CN115338875A - Intelligent tea leaf picking system and method based on image recognition - Google Patents
Intelligent tea leaf picking system and method based on image recognition Download PDFInfo
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- CN115338875A CN115338875A CN202211279589.4A CN202211279589A CN115338875A CN 115338875 A CN115338875 A CN 115338875A CN 202211279589 A CN202211279589 A CN 202211279589A CN 115338875 A CN115338875 A CN 115338875A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D46/00—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs
- A01D46/04—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs of tea
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D46/00—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs
- A01D46/30—Robotic devices for individually picking crops
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
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Abstract
The invention relates to the field of tea picking, in particular to an intelligent tea picking system and method based on image recognition. The intelligent system comprises a data acquisition unit, a server, a control unit, a motor driver, at least one sliding table, at least one manipulator and a communication unit. According to the tea leaf picking device, the tea leaf picking efficiency is improved in a mode of mechanically picking tea leaves by combining the control unit, the sliding table and the mechanical arm; the tea leaf image recognition is carried out through the Yolov5 algorithm, so that the tea leaf picking accuracy is improved; a large amount of operating equipment can be arranged in a complex data processing mode through the server, and the method is suitable for large-scale picking; the price of a single device is reduced by means of image recognition and data processing through the server; through the way of route planning and infrared induction, can reduce the damage to the tealeaves that can not pick.
Description
Technical Field
The invention relates to the field of tea picking, in particular to an intelligent tea picking system and method based on image recognition.
Background
Tea picking becomes the most troublesome problem for farmers due to strong seasonal picking, high cost and low efficiency, and along with the current social development, the situation is required, the tea planting area is wider and wider, along with the fact that the area needing to be picked is also larger and larger, the labor force of the work is reduced more and less due to high-intensity repeated work, the required labor cost is naturally improved more greatly, but the requirement of the current picking work cannot be met by selecting manual picking. With the improvement of image processing technology and sensor technology, the rapid development of intelligent control theory and the interactive derivation of a series of subjects such as computer science, electronics, information, intelligent technology and the like, the possibility of machine picking industrial production is brought.
Aiming at the problems of high cost and low efficiency of manual tea leaf picking, it is necessary to design an efficient tea leaf image recognition and processing system and method through technologies such as artificial intelligence, sensors and cloud servers.
Disclosure of Invention
The invention aims to provide an intelligent tea leaf picking system and method based on image recognition, and solves the problems of low efficiency and high cost of manual tea leaf picking.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an intelligent tea leaf picking system based on image recognition comprises a data acquisition unit, a server, a control unit, a motor driver, at least one sliding table, at least one manipulator and a communication unit;
the data acquisition unit acquires image information and distance information of tea leaves in an area and transmits the acquired information to the server;
the server performs image recognition on the acquired image information, converts distance information of the information of which the recognition result meets the requirement into coordinate information and sends the coordinate information to the control unit;
the control unit converts the coordinate information into a variable pulse width signal and sends the signal to the motor driver;
the motor driver is controlled to move to multiple shafts according to the variable pulse width signals, and the mechanical arm is controlled at the signal indication position to pick and collect tea leaves;
the data acquisition unit and the server, the server and the control unit, the control unit and the motor driver, and the motor driver and the sliding table realize data transmission through the communication module.
Preferably, the data acquisition unit acquires images through a USB drive-free binocular distance measurement synchronization module, and acquires position information of the images through a laser distance measurement module.
Preferably, the specific steps of the server performing image recognition on the acquired image information and converting the distance information of the information whose recognition result meets the requirement into the coordinate information include: and the server identifies the image through a Yolov5 algorithm, judges whether the image is a leaf bud which can be picked, acquires an image of the next area if the image is not the leaf bud which can be picked, extracts position information of the image if the image is the leaf bud which can be picked, and converts the position information of the image into coordinate information.
