CN114266969A - Inspection equipment and inspection method - Google Patents
Inspection equipment and inspection method Download PDFInfo
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Abstract
The invention discloses inspection equipment and an inspection method, and relates to the technical field of crop inspection, wherein the inspection equipment runs on a track in an agricultural area and comprises a binocular camera module, an image processing module and a wireless transmission module, wherein the binocular camera module comprises a first camera and a second camera; the image processing module is connected with the binocular camera module and used for processing the first image and the second image; the wireless transmission module is connected with the image processing module and used for transmitting the growth condition information of the crops to the cloud. According to the invention, the inspection equipment capable of regularly inspecting the crops is arranged, the binocular camera module is arranged on the inspection equipment, so that two cameras of the binocular camera module can respectively photograph each crop while moving, the photographed picture is processed by the processing module and the crop growth information is transmitted to the far end for management and analysis, and automatic regular high-precision inspection of the crops is realized.
Description
Technical Field
The invention relates to the technical field of crop inspection, in particular to inspection equipment and an inspection method.
Background
With the development of science and technology, modern agriculture is developing towards a larger scale. In small-scale crop planting, the inspection of crops is generally that a manager personally observes crop growth condition information in a crop planting area through naked eyes, judges the growth condition of the crops through planting experience and rapidly takes corresponding effective measures.
However, for large-scale crops, inspection generally relies on manual inspection or unmanned inspection. When the manual inspection is adopted, an inspector can only carry portable equipment to sample and inspect crops and record related conditions, the inspection mode has the defects of low inspection coverage rate and long time consumption for one-time inspection, and particularly, the crops are planted in hilly lands, so that the difficulty and the workload of the manual inspection are increased due to the complex terrain of a planting area.
And adopt unmanned aerial vehicle to patrol and examine specifically to shoot from last down through the camera, but this kind of mode of patrolling and examining can only follow a dimension and patrol and examine crops for patrol and examine not meticulously enough, can not carry out the omnidirectional to crops moreover and patrol and examine.
The two modes of inspecting the crops are difficult to comprehensively master the growth conditions of the crops in real time.
Disclosure of Invention
In order to solve the problems of the prior art, the invention provides inspection equipment, which runs on a track in an agricultural area, wherein the track is a single track arranged along a crop planting position in the agricultural area; the method comprises the following steps:
binocular camera module includes:
the first camera is used for acquiring a first image of the crop;
the second camera is used for acquiring a second image of the crop;
the image processing module is connected with the binocular camera module and used for processing the first image and the second image to obtain the actual size of the target features of the crops and the growth information of the non-target features of the crops; determining growth condition information of the crop according to the actual size of the target feature of the crop and the growth information of the non-target feature of the crop;
and the wireless transmission module is connected with the image processing module and is used for transmitting the growth condition information of the crops to a cloud.
Preferably, the image processing module further comprises: a distance measuring module;
the range finding module includes:
a target determination unit for determining target features of crops appearing in the first image and the second image simultaneously, respectively, and detecting an imaging size of the target features:
the depth calculation unit is connected with the target determination unit and used for calculating the depth of the target feature according to the imaging size of the target feature; and determining the actual size of the target feature according to the depth of the target feature.
Preferably, the image processing module further comprises: the image splicing module is used for splicing the first image information and the second image to obtain a spliced image; the image information of the spliced image is subjected to convolution extraction on the non-target characteristics of the crops in the spliced image through a convolution neural network of the image processing module to obtain a non-target characteristic diagram, network parameters of the full connection layer, the Softmax classifier and the frame regression are trained and adjusted through a transfer learning method, and the detection results of the non-target characteristics are output to obtain the growth information of the non-target characteristics of the crops.
Preferably, the binocular camera module is further used for detecting the track information of the running track of the inspection equipment;
the inspection equipment further comprises:
the control module is connected with the binocular camera module and used for at least one of the following:
according to the track information of the running track, when the running track information indicates that the inspection equipment turns in advance on the track, the inspection equipment is controlled to turn;
and according to the track information of the running track, when the running track information indicates that the inspection equipment is on the track to climb in advance, the inspection equipment is controlled to climb.
