TWI759069B - Method and device for measuring plant growth height, computer device and medium - Google Patents

Abstract

The present application relates to an image analysis technology, and the present application provides a method and a device for measuring a plant growth height, a computer device and a medium. The method controls a camera device to shoot detected plants to obtain a color image of detected plants and a depth image of detected plants, the color image includes detected plants, the depth image includes detected plants, uses a pre-trained detection model to detect the color image to obtain a plurality of detection frames corresponding to detected plants, uses an image alignment algorithm to align the color image with the depth image to obtain an alignment image, obtains a plurality of target frames corresponding to the plurality of detection frames, and determines a depth of target frames. The method further determines the number of target frames and determines a depth of detected plants based on the depth and quantities. This present application can improve a measurement efficiency of the detected plant.

Description

Plant growth height measurement method, device, computer device and medium

The present application relates to the technical field of image analysis, and in particular, to a method, device, computer device and medium for measuring plant growth height.

At present, by analyzing the daily growth of plants, it is helpful to determine the best way of planting plants, so as to improve the yield and quality of plants, thereby reducing planting costs and bringing good news to growers. The traditional method is to analyze the plant growth by manually measuring and recording the plant growth height. However, the manual measurement method not only brings measurement errors, but also reduces the measurement efficiency and consumes manpower.

In view of the above, it is necessary to provide a method, device, computer device and medium for measuring plant growth height, which can improve the measurement efficiency of plant growth height.

A plant growth height measurement method, which is applied to a computer device, the computer device is connected with a camera device, and the plant growth height measurement method comprises: when a height measurement request is received, determining a to-be-detected from the height measurement request plants; controlling the camera to photograph the plants to be detected, to obtain a color image and a depth image of the plants to be detected, the color image includes a plurality of plants to be detected, and the depth image includes a plurality of Plants to be tested; Use a pre-trained detection model to detect the color image to obtain detection frames corresponding to the plurality of plants to be detected; use an image alignment algorithm to align the color image and the depth image, obtaining an alignment image; obtaining target frames corresponding to multiple detection frames from the aligned image; determining the depth values of multiple target frames from the aligned image, and determining the number of the multiple target frames; The height of the plant to be detected is determined according to a plurality of depth values and the number.

According to an optional embodiment of the present application, the determining the plant to be detected from the height measurement request includes: acquiring any idle thread from a preset thread connection pool; parsing the height measurement request by using the arbitrary idle thread The method body is to obtain all the information carried by the height measurement request; obtain a preset label, and obtain information corresponding to the preset label from all the information, as the plant to be detected.

According to an optional embodiment of the present application, the controlling the camera to photograph the plant to be detected and obtaining the color image and the depth image of the plant to be detected includes: determining a first orientation where the plant to be detected is located; Controlling the first lens of the camera to move to a second position corresponding to the first position, and controlling the first lens to shoot to obtain the color image; controlling the second lens of the camera to move to the second orientation, and controlling the second lens to shoot to obtain the depth image.

According to an optional embodiment of the present application, before using a pre-trained detection model to detect the color image to obtain detection frames corresponding to the plurality of plants to be detected, the method for measuring plant growth height further includes: using a net Road crawler technology to obtain historical data; Logging the historical data into the forget gate layer for forgetting processing to obtain training data; using the cross-validation method to divide the training data into a training set and a verification set; logging the data in the training set into the input gate layer for training, Obtain a learner; adjust the learner according to the data in the verification set to obtain the detection model.

According to an optional embodiment of the present application, the performing an alignment process on the color image and the depth image by using an image alignment algorithm to obtain an aligned image includes: acquiring all depth pixels on the depth image; Mapping all the depth pixels into a preset depth coordinate system, and obtaining the depth coordinates of all the depth pixels; determining the world coordinates of all the depth pixels according to all the depth coordinates and the preset world coordinate system; determining according to all the world coordinates the positions of all the depth pixels on the color image, and determine the color pixels of the positions on the color image; fuse each depth pixel with each color pixel to obtain the aligned image .

According to an optional embodiment of the present application, the acquiring target frames corresponding to multiple detection frames from the alignment image includes: establishing the same coordinate system for the color image and the alignment image; determining each The coordinates of the detection frame on the color image; the coordinates of each detection frame are mapped to the alignment image to obtain a target frame corresponding to each detection frame.

According to an optional embodiment of the present application, the determining the height of the plant to be detected according to the plurality of depth values and the quantity includes: determining the imaging height at which the imaging device is located; comparing the imaging height with each depth value Perform a subtraction operation to obtain a plurality of distance results; calculate the sum of the plurality of distance results; and divide the sum by the number to obtain the height of the plant to be detected.

