Patent Publication Number: US-2022222837-A1

Title: Method for measuring growth height of plant, electronic device, and storage medium

Description:
FIELD 
     The present application relates to a technical field of image analysis, and more particularly to a method for measuring a growth height of a plant, an electronic device, and a storage medium. 
     BACKGROUND 
     To increase yield and quality of plants, it is helpful to determine a better planting method for plants by analyzing a daily growth of the plants, thereby reducing planting costs. Measuring the growth height of the plants accurately is necessary. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a camera device in communication with an electronic device in an embodiment of the present application. 
         FIG. 2  is a flowchart diagram of a method of measuring a growth height of a plant in an embodiment of the present application. 
         FIG. 3  is a structural diagram of a measurement device for measuring a growth height of a plant in an embodiment of the present application. 
         FIG. 4  is a structural diagram of an electronic device housing the measurement device in an embodiment of the present application. 
     
    
    
     DETAILED DESCRIPTION 
     The accompanying drawings combined with the detailed description illustrate the embodiments of the present disclosure hereinafter. It is noted that embodiments of the present disclosure and features of the embodiments can be combined, when there is no conflict. 
     Various details are described in the following descriptions for a better understanding of the present disclosure, however, the present disclosure may also be implemented in other ways other than those described herein. The scope of the present disclosure is not to be limited by the specific embodiments disclosed below. 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 the present disclosure belongs. The terms used herein in the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. 
       FIG. 1  is a block diagram of a camera device in communication with an electronic device in an embodiment of the present application. As shown in  FIG. 1 , a camera device  2  communicates with an electronic device  1 , and the camera device  2  includes a first lens  20  and a second lens  21 . The first lens  20  can capture color images, and the second lens  21  can capture depth images. 
       FIG. 2  is a flowchart diagram of a method for measuring a growth height of a plant in an embodiment of the present application. 
     In one embodiment, the method for measuring a growth height of a plant may be applied to one or more electronic devices  1 . The electronic device  1  includes hardware such as, but is not limited to, a microprocessor and an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), embedded devices, for example. 
     The electronic device  1  may be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (Personal Digital Assistant, PDA), a game console, an interactive network television (Internet Protocol Television, IPTV), or smart wearable device, for example. 
     The electronic device  1  may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group including multiple network servers, or a cloud including a large quantity of hosts or network servers based on a cloud computing technology. 
     A network, which includes the electronic device  1 , can include, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, and a virtual private network (Virtual Private Network, VPN), for example. 
     In block S 10 , in response to a received height measurement request, the electronic device  1  determines a plant to be detected according to the received height measurement request. 
     In one embodiment, information carried in the height measurement request includes a plant identifier, and a name of the plant to be detected, for example. 
     Moreover, the plant to be detected may be any plant that needs to be analyzed for daily growth, such as roses, sunflowers, or rice plant, for example. 
     In one embodiment, the electronic device  1  obtains an idle thread from a preset thread connection pool, and parses a method body of the height measurement request with the idle thread. Information in the height measurement request can be obtained. The electronic device  1  further obtains a preset label and information corresponding to the preset label from the obtained information as the plant to be detected. 
     The preset label may be the plant identifier. 
     By obtaining the idle thread, from the preset thread connection pool to parse a method body of the height measurement request, not only is the time for creating the thread reduced, but also the efficiency of parsing the height measurement request can be improved. Based on a mapping relationship between the preset label and the plant to be detected, the plant to be detected can be accurately determined. 
     In block S 11 , the electronic device  1  controls the camera device  2  to capture the plant to be detected, and obtain a color image and a depth image of the plant to be detected, each of the color image and the depth image includes a plurality of plants to be detected. 
     In one embodiment, the camera device  2  includes dual lenses, such as the first lens  20  and the second lens  21  as mentioned above. The camera device  2  may be positioned above the plant to be detected. 
     In one embodiment, the color image can be a red, green, blue (RGB) three-channel color image, and the depth image can be an image of which pixel values indicate a distance from the camera device  2  to each point in a captured scene. 
