Insect situation monitoring method, device and equipment and computer readable storage medium

Abstract

The invention provides an insect condition monitoring method, device and equipment and a computer readable storage medium, and the method comprises the steps: transmitting infrared light to a monitoring area, the monitoring area is covered with a coating for absorbing the infrared light, and the wavelength of the transmitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating; acquiring a to-be-recognized image of the monitoring area by using an infrared digital camera; and performing insect condition identification on the to-be-identified image to obtain insect condition information. The infrared light is emitted to the monitoring area, the influence of insufficient illumination in the monitoring area can be avoided, the background and the monitored object can be distinguished by using the coating for absorbing the infrared light, the insect pest situation monitoring result can be more accurate, and the agricultural production yield is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of insect detection, and in particular to an insect monitoring method, device, equipment and computer-readable storage medium. Background Art

[0002] Harmful insects can seriously affect the normal growth of crops and further affect crop yields. When carrying out large-scale agricultural production, it is necessary to do a good job of forecasting and monitoring insect conditions.

[0003] Existing pest monitoring methods mainly include experience-based monitoring, statistical model monitoring, and machine learning monitoring. Although existing pest monitoring methods can provide references for agricultural pest management to a certain extent, they generally have shortcomings, resulting in poor pest monitoring results.

[0004] For example, sticky insect boards can be set up as traps to trap pests, and the number of pests trapped by the sticky insect boards can be counted to indirectly estimate the density of pest populations near the sticky insect boards as the result of pest monitoring. However, when counting the number of pests trapped by the sticky insect boards, it usually depends on the experience of technicians, resulting in the obtained pest monitoring results being highly subjective and inaccurate. Summary of the invention

[0005] In view of this, the purpose of this application is to provide an insect monitoring method, device, equipment and computer-readable storage medium, which can make the insect monitoring results more accurate. The specific technical solutions are as follows:

[0006] In a first aspect, the present application provides a method for monitoring insect infestation, the method comprising:

[0007] emitting infrared light to a monitoring area, wherein the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating;

[0008] Using an infrared digital camera to collect the image to be identified in the monitoring area;

[0009] Insect presence identification is performed on the image to be identified to obtain insect presence information.

[0010] In a possible implementation, the emitting infrared light to the monitoring area includes:

[0011] The infrared digital camera is used to emit infrared light to the monitoring area.

[0012] In a possible implementation, the step of performing insect identification on the image to be identified to obtain insect information includes:

[0013] The insect condition recognition model is used to perform insect condition recognition on the image to be recognized to obtain insect condition information, and the insect condition recognition model is trained based on a labeled data set.

[0014] In a possible implementation, the training process of the insect recognition model includes:

[0015] Obtain a training sample image set;

[0016] Performing insect labeling on the training sample images in the training sample image set to obtain the labeled data set;

[0017] The annotated data set is used to train a neural network model to obtain the insect identification model.

[0018] In a possible implementation, the step of collecting the image to be identified in the monitoring area by using an infrared digital camera includes:

[0019] When a trigger signal from an infrared sensor is received, the infrared digital camera is used to collect an image to be identified in the monitoring area.

[0020] In a possible implementation, the step of collecting the image to be identified in the monitoring area by using the infrared digital camera includes:

[0021] The infrared digital camera is used to collect the image to be identified in the monitoring area at a preset time interval.

[0022] In a possible implementation, before using the infrared digital camera to collect the image to be identified in the monitoring area, the method further includes:

[0023] emitting an infrared signal to the monitoring area using the infrared sensor;

[0024] A trigger signal is received from the infrared sensor, and the infrared sensor is used to generate and send the trigger signal when it is determined that there are insects in the monitoring area based on the received reflection signal.

[0025] In a second aspect, the present application further provides an insect monitoring device, the device comprising:

[0026] An emission module, used for emitting infrared light to a monitoring area, wherein the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating;

[0027] An acquisition module is used to acquire the image to be identified in the monitoring area using an infrared digital camera;

[0028] The recognition module is used to perform insect identification on the image to be identified to obtain insect information.

[0029] In a third aspect, the present application further provides a computer device, including: a memory and a processor;

[0030] Wherein, the memory is used to store computer programs;

[0031] The processor is used to execute the computer program in the memory to implement the method described in the first aspect or any one of the first aspects.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the method described in the first aspect or any one of the first aspects.