A plant growth height measurement device, running in a computer device, the computer device is connected with a camera device, characterized in that, the plant growth height measurement device comprises: a determining unit, for when receiving a height measurement request, from the The plant to be detected is determined in the height measurement request; the control unit is configured to control the camera to photograph the plant to be detected, and obtain a color image and a depth image of the plant to be detected, and the color image includes multiple plants to be detected, and the depth image includes a plurality of plants to be detected; a detection unit is used to detect the color image by using a pre-trained detection model to obtain detection frames corresponding to the plurality of plants to be detected processing unit, for utilizing the image alignment algorithm to carry out alignment processing on the color image and the depth image to obtain an aligned image; an acquisition unit for obtaining from the aligned image and a plurality of detection The target frame corresponding to the frame; the determining unit is further configured to determine the depth values of multiple target frames from the aligned image, and determine the number of the multiple target frames; the determining unit is also configured to The plurality of depth values and the number determine the height of the plant to be detected.

A computer device comprising: a storage for storing at least one instruction; and a processor for executing the instructions stored in the storage to implement the method for measuring plant growth height.

A computer-readable storage medium having at least one instruction stored in the computer-readable storage medium, the at least one instruction being executed by a processor in a computer device to implement the method for measuring plant growth height.

It can be seen from the above technical solutions that the present application determines the plant to be detected from the height measurement request, can accurately determine the plant to be detected, and control the camera to photograph the plant to be detected, A color image and a depth image can be quickly acquired, the color image is detected by a pre-trained detection model, the detection efficiency is improved, the depth values of multiple target frames are determined from the aligned image, and the The number of multiple target frames, the height of the plant to be detected is determined according to the multiple depth values and the number, and the measurement efficiency of the plant to be detected is improved.

S10~S16: Steps

11: Plant growth height measuring device

110: Determine unit

111: Control unit

112: Detection unit

113: Processing unit

114: Get Unit

115: Divide Units

116: Training Unit

117: Adjustment unit

118: Computing Unit

119: Enhancement Unit

120: Generate unit

121: encryption unit

122: sending unit

1: Computer device

12: Storage

13: Processor

2: Camera device

20: First Shot

21: Second Shot

FIG. 1 is an application environment diagram of a preferred embodiment of the plant growth height measurement method of the present application.

FIG. 2 is a flow chart of a preferred embodiment of the method for measuring plant growth height of the present application.

FIG. 3 is a functional module diagram of a preferred embodiment of the plant growth height measuring device of the present application.

FIG. 4 is a schematic structural diagram of a computer device for implementing a preferred embodiment of the method for measuring plant growth height in the present application.

In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments.

As shown in FIG. 1 , it is an application environment diagram of a preferred embodiment of the plant growth height measurement method of the present application. The camera device 2 communicates with the computer device 1 , and the camera device 2 includes a first lens 20 and a second lens 21 . A color image can be captured through the first lens 20 , and a depth image can be captured through the second lens 21 .

As shown in FIG. 2 , it is a flow chart of a preferred embodiment of the method for measuring plant growth height of the present application. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

The method for measuring plant growth height is applied to one or more computer devices 1. The computer device 1 is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions, and its hardware includes: But not limited to microprocessors, application specific integrated circuits (ASICs), programmable gate arrays (Field-Programmable Gates) Array, FPGA), Digital Signal Processor (DSP), embedded devices, etc.

The computer device 1 can 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 (PDA), a game console, and an interactive Internet TV. (Internet Protocol Television, IPTV), smart wearable devices, etc.

The computer device 1 may also include network equipment and/or user equipment. Wherein, the network device includes, but is not limited to, a single network server, a server group formed by multiple network servers, or a cloud formed by a large number of hosts or network servers based on cloud computing (Cloud Computing).

The network where the computer device 1 is located includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.

In at least one embodiment of the present application, the present application is applied to a computer device, and the computer device is connected to a camera device.

Step S10, when a height measurement request is received, determine the plant to be detected from the height measurement request.

In at least one embodiment of the present application, the information carried in the height measurement request includes, but is not limited to, a plant identifier, the plant to be detected, and the like.

In at least one embodiment of the present application, the plant to be detected can be any plant that needs to be analyzed for daily growth, such as roses, sunflowers, rice, and the like.

In at least one embodiment of the present application, the computer device determining the plant to be detected from the height measurement request includes: acquiring any idle thread from a preset thread connection pool; parsing all idle threads by using the arbitrary idle thread The method body of the height measurement request, obtains all the information carried by the height measurement request; obtains a preset label, and obtains information corresponding to the preset label from the all the information, as the plant to be detected.

