[0065] In this embodiment, a high-altitude light is installed within a 300-meter radius (i.e., L is set to 300 meters) of the high-resolution insect radar monitoring station in area A. This high-altitude light takes a picture of the insects it traps at a set first time interval T1. Based on the captured pictures, it performs species identification using a trained neural network and sends the identification results (the species and quantity of insects in the picture) to an aerial migratory insect identification system based on insect radar and the high-altitude light via a communication connection. It should be understood that the aerial migratory insect identification system based on insect radar and the high-altitude light receives data on the insects trapped by the high-altitude light at the set first time interval T1. This insect data includes species data for each trapped insect. Preferably, in this embodiment, T1 is set to 2 minutes.

[0066] After receiving insect species data sent by the high-altitude light, the aerial migratory insect identification system based on insect radar and high-altitude light counts the types of insects trapped by the high-altitude light within the most recent time interval T3 according to the data. In this embodiment, T2 is set to 1 hour and T3 is set to 72 hours. For example, if at 21:00 on July 18, 2024, the system counts 29 insect species recorded in the insect biological parameter database that were trapped by the high-altitude light within the most recent 72 hours, and compares this with the data counted at 20:00 on July 18, 2024, and finds a change in the types of insects (in this embodiment, an increase in the number of insect species), then the optimal insect species classification model is updated.

[0067] The specific method for updating the optimal insect species classification model is as follows.

[0068] First, based on the 29 insect species counted at 21:00 on July 18, 2024, all data for these 29 insect species were read from the insect biological parameter database. In this embodiment, a total of 8509 insect biological parameter data entries were read. It should be understood that each insect biological parameter data entry includes the insect's wingbeat frequency, body length, body width, weight, and species. After outlier detection, removal, and standardization of the four data items (wingbeat frequency, body length, body width, and weight) from the 8509 insect biological parameter data entries, a sample set of 8189 insect biological parameter data entries was obtained. It should be noted that in this embodiment, outlier detection and removal are performed using the quartile method. Specifically, if a data point is higher than its upper boundary value (upper = Q3 + 1.5 × (Q3 - Q1)) or lower than its lower boundary value (lower = Q1 - 1.5 × (Q3 - Q1)), that data point is removed. Here, Q1 and Q3 are the 0.25 and 0.75 quartiles of the data point, respectively. For standardization, Z-score standardization is used. Standardization eliminates the impact of differences in the dimensions of the data points and balances their contributions. It should be further noted that other methods disclosed in the prior art can also be used for outlier detection and removal and standardization; this application does not limit the methods used.

[0069] Then, using the sample set, it is divided into training and test sets according to a set ratio. Three classification algorithms—XGBoost, Random Forest, and Backpropagation (BP) neural network—are applied to construct three insect species classification models, respectively. The insect species classification model with the highest F1 score is selected as the optimal insect species classification model. It should be noted that in this embodiment, the sample set is divided into training and test sets in a 7:3 ratio. Four biological parameters of the insect—wingbeat frequency, body length, body width, and weight—are used as model inputs, and the insect species are used as model outputs. To improve model performance, hyperparameter grid search optimization is performed on each model to obtain its optimal hyperparameter combination and its model accuracy performance on the test set. In this embodiment, for the 29 insect species counted at 21:00 on July 18, 2024, the F1 scores of the three insect species classification models constructed using XGBoost, Random Forest, and BP Neural Network were 0.912, 0.874, and 0.843, respectively. Therefore, the insect species classification model constructed using XGBoost was selected as the optimal insect species classification model. It was used to identify the species of each migrating insect detected by the insect radar at 21:00 on July 18, 2024, and thereafter, until the insect species captured by high-altitude light traps in the last 72 hours were detected to have changed compared to the previous count, and the optimal insect species classification model was updated.

[0070] In this embodiment, after 21:00 on July 18, 2024, and again at 23:00 on July 20, 2024, it was detected that the insect species captured by high-altitude light traps in the most recent 72 hours had changed compared to the insect species counted at 22:00 on July 20, 2024. Therefore, the optimal insect species classification model was updated based on the insect species captured by high-altitude light traps in the most recent 72 hours detected at 23:00 on July 20, 2024. The updated optimal insect species classification model was then used to identify the species of each migratory insect detected by insect radar at 23:00 on July 20, 2024 and thereafter, until the optimal insect species classification model was updated again.

[0071] It should be noted that the number of insect species captured by high-altitude light traps as of 23:00 on July 20, 2024, has changed compared to the previous count (22:00 on July 20, 2024). This indicates that the types of migratory insects changed between 22:00 and 23:00 on July 20, 2024. Furthermore, the optimal insect species classification model has not yet been updated based on the insect species count at 23:00 on July 20, 2024, during this period. During this time period, the optimal insect species classification model determined based on insect species statistics at 21:00 on July 18, 2024, was still in use. However, this model was not always accurate. Therefore, an updated optimal insect species classification model based on insect species statistics at 23:00 on July 20, 2024, was used to re-identify each migratory insect detected by insect radar during the past T2 time period (within one hour, i.e., between 22:00 and 23:00 on July 20, 2024). It should be understood that whenever the optimal insect species classification model is updated, each migratory insect detected by insect radar during the past T2 time period needs to be re-identified using the updated optimal insect species classification model.

