CN113826602A - Small-size multi-functional agricultural robot based on thing networking - Google Patents
Small-size multi-functional agricultural robot based on thing networking Download PDFInfo
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- CN113826602A CN113826602A CN202111208286.9A CN202111208286A CN113826602A CN 113826602 A CN113826602 A CN 113826602A CN 202111208286 A CN202111208286 A CN 202111208286A CN 113826602 A CN113826602 A CN 113826602A
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0089—Regulating or controlling systems
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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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- 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
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
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Abstract
The invention discloses a small-sized multifunctional agricultural robot based on the Internet of things, which comprises a starting agricultural robot, a camera, a spraying robot and a control system, wherein the starting agricultural robot moves according to a specified line, identifies crops in the line through the camera, then acquires images of the crops, identifies the images, finally partitions the images, extracts features after the images are partitioned, classifies the features after the features are extracted, sprays the crops when the crops needing to be sprayed with pesticides are identified, continues to move after the spraying, and picks the fruits through a mechanical arm when mature fruits are identified; the agricultural robot implanted with the whole set of functional program can sense the growth condition of crops more systematically, comprehensively and comprehensively, and can automatically perform corresponding operation according to the collected data and the resource conditions of regional agricultural climate, weather and the like, thereby improving the intelligent and precise level of agricultural production.
Description
Technical Field
The invention relates to the technical field of Internet of things agricultural tools, in particular to a small multifunctional agricultural robot based on the Internet of things.
Background
Irrigation, fertilization and pesticide application in traditional agricultural production activities are finished by farmers through experience and feeling by means of manual assessment, and the problems that a series of crops are subjected to fuzzy processing in different growth periods by applying the Internet of things, such as watering time, fertilization and pesticide application of fruits and vegetables, how to keep accurate concentration, how to supply according to needs and the like are all precisely controlled by an information intelligent monitoring system in real time and quantitatively;
the agriculture of China is developing towards high mechanization and intellectualization rapidly, as a lot of agricultural equipment on the market at present are expensive and large in size, the agricultural equipment is not beneficial to individual farmers to purchase and use, and the regional wide and flushly of China, the natural condition difference of regional agriculture development is obvious, and regions such as mountainous regions, hills, hillsides, microminiature basins and the like can not be widely popularized and applied to large agricultural machinery for plain cultivation, and the agricultural machinery market itself further promotes the mechanical intellectualization and integration requirements, the invention provides a small-sized multifunctional agricultural robot based on the Internet of things to solve the problems in the prior art.
Disclosure of Invention
In view of the above problems, the invention aims to provide a small-sized multifunctional agricultural robot based on the internet of things, which solves the problems that many agricultural devices on the market are expensive, large in size and not beneficial to individual farmers to purchase and use, regions in China are wide and can not widely popularize large-sized agricultural machinery suitable for plain cultivation, and the mechanical intelligence and integration requirements of the agricultural machinery market are further improved.
In order to realize the purpose of the invention, the invention is realized by the following technical scheme: a small-size multi-functional agricultural robot based on thing networking includes following step:
the method comprises the following steps: when the agricultural robot is used, firstly, the agricultural robot is started through the TFT capacitive touch screen, secondly, a control system of the raspberry group is used for sending a moving control command to the STM32 auxiliary control system, and when the STM32 auxiliary control system receives the control command, the agricultural robot is driven to move;
step two: secondly, the agricultural robot automatically moves on a specified route according to a set route program;
step three: the method comprises the following steps that the agricultural robot moves in a specified route, simultaneously identifies crops in the route through a camera, stops moving after the crops are identified, then carries out image acquisition on the crops, identifies images and finally separates the images;
step four: the agricultural robot extracts features after the image separation is finished, classifies the features after the features are extracted, uses the classified data for identifying the plant leaves, and continues to move in a specified route after the image acquisition of the agricultural robot is finished;
step five: when the agricultural robot identifies crops in a pesticide spraying area along a specified route, stopping movement, acquiring weather data of the day, intelligently regulating and controlling the pesticides to a specified concentration according to the weather data, finally spraying the crops, and continuing to move according to the specified route after spraying is finished;
step six: in the process that the agricultural robot moves along the specified route, the agricultural robot collects images through the camera, when the mature fruit is identified, the agricultural robot picks the fruit through the mechanical arm, and when the agricultural robot identifies that the mature fruit is not identified and the fruit is not found, the agricultural robot continues to move according to the specified route.