     In one embodiment, the color image and the depth image of the plant to be detected can be obtained by performing a following procedure. A first position is determined to be the location of the plant to be detected, and the first lens  20  of the camera device  2  is controlled to move to a second position corresponding to the first position. The first lens  20  is controlled to capture the plant to be detected, and the color image is obtained. Then the second lens  21  of the camera device  2  is controlled to move to the second position, the second lens  21  is controlled to also capture the plant to be detected, and the depth image is obtained. 
     According to the above embodiments, the color image and the depth image of the plant to be detected can be quickly obtained. 
     In block S 12 , the electronic device  1  detects the color image by a pre-trained detection model, and obtains a plurality of detection boxes including the plurality of plants to be detected. 
     In one embodiment, the plurality of detection boxes is obtained by extracting features of the color image by the pre-trained detection model. 
     In one embodiment, before the electronic device  1  detects the color image by the pre-trained detection model, the electronic device  1  obtains all the plurality of detection boxes including the plurality of plants to be detected. Historical data is obtained by performing a web crawler method, and the historical data is inputted to a forgetting gate layer for performing a forgetting processing, and training data is obtained. The training data is divided into a training set and a validation set by utilizing a cross-validation method. Data of the training set is inputted to an input gate layer for training, and a learner is obtained. The learner is adjusted according to data of the verification set, and the pre-trained detection model is obtained. 
     According to the above embodiments, the pre-trained detection model suitable for the plant to be detected can be generated. 
     In one embodiment, before the training data is divided into the training set and the validation set with the cross-validation method, a quantity of color training images in the training data is counted. In response to the quantity of color training images in the training data is less than a preset number, the quantity of color training images in the training data is increased by using a data enhancement algorithm. 
     According to the above embodiments, the risk of poor generalization of the pre-trained detection model is thereby avoided. 
     In one embodiment, the training data is randomly divided into at least one data packet according to a preset ratio. Any one of the at least one data packet can be determined as the verification set, and the remaining data packets are determined to be the training set. The above embodiments are repeated until one of the at least one data packet is determined as the verification set. 
     The preset ratio can be customized, and the preset ratio is not limited in the present application. 
     According to the above embodiments, each of the color training images in the training data is involved in training and verification procedures, thereby improving the pre-training of the detection model. 
     In one embodiment, the electronic device  1  determines an optimal hyperparameter point from the verification set by performing a hyperparameter grid search method. Moreover, the electronic device  1  adjusts the learner according to the optimal hyperparameter point, and obtains the pre-trained detection model. 
     Specifically, the electronic device  1  splits the verification set according to a preset step size, and obtains a target subset. The electronic device  1  traverses parameters of two ends on the target subset, verifies the learner by using the parameters of the two ends on the target subset, and obtains a learning rate of each of the parameters. The electronic device  1  determines a parameter with the largest learning rate as a first hyperparameter point, and in the neighborhood of the first hyperparameter point. The electronic device  1  reduces the preset step size and continues traversing until the length of a step size after reduction is equal to a preset step length, and determines that an obtained hyperparameter point is the optimal hyperparameter point. Furthermore, the electronic device  1  adjusts the learner according to the optimal hyperparameter point, and obtains the pre-trained detection model. 
     The preset step length is not limited in the present application. 
     According to the above embodiments, the pre-trained detection model is better able for analysis of the color image. 
     In block S 13 , the electronic device  1  aligns the color image and the depth image by using an image alignment algorithm, and obtains an alignment image. 
     In one embodiment, the alignment image can be an image generated by merging pixels of the color image with pixels of the depth image. 
     In one embodiment, the electronic device  1  acquires depth pixels of the depth image, and maps the depth pixels to a preset depth coordinate system. Depth coordinates of the depth pixels can be obtained. The electronic device  1  determines global coordinates of the depth pixels according to the depth coordinates and a preset global coordinate system, and determines positions of the depth pixels on the color image according to the global coordinates. The electronic device  1  further determines color pixels corresponding to the positions on the color image, and obtains the alignment image by merging each of the depth pixels with a corresponding color pixel. 