Preferably, the inspection equipment further comprises:
the inspection mode selection module is connected with the control module;
the inspection mode selection module comprises:
the daily inspection mode module is used for regularly inspecting crops;
mode module patrols and examines temporarily for patrol and examine crops when weather is abominable.
The invention also provides a polling method for polling by adopting the polling equipment, which comprises the following steps:
driving the inspection equipment to run on a single track laid in the crop planting position area;
respectively acquiring a first image and a second image of the crop by utilizing a first camera and a second camera of the rotating binocular camera module;
processing the first image and the second image to obtain the actual size of the target feature of the crop and the growth information of the non-target feature of the crop; determining growth condition information of the crop according to the actual size of the target feature of the crop and the growth information of the non-target feature of the crop;
and transmitting the growth condition information of the crops to a cloud end by utilizing the wireless transmission module.
Preferably, the processing the first image and the second image to obtain the actual size of the target feature of the crop and the growth information of the non-target feature of the crop includes:
respectively determining target features of the target crops appearing in the first image and the second image simultaneously, and detecting the imaging size of the target features:
calculating the depth of the target feature according to the imaging size of the target feature; and determining the actual size of the target feature according to the depth of the target feature.
Preferably, the processing the first image and the second image to obtain the actual size of the target feature of the crop and the growth information of the non-target feature of the crop includes:
splicing the first image information and the second image to obtain a spliced image; the image information of the spliced image is subjected to convolution extraction on the non-target characteristics of the crops in the spliced image through a convolution neural network of the image processing module to obtain a non-target characteristic diagram, network parameters of the full connection layer, the Softmax classifier and the frame regression are trained and adjusted through a transfer learning method, and the detection results of the non-target characteristics are output to obtain the growth information of the non-target characteristics of the crops.
Preferably, the drive inspection device runs on a single track laid in the area of the crop planting position, and comprises one of the following:
in a daily inspection mode, in response to receiving timing prompt information, driving inspection equipment to run on a single track paved in a crop planting position area;
in the temporary inspection mode, in response to the detection of bad weather, the inspection equipment is driven to run on a single track paved in the crop planting position area.
The invention also provides inspection equipment, which comprises: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is configured to implement the inspection method described above when running the computer program.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the inspection equipment for inspecting crops is arranged, the binocular camera module is arranged on the inspection equipment, two cameras of the binocular camera module can respectively photograph each crop while moving, the photographed picture is processed by the processing module to obtain the actual size of the target characteristic of each crop and the growth information of the non-target characteristic of each crop, then the growth condition information of each crop is determined, finally the growth information of all the crops in the crop planting area is determined, and the crop growth information is transmitted to the far end for management and analysis, so that the accurate inspection of the crops is realized, the inspection coverage is comprehensive, and the inspection precision is high.
Drawings
Fig. 1 is a schematic structural diagram of inspection equipment according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an image processing module according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a distance measuring module according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of binocular ranging in accordance with an embodiment of the present invention;
fig. 5 is a schematic flow chart of a polling method according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a hardware structure of the inspection device according to an embodiment of the present invention.
The attached drawings are marked as follows: 1-a memory; 2-a processor.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In the embodiment of the invention, the application scene of the inspection equipment can be used for inspecting the planted crops, and it can be understood that in the prior art, for the large-scale planted crops, the inspection generally depends on manual inspection or unmanned aerial vehicle inspection. The defects of the manual inspection are that the inspection coverage rate is low, the inspection time is long, particularly, the crops are planted in hilly lands, and the difficulty and the workload of the manual inspection are increased due to the complex terrain of a planting area. And adopt unmanned aerial vehicle to patrol and examine specifically to shoot from last down through the camera, but this kind of mode of patrolling and examining can only follow a dimension and patrol and examine crops for patrol and examine not meticulously enough, can not carry out the omnidirectional to crops moreover and patrol and examine.