[0033] In this application, infrared light is emitted to the monitoring area, and the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating; an infrared digital camera is used to collect the image to be identified in the monitoring area; the image to be identified is subjected to insect identification to obtain insect information. This application emits infrared light to the monitoring area so that insect monitoring can be performed in a dark light environment, avoiding the influence of insufficient light in the monitoring area. The use of a coating that absorbs infrared light can distinguish the background from the monitored object, which can make the insect monitoring results more accurate and ensure agricultural production output. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A schematic diagram of a wave absorbing effect provided by an embodiment of the present application is shown;

[0036] Figure 2 A flow chart of an insect monitoring method provided by an embodiment of the present application is shown;

[0037] Figure 3 A schematic diagram showing the relative position relationship between an infrared digital camera and a monitoring area provided in an embodiment of the present application;

[0038] Figure 4 A schematic diagram showing an insect situation identification result provided by an embodiment of the present application is shown;

[0039] Figure 5 A schematic diagram of a deep learning algorithm based on CNN provided in an embodiment of the present application is shown;

[0040] Figure 6 A schematic diagram of a multi-target recognition effect of a deep learning algorithm based on CNN provided in an embodiment of the present application is shown;

[0041] Figure 7 A schematic diagram of image annotation provided by an embodiment of the present application is shown;

[0042] Figure 8 A schematic structural diagram of an insect monitoring device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] First, some terms that may appear in the embodiments of the present application are explained.

[0045] Tobacco meal moth belongs to the order Lepidoptera, family Pyralidae. Tobacco meal moth is distributed all over the world, and is distributed in various parts of China. In the larval stage, tobacco meal moth damages tobacco leaves and cigarettes, and also feeds on wheat, oats, soybeans, peas, broad beans, peanut kernels, cocoa beans, flour, etc. The first-instar larvae eat the leaf flesh and leave the epidermis, causing many translucent spots to appear on the tobacco leaves; after the second instar, they eat holes, and when the damage is serious, they can eat up the leaves, leaving only the veins. After hatching, the larvae first feed on both sides of the main veins of the leaves, and gradually expand outwards. After feeding, they leave a lot of tobacco leaf debris and brown insect feces on the tobacco leaves. The larvae like to spin silk, connect tobacco leaf debris and brown insect feces to form a nest, and lurk in the nest, with the most lurking in the wrinkled parts of the tobacco leaves. The damaged tobacco leaves are very easy to mold and deteriorate.

[0046] In entomology, "instar" is a special word for insect larvae, used to describe the growth stage of insect larvae. First-instar larvae usually refer to the first-instar larvae that hatch from eggs, also known as first-instar larvae. Second-instar larvae refer to the second-instar larvae that enter after the first-instar larvae molt. Both first-instar larvae and second-instar larvae are important periods for pest control.

[0047] Infrared absorbing materials based on iron-based oxides have excellent absorption effects on infrared light of specific wavelengths. When photographed by an infrared digital camera, an almost completely black photo is obtained. When an object is on the coating, it will reflect infrared light and be photographed by the infrared digital camera, appearing as almost completely white. In this way, a high-contrast photo with a completely black background and a completely white monitored object can be obtained, which is convenient for subsequent image processing and recognition, and can effectively improve the recognition rate. High-contrast photos such as Figure 1 shown.

[0048] Harmful insects can seriously affect the normal growth of crops, and further affect the yield of crops. When carrying out large-scale agricultural production, it is necessary to do a good job of forecasting and monitoring the insect situation. The embodiments of the present application are illustrated by taking tobacco leaves and tobacco powder borers as examples. The larvae of tobacco powder borers need to eat a large amount of tobacco leaves during their growth, which is extremely harmful to tobacco leaf storage. The current monitoring of tobacco powder borers in the tobacco industry is to monitor the number of adults by sticking insect glue boards. The larval growth stage that is most harmful to tobacco leaf storage has been missed, and the damage to tobacco leaves has already been caused, and it is easy to miss the best time for disinfection.