Wherein, the preset label may be the plant identification.

The method body for parsing the height measurement request by obtaining idle threads from the default thread connection pool can not only reduce the time for creating threads, but also improve the efficiency of parsing the height measurement request. The mapping relationship of the plants to be detected can accurately determine the plants to be detected.

Step S11, controlling the camera to photograph the plant to be detected, to obtain a color image and a depth image of the plant to be detected, the color image includes a plurality of plants to be detected, and the depth image includes a plurality of plants to be tested.

In at least one embodiment of the present application, the camera device includes dual lenses, which are a first lens and a second lens respectively. Further, the camera device may be installed directly above the detected plant for photographing.

In at least one embodiment of the present application, the color image refers to an RGB three-channel color image, and the depth image refers to an image in which the distance from the camera to each point in the scene is taken as a pixel value .

In at least one embodiment of the present application, the computer device controlling the camera device to photograph the plant to be detected, and obtaining a color image and a depth image of the plant to be detected includes: determining where the plant to be detected is located. the first orientation; control the first lens of the camera to move to the second orientation corresponding to the first orientation, and control the first lens to shoot to obtain the color image; control the camera of the camera The second lens moves to the second orientation, and controls the second lens to shoot to obtain the depth image.

Through the above-mentioned embodiments, a color image and a depth image including the plant to be detected can be quickly acquired.

Step S12, using a pre-trained detection model to detect the color image to obtain detection frames corresponding to the plurality of plants to be detected.

In at least one embodiment of the present application, the detection frame is obtained by using the detection model to perform feature extraction on the color image.

In at least one embodiment of the present application, before using a pre-trained detection model to detect the color image to obtain detection frames corresponding to the plurality of plants to be detected, the method for measuring plant growth height further includes: Use web crawler technology to obtain historical data; log the historical data into the forget gate layer for forgetting processing to obtain training data; use cross-validation method to divide the training data into a training set and a verification set; The data is logged into the input gate layer for training to obtain a learner; the learner is adjusted according to the data in the verification set to obtain the detection model.

Through the above-mentioned embodiments, a detection model suitable for the plant to be detected can be generated.

In at least one embodiment of the present application, before adopting a cross-validation method to divide the training data into a training set and a verification set, the method further includes: calculating, by the computer device, the difference between the color training images in the training data number, when the number is less than the preset number, the computer device uses a data enhancement algorithm to increase the number of color training images in the training data.

Through the above implementation, it can be avoided that the generalization ability of the detection model obtained by training is poor due to insufficient number of color training images.

In at least one embodiment of the present application, the computer device dividing the training data into a training set and a verification set by using a cross-validation method includes: the computer device randomly dividing the training data into at least one set according to a preset ratio Data package, determine any one of the at least one data package as the verification set, and determine the rest of the data packages as the training set, and repeat the above steps until all the data packages are sequentially used as the validation set.

The preset ratio can be set by yourself, which is not limited in this application.

The data set is divided by the above-mentioned embodiment, so that each color training image in the training data participates in training and verification, thereby improving the fitting degree of training the detection model.

In at least one embodiment of the present application, the computer device adjusts the learner according to the data in the verification set, and obtaining the detection model includes: the computer device uses a hyperparameter grid search method to select from the verification set An optimal hyperparameter point is determined, and further, the computer device adjusts the learner through the optimal hyperparameter point to obtain the detection model.

Specifically, the computer device splits the verification set according to a fixed step size to obtain a target subset, traverses the parameters of the endpoints at both ends of the target subset, and verifies all the endpoints through the parameters of the endpoints at both ends. The learner obtains the learning rate of each parameter, determines the parameter with the best learning rate as the first hyperparameter point, and in the neighborhood of the first hyperparameter point, reduces the step size and continues to traverse until The step size is a preset step size, that is, the obtained hyperparameter point is the optimal hyperparameter point. Further, the computer device adjusts the learner according to the optimal hyperparameter point, and obtains the Detection model.

Wherein, the present application does not limit the preset step size.

Through the above embodiments, the detection model can be more suitable for the detection of the color image of the plant to be detected.

Step S13, using an image alignment algorithm to align the color image and the depth image to obtain an aligned image.

In at least one embodiment of the present application, the aligned image refers to an image generated by fusing pixels of the color image and pixels of the depth image.