Therefore, how to comprehensively and efficiently and automatically patrol crops in agricultural land becomes a technical problem which needs to be solved urgently.
In the following, agricultural fields are taken as orchards, and crops are taken as fruit trees. Of course, in other embodiments, the agricultural land may be a vegetable garden, and the crops may be vegetables, and the like, which are not limited herein.
Fig. 1 is a schematic structural diagram of inspection equipment according to an embodiment of the present invention, and as shown in fig. 1, the inspection equipment runs on a track in a fruit tree planting area, where the track is a single track deployed along a fruit tree planting position in an agricultural area; the method comprises the following steps:
binocular camera module includes:
the first camera is used for acquiring a first image of each fruit tree;
the second camera is used for acquiring a second image of each fruit tree;
the image processing module is connected with the binocular camera module and used for processing the first image and the second image to obtain the actual size of the target features of the fruit tree and the growth information of the non-target features of the fruit tree; determining the growth condition information of the fruit tree according to the actual size of the target characteristic of the fruit tree and the growth information of the non-target characteristic of the fruit tree;
and the wireless transmission module is connected with the image processing module and is used for transmitting the growth condition information of the fruit trees to the cloud.
The agricultural area may be in a plain area or a hilly area. The planting of the fruit trees adopts the distribution according to a row or a row (or a strip),
the binocular camera module is arranged at the top of the inspection equipment, and the first camera and the second camera of the binocular camera module can rotate clockwise or anticlockwise. When advancing on the track for no matter be located orbital which side for the fruit tree, first camera and second camera homoenergetic are taken a picture to the fruit tree. And the first image collected by the first camera and the second image collected by the second camera for each fruit tree have an overlapping area.
The first camera and the second camera can be set as visible light cameras and can also be set as infrared cameras. The visible light camera is set to shoot the fruit trees under the condition of better illumination condition; the infrared camera forms a video image by receiving infrared rays which are diffused and reflected after irradiating a photographed object, so that the infrared camera is suitable for photographing fruit trees under poor illumination conditions or no illumination conditions.
The target characteristics of the fruit tree include, but are not limited to, fruit of the fruit tree and/or trunk of the fruit tree; and the actual dimensions of the target feature herein include, but are not limited to, the actual size of the fruit and/or the actual cross-sectional diameter of the trunk.
Non-target features of fruit trees herein include, but are not limited to, leaves of fruit trees and/or flowers of fruit trees. And the growth information of the non-target characteristics of the fruit tree herein includes, but is not limited to, the morphology of the fruit tree leaves at different times during one growth period of the fruit tree and/or the morphology of the flowers of the fruit tree during the growth period.
The growth condition information of the fruit tree refers to the growth situation of the fruit tree in one growth cycle.
In the embodiment, the binocular camera shooting assembly is used for shooting the picture of the fruit tree, target features such as fruits and/or trunks and non-target features such as leaves and/or flowers of the fruit tree are extracted from the picture, the actual size of the target features is obtained through calculation, the non-target features are extracted from the image, the shape of the non-target features is obtained, and therefore the growth situation of the fruit tree in one growth cycle is determined. And transmitting the growth situation data to a remote end for management and analysis. This embodiment realizes patrolling and examining the fruit tree, and patrols and examines and cover whole orchard, and it is high to patrol and examine the precision, makes the more accurate growth condition of understanding each fruit tree in a growth cycle of planting personnel.
Fig. 2 is a schematic structural diagram of an image processing module according to an embodiment of the present invention, and as shown in fig. 2, the image processing module includes a distance measurement module and an image stitching module, specifically:
fig. 3 is a schematic structural diagram of a distance measurement module according to an embodiment of the present invention, and as shown in fig. 3, the image processing module in this embodiment is used to obtain an actual size of a target feature of a fruit tree, and specifically includes: a distance measuring module; the range finding module includes:
the target determining unit is used for respectively determining the target features of the fruit trees appearing in the first image and the second image simultaneously and detecting the imaging sizes of the target features:
the depth calculation unit is connected with the target determination unit and used for calculating the depth of the target feature according to the imaging size of the target feature; and determining the actual size of the target feature according to the depth of the target feature.