[0049] The embodiment of the present application can directly monitor the number of larvae of tobacco leaf borer, which is the most harmful to tobacco storage, to provide a basic basis for pest control. Monitoring the larvae of tobacco leaf borer is only an example of insect monitoring, and the embodiment of the present application can also be applied to monitoring other insects.

[0050] See also Figure 2 , shows a flow chart of an insect monitoring method provided by an embodiment of the present application, and the embodiment of the present application at least includes the following steps:

[0051] S11, emitting infrared light to the monitoring area.

[0052] In the embodiment of the present application, an infrared absorbing material based on iron-based oxide is used to evenly spray the monitoring area, so that the monitoring area is covered with a coating for absorbing infrared light. The monitoring area can be a designated location on the ground or wall of a tobacco warehouse, or it can be an area where other insects need to be monitored. In the embodiment of the present application, the monitoring area is described as a designated location on the ground or wall of a tobacco warehouse.

[0053] Specifically, the embodiment of the present application uses an infrared absorbing material containing iron-based oxides to uniformly spray a standard area on the monitoring area, and a standard area is usually a rectangular area of ​​1 meter × 1 meter.

[0054] In order to lure tobacco moth larvae to crawl out of tobacco leaves, the embodiments of the present application may also use sex-derived attractants and / or food-derived attractants to lure tobacco moth larvae, so that the tobacco moth larvae are transferred from the tobacco leaves to the monitoring area.

[0055] Visible light affects the aroma of tobacco leaves, so the lights in tobacco warehouses are usually turned off. Considering the poor illumination in tobacco warehouses, the embodiment of the present application can use infrared sensors to supplement the monitoring area. The wavelength of the infrared light used for supplementary lighting is the same as the wavelength corresponding to the infrared absorbing material based on iron-based oxides. It can be understood that the wavelength of the infrared light emitted to the monitoring area is consistent with the wavelength of the infrared light absorbed by the coating covering the monitoring area, so that when there is no insect in the monitoring area, the infrared digital camera is used to take a picture of the monitoring area, and the result is an almost completely black photo.

[0056] The infrared sensor can be a component of the infrared digital camera or a component independent of the infrared digital camera. When the infrared sensor is a component of the infrared digital camera, the infrared digital camera is an active light-emitting infrared digital camera that can actively emit infrared light. When the infrared sensor is independent of the infrared digital camera, the infrared digital camera is a passive infrared digital camera that only relies on receiving infrared light from the outside world to form an image.

[0057] The embodiment of the present application uses infrared supplementary light to monitor insect conditions in a tobacco warehouse with weak light or even complete darkness, which can avoid the influence of light on aroma. And after using infrared light for supplementary light, the coating covering the monitoring area can play a role in absorbing infrared light, and the image to be identified is a black and white grayscale image with extremely strong contrast, which reduces the workload when identifying insect conditions and has a high recognition rate.

[0058] Generally speaking, the embodiments of the present application may also use visible light for fill-in. After using visible light for fill-in, the coating covering the monitoring area cannot play the role of absorbing infrared light, and the image to be identified is usually a color image. The tobacco powder borer larvae are grayish white, and the color contrast between the tobacco powder borer larvae and the surrounding background may not be high, which will reduce the recognition accuracy of insect identification. In order to improve the color contrast between the tobacco powder borer larvae and the surrounding background, a coating with a color that has a high color contrast with the tobacco powder borer larvae can be sprayed in the monitoring area.

[0059] S12, using an infrared digital camera to collect an image to be identified in the monitoring area.

[0060] After setting up the infrared digital camera in the tobacco warehouse, you can connect to Ethernet and adjust the focus of the infrared digital camera so that the camera lens can focus accurately on the monitoring area sprayed with iron-based oxide-based infrared absorbing materials. The relative position relationship between the infrared digital camera and the monitoring area is as follows: Figure 3 shown.

[0061] The embodiment of the present application can use an infrared sensor to transmit an infrared signal to the monitoring area, and after receiving a trigger signal from the infrared sensor, use an infrared digital camera to capture an image to be identified in the monitoring area.

[0062] The infrared sensor can emit infrared signals to the monitoring area, and when it is determined that there are insects in the monitoring area based on the received reflected signals, a trigger signal is generated and sent.

