CN113608551A - Unmanned agricultural machinery group cooperation system and application method thereof - Google Patents
Unmanned agricultural machinery group cooperation system and application method thereof Download PDFInfo
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Abstract
The invention discloses an unmanned agricultural machinery group coordination system and an application method thereof, wherein the unmanned agricultural machinery group coordination system comprises the following steps: the acquisition unit acquires crop growth data in real time; the central group control unit is in communication connection with the acquisition system; the execution unit is in communication connection with the central group control unit; the acquisition unit is configured to include: a plurality of probes pre-embedded in the crop park; a patrol robot; the unmanned aerial vehicle acquires the soil color in real time; the prediction module is used for regularly inspecting the growth condition of crops; the execution unit is configured to include: the spraying module, the picking module and the weeding module are matched with the central group control unit to manage crops. The invention provides an unmanned agricultural machinery group coordination system and an application method thereof, and provides an inspection robot matched with various operation modules, wherein the inspection robot is uniformly allocated by a central group control unit of a core, so that the monitoring of the whole operation park is realized, and the operations of inspection, fertilization, weeding, yield prediction and the like can be performed on different crop conditions in a targeted manner.
Description
Technical Field
The invention relates to the field of agricultural automatic planting. More particularly, the invention relates to an unmanned agricultural machinery group coordination system used in the process of automatically managing crops by adopting agricultural automation equipment and an application method thereof.
Background
The traditional agricultural operation is mainly performed by manpower, and is assisted by basic agricultural machines, such as tractors, rice transplanters and the like. However, the use of these large agricultural machines is limited greatly, and has strict requirements on fields, environment, cost and the like, so far, except that the large agricultural machines are used in a unified way in individual large agricultural bases, most rural areas still depend on manual cultivation, and experience is the main, but the cultivation mode is low in efficiency and yield, and obviously, the cultivation mode is not suitable for the development of the requirements of modern agriculture.
Although large-scale mechanized equipment which is put forward in various agricultural industrial parks can solve the problems of cultivation efficiency and the like, operators still need to carry out follow-up operation all the time, cultivation efficiency is improved only to a certain extent, and farmers are not released from the fields fundamentally. Moreover, the large-scale cultivation equipment is only suitable for large-scale flat areas, has great use limitation in structured plantation such as terraced fields, orchards, forest gardens and the like, has single function, can only realize certain functions, such as seedling transplanting, pesticide spraying, weeding and the like, needs manual independent operation, cannot realize one-key intelligent operation, and does not have a set of complete one-key agricultural equipment to meet all agricultural cultivation requirements at present.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and/or disadvantages and to provide at least the advantages described hereinafter.
To achieve these objects and other advantages in accordance with the purpose of the invention, there is provided an unmanned agricultural machinery group coordination system, comprising:
the acquisition unit acquires crop growth data in real time;
the central group control unit is in communication connection with the acquisition system;
the execution unit is in communication connection with the central group control unit;
wherein the acquisition unit is configured to include:
the probes are pre-embedded in a crop park to obtain the water and fertilizer content of crop growth;
the inspection robot is provided with a wireless data transmission module and a data processing module which are matched with the probes;
the unmanned aerial vehicle acquires the soil color in real time;
the prediction module is used for regularly inspecting the growth condition of crops;
the execution unit is configured to include:
the pesticide spraying module, the picking module and the weeding module are matched with the central group control unit to manage crops;
the water and fertilizer irrigation module is matched with the central group control unit to manage the soil;
the prediction module, the pesticide spraying module, the picking module and the weeding module are respectively arranged on the inspection robot through corresponding mounting assemblies.
Preferably, the inspection robot is provided with a laser radar, a GPS antenna and inertial navigation which are matched with each other;
the prediction module is configured to include:
the camera is used for acquiring the growth condition of crops in real time;
the telescopic bracket is used for supporting and limiting the camera;
the spraying module is configured to include:
the inner part of the medicine storage box is constructed by a plurality of clapboards to obtain mutually independent medicine storage areas;
the mounting frame is arranged on the inspection robot to fix the medicine storage box;
the spray head is fixed on the mounting frame through a matched telescopic multi-section bracket and is communicated with each medicine storage area through a matched first pipeline component and a medicine pump;
the liquid manure irrigation module is configured to include:
a plurality of liquid fertilizer storage tanks arranged at predetermined positions in the crop park;
and the second pipeline component is matched with each liquid type fertilizer storage box to carry out fixed-point water and fertilizer management on crops at the positions of the probes.