     The preset depth coordinate system and the preset global coordinate system can be obtained from an open source system, or can be preset by a user according to the actual requirements, not being limited in the present application. 
     According to the above embodiment, the alignment image that includes a depth value can be generated, thereby aiding subsequent determination of a growth height of the plant to be detected. 
     In block S 14 , the electronic device  1  acquires a plurality of target boxes corresponding to the plurality of detection boxes from the alignment image. 
     In one embodiment, the electronic device  1  establishes a same coordinate system for the color image and the alignment image. The electronic device  1  determines coordinates of each of the plurality of detection boxes in the color image, and the electronic device  1  maps the coordinates of each of the plurality of detection boxes to the alignment image. The target boxes corresponding to the plurality of detection boxes are obtained. 
     According to the above embodiments, the plurality of target boxes of the alignment image can be accurately determined. 
     In block S 15 , the electronic device  1  determines a plurality of depth values of the plurality of target boxes based on the alignment image, and determines a quantity of the plurality of target boxes. 
     In one embodiment, the electronic device  1  determines pixels of any one of the target boxes, and obtains pixel depth values of the pixels from the alignment image. The electronic device  1  obtains depth values corresponding to the any one of the target boxes by calculating a sum of the pixel depth values. 
     The pixel depth values refer to a height of a feature point from the camera device  2 . The feature point can be a pixel corresponding to the depth image of the plant to be detected. 
     In block S 16 , the electronic device  1  determines a height of the plant to be detected according to the plurality of depth values and the quantity of the plurality of target boxes. 
     In one embodiment, the electronic device  1  determines a camera height of a position where the camera device  2  is located. The electronic device  1  obtains a plurality of distances by subtracting the camera height from each of the depth values, and the electronic device  1  obtains a sum by calculating the plurality of distances. The electronic device  1  obtains the height of the plant to be detected by dividing the sum by the quantity of the plurality of target boxes. 
     According to the above embodiments, an efficiency in measuring the plant to be detected is improved, manual measurements of the plant are not required. 
     In one embodiment, in response that the height of the plant to be detected is less than a preset height, warning information is generated according to the height of the plant to be detected. The warning information is encrypted by using a symmetric encryption algorithm to obtain a cipher text, an alarm level of the cipher text is determined according to the plant to be detected. Then an alarm mode is determined according to the alarm level of the cipher text, and the cipher text is sent by the alarm mode. 
     The preset height can be set according to an expected growth rate of the plant to be detected, the above embodiments do not limit the value of the preset height. The alarm level includes level one, level two, and so on. The alarm mode includes an audio alarm using a loudspeaker, an email mode, and a telephone mode, for example. 
     According to the above embodiments, in response that the height of the plant to be detected is less than the preset height, the warning information can be issued. In addition, the warning information can be protected against tampering by encrypting the warning information, and security of the warning information can be improved. Moreover, the warning information can be sent in an appropriate alarm mode by determining the alarm mode according to the alarm level. Thus, the warning information can be output in a more user-friendly way. 
     In the above embodiments, the electronic device  1  determines the plant to be detected according to the received height measurement request, thereby accurately determining the plant to be detected, whose height needs to be measured. The electronic device  1  controls the camera device  2  to capture the plant to be detected and can quickly obtain the color image and the depth image of the plant to be detected. The electronic device  1  detects the color image by the pre-trained detection model, improving the efficiency of detecting. By determining a plurality of depth values of the plurality of target boxes based on the alignment image, and determining a quantity of the plurality of target boxes, and by determining a height of the plant to be detected according to the plurality of depth values and the quantity of the plurality of target boxes, measurement of the height of the plant is more accurate and more efficient. 
       FIG. 3  is a structural diagram of a measurement device for measuring a growth height of a plant in an embodiment of the present application. 
