CN117933755A

CN117933755A - Method and system for automatically managing lawn

Info

Publication number: CN117933755A
Application number: CN202410124757.5A
Authority: CN
Inventors: 江卫忠; 邓智毅; 何冠荣; 陈昊; 陆韵
Original assignee: Guangzhou Longing For Innovation Technology Co ltd
Current assignee: Guangzhou Longing For Innovation Technology Co ltd
Priority date: 2024-01-30
Filing date: 2024-01-30
Publication date: 2024-04-26

Abstract

The invention discloses a method and a system for automatically managing lawns, which are applied to a mobile robot, wherein the method comprises the following steps: shooting a lawn based on the mobile robot to obtain a lawn image; inputting the lawn image into a trained lawn state recognition model to obtain a lawn state; and acquiring a corresponding lawn maintenance strategy and lawn maintenance advice according to the lawn state, controlling the mobile robot to execute the lawn maintenance strategy, and transmitting the lawn maintenance advice to a user terminal. The invention can realize continuous maintenance of the set working state for a long time to work, monitor the target in real time for a long time, discover the lawn state to be processed in time, accurately identify the lawn state, provide an effective method for different lawn states and severity, process the lawn state, promote the health and beauty of the lawn, and reduce the manpower management cost.

Description

Method and system for automatically managing lawn

Technical Field

The invention relates to the technical field of data processing, in particular to a method and a system for automatically managing lawns.

Background

Lawns are increasingly used in parks, school playgrounds, sports grounds (football, baseball, golf, etc.), and in general residential gardens. For example, when the application of the lawn is sports, it must be very carefully managed. Compared with wild grass, grass on the lawn has lower disease resistance and is easy to cause health problems such as various diseases.

In the prior art, if the lawn state needs to be found, a manual patrol checking mode or a low-flight patrol mode of the unmanned aerial vehicle is generally adopted. The manual patrol checking mode has higher requirements on human observation force and lawn profession; identifying and analyzing the problems according to subjective standards of people; problems are found after manual patrol, and the problems are handled manually, so that the efficiency is low.

Therefore, when the lawn is managed in the prior art, the lawn problem is identified by manpower, and a corresponding processing mode is adopted after the lawn problem is identified, so that the workload is high, and a large amount of labor cost is consumed.

The prior art is therefore still in need of further development.

Disclosure of Invention

Aiming at the technical problems, the embodiment of the invention provides a method and a system for automatically managing lawns, which can solve the technical problems that when the lawns are managed in the prior art, the problems of the lawns are mainly identified by manpower, and a corresponding processing mode is adopted after the identification, so that the workload is high and a large amount of labor cost is consumed.

A first aspect of an embodiment of the present invention provides a method for automatically managing a lawn, applied to a mobile robot, where the method includes:

Shooting a lawn based on the mobile robot to obtain a lawn image;

inputting the lawn image into a trained lawn state recognition model to obtain a lawn state;

And acquiring a corresponding lawn maintenance strategy and lawn maintenance advice according to the lawn state, controlling the mobile robot to execute the lawn maintenance strategy, and transmitting the lawn maintenance advice to a user terminal.

Optionally, a mowing module is provided on the mobile robot, and obtains a corresponding lawn maintenance policy and a lawn maintenance suggestion according to a lawn state, and controls the mobile robot to execute the lawn maintenance policy and transmit the lawn maintenance suggestion to a user terminal, including:

And if the lawn state is that the lawn withered and yellow area is larger than the first preset area threshold or the lawn alopecia areata area is larger than the second preset area threshold, controlling the mobile robot to increase the mowing frequency, and transmitting the fertilization and sowing maintenance advice to the user terminal.

And if the lawn state is that weeds exist, controlling the mobile robot to acquire the positions and the types of the weeds, performing fixed-point cutting on the weeds, acquiring corresponding herbicide information according to the types of the weeds, and transmitting the grass-cutting agent information to a user terminal.

And if the lawn state is that the growth speed of grass in different areas is uneven, controlling the robot to adjust the mowing frequency in the areas with uneven growth speed.

if the lawn state is that diseases and/or insect pests exist, the control robot marks the position points of the diseases and/or insect pests, generates maintenance reminding information corresponding to the diseases and/or insect pests, and transmits the marking information and the maintenance reminding information to the user terminal.