In at least one embodiment of the present application, the computer device performs an alignment process on the color image and the depth image by using an image alignment algorithm, and obtaining an aligned image includes: acquiring an image on the depth image. All depth pixels; mapping all depth pixels to a preset depth coordinate system to obtain the depth coordinates of all depth pixels; Determine the world coordinates of all the depth pixels according to all the depth coordinates and the preset world coordinate system; determine the positions of all the depth pixels on the color image according to all the world coordinates, and determine that the positions are in the color map color pixels on the image; each depth pixel is fused with each color pixel to obtain the aligned image.

Wherein, the preset depth coordinate system and the preset world coordinate system can be obtained from an open source system, or can be arbitrarily set by a user according to an application scenario, which is not limited in this application.

Through the above-mentioned embodiments, an aligned image containing depth values can be generated, so as to subsequently determine the height of the plant to be detected.

Step S14, acquiring target frames corresponding to multiple detection frames from the aligned image.

In at least one embodiment of the present application, the computer device acquiring target frames corresponding to the plurality of detection frames from the alignment image includes: establishing the same coordinate system for the color image and the alignment image ; determine the coordinates of each detection frame on the color image; map the coordinates of each detection frame to the alignment image to obtain a target frame corresponding to each detection frame.

Through the above-mentioned embodiments, the target frame of the aligned image can be accurately determined.

Step S15: Determine the depth values of multiple target frames from the aligned image, and determine the number of the multiple target frames.

In at least one embodiment of the present application, the computer device determining the depth values of multiple target frames from the aligned image includes: for any target frame, determining all pixels of the arbitrary target frame; from the Acquire the pixel depth value of each pixel in the aligned image; calculate the sum of the pixel depth values of all the pixels as the depth value of the arbitrary target frame.

The pixel depth value refers to the height of the pixel corresponding to the feature point on the to-be-detected plant from the camera device.

Step S16, determining the height of the plant to be detected according to the plurality of depth values and the quantity.

In at least one embodiment of the present application, the computer device determining the height of the plant to be detected according to a plurality of depth values and the quantity includes: determining a camera height at which the camera device is located; comparing the camera height with Subtract each depth value to obtain a plurality of distance results; calculate the sum of the plurality of distance results; and divide the sum by the number to obtain the height of the plant to be detected.

Through the above embodiments, it is unnecessary to manually measure the plants to be detected, and the measurement efficiency of the plants to be detected can be improved.

In at least one embodiment of the present application, after determining the height of the plant to be detected according to a plurality of depth values and the quantity, the method for measuring plant growth height further includes: when the height is less than a preset height, The computer device generates alarm information according to the height. Further, the computer device encrypts the alarm information by using a symmetric encryption algorithm to obtain ciphertext. Further, the computer device determines the alarm information according to the plant to be detected. the alarm level of the ciphertext, the computer device determines an alarm mode according to the alarm level, and further, the computer device sends the ciphertext in the alarm mode.

Wherein, the preset height can be set according to the expected generation rate of the plants to be detected, and the application does not limit the value of the preset height.

Further, the alarm levels include: level one, level two, and the like.

Further, the alarm method includes: the alarm sound of the speaker, the mail method, the telephone method, and the like.

Through the above-mentioned embodiment, when the height is less than the preset height, alarm information can be issued. In addition, by encrypting the alarm information, the alarm information can be prevented from being tampered with, and the alarm information can be improved. At the same time, the alarm mode is determined according to the alarm level, and the alarm information can be sent in an appropriate alarm mode, which makes the sending of the alarm information more humanized.

It can be seen from the above technical solutions that the present application can determine the plant to be detected from the height measurement request, can accurately determine the plant to be detected, control the camera device to photograph the plant to be detected, and can quickly obtain color images and depths. image, use the pre-trained detection model to detect the color image, improve the detection efficiency, determine the depth values of multiple target frames from the aligned image, and determine the number of the multiple target frames, according to the number of target frames. The depth value and the number determine the height of the plant to be detected, so as to improve the measurement efficiency of the plant to be detected.

As shown in FIG. 3 , it is a functional module diagram of a preferred embodiment of the plant growth height measuring device of the present application. The plant growth height measuring device 11 includes a determination unit 110, a control unit 111, a detection unit 112, a processing unit 113, an acquisition unit 114, a division unit 115, a training unit 116, an adjustment unit 117, a calculation unit 118, an enhancement unit 119, a generation unit unit 120 , encryption unit 121 and sending unit 122 . The module/unit referred to in this application refers to a series of computer program segments that can be executed by the processor 13 and can perform fixed functions, and are stored in the storage 12 . In this embodiment, the functions of each module/unit will be described in detail in subsequent embodiments.

