Intensive wheat ear head detection assembly based on Raspberry Pi

Abstract

The utility model discloses an intensive wheat ear head detection assembly based on Raspberry Pi, which belongs to the technical field of intensive wheat ear head detection equipment and comprises telescopic support rods positioned on the periphery and a mounting plate positioned in the center. The upper ends of the telescopic sections of the telescopic support rods are inwards and fixedly connected to the side faces of the periphery of the mounting plate through connecting rods, the lower ends of the non-telescopic sections of the telescopic support rods are connected with traveling wheels through rotating shafts, and the side faces of the upper ends of the non-telescopic sections of the two telescopic support rods located on the same side are backwards connected with concave push rods. And a control button seat is arranged on the rod body of the push rod. The detection assembly is designed based on the Raspberry Pi which is small in size and easy to carry and move, the detection assembly is moved and adjusted in a wheat field through a related adjusting assembly, multiple sets of images at different angles can be collected for dense wheat ears in the same unit area, the number of samples detected by a subsequent computer algorithm is increased, and the detection accuracy is improved. And the detection accuracy of the number of dense wheat ears is improved.

Description

TECHNICAL FIELD

[0001] The utility model relates to intensive wheat head detection equipment technical field more specifically, relate to a kind of intensive wheat head detection assembly based on raspberry pi. BACKGROUND

[0002] Wheat is the most widely planted, largest area and highest yield of grain crops in the world, timely estimation of wheat yield has important influence on crop production, food prices and food security, and the number of heads in unit area is the most important in wheat yield estimation, at present, artificial estimation method is based on expert visual estimation yield, accuracy cannot be guaranteed, sampling estimation method is through collecting part of area to manually count, weigh, time-consuming and laborious.

[0003] With the development of computer vision technology, for the wheat field in different regions of wheat head image, the learning ability of convolutional neural network can be used to extract features of intensive wheat head, realize wheat head quantity statistics and further realize yield estimation, and the accuracy of yield estimation can be further improved by training model with a large amount of data. The premise of computer vision yield estimation is to collect multiple wheat head images in wheat field to construct the data set required for training model, the existing acquisition and detection assembly is relatively bulky, including computer, camera and multiple lines and other components with large volume, carrying is relatively cumbersome, and in the acquisition and detection process, multiple people are needed to move the detection assembly frequently, the detection efficiency is low, and it cannot be well adapted to real environment of wheat field, and the portability and ease of use are poor. UTILITARIAN CONTENT

[0004] The utility model aims at providing a kind of intensive wheat head detection assembly based on raspberry pi, the detection assembly is designed based on the raspberry pi with smaller volume and easy to carry and move, through relevant adjusting assembly, the movement of detection assembly and adjustment are realized in wheat field, and real-time fixed-point wheat head quantity statistics are carried out, and for the intensive wheat head in the same unit area, multiple groups of images of different angles are collected, the sample number of subsequent computer algorithm training is increased, and the accuracy of intensive wheat head quantity detection is improved.

[0005] In order to realize the above-mentioned purpose, the technical scheme adopted by the utility model is as follows:

[0006] A kind of dense ear head detection component based on raspberry pi, including telescopic support pole located in four around and mounting plate located at central position, the telescopic support pole telescopic section upper end is fixedly connected to the mounting plate four around side position by connecting rod inward, the telescopic support pole non-telescopic section lower end is connected with traveling wheel by pivot, wherein the upper end side of the non-telescopic section of two telescopic support poles located in the same side is connected with concave push rod backward, the push rod is equipped with control button seat on the rod body, the mounting plate upper surface one side is equipped with portable power supply box and the portable power supply box is connected with solar photovoltaic module above.

[0007] As further optimization of the present scheme, the mounting plate central position is provided with through slot and small drive motor is fixedly installed in the through slot, the small drive motor lower output shaft is fixedly sleeved with telescopic connecting rod, the telescopic connecting rod telescopic section outer end is installed with raspberry pi casing, the small drive motor and raspberry pi casing are connected with portable power supply box by line and the portable power supply box is connected with control button seat by line.

[0008] As further optimization of the present scheme, the raspberry pi casing lower surface is equipped with dense ear head image acquisition camera, the dense ear head image acquisition camera is connected with the corresponding interface of raspberry pi mainboard in raspberry pi casing by FPC line.

[0009] As further optimization of the present scheme, the telescopic connecting rod telescopic section outer end is fixed with clamping plate and rotating seat is inwardly tightened and installed in the region between the clamping plate by angle adjusting bolt, the recessed clamping plate is connected with recessed clamping plate in the middle position of both sides by several spring rods, the recessed clamping plate is inserted into guide rod and the guide rod inner end is fixed in rotating seat both sides, and the recessed clamping plate is inwardly clamped raspberry pi casing.