Preferably, before the position information is transmitted to the control unit, the image is divided into N areas, and coordinate information of centers of the N areas is computationally acquired, where N is the number of the manipulators.
Preferably, in each of the regions, the coordinates of the progress path of the manipulator are obtained by ant colony algorithm calculation.
Preferably, the control unit comprises three variable pulse width signal interfaces.
Preferably, the sliding table is formed by matching a 2060V-Slot aluminum section lead screw sliding table with a 42 stepping motor sliding table, wherein the effective stroke of the lead screw is 600mm, and the load is 50kg.
Preferably, the manipulator senses the leaf buds through an infrared sensor and controls the blades to close to complete the picking of the tea leaves.
Preferably, the system comprises a power supply unit, the power supply unit is provided with 2 12V/2A batteries, and a 24V-to-12V direct current converter is connected in series for voltage reduction output.
In another aspect, an intelligent tea leaf picking method based on image recognition is provided, which comprises the following steps,
step A, acquiring images and position information of a set area through a data acquisition unit, and uploading the images and the position information to a server;
b, the server identifies whether the leaf buds in the image can be picked or not through an image identification algorithm, converts the position information into coordinate information and transmits the coordinate information to the control unit;
step C, the control unit converts the coordinate information into a variable pulse width signal and transmits the variable pulse width signal to a motor driver;
and D, controlling the sliding table to move to multiple shafts according to the variable pulse width signals, and controlling the mechanical arm to pick and collect tea leaves at the signal indication position.
Compared with the prior art, the invention has the beneficial effects that:
1) The tea leaf picking efficiency is improved through a mechanical tea leaf picking mode combining the control unit, the sliding table and the mechanical arm;
2) The image recognition is carried out through a Yolov5 algorithm, so that the tea picking accuracy is improved;
3) The price of a single device is reduced by means of image recognition and data processing through the server, and popularization is facilitated;
4) A server is used for complex data processing, a large amount of operating equipment can be arranged, the method is suitable for large-scale picking, and the time for large-area picking is shortened;
5) Through the route planning and the infrared induction mode, the damage to tea which cannot be picked can be reduced.
Drawings
Fig. 1 is a schematic workflow diagram of embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention as claimed, but is merely representative of some embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other without conflict.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures.
Example 1
An intelligent tea leaf picking system based on image recognition comprises a data acquisition unit, a server, a control unit, a motor driver, at least one sliding table, at least one manipulator and a communication unit, wherein the data acquisition unit is used for acquiring data;
the data acquisition unit acquires image information and distance information of tea leaves in an area and transmits the acquired information to the server; the server performs image recognition on the acquired image information, converts distance information of the information of which the recognition result meets the requirement into coordinate information and sends the coordinate information to the control unit; the control unit converts the coordinate information into a variable pulse width signal and sends the variable pulse width signal to the motor driver;
the motor driver is controlled to move to multiple shafts according to the variable pulse width signals, and the mechanical arm is controlled at the signal indication position to pick and collect tea leaves; the data acquisition unit and the server, the server and the control unit, the control unit and the motor driver, and the motor driver and the sliding table realize data transmission through the communication module.
The work flow diagram of the system is shown in fig. 1.
Example 2
On the basis of the embodiment 1, the data acquisition unit acquires images through a USB drive-free binocular distance measurement synchronization module, and acquires position information of the images through a laser distance measurement module. The server carries out image recognition on the acquired image information, and the specific steps of converting the distance information of the information with the recognition result meeting the requirements into coordinate information are as follows: and the server identifies the image through a Yolov5 algorithm, judges whether the image is a leaf bud which can be picked, acquires an image of the next area if the image is not the leaf bud which can be picked, extracts position information of the image if the image is the leaf bud which can be picked, and converts the position information of the image into coordinate information.