A second aspect of an embodiment of the present invention provides a system for automatically managing a lawn, applied to a mobile robot, where one or more cameras are disposed on the mobile robot, the system includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:

Shooting a lawn based on the mobile robot to obtain a lawn image;

Optionally, the computer program when executed by the processor implements the steps of:

Optionally, the computer program when executed by the processor further implements the steps of:

A third aspect of the embodiment of the present invention provides a non-volatile computer-readable storage medium, where the non-volatile computer-readable storage medium stores computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method for automatically managing a lawn as described above.

The technical scheme provided by the embodiment of the invention is applied to the mobile robot, and the method comprises the following steps: shooting a lawn based on the mobile robot to obtain a lawn image; inputting the lawn image into a trained lawn state recognition model to obtain a lawn state; and acquiring a corresponding lawn maintenance strategy and lawn maintenance advice according to the lawn state, controlling the mobile robot to execute the lawn maintenance strategy, and transmitting the lawn maintenance advice to a user terminal. The invention can realize continuous maintenance of the set working state for a long time to work, monitor the target in real time for a long time, discover the lawn state to be processed in time, accurately identify the lawn state, provide an effective method for different lawn states and severity, process the lawn state, promote the health and beauty of the lawn, and reduce the manpower management cost.

Drawings

FIG. 1 is a flowchart of a method for automatically managing lawns according to an embodiment of the present invention;

Fig. 2 is a schematic hardware structure diagram of another embodiment of a system for automatically managing lawns according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

Referring to fig. 1, fig. 1 is a flowchart illustrating an embodiment of a method for automatically managing a lawn according to an embodiment of the present invention. As shown in fig. 1, includes:

step S100, shooting a lawn based on the mobile robot to obtain a lawn image;

Step 200, inputting the lawn image into a trained lawn state recognition model to obtain a lawn state;

And step S300, acquiring a corresponding lawn maintenance strategy and lawn maintenance suggestions according to the lawn state, controlling the mobile robot to execute the lawn maintenance strategy, and transmitting the lawn maintenance suggestions to a user terminal.

In specific implementation, the embodiment of the invention is used for automatically managing the lawn. The method for automatically managing the lawn is applied to the mobile robot.

One or more cameras are arranged on the mobile robot. The method comprises the steps of shooting a lawn image through a camera, inputting the lawn image into a lawn state identification model, obtaining a lawn state, wherein the lawn state comprises, but is not limited to, withered and yellow lawn (whole piece), partial area yellowing/brown lawn, alopecia areata, weeds, uneven growth, diseases, insect pests and the like, obtaining the type and severity of a problem point identified by the lawn state identification model, obtaining lawn health state position information, transmitting the type and severity information of the problem point, the position information and other data to a user side, and displaying the data on intelligent equipment APP of the user side. Aiming at different lawn states and severity degrees, an effective method is provided for processing the lawn states and improving the health and the beauty of the lawn.

Smart devices include, but are not limited to, cell phones, tablet computers, smart televisions, and the like. The lawn status identified in some other embodiments may be replaced by telephone, text message, mail, etc. in addition to notifying the user via the mobile application.

In some other embodiments, the mobile robot may be replaced with other land vehicles. The camera in the visual identification method can be partially or completely replaced by other sensors, such as millimeter wave radar, a capacitance sensor, a laser radar sensor and the like, and visual model identification can be replaced by a mode of equipment photographing and remote manual image reading.

Further, recognition of the lawn state is completed through a visual segmentation method, data acquisition of a sufficient quantity is carried out on potential lawn states, corresponding labeling and training are carried out on the acquired data, and an algorithm model is corrected through continuous data training, so that recognition of the lawn state with high accuracy is achieved. The lawn state judgment module is mainly composed of a lawn state recognition module, a global positioning module and a lawn state analysis module, wherein the lawn state recognition module outputs the type and severity of the lawn state at the current position, the global positioning module determines the position of a lawn where a mower is located, and the lawn state analysis module fuses based on the information to obtain the health condition of different areas of the whole lawn finally.

Classifying the lawn state, performing semantic segmentation and region positioning on the type to be identified through a lawn state identification model, performing level evaluation on the severity of the problem, generating a lawn problem map, and transmitting the evaluation result to a user side in a wireless communication mode.