[0010] As further optimization of the present scheme, the raspberry pi casing side is equipped with several USB interfaces and network port, and the network port is inserted with wireless network card.

[0011] Compared with the prior art, the beneficial effects of the present application are as follows:

[0012] In the present application, the telescopic support pole, traveling wheel and push rod are arranged, so that the raspberry pi casing can be moved to any position in the wheat field, and the height of the telescopic support pole can be adjusted by the locking nut, so that the shooting height of the dense ear head image acquisition camera can be quickly adjusted to adapt to the ear height of different growth periods.

[0013] The utility model discloses a control button seat, small -size drive motor, telescopic connecting rod and recessed clamping plate etc. structure are designed, convenient to take and fix raspberry pi casing's while, can through the angle adjusting bolt adjustment dense wheat head image collection camera's shooting angle, telescopic connecting rod can adjust dense wheat head image collection camera distance installation board's distance, and the small -size drive motor drives dense wheat head image collection camera to rotate around the center installation board, and the same unit area's dense wheat head is gathered to multiple groups of different angle's image, increased subsequent computer algorithm training's sample number, improved dense wheat head quantity detection's accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is the whole structure schematic diagram of the utility model dense wheat head detection assembly;

[0015] Figure 2 It is the installation plate top structure schematic diagram of the utility model;

[0016] Figure 3 It is the installation plate below structure schematic diagram of the utility model;

[0017] Figure 4 It is the rotating seat connecting structure schematic diagram of the utility model;

[0018] In the drawing: 1, telescopic support rod;2, push rod;3, control button seat;4, connecting rod;5, installation plate;6, portable power supply box;7, solar photovoltaic module;8, line;9, flange;10, small -size drive motor;11, telescopic connecting rod;12, raspberry pi casing;13, dense wheat head image collection camera;14, USB interface;15, wireless network card;16, clamping plate;17, angle adjusting bolt;18, rotating seat;19, guide rod;20, spring rod;21, recessed clamping plate. DETAILED DESCRIPTION

[0019] In order to make the technical means, creation characteristics, achieve the purpose and effect of the utility model easy to understand, the following further elaborates the utility model in combination with specific drawing.

[0020] In order to solve the existing collection detection assembly is relatively bloated, including the computer of relatively large size, camera and multiple lines etc. assembly, carrying is relatively complicated, and in the collection detection process, needs to move detection assembly frequently, and the detection efficiency is relatively low, can not be better adapted to the real environment of wheat field, is not enough portable and easy to use, such as Figure 1As shown, the application includes four telescopic support rods 1 located around the periphery and a mounting plate 5 located at the center position, the upper end of the telescopic section of the telescopic support rod 1 is fixedly connected to the mounting plate 5 at the side position around the periphery through a connecting rod 4, the lower end of the non-telescopic section of the telescopic support rod 1 is connected with a traveling wheel through a rotating shaft, and the upper end of the non-telescopic section of the telescopic support rod 1 located at the same side is connected with a concave push rod 2 on the side surface, the push rod 2 is provided with a control button seat 3 on the rod body, a mobile power supply box 6 is arranged on one side of the upper surface of the mounting plate 5, and a solar photovoltaic module 7 is connected above the mobile power supply box 6, which is used to further improve the working endurance time of the detection assembly in the wheat field.

[0021] As shown in the figure, Figure 2 A through slot is formed in the center position of the mounting plate 5, and a small driving motor 10 is fixedly installed in the through slot through a flange 9, a telescopic connecting rod 11 is fixedly sleeved on the output shaft below the small driving motor 10, and a Raspberry Pi casing 12 is installed on the outer end of the telescopic section of the telescopic connecting rod 11. The small driving motor 10 and the Raspberry Pi casing 12 are connected with the mobile power supply box 6 through the line 8, and the mobile power supply box 6 is connected with the control button seat 3 through the line 8.

[0022] As shown in the figure, Figure 3 A dense ear image acquisition camera 13 is arranged on the lower surface of the Raspberry Pi casing 12, and the dense ear image acquisition camera 13 is connected with the corresponding interface of the Raspberry Pi mainboard in the Raspberry Pi casing 12 through FPC line.

[0023] As shown in the figure, Figure 4 The clamping plates 16 are fixed on the outer end of the telescopic section of the telescopic connecting rod 11, and the rotating seats 18 are tightly installed in the area between the clamping plates 16 through the angle adjusting bolts 17, the concave clamping plates 21 are connected with the rotating seats 18 through the spring rods 20 at the middle positions on both sides of the rotating seats 18, the concave clamping plates 21 are tightly clamped with the Raspberry Pi casing 12, and the Raspberry Pi casing 12 is provided with a plurality of USB interfaces 14 and a network port on the side surface, and the wireless network card 15 is inserted into the network port.