A method for applying an unmanned agricultural machinery group cooperative system comprises the following steps:
firstly, a rasterized map of a crop park is constructed through an inspection robot;
step two, carrying a prediction module on the inspection robot to detect the growth state of the crops in real time, and transmitting the detected growth data of the crops back to the central group control unit;
step three, the central group control unit determines whether to start one of a pesticide spraying module, a picking module and a weeding module based on the judgment of the crop growth data;
a wireless data transmission module and a data processing module arranged on the inspection robot receive the soil environment data detected by each probe;
and step five, the central group control unit determines whether to start the water and fertilizer irrigation module based on the judgment of the soil environment data.
Preferably, in step one, the construction of the rasterized map is configured to include:
s10, downloading a 22-level high-resolution map corresponding to the crop park, and selecting a regular area where the crop structured planting is just finished as an operation area;
s11, marking the corresponding obstacles in the operation area as black;
s12, manually defining a passable area, and planning a corresponding communication road and marking the communication road as white;
s13, acquiring a real value and a proportional value of the map, selecting a special point as an origin, calculating each offset through the position transformation of the map resolution and the corresponding position of the map, and storing the offset into a yaml file;
and S14, flying around the field by using the unmanned aerial vehicle, and scanning the field of the whole operation area to correct the offset data so as to ensure that the map is more accurate.
Preferably, in the second to fifth steps, an input interface group and a corresponding output interface group are reserved on the central group control unit;
wherein the input interface group is configured to include:
a first input interface in communication with the probe;
a second input interface in communicative connection with the prediction module;
the output interface group is configured to include:
and the irrigation interface, the weeding interface, the spraying interface and the picking interface are used for outputting the instruction sent by the middle inner processor to the corresponding modules.
Preferably, in the second to fifth steps, the central group control unit divides the internal storage module into corresponding areas as data receiving containers;
the data receiving container is internally divided into corresponding storage intervals for two times based on the number of the probes, each storage interval receives soil environment data returned by the probes, and the soil environment data types comprise trace element content, soil temperature, soil humidity, illumination intensity and soil carbon dioxide concentration;
and after receiving the data, the central group control unit analyzes each group of data in each container respectively and switches the working mode of the execution unit through the corresponding output interface according to the analysis structure.
Preferably, in the second step, the real-time detection method includes:
s20, the camera for image collection carried on the inspection robot collects the images of the crops in the moving process of the robot and transmits the images back to the central group control unit;
and S21, the central group control unit performs area identification according to the color characteristics of the returned crop image, and then inputs the crop characteristics into the extended Kalman production measurement model to predict the yield of the crop.
Preferably, in step three, the operation mode of the spraying module includes:
s30, loading two to three kinds of pesticides in the pesticide storage box;
s31, collecting samples of various pests by a data processing module on the inspection robot, and training by adopting a neural network to obtain recognition models of the various pests;
s32, automatically navigating and patrolling the operation area by the inspection robot according to the rasterized map, scanning crops by using a camera on the prediction module, and extracting corresponding pesticides to spray the area where the pests are located after identifying the corresponding pests;
and S33, when the inspection robot enters the end of a line in the working area, obtaining turn-around information by scanning the specific information of the two trees at the head of the line, and when the radar installation center line of the inspection robot is parallel to the trunk of the line, selecting a corresponding turn-around mode to enter the next line to continue driving until the inspection operation of all lines in the working area is completed.
Preferably, in the fifth step, when the central group control unit judges the content of the trace elements in the soil environment data and when the value of any element is lower than the required value for crop growth, the water and fertilizer irrigation system directly extracts the corresponding fertilizing amount from the corresponding stored fertilizer for mixed irrigation, so that the closed-loop control of water and fertilizer irrigation controlled by the data transmitted from the probe is realized;
the water and fertilizer irrigation module controls the fertilizing amount of each crop based on the following formula:
wherein P represents the actual fertilizer content concentration, PmaxExpressing the maximum demand, N expressing the growth period from seed loading to fruit bearing of the fruit tree, and t tableShowing the proportion of the grown time of the fruit tree in the total growth cycle.