The further improvement lies in that: in the first step, when the STM32 auxiliary control system receives a control command, the motor driving board is controlled to drive the motor to control the motion of the agricultural robot, and data obtained by the inductance transporting and placing board is acquired and processed to enable the trolley to run according to a specified route.
The further improvement lies in that: in the second step, the agricultural robot moves along a specified route through electromagnetic navigation and PID control, wherein the electromagnetic navigation is that a magnetic field is generated around a power-on wire laid on a specified route, then an I-shaped inductor generates induced electromotive force in the alternating magnetic field, the agricultural robot performs frequency selection, amplification and detection on the induced electromotive force, then a stable signal is obtained and input to a raspberry dispatching main control system to identify and process route information, and the robot is guaranteed to operate on the wire.
The further improvement lies in that: in the third step, the image recognition of crops is mainly realized through a computer vision technology, the image segmentation is to divide the whole image area collected by a camera into specific non-empty sub-areas which are not overlapped mutually, the color images of plants are mainly segmented, and flowers and leaves are obviously prominent in color, so that an automatic threshold segmentation method based on color is adopted.
The further improvement lies in that: the automatic threshold segmentation method comprises the steps of firstly converting an image from an RGB color space to an Lab color space, secondly extracting corresponding L, a and b components according to the color of a research object in the image, then carrying out segmentation by an automatic threshold method through an OTSU, and finally removing noise through mathematical morphology operation to obtain a binary image.
The further improvement lies in that: in the fourth step, three characteristics of color, shape and texture are extracted from the image after the separation is finished, and the characteristics of the hue H and the saturation S are extracted from the color characteristics.
The further improvement lies in that: and in the fourth step, after the image with the characteristics extracted is subjected to feature extraction, taking the three types of characteristics of each flower and each leaf as training characteristics of the SVM, then obtaining an SVM classifier, and after the SVM classifier obtains vectors of the color, shape and texture characteristics of the leaves, taking the color, shape and texture characteristics of the leaves as input vectors of a classification method for identifying the plant leaves.
The further improvement lies in that: in the fifth step, the agricultural robot achieves the function of the internet of things through the ESP32 module, sends a command through the raspberry pie, and controls the water pump to spray pesticides through the motor drive plate when the STM32 receives the command of the raspberry pie.
The further improvement lies in that: in the sixth step, a command is sent through the raspberry pi, and when the STM32 receives the command of the raspberry pi, the mechanical arm is controlled to complete picking operation.
The invention has the beneficial effects that: the agricultural robot implanted with the whole set of functional program can sense the growth condition of crops more systematically, comprehensively and comprehensively, and can automatically perform corresponding operation according to the collected data and the resource conditions of regional agricultural climate, weather and the like, so that the intelligent and accurate level of agricultural production is improved; the aim of fine tillage and fine cropping is achieved by utilizing automatic equipment to replace manpower and fully utilizing an image processing technology. The computer vision technology is adopted to mark and classify crop plants, so that a manual screening mode is replaced, the workload is reduced, and the cultivation efficiency is improved; provides a simple GUI interface and has high automation degree. The difficulty of operating the robot is reduced, so that most users of the product can use the robot more easily; meanwhile, in the visual processing part, noise reduction algorithms such as Gaussian filtering and BM3D denoising are added, so that the purity of the image is improved, the difficulty in lifting the subsequent image characteristics is reduced, and the image recognition precision is improved; a closed-loop control system is adopted in the aspect of motion, and a classical PID algorithm is applied, so that the robot has better passing capacity for complex terrains, and the practicability of the robot is greatly improved; by adopting the idea of modular design, all the functions are separated, the functional modules are not interfered with each other, and only corresponding modules need to be assembled when corresponding functions are needed. The size of the machine is reduced as much as possible, the flexibility of the machine is improved, and the difficulty of maintenance and management is reduced.
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FIG. 1 is a schematic view of the process of the present invention.
FIG. 2 is a block diagram of the hardware module connections of the present invention.
FIG. 3 is a diagram of the automatic color-based thresholding of the present invention.
Detailed Description
In order to further understand the present invention, the following detailed description will be made with reference to the following examples, which are only used for explaining the present invention and are not to be construed as limiting the scope of the present invention.
