Method for measuring feeding rate and intake of small piercing-sucking insects

Abstract

The invention discloses a method for measuring the feeding rate and intake of small piercing-sucking insects. The method comprises the following steps: acquiring an insect feeding video; the insect feeding video is processed, continuous pictures of the optimal insect feeding process are obtained, a tool is used for measuring the insect feeding continuous pictures, and data of the insect projection area are obtained; and establishing a prediction model according to the acquired data, and acquiring insect feeding rates and intakes at different moments according to the prediction model. According to the invention, the measurement cost can be reduced, and the continuous feeding rate and the accumulated intake of small and medium-sized piercing-sucking insects in the feeding process can be measured. The method is greatly helpful for deeply analyzing the feeding duration and feeding sites of small piercing-sucking insects.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and in particular relates to a method for measuring the feeding rate and intake of small piercing-sucking insects. Background Art

[0002] At present, due to the size of small piercing-sucking insects, there is no method that can measure the feeding rate of small piercing-sucking insects well. Most methods for measuring feeding intake are weighing, which cannot continuously measure the insect's intake and can only measure the intake at a certain point in time. Conventional weighing methods, such as analytical balances, have the problem of large sample collection volume when measuring small piercing-sucking insects, and equipment with small sample requirements is expensive. Although some scholars have proposed that using energetics to measure insect respiratory metabolism can accurately measure the energy gain and loss of pests and natural enemy insects, the size of small piercing-sucking insects severely limits the application of the above two main methods. Therefore, it is extremely important to quickly and accurately measure the feeding benefits of small piercing-sucking insects. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method for measuring the feeding rate and intake of small piercing-sucking insects, which can reduce the measurement cost and measure the continuous feeding rate and cumulative intake of small piercing-sucking insects during the feeding process. It is of great help for in-depth analysis of the feeding behavior of small piercing-sucking insects.

[0004] To achieve the above object, the present invention provides a method for measuring the feeding rate and intake of small piercing-sucking insects, comprising:

[0005] Get videos of insect feeding;

[0006] Processing the insect feeding video to obtain the best insect feeding picture;

[0007] Acquire the projected area data of the insects, and establish a prediction model based on the projected area data;

[0008] The optimal insect feeding picture is input into the prediction model to obtain the insect feeding rate and intake.

[0009] Optionally, obtaining the insect feeding video includes: photographing the insect using a photographing tool.

[0010] Optionally, processing the insect feeding video to obtain the best insect feeding picture includes:

[0011] Extracting video frame images of the insect feeding video;

[0012] Based on the video frame image, an RGB image, namely the optimal insect feeding image, is extracted.

[0013] Optionally, the method for extracting the video frame image of the insect feeding video is:

[0014] The insect feeding video is extracted into video frame pictures according to the insect morphology and feeding rate.

[0015] Optionally, based on the video frame picture, extracting the RGB picture includes:

[0016] Get the RGB channel with the largest color contrast between the insect under test and the background;

[0017] Based on the RGB channels, the RGB picture is extracted.

[0018] Optionally, obtaining the projected area data of the insect includes:

[0019] Performing grayscale processing on the best insect feeding picture to obtain a grayscale picture;

[0020] Based on the grayscale image, the projected area of ​​the insect is measured.

[0021] Optionally, based on the grayscale image, measuring the projection area of ​​the insect includes:

[0022] Based on the grayscale image, measuring the number of pixel blocks;

[0023] Based on the number of pixel blocks, the projected area of ​​the insect is obtained.

[0024] Optionally, based on the number of pixel blocks, obtaining the insect's projection area includes:

[0025] Using a ruler, the area of ​​the best insect feeding picture is obtained, and the area of ​​a pixel block is calculated according to the clarity of the insect feeding video;

[0026] Based on the number of shadow pixel blocks and the area of ​​a pixel block, the insect's projection area is obtained.

[0027] Compared with the prior art, the present invention has the following advantages and technical effects: the existing methods can only measure the feeding rate and food intake of insects at a certain point in time, and most methods are not suitable for small piercing-sucking insects; the present invention can quickly and continuously measure the feeding rate and food intake of small piercing-sucking insects, obtain the functional relationship between time and the feeding rate and intake of small piercing-sucking insects, and establish a mathematical model of the feeding rate and intake of small piercing-sucking insects under certain experimental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0029] Figure 1 The present invention is a flowchart of a method for measuring the feeding rate and intake of small piercing-sucking insects according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0031] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] The present invention takes the fact that the body wall of piercing-sucking insects will expand or contract when they are feeding or hungry as a starting point, and studies and invents a simple and easy method for measuring the feeding rate and food intake of small piercing-sucking insects. For small piercing-sucking insects, whether they are pests or natural enemies, studying their feeding rate and food intake has important scientific significance for insect behavior. The feeding rate and intake of insects represent their adaptability to the environment and food (prey). Efficient feeding rate and sufficient intake will affect the degree of damage to crops by pests or the degree of lethality to prey by natural enemies; for natural enemy piercing-sucking insects, these parameters can also be used to evaluate whether they have the ability to colonize in the field, expand populations, and carry out long-term prevention and control. All of the above are of great significance to scientific research such as insect behavior and biological control.