Example 3
On the basis of embodiment 2, before the position information is sent to the control unit, the image is divided into N areas, and coordinate information of the centers of the N areas is computationally obtained, where N is the number of the manipulators. And in each region, calculating and acquiring the path coordinates of the manipulator through an ant colony algorithm.
Example 4
On the basis of embodiment 1, the control unit comprises three variable pulse width interfaces. The sliding table is formed by matching a 2060V-Slot aluminum section lead screw sliding table with a 42 stepping motor sliding table, wherein the effective stroke of the lead screw is 600mm, and the load is 50kg. The manipulator senses leaf buds through the infrared sensor and controls the blades to close to complete picking of the tea leaves. The system comprises a power supply unit, wherein the power supply unit is provided with 2 12V/2A batteries which are connected in series, and a 24V-to-12V direct current converter is used for carrying out voltage reduction output.
Example 5
An intelligent tea leaf picking method based on image recognition comprises the following steps,
a, acquiring images and position information of a set area through a data acquisition unit, and uploading the images and the position information to a server;
b, the server identifies whether the leaf buds in the image can be picked or not through an image identification algorithm, converts the position information into coordinate information and transmits the coordinate information to the control unit;
step C, the control unit converts the coordinate information into a variable pulse width signal and transmits the variable pulse width signal to a motor driver;
and D, controlling the sliding table to move towards multiple shafts according to the variable pulse width signals, and controlling the mechanical arm to pick and collect the tea at the signal indication position.
In addition, the concept of partitioning in the present invention means to partition an image into regions, 2 regions means to partition the image into 2 regions on the left and right, each manipulator is responsible for its corresponding region, 4 regions means to partition the image into 4 regions, the left 2 regions are responsible for the left manipulator, the right 2 regions are responsible for the right manipulator, 6 regions means to partition the image into 6 regions, the left 3 regions are the working range of the left manipulator, the right 3 regions are the working range of the right manipulator, and so on.
The process of planning the tea picking path by using the basic ant colony algorithm comprises the following steps: firstly, carrying out normalization processing on coordinate data, then carrying out partitioning according to the principle that the number of fresh leaves is equal or the area of the fresh leaves is the same, then initializing parameters of an ant colony algorithm, starting to construct a solution space for a picking path, selecting the position of the next fresh leaf to be removed by ants according to a greedy method, obtaining an optimal solution after one cycle is completed, globally updating pheromones, carrying out the next iteration, adding 1 to the iteration number, restarting a new round of solution, outputting the optimal solution after the set value of the iteration number is reached, and finishing path planning.
The sliding table adopted by the invention is a synchronous belt type linear sliding table, and the structure at least comprises: the device comprises a belt, a linear guide rail, an aluminum alloy section, a coupler, a motor and a photoelectric switch.
The precision of the sliding table depends on the quality of a belt and the processing process in combination, the precision can be influenced by the control of power input, and the precision is generally higher than 0.1mm, so that the production cost can be controlled by adopting the synchronous belt types required by the sliding table for different production process requirements. The rigidity of the rigid guide rail can be increased according to different load requirements. The upper limit of the load is different for different specifications.
Usually through specific design, can control the elasticity of belt motion in one side of slip table, make things convenient for the debugging of equipment in process of production, the elasticity control of synchronous belt type all adopts screw control in the left and right sides, generally.
When picking fresh leaves, the mechanical arm moves in a 3-dimensional space, in order to reduce the influence of other branches and leaves on the movement of the mechanical arm, the mechanical arm is controlled to firstly reach a fixed position in an XY plane and then move downwards along a Z-axis, when the mechanical arm reaches the position of the fresh leaves, the blade is controlled to be closed, the fresh leaves are pinched off, then the mechanical arm moves upwards along the Z-axis to a set point, and the mechanical arm moves to the position of the next fresh leaf. Repeating the above process until fresh leaves are picked. In the fresh leaf picking process, the control system enables the mechanical arm moving process and the fresh leaf collecting process to be carried out simultaneously, the collecting process and the process of moving forwards to the next picking area are carried out simultaneously, and the picking speed is further improved.