The target determining unit extracts fruit tree target features in the first image and the second image and determines the imaging size of the target features by selecting a neural network model which is trained in a large data set, and the depth of the target features is determined by the imaging size of the target features.
There are many ways to calculate the depth through the imaging size, and the depth may be a relative depth or an absolute depth; the relative depth refers to the change of the depth of the target object, for example, the depth is larger when the imaging size is smaller; the absolute depth may then correspond to a depth value. The depth of the target object can be determined by continuous measurements of the physical relative depth or absolute depth of the target.
Determining the depth of the fruit tree target feature according to the imaging size of the target feature, wherein the relative depth of the target feature can be determined according to the length or width or the proportion of the area of the imaging of the target feature in the whole image; or, determining the relative depth of the target feature by the continuous imaging size of the target feature; or inquiring the preset contrast relation between the imaging size and the depth through the imaging size of the target feature to determine the depth of the target feature.
And obtaining the actual size of the target feature by adopting proportion conversion after the depth of the target feature is determined.
Determining the depth of the target feature may also be obtained by ranging, as shown in fig. 4:
p is a certain point on the target feature, OR and OT are optical centers of the first camera and the second camera respectively, imaging points of the point P on the photoreceptors of the two cameras are P and P 'respectively (an imaging plane of the camera is placed in front of a lens after being rotated), f is a focal length of the camera, B is a center distance of the two cameras, Z is a depth of the target feature, and if the distance from the point P to the point P' is dis:
dis＝B-(XR-XT)；
according to the similar triangle principle:
then, the following results are obtained:
the focal length f and the center distance B between the two cameras can be obtained by calibration measurement,
therefore, the depth information can be obtained by obtaining the parallax between the first image and the second image.
And a method of obtaining a parallax of a first image and a second image:
estimating a left-to-right disparity map sequence and a right-to-left disparity map sequence of the first image and the second image frame by adopting a stereo region matching method;
specifically, a mean shift algorithm is used for carrying out over-segmentation on a binocular stereo video image;
secondly, the energy function is constructed as follows:
Eenergy＝Edata+λEsmooth
wherein E isdataFor data items, EsmoothFor the smoothing term, λ is the weighting coefficient,
and constructing a corresponding grid map according to the data item and the smoothing item, and calculating parallax information corresponding to each pixel by adopting an algorithm to obtain a parallax map.
Wherein, the data items of the energy function take both intraframe errors and interframe errors into account; the intra-frame error comprehensively uses a brightness absolute difference Mean (MAD) criterion and a gradient absolute difference Sum (SGRAD) criterion, and each pixel self-adaptively adjusts the weight according to the importance degree in a window;
parallax image consistency check: and respectively carrying out consistency check on the left-to-right disparity map sequence and the right-to-left disparity map sequence, correcting unreliable visual field matching information and then calculating the disparity. And finally, obtaining depth information through the calculated parallax.
In other embodiments, the image processing module is used for splicing crop pictures shot by the first camera and the second camera to obtain a spliced image, and obtaining images of all fruit trees in the planting area through the spliced image, so that the situation that the first camera or the second camera only shoots partial images of a certain fruit tree and cannot obtain growth information of the fruit tree is avoided.
In order to achieve the effect, the image processing module comprises an image splicing module, and the image splicing module is used for splicing the first image information and the second image to obtain a spliced image; the splicing method specifically comprises the following steps:
shooting a first image and a second image of the same fruit tree, selecting one of the first image and the second image as a reference image and the other image as an image to be spliced, wherein the reference image and the image to be spliced have an overlapping area;
respectively carrying out first remapping on the reference image and the image to be spliced;
extracting rough matching characteristic point pairs between the remapped reference image and the remapped image to be spliced;
obtaining fine matching feature point pairs from the coarse matching feature point pairs by using second remapping;
estimating a rotation matrix and an offset matrix of the first camera and the second camera by using the fine matching characteristic point pairs;
and respectively splicing the reference image and the image to be spliced according to the rotation matrix and the offset matrix to obtain a spliced image.