Preferably, in the third step, the unmanned aerial vehicle is used for scanning the garden along the rasterized map, the GPS point location information of the known crops is abandoned based on the color identification mode, further weeds on the land of the operation area are identified, and the coordinate information is transmitted to the central group control unit;
the central group control unit marks all target points based on a previously established rasterized map, converts the target points into a TSP problem, and plans an optimized path traversed once by using an improved genetic algorithm;
and carrying out weeding modules on the inspection robot, and weeding along the planned optimized path until all target points are traversed.
The invention at least comprises the following beneficial effects: the system provides a plurality of operation modules to be matched with the inspection robot, the central group control unit of the core is used for allocating uniformly, the monitoring of the whole operation park is realized, and the operations of inspection, fertilization, weeding, yield prediction and the like can be performed on different crop conditions in a targeted manner.
The system collects the park data through the collection unit, analyzes the crop growth condition and facilitates the central group control unit to perform targeted operation on the execution unit.
The inspection robot sensor for the operation of the system is complete in equipment and excellent in performance, functions such as autonomous positioning navigation and autonomous operation can be achieved, all execution units can be carried on the same inspection robot or a plurality of inspection robots according to needs, all the robots belong to a linkage system, all the functions are achieved by one key, and the research result of the system is significant for promoting the modernization process of an agricultural park.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is a schematic diagram of the main operation flow of the central group control unit according to the present invention;
FIG. 2 is a diagram illustrating a comparison between the output value of the prediction module and the final result number;
FIG. 3 is a comparison graph of yield prediction in one embodiment of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
The method for applying the unmanned agricultural machinery group cooperation system comprises the following steps:
1. garden map construction
A grid map of the campus needs to be constructed first. The specific operation is as follows: downloading a 22-level high-grade map corresponding to the park, selecting a proper operation area (a regular area just finished with the structured planting of the saplings), and marking corresponding obstacles as black, such as fences, fruit trees, steps, ditches and the like. The passable area is manually drawn, reasonably communicated roads are planned, and the roads are marked as white. And acquiring a real value and a proportional value of the map, selecting a special point as an origin, calculating each offset through the map resolution and the corresponding position transformation of the map, and storing the offset into a yaml file. The unmanned aerial vehicle flies around the field, scans the whole operation field, corrects offset data and makes the map more accurate.
2. Crop data acquisition
Taking an orchard as an example, after fruit trees are planted in the orchard, the probes are used as main acquisition sensors to be buried within 5CM of the front of the fruit trees, and the probes are buried in the fruit trees. The peak period of the fruit tree growth is that the falling leaves are harvested in autumn and the new shoots stop growing to the fruit expanding period in spring. The requirements of the two periods of time on different nutrients are higher, for example, more nitrogen fertilizer is needed for growing roots, and phosphate fertilizer and potash fertilizer are needed for growing roots. Therefore, the probe can acquire the water and fertilizer content in the soil of each fruit tree, transmit the data to the central control system, further analyze the growth condition of the fruit tree and perform corresponding required operation aiming at the problem.
3. Design of central group control system
Based on the map with the correction completed, the following interfaces are set aside: the system comprises an input interface, an irrigation interface, a prediction interface, a weeding interface, a spraying interface and a picking interface, wherein the interfaces are used for issuing instructions later. The probe in the last step is used as the most key input interface, the growth environment data of the crops are transmitted back to the central control system, the system divides corresponding areas, the data are respectively received, and the number of containers is divided as the receiving area according to the number of the fruit trees. After receiving the data, analyzing each group of data in each container respectively, wherein the data types comprise trace element content, soil temperature, soil humidity, illumination intensity and soil carbon dioxide concentration. When the input interface transmits corresponding data into the system, the system analyzes each group of data, such as the output quantity of the irrigation interface determined by the content of trace elements and the soil humidity, whether the weeding interface outputs or not determined by the result of land color detection, the output quantity of the pesticide spraying interface determined by the input of the prediction interface, and the like. The main flow chart of the central group control system is shown in fig. 1.
4. Construction of yield prediction robot
And guiding the corrected map into a prediction robot, carrying a support with the height being the middle-lower part of the crown for the prediction robot, and carrying a camera to collect information of the crown. The method for early predicting the yield of the fruit trees comprises the steps of firstly determining image acquisition time according to information transmitted by a probe, judging that when the fruit trees are in an initial fruiting stage, a predicting robot starts to move forward along each row of the fruit trees, turns around when the fruit trees reach the tail of the row, and enters the next row. When the robot starts to operate, spraying pesticide along each row of fruit trees, when the robot travels to the end of the row of the orchard, scanning specific information of the two trees at the head of the row to obtain turning information, when the radar mounting center line of the robot is parallel to the trunk at the head of the row, detecting the distance between the robot and the fence at the moment, and selecting a proper turning mode to enter a new orchard row to continue traveling. The turn around mode is judged by scanning the distance of the boundary line.