The above embodiments are only used for illustrating the invention and not for limiting the technical solutions described in the invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above embodiments, and therefore, any modification or equivalent replacement of the present invention is made; all such modifications and variations are intended to be included herein within the scope of this disclosure and the appended claims.
Claims (10)
1. An intelligent tea leaf picking system based on image recognition is characterized by comprising a data acquisition unit, a server, a control unit, a motor driver, at least one sliding table, at least one manipulator and a communication unit, wherein the data acquisition unit is used for acquiring data of tea leaves;
the data acquisition unit acquires image information and distance information of tea leaves in an area and transmits the acquired information to the server;
the server performs image recognition on the acquired image information, converts the distance information of the information with the recognition result meeting the requirements into coordinate information and sends the coordinate information to the control unit;
the control unit converts the coordinate information into a variable pulse width signal and sends the signal to the motor driver;
the motor driver is controlled to move to multiple shafts according to the variable pulse width signals, and the mechanical arm is controlled at the signal indication position to pick and collect tea leaves;
the data acquisition unit and the server, the server and the control unit, the control unit and the motor driver, and the motor driver and the sliding table realize data transmission through the communication module.
2. The intelligent tea leaf picking system based on image recognition as claimed in claim 1, wherein the data acquisition unit acquires images through a USB drive-free binocular distance measurement synchronization module, and acquires position information of the images through a laser distance measurement module.
3. The intelligent tea leaf picking system based on image recognition as claimed in claim 1, wherein the server performs image recognition on the acquired image information, and the specific steps of converting the distance information of the information with the recognition result meeting the requirements into coordinate information are as follows: the server identifies the image through a Yolov5 algorithm, judges whether the image is a leaf bud which can be picked or not, acquires the image of the next area if the image is not the leaf bud which can be picked, extracts the position information of the image if the image is the leaf bud which can be picked, and converts the position information of the image into coordinate information.
4. An intelligent tea leaf picking system based on image recognition as claimed in claim 3, wherein before the position information is sent to the control unit, the image is divided into N areas, and coordinate information of the centers of the N areas is obtained through calculation, wherein N is the number of mechanical arms.
5. The intelligent tea leaf picking system based on image recognition as claimed in claim 4, wherein in each region, the carrying path coordinates of the mechanical arm are obtained through ant colony algorithm calculation.
6. The intelligent tea leaf picking system based on image recognition as claimed in claim 1, wherein the control unit comprises three variable pulse width signal interfaces.
7. The intelligent tea leaf picking system based on image recognition is characterized in that the sliding table is a 2060V-Slot aluminum section lead screw sliding table matched with a 42-step motor sliding table, wherein the effective stroke of the lead screw is 600mm, and the load is 50kg.
8. The intelligent tea leaf picking system based on image recognition as claimed in claim 1, wherein the manipulator senses leaf buds through an infrared sensor and controls blades to close to complete tea leaf picking.
9. The intelligent tea leaf picking system based on image recognition as claimed in claim 1, wherein the system comprises a power supply unit, the power supply unit is provided with 2 batteries of 12V/2A, and a 24V-to-12V DC converter is connected in series for voltage reduction output.
10. An intelligent tea leaf picking method based on image recognition is characterized by comprising the following steps,
a, acquiring images and position information of a set area through a data acquisition unit, and uploading the images and the position information to a server;
b, the server identifies whether the leaf buds in the image can be picked or not through an image identification algorithm, converts the position information into coordinate information and transmits the coordinate information to the control unit;
step C, the control unit converts the coordinate information into a variable pulse width signal and transmits the variable pulse width signal to a motor driver;
and D, controlling the sliding table to move towards multiple shafts according to the variable pulse width signals, and controlling the mechanical arm to pick and collect the tea at the signal indication position.
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Application publication date: 20221115 |