     As shown in  FIG. 3 , a measurement device  11  for measuring a growth height of a plant includes a determination module  110 , a control module  111 , a detection module  112 , a processing module  113 , an acquisition module  114 , a dividing module  115 , a training module  116 , an adjustment module  117 , a calculation module  118 , an enhancement module  119 , a generation module  120 , an encryption module  121 , and a sending module  122 . The modules in the present application refer to one of a series of computer-readable instruction segments that can be executed by at least one processor and that are capable of performing preset functions, which are stored in a storage device. In some embodiments, the functions of each module will be described. 
     In response to a received height measurement request, the determination module  110  determines a plant to be detected according to the received height measurement request. 
     In one embodiment, information carried in the height measurement request includes a plant identifier, and a name of the plant to be detected, for example. 
     Moreover, the plant to be detected may be any plant that needs to be analyzed for daily growth, such as roses, sunflowers, or rice plant, for example. 
     In one embodiment, the determination module  110  obtains an idle thread from a preset thread connection pool, and parses a method body of the height measurement request with the idle thread. Information in the height measurement request can be obtained. The determination module  110  further a preset label and obtains information corresponding to the preset label from the obtained information as the plant to be detected. 
     The preset label may be the plant identifier. 
     By obtaining the idle thread, from the preset thread connection pool to parse a method body of the height measurement request, not only is the time for creating the thread reduced, but also the efficiency of parsing the height measurement request can be improved. Based on a mapping relationship between the preset label and the plant to be detected, the plant to be detected can be accurately determined. 
     The control module  111  controls the camera device  2  to capture the plant to be detected, and obtain a color image and a depth image of the plant to be detected, each of the color image and the depth image includes a plurality of plants to be detected. 
     In one embodiment, the camera device  2  includes dual lenses, such as the first lens  20  and the second lens  21  as mentioned above. The camera device  2  may be positioned above the plant to be detected. 
     In one embodiment, the color image can be a red, green, blue (RGB) three-channel color image, and the depth image can be an image of which pixel values indicate a distance from the camera device  2  to each point in a captured scene. 
     In one embodiment, the color image and the depth image of the plant to be detected can be obtained by performing a following procedure. A first position is determined to be the location of the plant to be detected, and the first lens  20  of the camera device  2  is controlled to move to a second position corresponding to the first position. The first lens  20  is controlled to capture the plant to be detected, and the color image is obtained. Then the second lens  21  of the camera device  2  is controlled to move to the second position, the second lens  21  is controlled to also capture the plant to be detected, and the depth image is obtained. 
     According to the above embodiments, the color image and the depth image of the plant to be detected can be quickly obtained. 
     The detection module  112  detects the color image by a pre-trained detection model, and obtains a plurality of detection boxes including the plurality of plants to be detected. 
     In one embodiment, the plurality of detection boxes is obtained by extracting features of the color image by the pre-trained detection model. 
     In one embodiment, before the detection module  112  detects the color image by the pre-trained detection model, the detection module  112  obtains all the plurality of detection boxes including the plurality of plants to be detected. The acquisition module  114  obtains historical data by performing a web crawler method, and the historical data is inputted to a forgetting gate layer for the processing module  113  performing a forgetting processing, and training data is obtained. The dividing module  115  divides the training data into a training set and a validation set by utilizing a cross-validation method. The training module  116  inputs data of the training set to an input gate layer for training, and a learner is obtained. The adjustment module  117  adjusts the learner according to data of the verification set, and the pre-trained detection model is obtained. 
     According to the above embodiments, the pre-trained detection model suitable for the plant to be detected can be generated. 
     In one embodiment, before the training data is divided into the training set and the validation set with the cross-validation method, the calculation module  118  counts a quantity of color training images in the training data. In response to the quantity of color training images in the training data is less than a preset number, the enhancement module  119  increases the quantity of color training images in the training data by using a data enhancement algorithm. 
     According to the above embodiments, the risk of poor generalization of the pre-trained detection model is thereby avoided. 
