A plant high-definition video image comparison big data analysis method and system

Abstract

The present invention relates to the technical field of plant state analysis, and more specifically, to a method and system for analyzing plant high-definition video images and big data. The scheme includes performing image extraction to generate a plant growth collection database; dividing the plant growth period, and extracting first matching data according to the focus points of different growth periods; screening to obtain second matching data according to the first matching data; performing size extraction and analysis on the second matching data to generate real-time size data information; performing color extraction and analysis on the second matching data to generate real-time color data information; and extracting the optimal state index based on the real-time color data information and the real-time size data information. The scheme matches the focus points and morphological information by online analyzing the growth period of the plant, and then evaluates the plant state in different growth periods.

Description

Technical Field

[0001] The present invention relates to the technical field of plant status analysis, and more specifically, to a method and system for analyzing plant high-definition video and picture comparison big data. Background Art

[0002] High-definition data collection of plants is very necessary. This is because, with the development of the Internet of Things and artificial intelligence technologies, more and more technologies can be directly applied to the dynamic control of plant growth and development processes. However, without the core growth data of plants, it is difficult to effectively implement these dynamic control strategies.

[0003] Prior to the present invention, the prior art mainly used high-definition video recording to obtain dynamic videos of plant growth, and obtained the state of the plant growth process by accelerating and decelerating, zooming in and out, and professional technicians analyzed the current state of the plant. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method and system for analyzing big data of plant high-definition video images, which matches the focus and morphological information through online analysis of the growth period of plants, and then evaluates the status of plants in different growth periods.

[0005] According to a first aspect of an embodiment of the present invention, a big data analysis method for comparing high-definition plant video images is provided.

[0006] In one or more embodiments, preferably, the plant high-definition video picture comparison big data analysis method includes:

[0007] Perform image extraction to generate a plant growth collection database;

[0008] Divide the plant growth period, and extract the first matching data according to the focus points in different growth periods;

[0009] Screening the first matching data to obtain second matching data;

[0010] Extracting and analyzing the size of the second matching data to generate real-time size data information;

[0011] Performing color extraction and analysis on the second matching data to generate real-time color data information;

[0012] The optimal state index is extracted based on the real-time color data information and the real-time size data information.

[0013] In one or more embodiments, preferably, the performing image extraction to generate a plant growth collection database specifically includes:

[0014] Set the video acquisition cycle, and collect images of all plant growth videos according to the video acquisition cycle;

[0015] For data that are images themselves, the images obtained by plant growth video collection are collected and stored together in the plant growth collection database.

[0016] In one or more embodiments, preferably, dividing the plant growth period and extracting the first matching data according to the focus points in different growth periods specifically includes:

[0017] The plant growth process is divided into seedling stage, maturity stage, flowering stage and senescence stage;

[0018] Set the focus area for the seedling stage to leaves;

[0019] Set the maturity focus area to the neck;

[0020] Set the flowering period focus area to flowers;

[0021] Set the focus area of ​​senescence period to fruit;

[0022] Extract clear images with focus points corresponding to different periods and save them as high-quality information images;

[0023] All high-quality information images are marked with corresponding time periods, and the corresponding data are stored as first matching data.

[0024] In one or more embodiments, preferably, the step of screening the first matching data to obtain the second matching data specifically includes:

[0025] The corresponding matching diagrams of leaves, necks, fruits and flowers are preset;

[0026] All first matching data are intelligently identified, and if matching graphs that are identical or highly similar are found, they are considered to be second matching data.

[0027] In one or more embodiments, preferably, extracting and analyzing the size of the second matching data to generate real-time size data information specifically includes:

[0028] Performing size analysis on the second matching data to generate a total area of ​​the region of interest;

[0029] The first calculation formula is used to extract the total area growth rate;

[0030] The total area of ​​the concerned region and the total area size growth rate are used as real-time size data information;

[0031] The first calculation formula is:

[0032] C=(A + -A - )÷(T + -T - )

[0033] Among them, C is the growth rate of total area, A + is the total area of ​​the region of interest at the current acquisition time in the second matching data, A - is the total area of ​​the region of interest at the last acquisition time in the second matching data, T + is the current acquisition time of the second matching data, T - is the collection time of the last moment in the second matching data.

[0034] In one or more embodiments, preferably, the color extraction and analysis of the second matching data to generate real-time color data information specifically includes:

[0035] Performing color saturation analysis on the second matching data to generate an average saturation of the focus area;

[0036] The saturation growth rate is extracted using the second calculation formula;

[0037] The saturation growth rate and the average saturation are used together as real-time color data information;

[0038] The second calculation formula is:

[0039] Z=(B + -B - )÷(T + -T - )

[0040] Among them, Z is the saturation growth rate, B + is the saturation of the current acquisition time in the second matching data, B - is the saturation of the acquisition time of the last moment in the second matching data, T + is the current acquisition time of the second matching data, T - is the collection time of the last moment in the second matching data.

[0041] In one or more embodiments, preferably, extracting the optimal state index according to the real-time color data information and the real-time size data information specifically includes:

[0042] According to the historical data of the actual evaluation values ​​of some state indexes, the optimal state coefficient is extracted by using the third calculation formula;

[0043] Calculating a state index using a fourth calculation according to the optimal state coefficient;

[0044] The third calculation formula is:

[0045] {k1,k2,k3,k4}=argmin l (Σ|s-k1×A + +k2×C+k3×B + +k4×Z|)

[0046] Among them, {k1, k2, k3, k4} are the optimal state coefficients, s is the actual evaluation value, argmin l To extract Σ(s-k1×A + +k2×C+k3×B + +k4×Z) is the smallest, the corresponding optimal state coefficient function, Σ|s-k1×A + +k2×C+k3×B + +k4×Z| is the absolute value sum of the calculation errors of all actual evaluation values ​​in the historical data;

[0047] The fourth calculation formula is:

[0048] X=k1×A + +k2×C+k3×B + +k4×Z

[0049] Where X is the state index.