Under normal conditions, the front reserved position is sufficient, and a traditional semicircular arc turning method is selected.
When the width of a fruit tree row is smaller than the turning radius of the robot and the width of a row head boundary line is also smaller than the turning radius, the traditional semicircular arc turning strategy cannot meet the narrow space, and therefore a new fruit tree row can be entered only by adopting an interlaced turning method.
When the two turning methods are not applicable, the space in the opposite direction of the turning of the robot is large, and the width of the line-head boundary line meets the requirement, an 3/4 arc turning method can be adopted.
The three methods can meet the turning scene among most fruit tree lines.
The following table will illustrate the applicability of the three u-turn modes, where r represents the minimum turning radius of the robot, d represents the distance between the line head and the fence, and L represents the line spacing.
| Turning around mode | Application scope |
| Semicircular arc head dropping method | d>r，L>2r |
| Interlaced head dropping method | d< |
| 3/4 arc end-cutting method | d>2r，L<r |
Carrying high-precision image acquisition equipment to acquire images of fruit trees in the advancing process of the robot, and then identifying the fruit areas according to the characteristics of the images. The method for predicting through the artificial intelligent neural network model needs to do a lot of preparation work on model building, and is too complex. The method adopts a color distinguishing method, carries out fruit region identification according to color characteristics, and finally inputs the crown characteristics of the fruit tree into an extended Kalman production measurement model to predict the yield of the fruit tree. The traditional prediction is that the yield of the fruits is predicted through the existing fruit quantity, and the method introduces a color recognition mechanism, so that bad fruits can be abandoned on the original basis, and misdetection is reduced.
The fruit yield prediction belongs to a nonlinear system, and if the fruit yield prediction is processed by a linear system, data is distorted, so that the deviation is large. And inputting the observed value into the model as a single input, predicting based on the observed value, and ensuring that the predicted value is fitted with a true value by utilizing the superiority of the processing nonlinear system of the extended Kalman filtering.
In the model, the value of the observation matrix F is set to [1, dt,0.5 x dt ^ 2; 0,1, dt; 0,0,1], error matrix Q is set to [1,0, 0; 0,0.01, 0; 0,0,0.0001], ensuring the robustness of the nonlinear system. The number of results identified by the camera scan is used as input, and the obtained output value is compared with the number of final results, and the comparison result is shown in fig. 2, wherein the solid line represents the real yield, and the dotted line represents the predicted yield, and the unit is K.
5. Construction of pesticide spraying robot
In the agricultural production activity, crops need spout medicine, irrigation work often, and traditional medicine mode of spouting uses backpack medicine spraying barrel mostly, and this kind of mode is inefficiency not only, and it is low to spout the quality, needs a large amount of manpowers moreover, and the liquid medicine is huge to the human injury, in hot summer period, can cause the personal safety hidden danger even.
And guiding the corrected map into a pesticide spraying robot, carrying a bracket which is not lower than the tree crown for the pesticide spraying robot, and carrying a pesticide spraying probe. The bottom layer of the pesticide spraying robot is an intelligent mobile robot platform, wherein a laser radar, a GPS antenna and inertial navigation are carried. And performing data fusion by using the GPS and inertial navigation to determine the current pose of the robot. However, in an orchard, due to the shielding of a tree crown, the original input precision of a GPS is reduced, namely, the auxiliary of inertial navigation still has large offset, so that a laser radar is added, the surrounding environment is scanned by the 32-line laser radar and is matched with a map, and the current position is obtained.