[0033] The present invention proposes a method for approximately measuring the feeding rate and intake of small piercing-sucking insects. Use video to record the changes in the projection area of ​​small piercing-sucking insects during their feeding process, and use software such as Potplayer to extract the recorded video into frames; after extracting RGB images from fixed-interval frame images with the help of software such as imageJ, select appropriate RGB channels and use polygon tools to measure the changes in the projection area of ​​the insects in each frame, establish a mathematical model with the measured data, and obtain the functional relationship between the feeding rate and intake of piercing-sucking natural enemy insects over time under specific conditions, such as different densities between piercing-sucking natural enemy insects and prey. The specific functional relationship needs to be determined based on specific experimental data. Functional relationship between time and the feeding rate and intake of small piercing-sucking insects.

[0034] The present invention proposes a method for measuring the feeding rate and intake of small piercing-sucking insects. Figure 1 As shown, the specific steps include:

[0035] Get videos of insect feeding;

[0036] Process the insect feeding video to obtain the best insect feeding picture;

[0037] Obtain the projected area data of insects, and establish a prediction model based on the projected area data;

[0038] The best insect feeding images were input into the prediction model to obtain the insect feeding rate and intake.

[0039] Furthermore, obtaining the video of insects feeding includes: obtaining the video of insects feeding includes: using a shooting tool to shoot the insects.

[0040] Place insects and food in a disc of appropriate size. There should be a contrast between the background color of the disc and the body color of the insect to be measured. Use a stereoscope and a CCD camera to film the insect's feeding process. The shooting tools include but are not limited to CCD cameras, SONY cameras, DJI cameras, Canon cameras, etc. The shooting time varies from 10 minutes to 4 hours depending on the insect's feeding speed. The number of frames and clarity of the captured video can also be selected in different combinations according to the insect morphology and feeding rate. The number of frames is at least 24 frames and the clarity is at least 1080p.

[0041] Furthermore, the insect feeding video is processed to obtain the best insect feeding picture including:

[0042] Extract video frame images of insect feeding videos;

[0043] Based on the video frame images, RGB images are extracted, i.e., the best insect feeding images.

[0044] Furthermore, the method for extracting video frame images of insect feeding videos is as follows:

[0045] Insect feeding videos are extracted into video frame images based on insect morphology and feeding rate.

[0046] Specifically, different interval frames may be selected to extract pictures according to the feeding rate of the insects, such as extracting a picture every 12 frames, to ensure that more than 60% of the pixels in each picture are the insect being tested.

[0047] Furthermore, based on the video frame image, extracting the RGB image includes:

[0048] Get the RGB channel with the largest color contrast between the insect under test and the background;

[0049] Extract RGB images based on RGB channels.

[0050] Furthermore, obtaining the projection area data of the insect includes:

[0051] Perform grayscale processing on the best insect feeding picture to obtain a grayscale picture;

[0052] Based on the grayscale image, the projected area of ​​the insect is measured.

[0053] Furthermore, based on the grayscale image, the projected area of ​​the insect is measured including:

[0054] Based on the grayscale image, measure the area of ​​the pixel block;

[0055] Based on the pixel block area, the projection area of ​​the insect is obtained.

[0056] Specifically, first measure the actual size of the picture, use a ruler to place it under the video, and you can get the actual length and width of the photo. Then calculate the size of a pixel block based on the clarity of the video. For example, a 1080p picture has 1920*1080 pixel blocks, and a 4k picture has 4096*2160 pixel blocks. Divide the actual area of ​​the picture by the number of pixel blocks to get the area of ​​a pixel block. Finally, multiply it by the number of pixel blocks to get the projection area of ​​the insect.

[0057] Specifically, imageJ, Adobe Photoshop, ACDSee, Snapseed, and Convertio were used to perform grayscale processing on the images. The purpose of the processing was to separate the background and the measured insect as completely as possible to facilitate the measurement of the insect's projection area and reduce errors.

[0058] Use imageJ, Adobe Photoshop, ACDSee, Snapseed, and Convertio to measure the area to be measured in the picture using the polygon measurement tool. The measurement circle drawn should enclose the entire projected area of ​​the insect being measured. When drawing the measurement circle, it is necessary to ensure the standard statistics of the drawing, and the error between multiple measurement data of the same picture is within ±5%. Measure all the extracted RGB pictures and finally export the data.

[0059] Since the above software measures the area of ​​the picture, not the actual area, it is necessary to use pixels as a medium to calculate the actual area. First measure the actual area of ​​a pixel block, then convert the area calculated in the software with the area of ​​the actual pixel to get the actual projected area.

