Detection device and detection method capable of autonomously detecting crop growth state

Abstract

The invention belongs to the technical field of agricultural inspection robots, and particularly provides a detection device and a detection method capable of autonomously detecting the growth state of crops, by arranging a movable chassis, a lifting platform, a sliding rail, an image acquisition assembly, a navigation assembly and an interaction system, autonomous detection of physiological and biochemical characteristics of the crops is realized, and the detection efficiency is improved. The detection efficiency and accuracy are improved, and the detection requirements of various crops are met. An environment map is automatically established through the navigation assembly, path planning and obstacle avoidance are carried out according to a target point, and the movable chassis automatically moves to a target position. And after the target position is reached, fine adjustment is performed through the lifting platform and the slide rail to ensure that the image acquisition assembly reaches the optimal shooting position. And the image acquisition assembly starts to work, and the acquired data is uploaded to the cloud of the Internet of Things through the interaction system, so that remote monitoring and analysis of the data are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural inspection robots, and in particular relates to a detection device and a detection method capable of autonomously detecting the growth status of crops. Background Art

[0002] In agricultural production management, real-time monitoring of crop physiological and biochemical characteristics is crucial. It provides key data support for pest and disease monitoring, crop growth monitoring, water management, nutritional status assessment, yield prediction, weed identification, soil analysis and precision agriculture, and provides strong technical support for the research, management and protection of crops, effectively promoting the sustainable development of agricultural production.

[0003] However, the existing technology still has the following shortcomings: First, domestic and foreign scholars currently mainly use drones equipped with visible light imaging technology, infrared imaging technology or hyperspectral imaging technology to collect crop information. However, in complex environments such as greenhouses and small test fields, drones are difficult to operate flexibly, and manual handheld visible light cameras, infrared cameras and multispectral cameras are required for information collection, which is inefficient and costly. Secondly, the growth rates of crops vary significantly at different growth stages, and the time requirements for information collection are also different. Existing fixed sensors or cameras are difficult to meet the needs of data collection frequency at different growth stages, resulting in poor data timeliness and difficulty in accurately reflecting the growth status of crops. Finally, in some environments, such as greenhouses and test fields, it is necessary to arrange cameras, brackets, networks and other infrastructure for information collection, which undoubtedly increases the investment in manpower and material resources, and is difficult to adjust flexibly, and it is difficult to meet the needs of different crops and different growth stages.

[0004] Therefore, how to collect crop information more conveniently is an urgent problem to be solved.

[0005] It should be noted that the above information disclosed in this background technology section is only used to understand the background technology of the present application concept, and therefore, the above description is not considered to constitute information of the prior art. Summary of the invention

[0006] The embodiments of the present disclosure at least provide a detection device and a detection method that can autonomously detect the growth status of crops.

[0007] In a first aspect, an embodiment of the present disclosure provides a detection device capable of autonomously detecting the growth status of crops, comprising:

[0008] Mobile chassis;

[0009] A lifting platform, which is arranged on the mobile chassis;

[0010] A slide rail, which is arranged on the lifting platform;

[0011] An image acquisition component is slidably arranged on the slide rail;

[0012] A navigation assembly, which is arranged on the top of the lifting platform;

[0013] An interactive system, which is arranged on the mobile chassis;

[0014] During detection, the interactive system is suitable for receiving navigation instructions from the navigation component, and controlling the mobile chassis to reach the crop point to be photographed according to the navigation instructions. The interactive system is suitable for adjusting the position of the image acquisition component through the lifting platform and the slide rail, and controlling the image acquisition component to take pictures.

[0015] In an optional embodiment, the image acquisition assembly includes: a connecting bracket, a rotating camera mounting platform, a visible light camera, an infrared camera, and a multi-spectral camera;

[0016] The connecting bracket is slidably arranged on the slide rail;

[0017] The rotating camera mounting platform is rotatably disposed on the connecting bracket;

[0018] The visible light camera, infrared camera and multi-spectral camera are all arranged on the rotating camera mounting platform.