Example one
According to fig. 1-3, the embodiment provides a small-sized multifunctional agricultural robot based on the internet of things, which comprises the following steps:
the method comprises the following steps: when the agricultural robot is used, firstly, the agricultural robot is started through the TFT capacitive touch screen, secondly, a control system of the raspberry group is used for sending a moving control command to the STM32 auxiliary control system, and when the STM32 auxiliary control system receives the control command, the agricultural robot is driven to move;
step two: secondly, the agricultural robot automatically moves on a specified route according to a set route program;
step three: the method comprises the following steps that the agricultural robot moves in a specified route, simultaneously identifies crops in the route through a camera, stops moving after the crops are identified, then carries out image acquisition on the crops, identifies images and finally separates the images;
step four: the agricultural robot extracts features after the image separation is finished, classifies the features after the features are extracted, uses the classified data for identifying the plant leaves, and continues to move in a specified route after the image acquisition of the agricultural robot is finished;
step five: when the agricultural robot identifies crops in a pesticide spraying area along a specified route, stopping movement, acquiring weather data of the day, intelligently regulating and controlling the pesticides to a specified concentration according to the weather data, finally spraying the crops, and continuing to move according to the specified route after spraying is finished;
step six: in the process that the agricultural robot moves along the specified route, the agricultural robot collects images through the camera, when the mature fruit is identified, the agricultural robot picks the fruit through the mechanical arm, and when the agricultural robot identifies that the mature fruit is not identified and the fruit is not found, the agricultural robot continues to move according to the specified route.
In the first step, when the STM32 auxiliary control system receives a control command, the motor driving board is controlled to drive the motor to control the motion of the agricultural robot, and data obtained by the inductance transporting and placing board is acquired and processed to enable the trolley to run according to a specified route.
In the second step, the agricultural robot moves along a specified route through electromagnetic navigation and PID control, wherein the electromagnetic navigation is that a magnetic field is generated around a power-on wire laid on a specified route, then an I-shaped inductor generates induced electromotive force in the alternating magnetic field, the agricultural robot performs frequency selection, amplification and detection on the induced electromotive force, then a stable signal is obtained and input to a raspberry dispatching main control system to identify and process route information, and the robot is guaranteed to operate on the wire.
In the third step, the image recognition of crops is mainly realized through a computer vision technology, the image segmentation is to divide the whole image area collected by a camera into specific non-empty sub-areas which are not overlapped mutually, the color images of plants are mainly segmented, and flowers and leaves are obviously prominent in color, so that an automatic threshold segmentation method based on color is adopted.
The automatic threshold segmentation method comprises the steps of firstly converting an image from an RGB color space to an Lab color space, secondly extracting corresponding L, a and b components according to the color of a research object in the image, then carrying out segmentation by an automatic threshold method through an OTSU, and finally removing noise through mathematical morphology operation to obtain a binary image.
In the fourth step, three characteristics of color, shape and texture are extracted from the image after the separation is finished, and the characteristics of the hue H and the saturation S are extracted from the color characteristics.
And in the fourth step, after the image with the characteristics extracted is subjected to feature extraction, taking the three types of characteristics of each flower and each leaf as training characteristics of the SVM, then obtaining an SVM classifier, and after the SVM classifier obtains vectors of the color, shape and texture characteristics of the leaves, taking the color, shape and texture characteristics of the leaves as input vectors of a classification method for identifying the plant leaves.
In the fifth step, the agricultural robot achieves the function of the internet of things through the ESP32 module, sends a command through the raspberry pie, and controls the water pump to spray pesticides through the motor drive plate when the STM32 receives the command of the raspberry pie.
In the sixth step, a command is sent through the raspberry pi, and when the STM32 receives the command of the raspberry pi, the mechanical arm is controlled to complete picking operation.