The lawn state recognition model mainly comprises a backbone network, multi-layer pyramid feature fusion and multi-task branches, wherein the backbone network is designed by ResNet structures, and image features with different resolutions are extracted; the multi-layer pyramid feature fusion module fuses features with different resolutions; the multi-task branch mainly comprises five tasks of grass type, grass health state, weed, disease and insect pest, wherein grass health state (dead grass (whole grass), partial area yellowing/brown, alopecia areata and the like) outputs three categories of health, normal and unhealthy, weed, disease and insect pest output category and three grades of light, normal and serious.

The model training mainly acquires data of the type and the problem condition corresponding to the annotation to obtain the problem and the severity corresponding to the image, designs the model according to the structure and trains by using the annotation data to enable the model to have generalization performance, and finally deploys the model to the embedded equipment to run in real time.

The results of the following dimensions are weighted averaged: and (3) classifying and judging the severity degree under the training of the visual large model, and comparing the actual color and the growth speed of the lawn with the normal color and the growth speed of the lawn in the season, wherein the coverage rate of the lawn problems (withered and yellow, alopecia areata, weeds, diseases and insects and the like) is improved. Resulting in a final score/grade.

According to the embodiment of the invention, through an automatic driving and vision large model technology, the discovery of the lawn state is free from the dependence on manpower, the universality is better, the accuracy is improved, and the efficiency is higher. And completes the discovery-analysis-execution of the closed loop of the improvement measure.

Further, a mowing module is arranged on the mobile robot, and a corresponding lawn maintenance strategy and lawn maintenance advice are obtained according to the lawn state, and the mobile robot is controlled to execute the lawn maintenance strategy and transmit the lawn maintenance advice to a user terminal, which comprises:

In the specific implementation, when the lawn (whole piece) is withered and yellow, partial area is yellowing/brown and alopecia areata are caused, the robot autonomously increases the mowing frequency and informs a user to carry out necessary fertilization and sowing maintenance.

Further, the lawn color and growth rate of the growing season (the network-acquired local season and time) are combined to discriminatively determine whether and what action should be taken.

First, the location, severity/class classification, and suggested treatment of the problem lawn are provided. The notification content includes: the type, time and frequency of fertilization.

In specific implementation, aiming at weeds, after the robot obtains the positions of the weeds, the weeds are cut at fixed points, so that the weeds are prevented from continuing to grow; in addition, aiming at different weed types, the user is prompted to adopt different weed killers to conduct targeted weed killing, and the grass-order agent information is transmitted to the user terminal.

During implementation, the positions of the areas with vigorous growth and the areas with slow growth are obtained, the robot is controlled to autonomously increase the mowing frequency of the areas with vigorous growth, the mowing frequency of the areas with slow growth is reduced, the lawn grows uniformly, and the flatness and the attractiveness are improved.

The appropriate mowing frequency of the different areas is calculated according to the mowing height suggested by the different grass species and the growing speed of the grass.

For example, oggot grass has a natural height of 8-28cm, a pruning height of 3.5-7.5cm, and according to the principle of 1/3 of the grass pruning height, if the grass is to be left to be 6cm in height, mowing should be performed when the grass is 9cm in height, and 3cm of grass tips should be cut off. Calculated according to the growth speed of grass, if the length of the vigorous zone is 1cm after 2 days, the next mowing interval is 6 days later. Essentially 1 mowing per week. The growth speed of the grass is the difference between the current grass height (the visual recognition grass height) and the last grass leaving height divided by the two mowing time interval. The grass growth rate in the area is updated over time (e.g., every month) to reverse the frequency of mowing that should be performed.

Mowing heights are different for different grass species, but can be roughly divided into two large areas of the united states and europe to distinguish them respectively. The usual mowing height in the United states is 2.5cm-9cm and European is 2.5cm-6cm.

In specific implementation, aiming at diseases and insect pests, marking position points, displaying the positions of the diseases and the insect pests on an APP map, and reminding a user to maintain the lawn aiming at the diseases and the insect pests.

Compared with the prior art which only relies on manual identification, the embodiment of the invention has the following advantages:

And (5) continuously monitoring. The manual recognition of the lawn health state has physical energy limitation, and apart from the influence of the idle condition of the personal time, the long-time work can also cause labor interruption or efficiency reduction; the mobile robot can continuously keep a set working state for a long time to work, monitor a target in real time for a long time and timely find the lawn state to be processed.