[0019] In an optional implementation, the step of adjusting the position of the image acquisition component by means of a lifting platform and a slide rail includes:

[0020] S1: Control the visible light camera to obtain crop photos;

[0021] S2: Process photos using computer vision and image processing techniques;

[0022] S3: judging whether the crop is in the center of the picture according to the processing result, if so, proceeding to step S4, if not, proceeding to step S5;

[0023] S4: obtaining the area ratio of the crop in the entire photo. If the area ratio is within a preset range, the adjustment is completed. If the area ratio is not within the preset range, the process proceeds to step S6.

[0024] S5: adjusting the position of the mobile chassis and / or the connecting bracket according to the positional relationship between the crop and the center of the image, and entering step S1 after the adjustment is completed;

[0025] S6: Adjust the height of the lifting platform, and go to step S4 after the adjustment is completed.

[0026] In an optional implementation, the step of processing the photo by using computer vision and image processing technology includes:

[0027] S21: Image preprocessing, namely:

[0028] Grayscale: Convert a color image to a grayscale image. The formula is as follows:

[0029] I gray (xy)=0.299·I R (x,y)+0.587·I G (xy)+0.114·I B (x,y);

[0030] In the formula, I R ,I G ,I B They are the red, green, and blue channels of the image, respectively, and (x, y) represents the pixel point;

[0031] Gaussian blur, use the Gaussian module to reduce noise, the formula is as follows:

[0032]

[0033] Among them, G(i,j) is the Gaussian kernel, i and j represent the coordinate offsets in the Gaussian kernel, and k is the size of the kernel;

[0034] S22: Use threshold segmentation to binarize the image. The formula is as follows:

[0035]

[0036] Where T is the threshold;

[0037] S23: Use Canny edge detection to find contours in the image.

[0038] Contours = findContours(I thresholded );

[0039] S24: Calculate the area of ​​the crop area using the following formula:

[0040] A crop =contourArea(C largest )

[0041] Among them, C largest It is the largest outline;

[0042] Calculate the area ratio using the following formula:

[0043]

[0044] S25: Calculate the distance between the crop and the center of the photo. The formula is as follows;

[0045] Δx=c x -c image_x

[0046] Δy=c y -c image_y ;

[0047] In the formula, c image_x 、c image_y is the image center, c x and c y represents the contour center of the crop, Δx and Δy represent the difference between the contour center of the crop and the center of the image.

[0048] In an optional implementation, the step of adjusting the position of the mobile chassis and / or the connecting bracket according to the positional relationship between the crop and the center of the picture includes:

[0049] S51: When the center of the crop's outline is on the left side of the image center, the mobile chassis is controlled to move to the left; when the center of the crop's outline is on the right side of the image center, the mobile chassis is controlled to move to the right;

[0050] S52: When the center of the crop outline is located above the center of the image, the connecting bracket is controlled to move forward; when the center of the crop outline is located below the center of the image, the connecting bracket is controlled to move backward.

[0051] In an optional implementation, the step of adjusting the height of the lifting platform comprises:

[0052] If the area ratio is smaller than the preset range, the lifting platform is controlled to descend; if the area ratio is larger than the preset range, the lifting platform is controlled to ascend.

[0053] In an optional implementation, the step of controlling the image acquisition component to shoot includes:

[0054] S71: Control the visible light camera to take pictures. After the pictures are taken, control the rotating camera mounting platform to rotate 90° clockwise.

[0055] S72: Control the infrared camera to take pictures. After the pictures are taken, control the rotating camera mounting platform to rotate 90° clockwise.

[0056] S73: Control the multispectral camera to shoot. After shooting, control the rotating camera mounting platform to rotate 180° counterclockwise and return to the initial state.

[0057] In an optional embodiment, the detection device capable of autonomously detecting the growth status of crops further comprises a mechanical arm;

[0058] The mechanical arm is arranged on the mobile chassis, and an electrode clamp is arranged at the execution end of the mechanical arm;

[0059] The clamping jaw is suitable for detecting bioelectric signals of crops.