When receiving the height measurement request, the determination unit 110 determines the plant to be detected from the height measurement request.

In at least one embodiment of the present application, the information carried in the height measurement request includes, but is not limited to, a plant identifier, the plant to be detected, and the like.

In at least one embodiment of the present application, the plant to be detected can be any plant that needs to be analyzed for daily growth, such as roses, sunflowers, rice, and the like.

In at least one embodiment of the present application, the determining unit 110 determining the plant to be detected from the height measurement request includes: acquiring any idle thread from a preset thread connection pool; using the arbitrary idle thread to parse The method body of the altitude measurement request, obtains all the information carried by the altitude measurement request; Acquire a preset tag, and acquire information corresponding to the preset tag from all the information as the plant to be detected.

Wherein, the preset label may be the plant identification.

The method body for parsing the height measurement request by obtaining idle threads from the default thread connection pool can not only reduce the time for creating threads, but also improve the efficiency of parsing the height measurement request. The mapping relationship of the plants to be detected can accurately determine the plants to be detected.

The control unit 111 controls the camera to photograph the plant to be detected, and obtains a color image and a depth image of the plant to be detected, where the color image includes a plurality of plants to be detected, and the depth image includes a plurality of plants to be tested.

In at least one embodiment of the present application, the camera device includes dual lenses, which are a first lens and a second lens respectively. Further, the camera device may be installed directly above the detected plant for photographing.

In at least one embodiment of the present application, the color image refers to an RGB three-channel color image, and the depth image refers to an image in which the distance from the camera to each point in the scene is taken as a pixel value .

In at least one embodiment of the present application, the control unit 111 controls the camera device to photograph the plant to be detected, and obtaining a color image and a depth image of the plant to be detected includes: determining where the plant to be detected is located the first orientation of the camera; control the first lens of the camera to move to the second orientation corresponding to the first orientation, and control the first lens to shoot to obtain the color image; control the camera The second lens is moved to the second orientation, and the second lens is controlled to shoot to obtain the depth image.

Through the above-mentioned embodiments, a color image and a depth image including the plant to be detected can be quickly acquired.

The detection unit 112 detects the color image by using the pre-trained detection model, and obtains detection frames corresponding to the plurality of plants to be detected.

In at least one embodiment of the present application, the detection frame is obtained by using the detection model to perform feature extraction on the color image.

In at least one embodiment of the present application, before using a pre-trained detection model to detect the color image and obtain detection frames corresponding to the plurality of plants to be detected, the acquisition unit 114 acquires the history by using a web crawler technology data, the processing unit 113 logs the historical data into the forget gate layer for forgetting processing, and obtains training data, the dividing unit 115 adopts the cross-validation method to divide the training data into a training set and a verification set, and the training unit 116 divides the training data into The centralized data is logged into the input gate layer for training to obtain a learner, and the adjustment unit 117 adjusts the learner according to the data in the verification set to obtain the detection model.

Through the above-mentioned embodiments, a detection model suitable for the plant to be detected can be generated.

In at least one embodiment of the present application, before using the cross-validation method to divide the training data into a training set and a validation set, the computing unit 118 calculates the number of color training images in the training data, when the number is less than When the number is preset, the enhancement unit 119 uses a data enhancement algorithm to increase the number of color training images in the training data.

Through the above implementation, it can be avoided that the generalization ability of the detection model obtained by training is poor due to insufficient number of color training images.

In at least one embodiment of the present application, the dividing unit 115 adopts a cross-validation method to divide the training data into a training set and a validation set, including: the dividing unit 115 randomly divides the training data into two parts according to a preset ratio. For at least one data package, any one data package in the at least one data package is determined as the verification set, and the rest of the data packages are determined as the training set, and the above steps are repeated until all the data packages are used in turn. the validation set.

The preset ratio can be set by yourself, which is not limited in this application.

The data set is divided by the above-mentioned embodiment, so that each color training image in the training data participates in training and verification, thereby improving the fitting degree of training the detection model.

In at least one embodiment of the present application, the adjustment unit 117 adjusts the learner according to the data in the verification set, and obtaining the detection model includes: the adjustment unit 117 uses a hyperparameter grid search method to obtain the detection model from the The optimal hyperparameter point is determined in the verification set, and further, the adjustment unit 117 adjusts the learner through the optimal hyperparameter point to obtain the detection model.