[0063] When receiving a trigger signal from an infrared sensor, the embodiment of the present application uses an infrared digital camera to collect images to be identified in the monitoring area, which can avoid obtaining images to be identified in which no insects are present, ensure the validity of the images to be identified, and improve the efficiency of image recognition.

[0064] In order to ensure the difference of the images to be identified, the embodiments of the present application can use an infrared digital camera to collect the images to be identified in the monitoring area according to preset time intervals. If the preset time interval is too long, it is easy to cause the continuity to be lost. If the preset time interval is too short, it is easy to cause the difference between the images to be small and meaningless. Therefore, the setting of the preset time interval needs to be appropriate and cannot be too long or too short. The specific setting can be set in combination with experience and actual application scenarios. It should be noted that the preset time interval can be set according to the changes in the insect situation. If the growth rate of the number of insects is found to be accelerated during the insect monitoring process, the preset time interval can be shortened. If the growth rate of the number of insects is found to be slow during the insect monitoring process, the preset time interval can be extended.

[0065] Specifically, if the infrared digital camera is an active light-emitting infrared digital camera, the infrared digital camera is used to emit infrared light to the monitoring area, and the infrared digital camera is used to collect the image to be identified in the monitoring area. In the embodiment of the present application, the infrared digital camera can be used to perform infrared fill light and video recording of the monitoring area at a preset time interval to obtain the image to be identified, and store the image to be identified. The wavelength of the infrared light used by the active light-emitting infrared digital camera for fill light is the same as the wavelength corresponding to the infrared absorbing material based on iron-based oxide.

[0066] If the infrared digital camera is an infrared digital camera that passively receives external infrared light, the infrared sensor is used to emit infrared light to the monitoring area, and the infrared digital camera is used to collect the image to be identified in the monitoring area. In the embodiment of the present application, the infrared sensor can be used to perform infrared fill light on the monitoring area at a preset time interval, and the infrared digital camera can be used to photograph the monitoring area at a preset time interval to obtain the image to be identified, and the image to be identified is stored. The infrared fill light duration of the infrared sensor needs to ensure that the infrared digital camera can complete the photography of the monitoring area. The wavelength of the infrared light used by the infrared sensor for fill light is the same as the wavelength corresponding to the infrared absorbing material based on iron-based oxide.

[0067] S13, performing insect identification on the image to be identified to obtain insect information.

[0068] In the embodiment of the present application, the larvae of tobacco powder borer in the image to be identified can be identified by AI machine vision algorithm and the number of tobacco powder borer larvae can be counted. Specifically, the insect recognition model can be used to perform insect recognition on the image to be identified to obtain insect information. The insect recognition information is used to indicate the insect recognition result. The insect recognition result is as follows: Figure 4 As shown, the larvae can be marked. The insect recognition model is obtained by training the neural network model based on the labeled data set. The neural network model can automatically extract features, recognize patterns, and perform tasks such as classification and prediction from a large amount of data. There are many types of neural network models for image recognition. The embodiment of this application is described using a convolutional neural network model.

[0069] The morphology of tobacco powder borer larvae is relatively simple and fixed, and is also different from the morphology of tobacco powder borer adults, so the embodiments of the present application can be processed using a deep learning algorithm based on convolutional neural networks (CNN). CNN is one of the important components of AI machine vision algorithms. Through structures such as convolutional layers and pooling layers, CNN can automatically extract features from images and perform effective classification and detection.

[0070] CNN-based deep learning algorithms are very suitable for identifying targets in images, such as Figure 5 As shown in the figure, a car image is input and processed by the CNN-based deep learning algorithm to accurately identify it as a car. Multiple targets in the image can also be identified and counted, such as Figure 6 As shown in the figure, CNN's deep learning algorithm identified two people and two horses.

[0071] The training process of the insect recognition model can include the following steps:

[0072] S21, obtaining a training sample image set.

[0073] Since the environment in which tobacco leaf moth larvae crawl is too complex, tobacco leaf moth larvae can protect themselves by mimicking the surrounding environment, and tobacco leaf warehouses often lack light due to storage needs, it is difficult to obtain satisfactory tobacco leaf moth larvae image materials using ordinary visible light cameras. In the embodiment of the present application, an infrared absorbing material based on iron-based oxide is sprayed on the image acquisition area, and an infrared digital camera capable of emitting infrared light of a specific wavelength is used to acquire image materials.