[0060] In an optional embodiment, the mechanical arm is further provided with a depth camera;

[0061] The depth camera is suitable for capturing image information of crops and sending the image information to the interactive system;

[0062] The interactive system is also suitable for finding multiple groups of different leaves based on image recognition technology, clamping each group of leaves in turn through the electrode clamp, and uploading the acquired bioelectric signals to the interactive system.

[0063] In a second aspect, the embodiments of the present disclosure further provide a detection method of the detection device capable of autonomously detecting the growth status of crops as described above, the detection method comprising:

[0064] receiving a navigation instruction from the navigation component;

[0065] The mobile chassis reaches the crop point to be photographed according to the navigation instructions;

[0066] The position of the image acquisition component is adjusted through the lifting platform and the slide rail;

[0067] Control the image acquisition component to take pictures.

[0068] The beneficial effect of the present invention is that the detection device and detection method that can autonomously detect the growth status of crops realizes autonomous detection of crop physiological and biochemical characteristics by setting up a mobile chassis, a lifting platform, a slide rail, an image acquisition component, a navigation component and an interactive system, improves detection efficiency and accuracy, and is suitable for the detection needs of various crops. The navigation component automatically establishes an environmental map, and performs path planning and obstacle avoidance according to the target point, and the mobile chassis automatically moves to the target position. After reaching the target position, fine-tuning is performed through the lifting platform and the slide rail to ensure that the image acquisition component reaches the optimal shooting position. The image acquisition component starts working, and the collected data is uploaded to the Internet of Things cloud through the interactive system to realize remote monitoring and analysis of the data.

[0069] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, this article specifically cites preferred embodiments and provides detailed descriptions as follows in conjunction with the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 A schematic diagram of the structure of a detection device capable of autonomously detecting the growth status of crops provided in an embodiment of the present disclosure;

[0073] Figure 2 A communication structure diagram of a detection device capable of autonomously detecting the growth status of crops provided in an embodiment of the present disclosure;

[0074] Figure 3 A flowchart of the steps of adjusting the position of the image acquisition component through the lifting platform and the slide rail and controlling the image acquisition component to take pictures provided in the embodiment of the present disclosure;

[0075] Figure 4 A schematic diagram of the steering of a mobile chassis provided in an embodiment of the present disclosure;

[0076] Figure 5 A partial structural diagram of an image acquisition component provided in an embodiment of the present disclosure;

[0077] Figure 6 A top view of a detection device capable of autonomously detecting the growth status of crops provided in an embodiment of the present disclosure;

[0078] Figure 7 A partial schematic diagram of a lifting platform provided in an embodiment of the present disclosure;

[0079] Figure 8 A schematic diagram of the degree of freedom adjustment of a detection device capable of autonomously detecting the growth status of crops provided by an embodiment of the present disclosure;

[0080] Fig. 9 A block diagram of the communication methods of various parts of the detection device that can autonomously detect the growth status of crops provided in an embodiment of the present disclosure.

[0081] In the figure: 1. Image acquisition component; 2. Slide rail; 3. Navigation component; 4. Lifting platform; 5. Robotic arm; 6. Mobile chassis; 7. Interactive system;

[0082] 1.1. Camera mounting platform; 1.2. Connecting bracket;

[0083] 2.1, Dual output shaft stepper motor;

[0084] 3.1, laser radar; 3.2, control box;

[0085] 4.1, turntable; 4.2, push rod motor; 4.3, support frame; 4.4, U-shaped platform;

[0086] 5.1 End of the robotic arm;

[0087] 6.1. Steering linkage; 6.2. Double wishbone suspension; 6.3. Wheel hub motor control board. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, in the drawings, the thickness of the components may be exaggerated or reduced in order to effectively describe the technical content.