     In one embodiment, the training data is randomly divided into at least one data packet according to a preset ratio. Any one of the at least one data packet can be determined as the verification set, and the remaining data packets are determined to be the training set. The above embodiments are repeated until one of the at least one data packet is determined as the verification set. 
     The preset ratio can be customized, and the preset ratio is not limited in the present application. 
     According to the above embodiments, each of the color training images in the training data is involved in training and verification procedures, thereby improving the pre-training of the detection model. 
     In one embodiment, the adjustment module  117  determines an optimal hyperparameter point from the verification set by performing a hyperparameter grid search method. Moreover, the adjustment module  117  adjusts the learner according to the optimal hyperparameter point, and obtains the pre-trained detection model. 
     Specifically, the adjustment module  117  splits the verification set according to a preset step size, and obtains a target subset. The adjustment module  117  traverses parameters of two ends on the target subset, verifies the learner by using the parameters of the two ends on the target subset, and obtains a learning rate of each of the parameters. The adjustment module  117  determines a parameter with the largest learning rate as a first hyperparameter point, and in the neighborhood of the first hyperparameter point. The adjustment module  117  reduces the preset step size and continues traversing until the length of a step size after reduction is equal to a preset step length, and determines that an obtained hyperparameter point is the optimal hyperparameter point. Furthermore, the adjustment module  117  adjusts the learner according to the optimal hyperparameter point, and obtains the pre-trained detection model. 
     The preset step length is not limited in the present application. 
     According to the above embodiments, the pre-trained detection model is better able for analysis of the color image. 
     The processing module  113  aligns the color image and the depth image by using an image alignment algorithm, and obtains an alignment image. 
     In one embodiment, the alignment image can be an image generated by merging pixels of the color image with pixels of the depth image. 
     In one embodiment, the processing module  113  acquires depth pixels of the depth image, and maps the depth pixels to a preset depth coordinate system. Depth coordinates of the depth pixels can be obtained. The processing module  113  determines global coordinates of the depth pixels according to the depth coordinates and a preset global coordinate system, and determines positions of the depth pixels on the color image according to the global coordinates. The processing module  113  further determines color pixels corresponding to the positions on the color image, and obtains the alignment image by merging each of the depth pixels with a corresponding color pixel. 
     The preset depth coordinate system and the preset global coordinate system can be obtained from an open source system, or can be preset by a user according to the actual requirements, not being limited in the present application. 
     According to the above embodiment, the alignment image that includes a depth value can be generated, thereby aiding subsequent determination of a growth height of the plant to be detected. 
     The acquisition module  114  acquires a plurality of target boxes corresponding to the plurality of detection boxes from the alignment image. 
     In one embodiment, the acquisition module  114  establishes a same coordinate system for the color image and the alignment image. The acquisition module  114  determines coordinates of each of the plurality of detection boxes in the color image, and the acquisition module  114  maps the coordinates of each of the plurality of detection boxes to the alignment image. The target boxes corresponding to the plurality of detection boxes are obtained. 
     According to the above embodiments, the plurality of target boxes of the alignment image can be accurately determined. 
     The determination module  110  determines a plurality of depth values of the plurality of target boxes based on the alignment image, and determines a quantity of the plurality of target boxes. 
     In one embodiment, the determination module  110  determines pixels of any one of the target boxes, and obtains pixel depth values of the pixels from the alignment image. The determination module  110  obtains depth values corresponding to the any one of the target boxes by calculating a sum of the pixel depth values. 
     The pixel depth values refer to a height of a feature point from the camera device  2 . The feature point can be a pixel corresponding to the depth image of the plant to be detected. 
     The determination module  110  determines a height of the plant to be detected according to the plurality of depth values and the quantity of the plurality of target boxes. 
     In one embodiment, the determination module  110  determines a camera height of a position where the camera device  2  is located. The determination module  110  obtains a plurality of distances by subtracting the camera height from each of the depth values, and the determination module  110  obtains a sum by calculating the plurality of distances. The determination module  110  obtains the height of the plant to be detected by dividing the sum by the quantity of the plurality of target boxes. 