Specifically, the adjustment unit 117 splits the verification set according to a fixed step size to obtain a target subset, traverses the parameters of the endpoints at both ends of the target subset, and verifies the parameters of the endpoints at the two ends. The learner obtains the learning rate of each parameter, determines the parameter with the best learning rate as the first hyperparameter point, and in the neighborhood of the first hyperparameter point, reduces the step size and continues to traverse, Until the step size is the preset step size, that is, the obtained hyperparameter point is the optimal hyperparameter point. Further, the adjustment unit 117 adjusts the learner according to the optimal hyperparameter point, and obtains the detection model.

Wherein, the present application does not limit the preset step size.

Through the above embodiments, the detection model can be more suitable for the detection of the color image of the plant to be detected.

The processing unit 113 uses an image alignment algorithm to perform alignment processing on the color image and the depth image to obtain an aligned image.

In at least one embodiment of the present application, the aligned image refers to an image generated by fusing pixels of the color image and pixels of the depth image.

In at least one embodiment of the present application, the processing unit 113 performs an alignment process on the color image and the depth image by using an image alignment algorithm, and obtaining an aligned image includes: acquiring an image on the depth image All the depth pixels of ; map all the depth pixels to a preset depth coordinate system to obtain the depth coordinates of all the depth pixels; Determine the world coordinates of all the depth pixels according to all the depth coordinates and the preset world coordinate system; determine the positions of all the depth pixels on the color image according to all the world coordinates, and determine the positions in the color map color pixels on the image; each depth pixel is fused with each color pixel to obtain the aligned image.

Wherein, the preset depth coordinate system and the preset world coordinate system can be obtained from an open source system, or can be arbitrarily set by a user according to an application scenario, which is not limited in this application.

Through the above-mentioned embodiments, an aligned image containing depth values can be generated, so as to subsequently determine the height of the plant to be detected.

The obtaining unit 114 obtains target frames corresponding to a plurality of detection frames from the aligned image.

In at least one embodiment of the present application, the acquiring unit 114 acquiring target frames corresponding to multiple detection frames from the alignment image includes: establishing the same coordinates for the color image and the alignment image determine the coordinates of each detection frame on the color image; map the coordinates of each detection frame to the alignment image to obtain a target frame corresponding to each detection frame.

Through the above-mentioned embodiments, the target frame of the aligned image can be accurately determined.

The determining unit 110 determines depth values of multiple target frames from the aligned image, and determines the number of the multiple target frames.

In at least one embodiment of the present application, the determining unit 110 determining the depth values of multiple target frames from the aligned image includes: for any target frame, determining all pixels of the arbitrary target frame; Obtain the pixel depth value of each pixel in the aligned image; calculate the sum of the pixel depth values of all the pixels as the depth value of the arbitrary target frame.

The pixel depth value refers to the height of the pixel corresponding to the feature point on the to-be-detected plant from the camera device.

The determining unit 110 determines the height of the plant to be detected according to a plurality of depth values and the number.

In at least one embodiment of the present application, the determining unit 110 determining the height of the plant to be detected according to a plurality of depth values and the quantity includes: determining the imaging height at which the imaging device is located; Perform a subtraction operation with each depth value to obtain a plurality of distance results; calculate the sum of the plurality of distance results; and divide the sum by the number to obtain the height of the plant to be detected.

Through the above embodiments, it is unnecessary to manually measure the plants to be detected, and the measurement efficiency of the plants to be detected can be improved.

In at least one embodiment of the present application, after the height of the plant to be detected is determined according to a plurality of depth values and the quantity, when the height is less than a preset height, the generating unit 120 generates alarm information according to the height , further, the encryption unit 121 encrypts the alarm information by using a symmetric encryption algorithm to obtain a ciphertext, and further, the determination unit 110 determines the alarm level of the ciphertext according to the plant to be detected, and the determination unit 110 determines an alarm mode according to the alarm level, and further, the sending unit 122 sends the ciphertext in the alarm mode.

Wherein, the preset height may be set according to the expected generation rate of the plants to be detected, and the application does not limit the value of the preset height.

Further, the alarm levels include: level one, level two, and the like.

Further, the alarm method includes: the alarm sound of the speaker, the mail method, the telephone method, and the like.

Through the above-mentioned embodiment, when the height is less than the preset height, alarm information can be issued. In addition, by encrypting the alarm information, the alarm information can be prevented from being tampered with, and the alarm information can be improved. At the same time, the alarm mode is determined according to the alarm level, and the alarm information can be sent in an appropriate alarm mode, which makes the sending of the alarm information more humanized.

It can be seen from the above technical solutions that the present application can determine the plant to be detected from the height measurement request, can accurately determine the plant to be detected, control the camera device to photograph the plant to be detected, and can quickly obtain color images and depths. image, use the pre-trained detection model to detect the color image, improve the detection efficiency, determine the depth values of multiple target frames from the aligned image, and determine the number of the multiple target frames, according to the number of target frames. The depth value and the number determine the height of the plant to be detected, so as to improve the measurement efficiency of the plant to be detected.