[0090] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0091] See also Figure 1 and Figure 2 , Figure 1The schematic diagram of the structure of the detection device that can autonomously detect the growth status of crops is shown, which includes: a mobile chassis 6; a lifting platform 4, which is arranged on the mobile chassis 6; a slide rail 2, which is arranged on the lifting platform 4; an image acquisition component 1, which is slidably arranged on the slide rail 2; a navigation component 3, which is arranged on the top of the lifting platform 4; an interactive system 7, which is arranged on the mobile chassis 6; during detection, the interactive system 7 is suitable for receiving the navigation instructions of the navigation component 3, and controlling the mobile chassis 6 to reach the crop point to be photographed according to the navigation instructions, and the interactive system 7 is suitable for adjusting the position of the image acquisition component 1 through the lifting platform 4 and the slide rail 2, and controlling the image acquisition component 1 to shoot. By setting the mobile chassis 6, the lifting platform 4, the slide rail 2, the image acquisition component 1, the navigation component 3 and the interactive system 7, the autonomous detection of the physiological and biochemical characteristics of crops is realized, the detection efficiency and accuracy are improved, and it is suitable for the detection needs of various crops. The environment map is automatically established by the navigation component 3, and the path planning and obstacle avoidance are performed according to the target point, and the mobile chassis 6 automatically moves to the target position. After reaching the target position, fine adjustment is performed through the lifting platform 4 and the slide rail 2 to ensure that the image acquisition component 1 reaches the best shooting position. The image acquisition component 1 starts working, and the collected data is uploaded to the Internet of Things cloud through the interactive system 7 to realize remote monitoring and analysis of the data.

[0092] Among them, the interactive system uses an industrial computer as a processor to implement the operation of the algorithm, collect the collected data, control the mobile chassis, and control the robotic arm.

[0093] See also Figure 1 and Figure 4 In some embodiments, the mobile chassis 6 adopts a double rocker arm suspension 6.2, which has the advantages of high durability and reliability and is conducive to coping with harsh environments; the mobile chassis 6 also adopts a dual steering solution, and the front and rear wheels can be steered at the same time, effectively reducing the turning radius, such as Figure 4 As shown, when the whole machine turns left, the front and rear wheels of the whole machine reverse under the reverse movement of the steering link 6.1 to complete the left turn. The principle of right turning is the same as that of left turning.

[0094] The mobile chassis 6 is provided with a wheel hub motor control board 6.3 for driving the mobile chassis 6 to move.

[0095] See also Figure 5In some embodiments, the image acquisition component 1 includes: a connecting bracket 1.2, a rotating camera mounting platform 1.1, a visible light camera, an infrared camera and a multi-spectral camera; the connecting bracket 1.2 is slidably set on the slide rail 2; the rotating camera mounting platform 1.1 is rotatably set on the connecting bracket 1.2; the visible light camera, the infrared camera and the multi-spectral camera are all set on the rotating camera mounting platform 1.1.

[0096] Among them, the connecting bracket 1.2 is driven by a dual-output shaft stepper motor 2.1, and synchronous pulleys are installed at both ends of the dual-output shaft stepper motor 2.1. Both ends of the synchronous belt are fixed on the sliding plate under the connecting bracket 1.2. When the dual-output shaft stepper motor 2.1 rotates, the synchronous pulley drives the synchronous belt to move, and the sliding plate drives the entire image acquisition system to move in the Y-axis direction through the connecting bracket 1.2.

[0097] See also Figure 6 The navigation component 3 includes a laser radar 3.1 and a control box 3.2. The laser radar 3.1 is installed above the control box 3.2, and the control box 3.2 is fixed on the U-shaped platform 4.4 of the lifting platform 4. The control box 3.2 combines the robot operating system with the interactive system 7, and is equipped with a 16-line laser radar 3.1. The improved cartographer algorithm is used for mapping to generate an accurate environmental map. After the mapping is completed, the target point is selected, and the obstacle avoidance, path planning and target approaching algorithms will generate the best path. The whole machine will move along the best path to the appropriate area where crops need to be collected, completing unmanned automatic navigation.

[0098] See also Figure 7 The lifting platform 4 is set at the front end above the mobile chassis 6, the turntable 4.1 is set on the mobile chassis 6, the support frame 4.3 and the push rod motor 4.2 are fixed on the surface of the turntable 4.1, and the two jointly support the entire U-shaped platform 4.4. The turntable 4.1 can rotate 360 ​​degrees. Through the control of the rotating motor, the crops on the left and right sides and in front of the detection device that can autonomously detect the growth status of the crops can be collected.