When the robot is used, the robot can automatically complete corresponding functions at corresponding time according to a program set by a user, the robot moves along a set route through electromagnetic navigation and PID control, simultaneously, images are collected through a camera to identify crops, the robot stops moving after identifying the crops, when pesticide spraying operation is carried out, temperature and humidity data are collected in real time, weather data of the day are obtained through the Internet of things, pesticide is sprayed to the crops after the concentration of the pesticide is intelligently regulated, the robot continues moving after spraying is finished until the next crop is identified, when fruit picking operation is carried out, the robot collects the images through the camera to identify ripe fruits, if the ripe fruits are successfully identified, the robot finishes the fruit picking operation through a mechanical arm, and if the ripe fruits are not identified or all the fruits are picked, the robot continues to move until the next crop is identified, and the small multifunctional agricultural robot based on the Internet of things is used.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. The utility model provides a small-size multi-functional agricultural robot based on thing networking which characterized in that includes following step:
the method comprises the following steps: when the agricultural robot is used, firstly, the agricultural robot is started through the TFT capacitive touch screen, secondly, a control system of the raspberry group is used for sending a moving control command to the STM32 auxiliary control system, and when the STM32 auxiliary control system receives the control command, the agricultural robot is driven to move;
step two: secondly, the agricultural robot automatically moves on a specified route according to a set route program;
step three: the method comprises the following steps that the agricultural robot moves in a specified route, simultaneously identifies crops in the route through a camera, stops moving after the crops are identified, then carries out image acquisition on the crops, identifies images and finally separates the images;
step four: the agricultural robot extracts features after the image separation is finished, classifies the features after the features are extracted, uses the classified data for identifying the plant leaves, and continues to move in a specified route after the image acquisition of the agricultural robot is finished;
step five: when the agricultural robot identifies crops in a pesticide spraying area along a specified route, stopping movement, acquiring weather data of the day, intelligently regulating and controlling the pesticides to a specified concentration according to the weather data, finally spraying the crops, and continuing to move according to the specified route after spraying is finished;
step six: in the process that the agricultural robot moves along the specified route, the agricultural robot collects images through the camera, when the mature fruit is identified, the agricultural robot picks the fruit through the mechanical arm, and when the agricultural robot identifies that the mature fruit is not identified and the fruit is not found, the agricultural robot continues to move according to the specified route.
2. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 1, wherein: in the first step, when the STM32 auxiliary control system receives a control command, the motor driving board is controlled to drive the motor to control the motion of the agricultural robot, and data obtained by the inductance transporting and placing board is acquired and processed to enable the trolley to run according to a specified route.
3. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 1, wherein: in the second step, the agricultural robot moves along a specified route through electromagnetic navigation and PID control, wherein the electromagnetic navigation is that a magnetic field is generated around a power-on wire laid on a specified route, then an I-shaped inductor generates induced electromotive force in the alternating magnetic field, the agricultural robot performs frequency selection, amplification and detection on the induced electromotive force, then a stable signal is obtained and input to a raspberry dispatching main control system to identify and process route information, and the robot is guaranteed to operate on the wire.
4. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 1, wherein: in the third step, the image recognition of crops is mainly realized through a computer vision technology, the image segmentation is to divide the whole image area collected by a camera into specific non-empty sub-areas which are not overlapped mutually, the color images of plants are mainly segmented, and flowers and leaves are obviously prominent in color, so that an automatic threshold segmentation method based on color is adopted.
5. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 4, wherein: the automatic threshold segmentation method comprises the steps of firstly converting an image from an RGB color space to an Lab color space, secondly extracting corresponding L, a and b components according to the color of a research object in the image, then carrying out segmentation by an automatic threshold method through an OTSU, and finally removing noise through mathematical morphology operation to obtain a binary image.
6. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 1, wherein: in the fourth step, three characteristics of color, shape and texture are extracted from the image after the separation is finished, and the characteristics of the hue H and the saturation S are extracted from the color characteristics.
7. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 1, wherein: and in the fourth step, after the image with the characteristics extracted is subjected to feature extraction, taking the three types of characteristics of each flower and each leaf as training characteristics of the SVM, then obtaining an SVM classifier, and after the SVM classifier obtains vectors of the color, shape and texture characteristics of the leaves, taking the color, shape and texture characteristics of the leaves as input vectors of a classification method for identifying the plant leaves.
8. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 1, wherein: in the fifth step, the agricultural robot achieves the function of the internet of things through the ESP32 module, sends a command through the raspberry pie, and controls the water pump to spray pesticides through the motor drive plate when the STM32 receives the command of the raspberry pie.
9. The small-sized multifunctional agricultural robot based on the internet of things as claimed in claim 1, wherein: in the sixth step, a command is sent through the raspberry pi, and when the STM32 receives the command of the raspberry pi, the mechanical arm is controlled to complete picking operation.
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