The method for respectively carrying out first remapping on the reference image and the image to be spliced comprises the following steps:
and respectively carrying out first remapping on the reference image and the image to be spliced by utilizing equidistant cylindrical projection to obtain corresponding equidistant cylindrical projection images which are respectively used as the remapped reference image and the remapped image to be spliced.
Wherein the obtaining of the fine matching feature point pair from the coarse matching feature point pair by using the second remapping comprises:
carrying out second remapping on the coordinates of the coarse matching characteristic point pairs by using plane projection;
and obtaining the characteristic point pairs which accord with the homography in the rough matching characteristic point pairs as fine matching characteristic point pairs.
In the embodiment, by adopting a static splicing mode and utilizing the geometric properties of the projection of the acquired image, the feature points of the original image set are extracted and matched to obtain accurate control point pairs, so that the high quality of the spliced image is realized.
The spliced image of the embodiment is sent into a 101-layer convolutional neural network to output a non-target characteristic diagram of a fruit tree; integrating all the characteristics in the characteristic diagram;
training and adjusting the final full-connection layer, the Softmax classifier and the frame regression network parameters by a transfer learning method to obtain the recognition result of the non-target characteristics of the fruit tree; and judging the growth form of the non-target features according to the recognition result.
And obtaining the growth condition information of the fruit tree through the obtained growth form of the non-target characteristic of the fruit tree and the actual size of the target characteristic of the fruit tree.
In other embodiments, the binocular camera module can also be used for detecting the track information of the running track of the inspection equipment;
the inspection equipment further comprises a control module connected with the binocular camera module and used for at least one of the following:
according to the track information of the running track, when the running track information indicates that the inspection equipment turns in advance on the track, the inspection equipment is controlled to turn;
and according to the track information of the running track, when the running track information indicates that the inspection equipment climbs the slope on the track in advance, controlling the inspection equipment to climb the slope.
The information of the running track of this embodiment can be obtained through some special position information points on the track, and these special positions can be the turning of the track, and also can be the climbing of the track. The transceiver may be located at the particular location described above. For example, all the corners of the track can be provided with photoelectric sensors, or the climbing positions of the track can be provided with proximity switches, and the photoelectric sensors and the proximity switches are electrically connected with the control module. The inspection equipment is arranged in such a way that the turning and climbing can be automatically realized when the track runs.
After the inspection equipment shoots one fruit tree, the inspection equipment automatically moves along the track to shoot the next fruit tree.
The inspection equipment also comprises an inspection mode selection module, and the inspection mode selection module is connected with the control module;
patrol and examine mode selection module and include:
the daily inspection mode module is used for regularly inspecting the fruit trees;
mode module patrols and examines temporarily for patrol and examine crops when weather is abominable.
The regular inspection in the daily inspection mode can be set as the inspection of fruit trees in the orchard in an inspection period of every three days; and regular patrol can be performed in three time periods within a patrol day: and the fruit trees are patrolled six points in the morning, one point at noon and eight points at night. Of course, the period and time period of the polling can be determined according to actual requirements, and the invention is not limited in any way.
Further, the inspection mode can also be temporary inspection performed under certain special weather, and the control module drives the inspection equipment to perform temporary inspection when the air temperature of the orchard is higher than 30 ℃ or the air temperature of the orchard is lower than 4 ℃ or the air humidity of the orchard is higher than 85% in the special weather of the embodiment. The temperature sensor and the humidity sensor on the equipment are patrolled and examined again to the air temperature and the humidity accessible setting in orchard measure, and above-mentioned sensor also control module group links to each other. The temperature and humidity of the temporary inspection can be determined according to the variety and actual requirements of the fruit trees, and the invention is not limited in any way.