[0099] See also Figure 3 and Figure 8 , the mobile chassis 6 moves along the X-axis direction, which corresponds to the speed direction v1, the lifting platform 4 moves along the Z-axis direction, which corresponds to the speed direction v2, the image acquisition component 1 moves along the Y-axis direction on the slide rail 2, which corresponds to the speed direction v3, the turntable 4.1 can drive the entire lifting platform 4 to rotate around the Z-axis, which corresponds to the speed direction y1, and the stepper motor in the image acquisition component 1 will drive the camera installation platform 1.1 to rotate around the Y-axis, which corresponds to the speed direction y2. The steps of adjusting the position of the image acquisition component 1 through the lifting platform 4 and the slide rail 2 include:

[0100] S1: Control the visible light camera to obtain crop photos;

[0101] S2: Process photos using computer vision and image processing techniques;

[0102] S3: judging whether the crop is in the center of the picture according to the processing result, if so, proceeding to step S4, if not, proceeding to step S5;

[0103] S4: obtaining the area ratio of the crop in the entire photo. If the area ratio is within a preset range, the adjustment is completed. If the area ratio is not within the preset range, the process proceeds to step S6.

[0104] S5: adjusting the position of the mobile chassis 6 and / or the connecting bracket 1.2 according to the positional relationship between the crop and the center of the picture, and entering step S1 after the adjustment is completed;

[0105] S6: Adjust the height of the lifting platform 4, and enter step S4 after the adjustment is completed.

[0106] Specifically, the step of adjusting the height of the lifting platform 4 is: if the area ratio is less than the preset range, the lifting platform 4 is controlled to descend; if the area ratio is greater than the preset range, the lifting platform 4 is controlled to ascend.

[0107] In a preferred embodiment, the preset range is 60% to 80%.

[0108] Wherein, step S2 comprises the following steps:

[0109] S21: Image preprocessing, namely:

[0110] Grayscale: Convert a color image to a grayscale image. The formula is as follows:

[0111] I gray (xy)=0.299·I R (x,y)+0.587·I G (xy)+0.114·I B (x,y);

[0112] In the formula, I R ,I G ,I B They are the red, green, and blue channels of the image, respectively, and (x, y) represents the pixel point;

[0113] Gaussian blur, use the Gaussian module to reduce noise, the formula is as follows:

[0114]

[0115] Among them, G(i,j) is the Gaussian kernel, k is the size of the kernel;

[0116] S22: Use threshold segmentation to binarize the image. The formula is as follows:

[0117]

[0118] Where T is the threshold;

[0119] S23: Use Canny edge detection to find contours in the image.

[0120] Contours = findContours(I thresholded );

[0121] S24: Calculate the area of ​​the crop area using the following formula:

[0122] A crop =contourArea(C largest )

[0123] Among them, C largest It is the largest outline;

[0124] Calculate the area ratio using the following formula:

[0125]

[0126] S25: Calculate the distance between the crop and the center of the photo. The formula is as follows;

[0127] Δx=c x -c image_x

[0128] Δy=c y -c image_y ;

[0129] In the formula, c image_x 、c image_y is the image center, c x and c y represents the contour center of the crop, Δx and Δy represent the difference between the contour center of the crop and the center of the image.

[0130] Wherein, step S5 comprises:

[0131] S51: If the center of the crop's outline is located on the left side of the image center, the movable chassis 6 is controlled to move leftward; if the center of the crop's outline is located on the right side of the image center, the movable chassis 6 is controlled to move rightward;

[0132] S52: When the center of the crop's outline is above the center of the image, the connecting bracket 1.2 is controlled to move forward; when the center of the crop's outline is below the center of the image, the connecting bracket 1.2 is controlled to move backward.

[0133] If the area ratio is smaller than the preset range, the lifting platform 4 is controlled to descend; if the area ratio is larger than the preset range, the lifting platform 4 is controlled to ascend.