     According to the above embodiments, an efficiency in measuring the plant to be detected is improved, manual measurements of the plant are not required. 
     In one embodiment, in response that the height of the plant to be detected is less than a preset height, the generation module  120  generates warning information according to the height of the plant to be detected. The encryption module  121  encrypts the warning information by using a symmetric encryption algorithm to obtain a cipher text. An alarm level of the cipher text is determined according to the plant to be detected. Then an alarm mode is determined according to the alarm level of the cipher text, and the sending module  122  sent the cipher text by the alarm mode. 
     The preset height can be set according to an expected growth rate of the plant to be detected, the above embodiments do not limit the value of the preset height. The alarm level includes level one, level two, and so on. The alarm mode includes an audio alarm using a loudspeaker, an email mode, and a telephone mode, for example. 
     According to the above embodiments, in response that the height of the plant to be detected is less than the preset height, the warning information can be issued. In addition, the warning information can be protected against tampering by encrypting the warning information, and security of the warning information can be improved. Moreover, the warning information can be sent in an appropriate alarm mode by determining the alarm mode according to the alarm level. Thus, the warning information can be output in a more user-friendly way. 
     In the above embodiments, the electronic device  1  determines the plant to be detected according to the received height measurement request, thereby accurately determining the plant to be detected, whose height needs to be measured. The electronic device  1  controls the camera device  2  to capture the plant to be detected and can quickly obtain the color image and the depth image of the plant to be detected. The electronic device  1  detects the color image by the pre-trained detection model, improving the efficiency of detecting. By determining a plurality of depth values of the plurality of target boxes based on the alignment image, and determining a quantity of the plurality of target boxes, and by determining a height of the plant to be detected according to the plurality of depth values and the quantity of the plurality of target boxes, measurement of the height of the plant is more accurate and more efficient. 
       FIG. 4  is a structural diagram of an electronic device housing the measurement device in an embodiment of the present application. The electronic device  1  may include a storage device  12 , at least one processor  13 , and computer-readable instructions stored in the storage device  12  and executable by the at least one processor  13 , for example, a growth height of a plant determination programs. 
     Those skilled in the art will understand that  FIG. 4  is only an example of the electronic device  1  and does not constitute a limitation on the electronic device  1 . Another electronic device  1  may include more or fewer components than shown in the figures or may combine some components or have different components. For example, the electronic device  1  may further include an input/output device, a network access device, a bus, and the like. 
     The at least one processor  13  can be a central processing unit (CPU), or can be another general-purpose processor, digital signal processor (DSPs), application-specific integrated circuit (ASIC), Field-Programmable Gate Array (FPGA), another programmable logic device, discrete gate, transistor logic device, or discrete hardware component, etc. The processor  13  can be a microprocessor or any conventional processor. The processor  13  is a control center of the electronic device  1  and connects various parts of the entire electronic device  1  by using various interfaces and lines. 
     The processor  13  executes the computer-readable instructions to implement the method for determining a growth height of a plant as mentioned in the above embodiments, such as in block S 10 -S 16  shown in  FIG. 2 . Alternatively, the processor  13  executes the computer-readable instructions to implement the functions of the modules/units in the foregoing device embodiments, such as the modules  110 - 122  in  FIG. 3 . 
     For example, the computer-readable instructions can be divided into one or more modules/units, and the one or more modules/units are stored in the storage device  12  and executed by the at least one processor  13 . The one or more modules/units can be a series of computer-readable instruction segments capable of performing specific functions, and the instruction segments are used to describe execution processes of the computer-readable instructions in the electronic device  1 . For example, the computer-readable instruction can be divided into the determination module  110 , the control module  111 , the detection module  112 , the processing module  113 , the acquisition module  114 , the dividing module  115 , the training module  116 , the adjustment module  117 , the calculation module  118 , the enhancement module  119 , the generation module  120 , the encryption module  121 , and the sending module  122  as shown in  FIG. 3 . 