The pesticide storage box is characterized in that two to three pesticides are loaded in the pesticide storage box, mutually independent pesticide storage areas are constructed in the pesticide storage box through a plurality of partition plates, the pesticide storage box can be arranged on the inspection robot through a matched mounting frame, the spray heads are fixed on the mounting frame through a matched telescopic multi-section support, so that the height of the spray heads can be properly adjusted according to needs to adapt to the height needs of different crops, and the spray heads are communicated with the pesticide storage areas through a matched first pipeline component and a pesticide pump and are used for extracting pesticides in different areas to carry out fixed-point and quantitative pesticide killing treatment according to needs when specific pests are identified during actual operation, so that the pesticide spraying amount controllability is better, pesticide residues are prevented, and during actual operation, samples of various pests are collected firstly to be trained through a neural network, and obtaining identification models of various pests. The pesticide spraying robot performs automatic navigation patrol in the gaps of the farm, scans by using a camera, and recognizes that the corresponding pests spray the corresponding pesticides. The mode can greatly improve the efficiency of spraying the pest pesticide and cannot influence people. When the robot starts to operate, targeted pesticide spraying is carried out along each row of fruit trees, when the robot travels to the end of a row of the orchard, turning information is obtained by scanning specific information of two trees at the head of the row, when a radar mounting center line of the robot is parallel to a trunk at the head of the row, the distance between the robot and an enclosure at the moment is detected, and a proper turning mode is selected to enter a new orchard row to continue traveling. The turning mode is the same as the above, and after turning, the robot continues to run, so that the full-coverage spraying of the pesticide at the position with the demand is realized.
6. Water and fertilizer irrigation integrated design
For the growth of fruit trees, 17 essential nutrient elements are not necessary, and the corresponding diseases can be caused by the lack of any one element, and one element cannot replace the other element. Except that the fruit trees can be supplemented by three elements of carbon, hydrogen and oxygen automatically through respiration and rainwater supply, the rest 14 elements are required to be supplemented by manpower, namely nitrogen, phosphorus, potassium, silicon, calcium, magnesium, sulfur, iron, boron, copper, manganese, molybdenum, chlorine and zinc. 14 independent large barrels are used for respectively preparing fertilizers with proper concentration suitable for the growth of corresponding fruit trees, and the fertilizers are connected with a central control system and a conveying pipe.
The probe is used for obtaining data such as the content of trace elements in soil, the temperature of the soil, the humidity of the soil, the illumination intensity, the carbon dioxide concentration of the soil and the like, particularly the content of the trace elements, and when any necessary element is lower than a necessary value for crop growth, the water and fertilizer irrigation system directly extracts a corresponding required value from a corresponding stored fertilizer for mixed irrigation. The data transmitted by each probe is processed independently, and the conveying pipe bound on the probe also works independently. The system can work independently for each crop all day, namely at the same time, a plurality of conveying pipes work simultaneously, the content of elements of water and fertilizer conveyed by the conveying pipes is not completely consistent, and the system is operated independently for the requirement of each crop. The water and fertilizer irrigation closed-loop control of data control transmitted by the probe is completed, naturally, the optimal mode of the operation mode is to directly use liquid fertilizer and match with other fertilizer storage equipment, solid fertilizer is input into each liquid fertilizer storage box through the feeding unit, and further the effect of fertilization is achieved through the matching with water and/or other liquid fertilizers, and the pipeline used here can be a pipeline laid on the ground or a conveying hose arranged on the ground, and the water and fertilizer fine management of crops or crops in each block in an operation area is completed through the matching of a main pipe, a branch pipe and a capillary pipe.
However, the demand for fertilizers varies from fruit tree to fruit tree in different growth periods. For example, in the period from germination to flowering of fruit trees, a pre-emergence fertilizer, i.e., a fertilizer mainly composed of nitrogen, needs to be applied. And the phosphate fertilizer and the potash fertilizer are needed from the flowering period to the fruit expansion period of the fruit trees. After fruit harvest, which is also a critical period for flower bud differentiation, a large amount of medium elements is required. Therefore, if a single fertilization model is used, the fertilization effect of water and fertilizer integration is not as good as that of the traditional integral fertilization. Therefore, a self-adaptive dynamic programming system is introduced to regulate and control the content of the water and the fertilizer, which is exemplified as follows:
taking nitrogen fertilizer as an example, the fruit trees have high demand in the early growth stage, the medium-stage demand proportion is reduced, and the later-stage demand proportion is increased, so that the apple trees are taken as an example, the following formula is set for controlling the fertilizing amount.
Wherein P represents the actual fertilizer content concentration, PmaxThe maximum demand is shown, N represents the growth period from seed loading to fruit bearing of the fruit tree, and t represents the proportion of the grown-up time of the fruit tree in the total growth period. Therefore, according to the design of the fertilization concentration, all elements can be always kept in the range of the optimal growth concentration in the whole growth period of the fruit tree, and the defect of single concentration is avoided.