[0074] When there are no tobacco powder borer larvae in the image acquisition area, the image will appear as a completely black area. When tobacco powder borer larvae appear in the image acquisition area, the tobacco powder borer larvae in the image will appear as high-contrast white.

[0075] The training sample image set may include multiple images containing larvae. In order to improve the training accuracy, the number of images in the training sample image set is at least 10, and the images in the training sample image set include at least 100 larvae of various forms. The above values ​​are for reference only and can be set according to actual conditions.

[0076] Since insect infestation is greatly affected by the environment, a simulation environment for infrared digital cameras is built during the training process to make it as consistent as possible with the environment at the insect infestation monitoring site.

[0077] S22, performing insect labeling on the training sample images in the training sample image set to obtain a labeled data set.

[0078] In the embodiment of the present application, a special annotation tool can be used to mark all tobacco powder borer larvae in the training sample image set with rectangular frames. The annotation result is as follows: Figure 7 , so that CNN has learning samples.

[0079] The embodiment of the present application can also enhance the annotated data set by using image enhancement technology to add more forms of tobacco powder borer larvae to improve the learning efficiency of CNN.

[0080] S23, using the labeled data set to train a neural network model to obtain an insect recognition model.

[0081] The labeled data set is used to train CNN and gradually establish an insect recognition model.

[0082] After the insect recognition model is obtained, it can be applied to the tobacco warehouse for on-site testing. According to the test results, the on-site temperature and humidity data are supplemented and the model is corrected until the insect recognition model achieves the expected recognition effect.

[0083] The insect recognition model can be used to process the stored images to be recognized, identify and count the tobacco powder borer larvae in the images to be recognized, and save the counting results in a database so that the data statistics and analysis system can obtain the counting results from the database and perform analysis.

[0084] The embodiment of the present application uses infrared light for fill lighting and combines an infrared digital camera to shoot the monitored area. The acquired image is a black and white grayscale image. The AI ​​machine vision algorithm required for insect identification can be greatly simplified, thereby improving the algorithm efficiency and recognition accuracy.

[0085] In addition, the embodiment of the present application changes the conventional monitoring method of tobacco powder borer adults and directly monitors tobacco powder borer larvae, making the monitoring of tobacco powder borer insect conditions more timely and effective, and being able to timely reduce the damage of tobacco powder borer larvae to tobacco leaves.

[0086] In an embodiment of the present application, infrared light is emitted to the monitoring area, and the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating; an infrared digital camera is used to collect an image to be identified in the monitoring area; the image to be identified is subjected to insect identification to obtain insect information. The embodiment of the present application emits infrared light to the monitoring area so that insect monitoring can be performed in a dark light environment, avoiding the influence of insufficient light in the monitoring area. The use of a coating that absorbs infrared light can distinguish between the background and the monitored object, which can make the insect monitoring results more accurate and ensure agricultural production output.

[0087] Next, an insect monitoring device provided in an embodiment of the present application is introduced. The insect monitoring device introduced below and the insect monitoring method introduced above can refer to each other.

[0088] See also Figure 8 , shows a schematic structural diagram of an insect monitoring device provided in an embodiment of the present application, the device comprising:

[0089] The transmitting module 801 is used to transmit infrared light to the monitoring area, the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating;

[0090] The acquisition module 802 is used to acquire the image to be identified in the monitoring area using an infrared digital camera;

[0091] The identification module 803 is used to perform insect identification on the image to be identified to obtain insect information.

[0092] In the embodiment of the present application, the transmitting module 801 is specifically used for:

[0093] The infrared digital camera is used to emit infrared light to the monitoring area.

[0094] In the embodiment of the present application, the identification module 803 is specifically used for:

[0095] The insect condition recognition model is used to perform insect condition recognition on the image to be recognized to obtain insect condition information, and the insect condition recognition model is trained based on a labeled data set.

[0096] In the embodiment of the present application, the device further includes:

[0097] An acquisition module, used to acquire a training sample image set;

[0098] A labeling module, used for labeling insects on the training sample images in the training sample image set to obtain the labeling data set;

[0099] The training module is used to train the neural network model using the labeled data set to obtain the insect recognition model.