[0134] In some embodiments, the step of controlling the image acquisition component 1 to shoot includes:

[0135] S71: Control the visible light camera to take pictures. After the pictures are taken, control the rotating camera mounting platform 1.1 to rotate 90° clockwise;

[0136] S72: Control the infrared camera to take pictures. After the pictures are taken, control the rotating camera mounting platform 1.1 to rotate 90° clockwise;

[0137] S73: Control the multispectral camera to take pictures. After the pictures are taken, control the rotating camera mounting platform 1.1 to rotate 180° counterclockwise and return to the initial state.

[0138] See also Figure 1 and Figure 6 In some embodiments, the detection device capable of autonomously detecting the growth status of crops further comprises a mechanical arm 5; the mechanical arm 5 is arranged on the mobile chassis 6, and an electrode clamp is arranged at the execution end of the mechanical arm 5; the clamp is suitable for detecting the bioelectrical signal of the crop. Among them, a depth camera is also arranged on the mechanical arm 5; the depth camera is suitable for capturing the image information of the crop and sending it to the interactive system 7; the interactive system 7 is also suitable for finding multiple groups of different leaves based on image recognition technology, clamping each group of leaves in turn with the electrode clamp, and uploading the acquired bioelectrical signal to the interactive system 7.

[0139] See also Fig. 9 , Fig. 9 A partial communication block diagram between various parts of the detection device that can autonomously detect the growth status of crops and the interactive system 7 is shown.

[0140] At least one embodiment also provides a detection method for a detection device as described above that can autonomously detect the growth status of crops, the detection method comprising: receiving navigation instructions from the navigation component 3; the mobile chassis 6 reaches the crop point to be photographed according to the navigation instructions; adjusting the position of the image acquisition component 1 through the lifting platform 4 and the slide rail 2; and controlling the image acquisition component 1 to take pictures.

[0141] In summary, the present invention provides a detection device and a detection method that can autonomously detect the growth status of crops. By setting a mobile chassis, a lifting platform, a slide rail, an image acquisition component, a navigation component and an interactive system, autonomous detection of crop physiological and biochemical characteristics is achieved, detection efficiency and accuracy are improved, and it is suitable for the detection needs of various crops. The navigation component automatically establishes an environmental map, and performs path planning and obstacle avoidance according to the target point, and the mobile chassis automatically moves to the target position. After reaching the target position, fine-tuning is performed through the lifting platform and the slide rail to ensure that the image acquisition component reaches the optimal shooting position. The image acquisition component starts working, and the collected data is uploaded to the Internet of Things cloud through the interactive system to achieve remote monitoring and analysis of the data.

[0142] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A detection device capable of autonomously detecting the growth status of crops, characterized in that: include: Mobile chassis; A lifting platform, which is arranged on the mobile chassis; A slide rail, which is arranged on the lifting platform; An image acquisition component is slidably arranged on the slide rail; A navigation assembly, which is arranged on the top of the lifting platform; An interactive system, which is arranged on the mobile chassis; During detection, the interactive system is suitable for receiving navigation instructions from the navigation component, and controlling the mobile chassis to reach the crop point to be photographed according to the navigation instructions. The interactive system is suitable for adjusting the position of the image acquisition component through the lifting platform and the slide rail, and controlling the image acquisition component to take pictures.

2. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 1, characterized in that: The image acquisition assembly includes: a connecting bracket, a rotating camera mounting platform, a visible light camera, an infrared camera and a multi-spectral camera; The connecting bracket is slidably arranged on the slide rail; The rotating camera mounting platform is rotatably disposed on the connecting bracket; The visible light camera, infrared camera and multi-spectral camera are all arranged on the rotating camera mounting platform.

3. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 2, characterized in that: The step of adjusting the position of the image acquisition component by means of the lifting platform and the slide rail comprises: S1: Control the visible light camera to obtain crop photos; S2: Process photos using computer vision and image processing techniques; S3: judging whether the crop is in the center of the picture according to the processing result, if so, proceeding to step S4, if not, proceeding to step S5; S4: obtaining the area ratio of the crop in the entire photo. If the area ratio is within a preset range, the adjustment is completed. If the area ratio is not within the preset range, the process proceeds to step S6. S5: adjusting the position of the mobile chassis and / or the connecting bracket according to the positional relationship between the crop and the center of the image, and entering step S1 after the adjustment is completed; S6: Adjust the height of the lifting platform, and go to step S4 after the adjustment is completed.

4. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 3, characterized in that: The step of processing the photo by computer vision and image processing technology includes: S21: Image preprocessing, namely: Grayscale: Convert a color image to a grayscale image. The formula is as follows: I gray (x.y)=0.299·I R (x,y)+0.587·I G (x.y)+0.114·I B (x,y); In the formula, I R ,I G ,I B They are the red, green, and blue channels of the image, respectively, and (x, y) represents the pixel point; Gaussian blur, use the Gaussian module to reduce noise, the formula is as follows: Among them, G(i,j) is the Gaussian kernel, i and j represent the coordinate offsets in the Gaussian kernel, and k is the size of the kernel; S22: Use threshold segmentation to binarize the image. The formula is as follows: Where T is the threshold; S23: Use Canny edge detection to find contours in the image. Contours=findContours(I thresholded ); S24: Calculate the area of ​​the crop area using the following formula: A crop =contourArea(C largest ) Among them, C largest It is the largest outline; Calculate the area ratio using the following formula: S25: Calculate the distance between the crop and the center of the photo. The formula is as follows; Δx=c x -c image_x Δy=c y -c image_y ; In the formula, c image_x 、c image_y is the image center, c x and c y represents the contour center of the crop, Δx and Δy represent the difference between the contour center of the crop and the center of the image.

5. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 3, characterized in that: The step of adjusting the position of the mobile chassis and / or the connecting bracket according to the positional relationship between the crop and the center of the picture includes: S51: When the center of the crop's outline is on the left side of the image center, the mobile chassis is controlled to move to the left; when the center of the crop's outline is on the right side of the image center, the mobile chassis is controlled to move to the right; S52: When the center of the crop outline is located above the center of the image, the connecting bracket is controlled to move forward; when the center of the crop outline is located below the center of the image, the connecting bracket is controlled to move backward.

6. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 3, characterized in that: The steps of adjusting the height of the lifting platform are: If the area ratio is less than the preset range, the lifting platform is controlled to descend; if the area ratio is greater than the preset range, the lifting platform is controlled to ascend.

7. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 2, characterized in that: The step of controlling the image acquisition component to shoot comprises: S71: Control the visible light camera to take pictures. After the pictures are taken, control the rotating camera mounting platform to rotate 90° clockwise. S72: Control the infrared camera to take pictures. After the pictures are taken, control the rotating camera mounting platform to rotate 90° clockwise. S73: Control the multispectral camera to shoot. After shooting, control the rotating camera mounting platform to rotate 180° counterclockwise and return to the initial state.

8. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 1, characterized in that: The detection device capable of autonomously detecting the growth status of crops further comprises a mechanical arm; The mechanical arm is arranged on the mobile chassis, and an electrode clamp is arranged at the execution end of the mechanical arm; The clamping jaw is suitable for detecting bioelectric signals of crops.

9. The detection device capable of autonomously detecting the growth status of crops as claimed in claim 8, characterized in that: The mechanical arm is also provided with a depth camera; The depth camera is suitable for capturing image information of crops and sending the image information to the interactive system; The interactive system is also suitable for finding multiple groups of different leaves based on image recognition technology, clamping each group of leaves in turn through the electrode clamp, and uploading the acquired bioelectric signals to the interactive system.

10. A detection method for a detection device capable of autonomously detecting the growth status of crops as claimed in any one of claims 1 to 9, characterized in that: The detection method comprises: receiving a navigation instruction from the navigation component; The mobile chassis reaches the crop point to be photographed according to the navigation instructions; The position of the image acquisition component is adjusted through the lifting platform and the slide rail; Control the image acquisition component to take pictures.

Images