It should be noted that the parameter setting of regular timing polling and the temperature and humidity parameter setting of temporary polling in daily polling can be set through a control panel on the polling equipment, and the parameter setting can also be performed through a remote control terminal of the polling equipment.
In the embodiment of the invention, the regular routine inspection of the fruit trees and the temporary inspection of the fruit trees in severe weather such as high temperature, low temperature, heavy rain and the like can be realized by setting the daily inspection mode and the temporary inspection mode.
Fig. 5 is a schematic flow chart of an inspection method according to an embodiment of the present invention, as shown in fig. 1, the inspection method is applied to the inspection device to inspect a fruit tree, and the inspection method includes:
driving the inspection equipment to run on a single track laid in the fruit tree planting position area;
respectively acquiring a first image and a second image of each fruit tree by utilizing a first camera and a second camera of a rotating binocular camera module;
processing the first image and the second image to obtain the actual size of the target characteristic of the fruit tree and the growth information of the non-target characteristic of the fruit tree; determining growth condition information of the fruit tree according to the actual size of the target characteristic of the fruit tree and the growth information of the non-target characteristic of the fruit tree;
and transmitting the growth condition information of the fruit trees to the cloud end by using the wireless transmission module.
In some optional embodiments, processing the first image and the second image to obtain the actual size of the target feature of the fruit tree and the growth information of the non-target feature of the fruit tree includes:
respectively determining the target features of fruit trees appearing in the first image and the second image simultaneously, and detecting the imaging size of the target features:
calculating the depth of the target feature according to the imaging size of the target feature; and determining the actual size of the target feature according to the depth of the target feature.
In some optional embodiments, processing the first image and the second image to obtain the actual size of the target feature of the fruit tree and the growth information of the non-target feature of the fruit tree includes:
splicing the first image information and the second image to obtain a spliced image; and carrying out convolution extraction on the non-target characteristics of crops in the spliced image by the image information of the spliced image through a convolution neural network of the image processing module to obtain a non-target characteristic diagram, training and adjusting network parameters of the full connection layer, the Softmax classifier and the frame regression through a transfer learning method, and outputting a detection result of the non-target characteristics to obtain growth information of the non-target characteristics of the fruit tree.
In some optional embodiments, the driving inspection equipment runs on a single track paved in the area of the fruit tree planting position, and the driving inspection equipment comprises one of the following components:
in a daily inspection mode, in response to receiving timing prompt information, driving inspection equipment to run on a single track paved in a fruit tree planting position area;
under the mode of temporarily patrolling and examining, respond to and detect the weather bad, the drive is patrolled and examined the equipment and is operated on the monorail that the position region was laid is planted to the fruit tree.
Here, it should be noted that: the description of the inspection method item is similar to that of the inspection equipment item, and the description of the beneficial effects of the method is not repeated. For technical details that are not disclosed in the inspection method embodiment of the present invention, refer to the description of the embodiment of the inspection apparatus of the present invention.
As shown in fig. 6, an embodiment of the present invention further provides an inspection device, where the inspection device includes a memory 1, a processor 2, and computer instructions stored in the memory 1 and executable on the processor 2; the processor 2 implements the steps applied to the vulnerability scanning method when executing the instructions.
In some embodiments, memory 1 in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1 of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
And the processor 2 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 2. The Processor 2 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 1, and the processor 2 reads the information in the memory 2 and completes the steps of the method in combination with the hardware thereof.
In some embodiments, the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Yet another embodiment of the present invention provides a computer storage medium storing an executable program that, when executed by the processor 1, can implement the steps applied to the driving method. For example, as one or more of the methods shown in fig. 1.
In some embodiments, the computer storage medium may include: a U-disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: the technical schemes described in the embodiments of the present invention can be combined arbitrarily without conflict.