7. Design of weeding robot
The inter-row driving strategy used by the weeding robot is consistent with that of the spraying robot. The identification mode adopts the most efficient color identification, uses the unmanned aerial vehicle to scan the garden along the map, discards the information of the known fruit tree GPS point location, identifies the weeds on the land, and transmits the coordinate information to the group control center. And the group control center marks all target points based on the established map, converts the target points into a TSP problem, plans a traversed reasonable path by using an improved genetic algorithm, and operates the weeding robot along the planned path until all the target points are traversed.
By taking the yield prediction robot as an example, the yield of each fruit tree is predicted by utilizing the advantage of independent data analysis, and compared with the traditional manual prediction, the accuracy rate is greatly improved. As shown in fig. 3, the solid line is the real fruit tree yield, the dotted line is the artificial prediction result, and the star line is the prediction result obtained based on the training of the method. By effectively combining the image processing and identifying technology and the artificial intelligence technology, the defect of simply predicting by using the image processing and identifying technology is overcome, and therefore the yield of fruits in the orchard can be accurately predicted at an early stage. The prediction result of the method is more fit for the actual yield and has higher efficiency. Meanwhile, all agricultural machines can completely realize autonomous operation without manual intervention. The agricultural park using the system can save a large amount of manpower, improve the operation efficiency and realize the yield increase of more than 30 percent for the crop yield.
The above scheme is merely illustrative of a preferred example, and is not limiting. When the invention is implemented, appropriate replacement and/or modification can be carried out according to the requirements of users.
The number of apparatuses and the scale of the process described herein are intended to simplify the description of the present invention. Applications, modifications and variations of the present invention will be apparent to those skilled in the art.
While embodiments of the invention have been disclosed above, it is not intended to be limited to the uses set forth in the specification and examples. It can be applied to all kinds of fields suitable for the present invention. Additional modifications will readily occur to those skilled in the art. It is therefore intended that the invention not be limited to the exact details and illustrations described and illustrated herein, but fall within the scope of the appended claims and equivalents thereof.
Claims (10)
1. An unmanned agricultural machinery group cooperative system, comprising:
the acquisition unit acquires crop growth data in real time;
the central group control unit is in communication connection with the acquisition system;
the execution unit is in communication connection with the central group control unit;
wherein the acquisition unit is configured to include:
the probes are pre-embedded in a crop park to obtain the water and fertilizer content of crop growth;
the inspection robot is provided with a wireless data transmission module and a data processing module which are matched with the probes;
the unmanned aerial vehicle acquires the soil color in real time;
the prediction module is used for regularly inspecting the growth condition of crops;
the execution unit is configured to include:
the pesticide spraying module, the picking module and the weeding module are matched with the central group control unit to manage crops;
the water and fertilizer irrigation module is matched with the central group control unit to manage the soil;
the prediction module, the pesticide spraying module, the picking module and the weeding module are respectively arranged on the inspection robot through corresponding mounting assemblies.
2. The unmanned agricultural machinery group cooperative system of claim 1, wherein the inspection robot is equipped with a laser radar, a GPS antenna, and inertial navigation which are matched with each other;
the prediction module is configured to include:
the camera is used for acquiring the growth condition of crops in real time;
the telescopic bracket is used for supporting and limiting the camera;
the spraying module is configured to include:
the inner part of the medicine storage box is constructed by a plurality of clapboards to obtain mutually independent medicine storage areas;
the mounting frame is arranged on the inspection robot to fix the medicine storage box;
the spray head is fixed on the mounting frame through a matched telescopic multi-section bracket and is communicated with each medicine storage area through a matched first pipeline component and a medicine pump;
the liquid manure irrigation module is configured to include:
a plurality of liquid fertilizer storage tanks arranged at predetermined positions in the crop park;
and the second pipeline component is matched with each liquid type fertilizer storage box to carry out fixed-point water and fertilizer management on crops at the positions of the probes.
3. A method of using the unmanned agricultural machinery complex system of claims 1-2, comprising the steps of:
firstly, a rasterized map of a crop park is constructed through an inspection robot;
step two, carrying a prediction module on the inspection robot to detect the growth state of the crops in real time, and transmitting the detected growth data of the crops back to the central group control unit;
step three, the central group control unit determines whether to start one of a pesticide spraying module, a picking module and a weeding module based on the judgment of the crop growth data;
a wireless data transmission module and a data processing module arranged on the inspection robot receive the soil environment data detected by each probe;
and step five, the central group control unit determines whether to start the water and fertilizer irrigation module based on the judgment of the soil environment data.