[0060] Specifically, the exported data is saved in CSV file format, and then a linear regression is used to establish a prediction model using modeling software such as Matlab and Mathematica. Ten groups of data are sampled for verification, and the error between the feeding rate and feeding amount calculated by the model and the actual feeding rate and feeding amount is within ±5%, and the calculation formula is (predicted value-actual value) / actual value=error value. If the error value is within ±5%, it means that the model is qualified; if the error value is not within ±5%, the number of repetitions is increased and the model is established again to make the model more accurate and reliable.

[0061] Select appropriate mathematical models based on test data, mainly including linear models and nonlinear models, such as y=ax+b, y=ae bx , y = a1n(x) + b, y = ax b etc. (a, b are constants, e = 2.718, y is the feeding rate or feeding amount, x is the time), use excel, wps and other software to bring the data into the above mathematical model for fitting, where R 2 The largest one indicates that the model is the most suitable, that is, the mathematical model is used as the best prediction model.

[0062] Example 1

[0063] Measure the feeding rate and feeding amount of aphids when feeding on cotton leaves. Place aphids on cotton leaves and shoot a video of them feeding. Shoot directly above them for 20 minutes. Shoot a total of 100 sets of videos. Select 4k and 24 frames for video quality. Import the video into potplayer software, extract the video as fixed frame pictures, and extract one picture every 12 frames, that is, extract one picture every 0.5s. Import the fixed frame pictures into imageJ, extract all pictures as RGB pictures, select the picture of the Blue channel, and then adjust the grayscale value of the picture to 160, which can well separate the aphids and cotton leaves. Finally, use the polygon tool to measure the projection area of ​​the aphids, and then convert the measured projection area into the actual area of ​​the aphids according to the pixels. Export the data to CSV format, use Matlab linear regression to establish a prediction model, and finally randomly select 10 sets of data for verification. The average error is within 5%, which means that the model is qualified.

[0064] Example 2

[0065] Measure the feeding rate and feeding amount of predatory mites when feeding on spider mites. Place predatory mites and spider mites on bean leaves, and shoot videos of predatory mites feeding on spider mites. Shoot directly above them, with the predatory mite in a positive posture. Shoot for 60 minutes, shoot a total of 100 sets of videos, and select 8k and 24 frames for video quality. Import the video into Bledner software, extract the video as fixed frame images, and extract one image every 12 frames, that is, extract one image every 0.5s. Import the fixed frame images into Convertio, extract all images as RGB images, select the image of the Green channel, and then adjust the grayscale value of the image to 130, which can well separate the predatory mites, spider mites and bean leaves. Finally, use the polygon tool to measure the projection area of ​​the predatory mite, and then convert the measured projection area into the actual area of ​​the predatory mite according to the pixel. Export the data to CSV format, use Mathematica linear regression to establish a prediction model, and finally randomly select 10 groups of data for verification. The average error is within 5%, which means that the model is qualified.

[0066] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for measuring the feeding rate and intake of small piercing-sucking insects, characterized in that: include: Get videos of insect feeding; Processing the insect feeding video to obtain the best insect feeding picture; Acquire the projected area data of the insects, and establish a prediction model based on the projected area data; The optimal insect feeding picture is input into the prediction model to obtain the insect feeding rate and intake.

2. A method for measuring the feeding rate and intake of small piercing-sucking insects according to claim 1, characterized in that: Acquiring the insect feeding video includes: photographing the insect using a photographing tool.

3. A method for measuring feeding rate and intake of small piercing-sucking insects according to claim 1, characterized in that: Processing the insect feeding video to obtain the best insect feeding picture includes: Extracting video frame images of the insect feeding video; Based on the video frame image, an RGB image, namely the optimal insect feeding image, is extracted.

4. A method for measuring feeding rate and intake of small piercing-sucking insects according to claim 3, characterized in that: The method for extracting the video frame images of the insect feeding video is: The insect feeding video is extracted into video frame pictures according to the insect morphology and feeding rate.

5. A method for measuring feeding rate and intake of small piercing-sucking insects according to claim 3, characterized in that: Based on the video frame picture, extracting the RGB picture includes: Get the RGB channel with the largest color contrast between the insect under test and the background; Based on the RGB channels, the RGB picture is extracted.

6. A method for measuring feeding rate and intake of small piercing-sucking insects according to claim 1, characterized in that: Obtaining insect projection area data includes: Performing grayscale processing on the best insect feeding picture to obtain a grayscale picture; Based on the grayscale image, the projected area of ​​the insect is measured.

7. A method for measuring feeding rate and intake of small piercing-sucking insects according to claim 6, characterized in that: Based on the grayscale image, measuring the projected area of ​​the insect includes: Based on the grayscale image, measuring the number of pixel blocks; Based on the number of pixel blocks, the projected area of ​​the insect is obtained.

8. A method for measuring feeding rate and intake of small piercing-sucking insects according to claim 7, characterized in that: Based on the number of pixel blocks, the projected area of ​​the insect is obtained by: Using a ruler, the area of ​​the best insect feeding picture is obtained, and the area of ​​a pixel block is calculated according to the clarity of the insect feeding video; Based on the number of shadow pixel blocks and the area of ​​a pixel block, the insect's projection area is obtained.

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