High efficiency and accuracy. The artificial recognition of the lawn state needs a large amount of accumulated expertise for a long time to ensure the basic accuracy of recognition, and a large amount of time and energy cost are required for culturing a large amount of specialized recognition talents; by combining an image recognition technology with an artificial intelligence training model, after the model is fully trained by expert knowledge, the state of the lawn can be accurately recognized as soon as the model is carried on any robot, and compared with the method for culturing specialized talents, the time and energy cost are saved.

And (3) carrying out data-driven decision and updating iteration in real time. Manually identifying the state of the lawn, and if updating and iterating the prior knowledge are required, taking additional time to participate in learning; meanwhile, different people have subjectivity on judging different problems, and cannot unify quantitative standards and perform unified analysis and judgment; the robot data large model can update the optimized model data in real time by combining the past working data in the actual working, and the unified model is used for making the same standard. And the optimal solution can be judged according to all the data exhaustion decision schemes in one-to-one comparison.

Continuous work, simple and convenient operation and accurate identification of various problems.

It should be noted that, there is not necessarily a certain sequence between the steps, and those skilled in the art will understand that, in different embodiments, the steps may be performed in different orders, that is, may be performed in parallel, may be performed interchangeably, or the like.

The method for automatically managing lawns according to the embodiment of the present invention is described above, and the system for automatically managing lawns according to the embodiment of the present invention is described below, referring to fig. 2, fig. 2 is a schematic hardware structure of another embodiment of a system for automatically managing lawns according to the embodiment of the present invention, as shown in fig. 2, where the system 10 includes: a processor 101, a memory 102 and a computer program stored on the memory and executable on the processor, which when executed by the processor 101, performs the steps of:

Shooting a lawn based on the mobile robot to obtain a lawn image;

Specific implementation steps are the same as those of the method embodiment, and are not repeated here.

Optionally, the computer program when executed by the processor 101 also implements the steps of:

Embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer-executable instructions for execution by one or more processors, e.g., to perform the method steps S100-S300 of fig. 1 described above.

By way of example, nonvolatile storage media can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM may be available in many forms such as Synchronous RAM (SRAM), dynamic RAM, DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SYNCHLINK DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The disclosed memory components or memories of the operating environment described in embodiments of the present invention are intended to comprise one or more of these and/or any other suitable types of memory.

The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automatically managing a lawn, applied to a mobile robot, the method comprising:

Shooting a lawn based on the mobile robot to obtain a lawn image;

2. The method for automatically managing lawns according to claim 1, wherein the mobile robot is provided with a mowing module, obtains a corresponding lawn maintenance policy and a lawn maintenance suggestion according to a lawn state, controls the mobile robot to execute the lawn maintenance policy, and transmits the lawn maintenance suggestion to a user terminal, and includes:

3. The method for automatically managing lawns according to claim 1, wherein the mobile robot is provided with a mowing module, obtains a corresponding lawn maintenance policy and a lawn maintenance suggestion according to a lawn state, controls the mobile robot to execute the lawn maintenance policy, and transmits the lawn maintenance suggestion to a user terminal, and includes:

4. The method for automatically managing lawns according to claim 1, wherein the mobile robot is provided with a mowing module, obtains a corresponding lawn maintenance policy and a lawn maintenance suggestion according to a lawn state, controls the mobile robot to execute the lawn maintenance policy, and transmits the lawn maintenance suggestion to a user terminal, and includes:

5. The method for automatically managing lawns according to claim 1, wherein the mobile robot is provided with a mowing module, obtains a corresponding lawn maintenance policy and a lawn maintenance suggestion according to a lawn state, controls the mobile robot to execute the lawn maintenance policy, and transmits the lawn maintenance suggestion to a user terminal, and includes:

6. A system for automatically managing a lawn, for use with a mobile robot having one or more cameras disposed thereon, the system comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:

Shooting a lawn based on the mobile robot to obtain a lawn image;

7. The system for automatically managing a lawn according to claim 6, wherein the computer program, when executed by the processor, performs the steps of:

8. The system for automatically managing a lawn according to claim 6, wherein the computer program, when executed by the processor, further performs the steps of:

9. The system for automatically managing a lawn according to claim 6, wherein the computer program, when executed by the processor, further performs the steps of:

10. A non-transitory computer-readable storage medium storing computer-executable instructions which, when executed by one or more processors, cause the one or more processors to perform the method of automatically managing a lawn of any of claims 1-5.