[0024] Specifically, the height adjustable range of the dense ear image acquisition camera 13 is 70 to 145 cm, and in actual use, the Raspberry Pi casing 12 can be easily placed between the concave clamping plates 21 by manually opening and closing the concave clamping plates 21 and fixed by the inward elastic force of the spring rods 20, and at the same time, the wireless network card 15 is inserted into the network port of the Raspberry Pi casing 12 for remote data transmission with the processing end with a touch screen.

[0025] The detection personnel pushes the whole detection assembly to the corresponding detection point position of the wheat field through the push rod 2, then adjusts the height of the dense ear image acquisition camera 13 through the four telescopic support rods 1 around, according to the actual ear height, so that the camera plane is 50 cm away from the ear, then adjusts the horizontal position of the dense ear image acquisition camera 13 through the telescopic connecting rod 11, adjusts the shooting angle of the dense ear image acquisition camera 13 through the rotating seat 18, and the image acquisition work can be started, in the process, the detection personnel can start the small driving motor 10 to rotate and drive the dense ear image acquisition camera 13 to rotate around the center mounting plate 5 through the control button seat 3, different angle images of the dense ear are collected, image data is wirelessly transmitted to the processing end, the dense ear is extracted through the feature self-learning ability of the convolutional neural network, the YOLO model is trained through a large amount of data, and the estimation accuracy is improved.

[0026] The standard parts used in the utility model can be purchased from the market, the special-shaped parts can be ordered according to the description and the drawings, the specific connection mode of each part adopts the conventional means such as bolt, rivet and welding in the prior art, the machinery, parts and equipment adopt the conventional type in the prior art, and the circuit connection adopts the conventional connection mode in the prior art, which will not be described in detail here.

[0027] The basic principle and main features of the utility model and the advantages of the utility model are shown and described above. It should be understood by the person skilled in the art that the utility model is not limited by the above-mentioned embodiments, and the above-mentioned embodiments and the description in the specification only illustrate the principle of the utility model, various changes and improvements can be made to the utility model without departing from the spirit and scope of the utility model, and these changes and improvements all fall within the scope of the utility model claimed. The protection scope of the utility model is defined by the appended claims and their equivalents.

Claims

1. A dense wheat head detection component based on Raspberry Pi, characterized in that: The system includes telescopic support rods located around the perimeter and a mounting plate located at the center. The upper ends of the telescopic sections of the telescopic support rods are fixedly connected inward to the sides of the mounting plate via connecting rods. The lower ends of the non-telescopic sections of the telescopic support rods are connected to driving wheels via pivots. The upper sides of the non-telescopic sections of two telescopic support rods on the same side are connected to concave push rods. The push rods are equipped with control button seats. A mobile power supply box is located on one side of the upper surface of the mounting plate, and a solar photovoltaic module is connected above the mobile power supply box.

2. The dense wheat head detection component based on Raspberry Pi according to claim 1, characterized in that: The mounting plate has a through slot at its center, and a small drive motor is fixedly installed inside the through slot via a flange. A telescopic connecting rod is fixedly mounted on the output shaft below the small drive motor. A Raspberry Pi casing is installed on the outer end of the telescopic section of the telescopic connecting rod. The small drive motor and the Raspberry Pi casing are both connected to a mobile power supply box via wiring, and the mobile power supply box is connected to a control button socket via wiring.

3. The dense wheat head detection component based on Raspberry Pi according to claim 2, characterized in that: The lower surface of the Raspberry Pi casing is equipped with a dense wheat-head image acquisition camera, which is connected to the corresponding interface of the Raspberry Pi motherboard inside the Raspberry Pi casing via an FPC cable.

4. The dense wheat head detection component based on Raspberry Pi according to claim 2, characterized in that: The telescopic link has a clamp plate fixed to its outer end, and a rotating seat is installed between the clamp plates by tightening the angle adjustment bolts inward. The rotating seat has a concave clamp plate connected to the middle position on both sides by several spring rods. A guide rod is inserted into the concave clamp plate and the inner end of the guide rod is fixed to both sides of the rotating seat. The concave clamp plate clamps the Raspberry Pi casing inward.

5. A dense wheat head detection component based on Raspberry Pi according to claim 4, characterized in that: The Raspberry Pi casing has several USB ports and a network port on its side, and a wireless network card is inserted into the network port.

Images