     The storage device  12  can be configured to store the computer-readable instructions and/or modules/units. The processor  13  may run or execute the computer-readable instructions and/or modules/units stored in the storage device  12  and may call up data stored in the storage device  12  to implement various functions of the electronic device  1 . The storage device  12  mainly includes a storage program area and a storage data area. The storage program area may store an operating system, and an application program required for at least one function (such as a sound playback function, an image playback function, for example), for example. The storage data area may store data (such as audio data, phone book data, for example) created according to the use of the electronic device  1 . In addition, the storage device  12  may include a high-speed random access memory, and may also include a non-transitory storage medium, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) Card, a flashcard, at least one disk storage device, a flash memory device, or another non-transitory solid-state storage device. 
     The storage device  12  may be an external memory and/or an internal memory of the electronic device  1 . The storage device  12  may be a memory in a physical form, such as a memory stick, a Trans-flash Card (TF card), for example. 
     When the modules/units integrated into the electronic device  1  are implemented in the form of software functional units having been sold or used as independent products, they can be stored in a non-transitory readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments implemented by the present disclosure can also be completed by related hardware instructed by computer-readable instructions. The computer-readable instructions can be stored in a non-transitory readable storage medium. The computer-readable instructions, when executed by the processor, may implement the steps of the foregoing method embodiments. The computer-readable instructions include computer-readable instruction codes, and the computer-readable instruction codes can be in a source code form, an object code form, an executable file, or some intermediate form. The non-transitory readable storage medium can include any entity or device capable of carrying the computer-readable instruction code, such as a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM). 
     With reference to  FIG. 2 , the storage device  12  in the electronic device  1  stores a plurality of instructions to implement a method for measuring a growth height of a plant, and the processor  13  can execute the multiple instructions to: in response to a received height measurement request, determining a plant to be detected according to the received height measurement request; controlling the camera device to capture the plant to be detected, and obtaining a color image and a depth image of the plant to be detected, each of the color image and the depth image comprising a plurality of plants to be detected; detecting the color image by a pre-trained detection model, and obtaining a plurality of detection boxes comprising the plurality of plants to be detected; aligning the color image and the depth image by an image alignment algorithm and obtaining an alignment image; acquiring a plurality of target boxes corresponding to the plurality of detection boxes from the alignment image; determining a plurality of depth values of the plurality of target boxes based on the alignment image, and determining a quantity of the plurality of target boxes; and determining a height of the plant to be detected according to the plurality of depth values and the quantity of the plurality of target boxes. 
     The computer-readable instructions are executed by the processor  13  to realize the functions of each module/unit in the above-mentioned device embodiments, which will not be repeated here. 
     In the several embodiments provided in the preset application, the disclosed electronic device and method can be implemented in other ways. For example, the embodiments of the devices described above are merely illustrative. For example, divisions of the modules are only logical function divisions, and there can be other manners of division in actual implementation. 
     In addition, each functional module in each embodiment of the present disclosure can be integrated into one processing module, or can be physically present separately in each unit or two or more modules can be integrated into one module. The above modules can be implemented in a form of hardware or in a form of a software functional unit. 
     Therefore, the present embodiments are considered as illustrative and not restrictive, and the scope of the present disclosure is defined by the appended claims. All changes and variations in the meaning and scope of equivalent elements are included in the present disclosure. Any reference sign in the claims should not be construed as limiting the claim. 
     Moreover, the word “comprising” does not exclude other units nor does the singular exclude the plural. A plurality of units or devices stated in the system claims may also be implemented by one unit or device through software or hardware. Words such as “first” and “second” are used to indicate names, but not in any particular order. 
     Finally, the above embodiments are only used to illustrate technical solutions of the present disclosure and are not to be taken as restrictions on the technical solutions. Although the present disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in one embodiment can be modified, or some of the technical features can be equivalently substituted, and that these modifications or substitutions are not to detract from the essence of the technical solutions or from the scope of the technical solutions of the embodiments of the present disclosure.