As shown in FIG. 4 , it is a schematic structural diagram of a computer device for realizing a preferred embodiment of the plant growth height measurement method of the present application.

In one embodiment of the present application, the computer device 1 includes, but is not limited to, a storage 12 , a processor 13 , and a computer program stored in the storage 12 and running on the processor 13 , such as the plant height measurement program.

Those skilled in the art can understand that the schematic diagram is only an example of the computer device 1, and does not constitute a limitation on the computer device 1. It may include more or less components than the one shown, or combine some components, or different Components, such as the computer device 1, may also include input and output devices, network access devices, bus bars, and the like.

The processor 13 may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), or an Application Specific Integrated Circuit (ASIC). , Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor 13 is the computing core and control center of the computer device 1, and uses various interfaces and lines to connect the entire computer device 1 various parts of the computer device 1, and the operating system that executes the computer device 1, as well as various installed applications, code, and the like.

The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the application program to implement the steps in each of the above embodiments of the method for measuring plant growth height, for example, the steps shown in FIG. 2 .

Exemplarily, the computer program can be divided into one or more modules/units, and the one or more modules/units are stored in the storage 12 and executed by the processor 13, to complete this application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device 1 . For example, the computer program can be divided into a determination unit 110, a control unit 111, a detection unit 112, a processing unit 113, an acquisition unit 114, a division unit 115, a training unit 116, an adjustment unit 117, a calculation unit 118, an enhancement unit 119, Generation unit 120 , encryption unit 121 and transmission unit 122 .

The storage 12 can be used to store the computer programs and/or modules. The processor 13 executes or executes the computer programs and/or modules stored in the storage 12, and calls the computer programs and/or modules stored in the storage 12. 12 to realize various functions of the computer device 1 . The storage 12 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; storage data The area can store data etc. created according to the use of the computer device. In addition, the storage 12 may include non-volatile storage such as hard disk, storage, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, flash memory A memory card (Flash Card), at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.

The storage 12 may be an external storage and/or an internal storage of the computer device 1 . Further, the storage 12 may be a storage in physical form, such as a storage bar, a TF card (Trans-flash Card), and the like.

If the modules/units integrated in the computer device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the methods of the above embodiments, and can also use The computer program instructs the relevant hardware to complete, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be implemented.

Wherein, the computer program includes computer program code, and the computer program code may be in the form of original code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, pen drive, removable hard disk, magnetic disk, optical disk, computer storage, read-only storage (ROM, Read-only storage) Only Memory).

Referring to FIG. 2 , the storage 12 in the computer device 1 stores a plurality of instructions to implement a method for measuring plant growth height, and the processor 13 can execute the plurality of instructions to implement: when a height measurement request is received At the time of detection, determine the plant to be detected from the height measurement request; control the camera to photograph the plant to be detected, and obtain a color image and a depth image of the plant to be detected, and the color image includes a plurality of to-be-detected plants. Detecting plants, and the depth image includes a plurality of plants to be detected; using a pre-trained detection model to detect the color image to obtain detection frames corresponding to the plurality of plants to be detected; using an image alignment algorithm Aligning the color image and the depth image to obtain an aligned image; acquiring target frames corresponding to multiple detection frames from the aligned image; determining multiple targets from the aligned image The depth value of the frame is determined, and the number of the multiple target frames is determined; the height of the plant to be detected is determined according to the multiple depth values and the number.

Specifically, for the specific implementation method of the above-mentioned instruction by the processor 13, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG. 2, and details are not described herein.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only It is illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or may distribute images to multiple on the network unit. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

Accordingly, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in this application. Any associated icon indicia in a claim should not be considered to limit the claim to which it relates.

Furthermore, it is clear that the word "comprising" does not exclude other units or steps and the singular does not exclude the plural. A plurality of units or devices stated in this application may also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be Modifications or equivalent substitutions can be made without departing from the spirit and scope of the technical solutions of the present application.