4. The method for applying the unmanned agricultural machinery group cooperative system of claim 3, wherein in the step one, the construction of the rasterized map is configured to include:
s10, downloading a 22-level high-resolution map corresponding to the crop park, and selecting a regular area where the crop structured planting is just finished as an operation area;
s11, marking the corresponding obstacles in the operation area as black;
s12, manually defining a passable area, and planning a corresponding communication road and marking the communication road as white;
s13, acquiring a real value and a proportional value of the map, selecting a special point as an origin, calculating each offset through the position transformation of the map resolution and the corresponding position of the map, and storing the offset into a yaml file;
and S14, flying around the field by using the unmanned aerial vehicle, and scanning the field of the whole operation area to correct the offset data so as to ensure that the map is more accurate.
5. The unmanned agricultural machinery group cooperative system application method of claim 3, wherein in the second to fifth steps, an input interface group and a corresponding output interface group are reserved on the central group control unit;
wherein the input interface group is configured to include:
a first input interface in communication with the probe;
a second input interface in communicative connection with the prediction module;
the output interface group is configured to include:
and the irrigation interface, the weeding interface, the spraying interface and the picking interface are used for outputting the instruction sent by the middle inner processor to the corresponding modules.
6. The method as claimed in claim 3, wherein in steps two to five, the central group control unit divides the internal storage module into corresponding areas as data receiving containers;
the data receiving container is internally divided into corresponding storage intervals for two times based on the number of the probes, each storage interval receives soil environment data returned by the probes, and the soil environment data types comprise trace element content, soil temperature, soil humidity, illumination intensity and soil carbon dioxide concentration;
and after receiving the data, the central group control unit analyzes each group of data in each container respectively and switches the working mode of the execution unit through the corresponding output interface according to the analysis structure.
7. The method for applying the unmanned agricultural machinery group cooperative system of claim 3, wherein in the second step, the real-time detection mode comprises:
s20, the camera for image collection carried on the inspection robot collects the images of the crops in the moving process of the robot and transmits the images back to the central group control unit;
and S21, the central group control unit performs area identification according to the color characteristics of the returned crop image, and then inputs the crop characteristics into the extended Kalman production measurement model to predict the yield of the crop.
8. The method for applying the unmanned agricultural machinery group cooperative system of claim 3, wherein in step three, the operation mode of the spraying module comprises:
s30, loading two to three kinds of pesticides in the pesticide storage box;
s31, collecting samples of various pests by a data processing module on the inspection robot, and training by adopting a neural network to obtain recognition models of the various pests;
s32, automatically navigating and patrolling the operation area by the inspection robot according to the rasterized map, scanning crops by using a camera on the prediction module, and extracting corresponding pesticides to spray the area where the pests are located after identifying the corresponding pests;
and S33, when the inspection robot enters the end of a line in the working area, obtaining turn-around information by scanning the specific information of the two trees at the head of the line, and when the radar installation center line of the inspection robot is parallel to the trunk of the line, selecting a corresponding turn-around mode to enter the next line to continue driving until the inspection operation of all lines in the working area is completed.
9. The method according to claim 3, wherein in step five, when the central group control unit determines the content of trace elements in the soil environment data, and when the value of any element is lower than the required value for crop growth, the water and fertilizer irrigation system directly extracts the corresponding fertilizing amount from the corresponding reserved fertilizer for mixed irrigation, so as to realize closed-loop control of water and fertilizer irrigation by the data transmitted from the probe;
the water and fertilizer irrigation module controls the fertilizing amount of each crop based on the following formula:
wherein P represents the actual fertilizer content concentration, PmaxThe maximum demand is shown, N represents the growth period from seed loading to fruit bearing of the fruit tree, and t represents the proportion of the grown-up time of the fruit tree in the total growth period.
10. The method for applying the unmanned agricultural machinery group cooperative system of claim 3, wherein in step three, the unmanned aerial vehicle is used to scan the park along the rasterized map, the GPS point location information of the known crops is discarded based on the color recognition mode, further weeds on the land of the working area are recognized, and the coordinate information is transmitted to the central group control unit;
the central group control unit marks all target points based on a previously established rasterized map, converts the target points into a TSP problem, and plans an optimized path traversed once by using an improved genetic algorithm;
and carrying out weeding modules on the inspection robot, and weeding along the planned optimized path until all target points are traversed.
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