Finally, only specific embodiments of the present invention have been described in detail above. The invention is not limited to the specific embodiments described above. Equivalent modifications and substitutions by those skilled in the art are also within the scope of the present invention. Accordingly, equivalent alterations and modifications are intended to be included within the scope of the invention, without departing from the spirit and scope of the invention.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118426493A (en) * | 2024-07-05 | 2024-08-02 | 山东字节信息科技有限公司 | Unmanned aerial vehicle inspection system and method based on cloud platform |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355550A (en) * | 2016-10-31 | 2017-01-25 | 微景天下（北京）科技有限公司 | Image stitching system and image stitching method |
| CN109977924A (en) * | 2019-04-15 | 2019-07-05 | 北京麦飞科技有限公司 | For real time image processing and system on the unmanned plane machine of crops |
| CN110458106A (en) * | 2019-08-13 | 2019-11-15 | 深圳市睿海智电子科技有限公司 | A kind of intelligent analysis method and intellectual analysis device of tomato growth state |
| US20200253127A1 (en) * | 2018-09-11 | 2020-08-13 | Pollen Systems Corporation | Crop management method and apparatus with autonomous vehicles |
| CN111929253A (en) * | 2013-03-25 | 2020-11-13 | 索尼公司 | Information processing system, device and method, and method for producing crop product |
| CN111985724A (en) * | 2020-08-28 | 2020-11-24 | 深圳前海微众银行股份有限公司 | A method, device, equipment and storage medium for crop yield estimation |
| CN112800665A (en) * | 2021-01-13 | 2021-05-14 | 电凯(常州)新能源技术研究院有限公司 | Plant growth self-feedback learning cultivation method based on artificial intelligence |
| CN112862272A (en) * | 2021-01-21 | 2021-05-28 | 华中科技大学 | Accurate agricultural management and analysis system, positioning method, terminal and storage medium |
| CN113724210A (en) * | 2021-08-13 | 2021-11-30 | 广州华农大智慧农业科技有限公司 | Crop growth recognition method and system |
| CN113763304A (en) * | 2020-05-19 | 2021-12-07 | 中移(成都)信息通信科技有限公司 | Method, device, equipment and medium for identification of crop diseases and insect pests |
-
2021
- 2021-12-16 CN CN202111547864.1A patent/CN114266969B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111929253A (en) * | 2013-03-25 | 2020-11-13 | 索尼公司 | Information processing system, device and method, and method for producing crop product |
| CN106355550A (en) * | 2016-10-31 | 2017-01-25 | 微景天下（北京）科技有限公司 | Image stitching system and image stitching method |
| US20200253127A1 (en) * | 2018-09-11 | 2020-08-13 | Pollen Systems Corporation | Crop management method and apparatus with autonomous vehicles |
| CN109977924A (en) * | 2019-04-15 | 2019-07-05 | 北京麦飞科技有限公司 | For real time image processing and system on the unmanned plane machine of crops |
| CN110458106A (en) * | 2019-08-13 | 2019-11-15 | 深圳市睿海智电子科技有限公司 | A kind of intelligent analysis method and intellectual analysis device of tomato growth state |
| CN113763304A (en) * | 2020-05-19 | 2021-12-07 | 中移(成都)信息通信科技有限公司 | Method, device, equipment and medium for identification of crop diseases and insect pests |
| CN111985724A (en) * | 2020-08-28 | 2020-11-24 | 深圳前海微众银行股份有限公司 | A method, device, equipment and storage medium for crop yield estimation |
| CN112800665A (en) * | 2021-01-13 | 2021-05-14 | 电凯(常州)新能源技术研究院有限公司 | Plant growth self-feedback learning cultivation method based on artificial intelligence |
| CN112862272A (en) * | 2021-01-21 | 2021-05-28 | 华中科技大学 | Accurate agricultural management and analysis system, positioning method, terminal and storage medium |
| CN113724210A (en) * | 2021-08-13 | 2021-11-30 | 广州华农大智慧农业科技有限公司 | Crop growth recognition method and system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118426493A (en) * | 2024-07-05 | 2024-08-02 | 山东字节信息科技有限公司 | Unmanned aerial vehicle inspection system and method based on cloud platform |
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