S10~S16: Steps

Claims (9)

A method for measuring plant growth height, which is applied to a computer device, wherein the computer device is connected with a camera device, wherein the method for measuring plant growth height comprises: when a height measurement request is received, determining from the height measurement request Plants to be detected; control the camera to photograph the plants to be detected, to obtain a color image and a depth image of the plants to be detected, the color image includes a plurality of plants to be detected, and the depth image includes a plurality of plants to be detected; use a pre-trained detection model to detect the color image to obtain detection frames corresponding to the plurality of plants to be detected; use an image alignment algorithm to align the color image with the depth The images are aligned to obtain an aligned image; target frames corresponding to multiple detection frames are obtained from the aligned image; depth values of multiple target frames are determined from the aligned image, and the multiple target frames are determined from the aligned images. The number of target frames; determining the height of the plant to be detected according to a plurality of depth values and the number, including: determining the camera height where the camera is located; subtracting the camera height from each depth value Operation to obtain multiple distance results, wherein each depth value refers to the sum of the pixel depth values of all pixels in each target frame, and the pixel depth value refers to the pixel corresponding to the feature on the plant to be detected The height of the point from the camera device; calculating the sum of the plurality of distance results; dividing the sum by the number to obtain the height of the plant to be detected. The method for measuring plant growth height according to claim 1, wherein the determining the plant to be detected from the height measurement request comprises: acquiring any idle thread from a preset thread connection pool; using the arbitrary idle thread to execute thread to parse the method body of the altitude measurement request, and obtain all the information carried by the altitude measurement request; Acquire a preset tag, and acquire information corresponding to the preset tag from all the information as the plant to be detected. The method for measuring plant growth height according to claim 1, wherein the controlling the camera device to photograph the plant to be detected to obtain a color image and a depth image of the plant to be detected comprises: determining the to-be-detected plant the first orientation of the plant; control the first lens of the camera to move to the second orientation corresponding to the first orientation, and control the first lens to shoot to obtain the color image; control the The second lens of the camera is moved to the second orientation, and the second lens is controlled to shoot to obtain the depth image. The method for measuring plant growth height according to claim 1, wherein before detecting the color image by using a pre-trained detection model to obtain detection frames corresponding to the plurality of plants to be detected, the plant growth height The measurement method further includes: using a web crawler technology to obtain historical data; logging the historical data into the forget gate layer for forgetting processing to obtain training data; using a cross-validation method to divide the training data into a training set and a verification set; The data in the training set is logged into the input gate layer for training to obtain a learner; the learner is adjusted according to the data in the verification set to obtain the detection model. The method for measuring plant growth height according to claim 1, wherein said using an image alignment algorithm to align the color image and the depth image to obtain the aligned image comprises: acquiring the depth map All the depth pixels on the image; map all the depth pixels to a preset depth coordinate system to obtain the depth coordinates of all the depth pixels; determine the world of all the depth pixels according to all the depth coordinates and the preset world coordinate system coordinate; Determine the positions of all the depth pixels on the color image according to all the world coordinates, and determine the color pixels of the positions on the color image; fuse each depth pixel with each color pixel to obtain the aligned image. The method for measuring plant growth height according to claim 1, wherein the obtaining target frames corresponding to a plurality of detection frames from the aligned image comprises: establishing an identical image for the color image and the aligned image determine the coordinates of each detection frame on the color image; map the coordinates of each detection frame to the alignment image to obtain a target frame corresponding to each detection frame. A plant growth height measurement device, which runs in a computer device, the computer device is connected with a camera device, wherein the plant growth height measurement device comprises: a determination unit, for receiving a height measurement request, from the The plant to be detected is determined in the height measurement request; the control unit is configured to control the camera to photograph the plant to be detected, and obtain a color image and a depth image of the plant to be detected, and the color image includes a plurality of to-be-detected plants. Detecting plants, and the depth image includes a plurality of plants to be detected; a detection unit is used to detect the color image by using a pre-trained detection model to obtain detection frames corresponding to the plurality of plants to be detected; processing a unit for aligning the color image and the depth image using an image alignment algorithm to obtain an aligned image; an acquiring unit for acquiring from the aligned image corresponding to a plurality of detection frames The target frame of the The depth value and the quantity determine the height of the plant to be detected, including: determining the imaging height at which the imaging device is located; subtracting the imaging height and each depth value to obtain a plurality of distance results, wherein , each depth value refers to the There is the sum of the pixel depth values of the pixel points, and the pixel depth value refers to the height of the pixel point corresponding to the feature point on the plant to be detected from the camera device; calculate the sum of the plurality of distance results; Divide by the number to obtain the height of the plant to be detected. A computer device, wherein the computer device comprises: a storage, storing at least one instruction; and a processor, acquiring the instructions stored in the storage to realize the plant growth according to any one of claim 1 to 6 Height measurement method. A computer-readable storage medium, wherein: the computer-readable storage medium stores at least one instruction, and the at least one instruction is acquired by a processor in a computer device to implement any one of claim 1 to 6. method for measuring plant growth height.

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