[0100] In the embodiment of the present application, the acquisition module 802 is specifically used for:

[0101] When a trigger signal from an infrared sensor is received, the infrared digital camera is used to collect an image to be identified in the monitoring area.

[0102] In the embodiment of the present application, when the acquisition module 802 uses the infrared digital camera to acquire the image to be identified in the monitoring area, it is specifically used to:

[0103] The infrared digital camera is used to collect the image to be identified in the monitoring area at a preset time interval.

[0104] In the embodiment of the present application, the device further includes:

[0105] A signal transmitting module, used for transmitting an infrared signal to the monitoring area using the infrared sensor;

[0106] The receiving module is used to receive a trigger signal from the infrared sensor, and the infrared sensor is used to generate and send the trigger signal when it is determined that there are insects in the monitoring area based on the received reflection signal.

[0107] The embodiment of the present application also provides a computer device, including: a memory and a processor;

[0108] Wherein, the memory is used to store computer programs;

[0109] The processor is used to execute the computer program in the memory to implement the method described in the above method embodiment.

[0110] The embodiment of the present application also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the method described in the above method embodiment.

[0111] In the embodiment of the present application, the transmitting module transmits infrared light to the monitoring area, the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating; the acquisition module uses an infrared digital camera to acquire the image to be identified in the monitoring area; the recognition module performs insect identification on the image to be identified to obtain insect information. The embodiment of the present application transmits infrared light to the monitoring area so that insect monitoring can be performed in a dark light environment, avoiding the influence of insufficient light in the monitoring area. The use of a coating that absorbs infrared light can distinguish the background from the monitored object, which can make the insect monitoring results more accurate and ensure agricultural production output.

[0112] It should be noted that the same or similar parts between the various embodiments can be referred to each other. As for the device embodiment and the system embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0113] For the above-mentioned embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0114] Finally, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0115] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0116] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring insect pests, characterized in that: The method comprises: emitting infrared light to a monitoring area, wherein the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating; Using an infrared digital camera to collect the image to be identified in the monitoring area; Insect presence identification is performed on the image to be identified to obtain insect presence information.

2. The method according to claim 1, characterized in that The emitting infrared light to the monitoring area comprises: The infrared digital camera is used to emit infrared light to the monitoring area.

3. The method according to claim 1, characterized in that: The step of performing insect identification on the image to be identified to obtain insect information includes: The insect condition recognition model is used to perform insect condition recognition on the image to be recognized to obtain insect condition information, and the insect condition recognition model is trained based on a labeled data set.

4. The method according to claim 3, characterized in that The training process of the insect recognition model includes: Obtain a training sample image set; Performing insect labeling on the training sample images in the training sample image set to obtain the labeled data set; The annotated data set is used to train a neural network model to obtain the insect identification model.

5. The method according to any one of claims 1 to 4, characterized in that: The method of collecting the image to be identified in the monitoring area by using an infrared digital camera includes: When a trigger signal from an infrared sensor is received, the infrared digital camera is used to collect an image to be identified in the monitoring area.

6. The method according to claim 5, characterized in that The method of collecting the image to be identified in the monitoring area by using the infrared digital camera includes: The infrared digital camera is used to collect the image to be identified in the monitoring area at a preset time interval.

7. The method according to claim 5, characterized in that Before using the infrared digital camera to collect the image to be identified in the monitoring area, the method further includes: emitting an infrared signal to the monitoring area using the infrared sensor; A trigger signal is received from the infrared sensor, and the infrared sensor is used to generate and send the trigger signal when it is determined that there are insects in the monitoring area based on the received reflection signal.

8. An insect monitoring device, characterized in that: The device comprises: An emission module, used for emitting infrared light to a monitoring area, wherein the monitoring area is covered with a coating for absorbing infrared light, and the wavelength of the emitted infrared light is consistent with the wavelength of the infrared light absorbed by the coating; An acquisition module is used to acquire the image to be identified in the monitoring area using an infrared digital camera; The recognition module is used to perform insect identification on the image to be identified to obtain insect information.

9. A computer device, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is configured to execute the computer program in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The device stores instructions which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 7.

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