[0072] The insect radar monitoring station is equipped with a radar industrial control computer. This computer can calculate in real-time four biological parameters of each migratory insect monitored by the insect radar: wingbeat frequency, body length, body width, and weight. These calculated parameters are then sent to the aerial migratory insect identification system based on insect radar and high-altitude lights. The system inputs these parameters into an optimal insect species classification model to determine the species of each migratory insect. It should be noted that the radar industrial control computer can also calculate in real-time flight parameters such as flight altitude, flight direction, vertical speed, and horizontal speed of each migratory insect monitored by the insect radar. By integrating the target insect species identified by the system with the biological and flight parameters calculated by the radar industrial control computer, it can provide data support for constructing a monitoring and early warning system for migratory agricultural and forestry pests. Furthermore, it can provide accurate data support for simulating the migration trajectories of different insect species, monitoring population dynamics, and formulating control strategies, thereby improving the scientific rigor and practicality of insect pest monitoring and early warning.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the foregoing embodiments have described the present invention in detail, those skilled in the art should understand that modifications can be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein, and these substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.

Claims

1. A method for identifying migratory insects in the air based on insect radar and high-altitude lights, comprising: Step 1: Receive four biological parameters of each migratory insect in the air, namely wingbeat frequency, body length, body width, and weight, as monitored in real time by the insect radar; Step 2: Input the received four biological parameters into the optimal insect species classification model to obtain the species of each aerial migratory insect; The feature is that, in step 2, the optimal insect species classification model is determined through the following repeated steps: Step 201: Receive data on insects captured by the high-altitude light trap according to the set first time interval T1; Step 202: Based on the received data of insects trapped by the high-altitude lights, count the types of insects trapped by the high-altitude lights in the most recent time interval T3 according to the set second time interval T2, and compare them with the insect types counted in the previous time. If the insect types have changed, proceed to step 203; if the insect types have not changed, proceed to step 204. Step 203: Update the optimal insect species classification model; Step 204: Do not update the optimal insect species classification model; In step 203, the optimal insect species classification model is updated, including: Step 2031: Based on the latest insect species counted in Step 202, read the corresponding insect biological parameter data from the insect biological parameter database to form a sample set; Step 2032: Using the sample set, construct three insect species classification models using XGBoost, Random Forest, and BP Neural Network respectively, and select the insect species classification model with the highest F1 score as the optimal insect species classification model.

2. The method for identifying migratory insects in the air based on insect radar and high-altitude lights as described in claim 1, characterized in that: In step 1, the industrial control computer of the insect radar performs real-time inversion calculations of four biological parameters for each migratory insect in the air monitored by the insect radar: wingbeat frequency, body length, body width, and weight.

3. The method for identifying migratory insects in the air based on insect radar and high-altitude lights as described in claim 1, characterized in that: In step 201, the data on the insects captured by the high-altitude light includes data on the species of each insect captured.

4. The method for identifying migratory insects in the air based on insect radar and high-altitude lights as described in claim 1, characterized in that: In step 201, the data of the insects trapped by the high-altitude light includes image data of each trapped insect. In step 202, the species of each insect is identified based on the image data.

5. The method for identifying migratory insects in the air based on insect radar and high-altitude lights as described in claim 1, characterized in that: The second time interval T2 is not less than the value of the first time interval T1, and the value of T3 is not less than 48h.

6. The method for identifying migratory insects in the air based on insect radar and high-altitude lights as described in claim 1, characterized in that: Each insect biological parameter data in the insect biological parameter database includes the insect's wingbeat frequency, body length, body width, weight, and species. In step 2031, based on the latest statistically analyzed insect species, the corresponding insect biological parameter data is read from the insect biological parameter database. Then, outlier detection and removal, and standardization processing are performed on the four data items of wingbeat frequency, body length, body width, and weight in the read insect biological parameter data to form a sample set.

7. The method for identifying migratory insects in the air based on insect radar and high-altitude lights as described in claim 1, characterized in that: In step 2, if the optimal insect species classification model is updated, the four biological parameters of each migratory insect in the air, namely wingbeat frequency, body length, body width and weight, which were received by the insect radar in real time during the past T2 time period, are input into the latest optimal insect species classification model to reclassify and obtain the species of each migratory insect in the air.

8. A system for identifying migratory insects in the air based on insect radar and high-altitude lights, characterized in that: Used to perform the aerial migratory insect identification method based on insect radar and high-altitude light as described in any one of claims 1-7.

9. The aerial migratory insect identification system based on insect radar and high-altitude light as described in claim 8, characterized in that: It is communicatively connected to a high-altitude light and an insect radar monitoring station, respectively, with the high-altitude light positioned within the L-distance range of the insect radar monitoring station.

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