[0050] According to a second aspect of an embodiment of the present invention, a plant high-definition video image comparison big data analysis system is provided.

[0051] In one or more embodiments, preferably, the plant high-definition video image comparison big data analysis system includes:

[0052] A data acquisition module, used for image extraction to generate a plant growth collection database;

[0053] A focus extraction module is used to divide the plant growth period and extract the first matching data according to the focus points in different growth periods;

[0054] A focus region extraction module, configured to obtain second matching data by screening the first matching data;

[0055] A size analysis module, used for extracting and analyzing the size of the second matching data to generate real-time size data information;

[0056] A color analysis module, used for extracting and analyzing the color of the second matching data to generate real-time color data information;

[0057] The state indexing module is used to extract the optimal state index according to the real-time color data information and the real-time size data information.

[0058] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0059] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0060] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0061] In the scheme of the present invention, a big data analysis and screening method for plant high-definition videos and pictures focusing on the analysis of plant growth status is provided.

[0062] In the scheme of the present invention, by paying attention to the size, shape and corresponding change rate of the information during the growth process, a rapid analysis result for the plant at different stages is formed.

[0063] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0064] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 It is a flow chart of a method for analyzing plant high-definition video and picture comparison big data according to an embodiment of the present invention.

[0067] Figure 2 It is a flowchart of performing image extraction and generating a plant growth collection database in a big data analysis method for comparing plant high-definition video images in one embodiment of the present invention.

[0068] Figure 3 It is a flowchart of dividing plant growth periods in a big data analysis method for comparing plant high-definition video images in an embodiment of the present invention, and extracting first matching data according to focus points in different growth periods.

[0069] Figure 4 It is a flowchart of obtaining second matching data by screening according to the first matching data in a method for analyzing plant high-definition video image comparison big data according to an embodiment of the present invention.

[0070] Figure 5 It is a flowchart of extracting and analyzing the size of the second matching data in a big data analysis method for comparing plant high-definition video images in one embodiment of the present invention to generate real-time size data information.

[0071] Figure 6 It is a flow chart of extracting and analyzing the color of the second matching data to generate real-time color data information in a big data analysis method for comparing high-definition plant video images according to an embodiment of the present invention.

[0072] Figure 7 It is a flow chart of extracting the optimal state index based on the real-time color data information and the real-time size data information in a big data analysis method for comparing plant high-definition video images in one embodiment of the present invention.

[0073] Figure 8 It is a structural diagram of a plant high-definition video image comparison big data analysis system according to an embodiment of the present invention.

[0074] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0075] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0077] High-definition data collection of plants is very necessary. This is because, with the development of the Internet of Things and artificial intelligence technologies, more and more technologies can be directly applied to the dynamic control of plant growth and development processes. However, without the core growth data of plants, it is difficult to effectively implement these dynamic control strategies.

[0078] Prior to the present invention, the prior art mainly used high-definition video recording to obtain dynamic videos of plant growth, and obtained the state of the plant growth process by accelerating and decelerating, zooming in and out, and professional technicians analyzed the current state of the plant.

[0079] In an embodiment of the present invention, a method and system for analyzing plant high-definition video and image comparison big data is provided. The solution matches the focus and morphological information by online analyzing the growth period of the plant, and then evaluates the plant status in different growth periods.

[0080] According to a first aspect of an embodiment of the present invention, a big data analysis method for comparing high-definition plant video images is provided.

[0081] Figure 1 It is a flow chart of a method for analyzing plant high-definition video and picture comparison big data according to an embodiment of the present invention.

[0082] In one or more embodiments, preferably, the plant high-definition video picture comparison big data analysis method includes:

[0083] S101, extracting images to generate a plant growth collection database;

[0084] S102, dividing the plant growth period, and extracting first matching data according to the focus points in different growth periods;

[0085] S103, screening the first matching data to obtain second matching data;

[0086] S104, extracting and analyzing the size of the second matching data to generate real-time size data information;

[0087] S105, extracting and analyzing the color of the second matching data to generate real-time color data information;

[0088] S106: Extract an optimal state index based on the real-time color data information and the real-time size data information.

[0089] In an embodiment of the present invention, the current state of the plant can be quickly calibrated through plant image analysis. This calibrated state will provide a basis for future artificial intelligence plant growth management. When the plant is in different growth states, the analysis will form a corresponding growth state index. The final indexed growth state will be the basic data for subsequent control. In the present invention, subsequent control is not limited, but mainly limits how to analyze the growth state.

[0090] Figure 2 It is a flowchart of performing image extraction and generating a plant growth collection database in a big data analysis method for comparing plant high-definition video images in one embodiment of the present invention.

[0091] like Figure 2 As shown, in one or more embodiments, preferably, the performing of image extraction to generate a plant growth collection database specifically includes:

[0092] S201, setting a video acquisition cycle, and performing image acquisition on all plant growth videos according to the video acquisition cycle;

[0093] S202: For the data which are images themselves, the images obtained by collecting plant growth videos are collected and stored together in a plant growth collection database.

[0094] In an embodiment of the present invention, the core of image acquisition of plant growth information is to extract images. If the acquired information is a video, the image is acquired from the video according to a preset period. If it is an image itself, no processing is required. Finally, a plant growth acquisition database is produced, which only contains image information.

[0095] Figure 3 It is a flowchart of dividing plant growth periods in a big data analysis method for comparing plant high-definition video images in an embodiment of the present invention, and extracting first matching data according to focus points in different growth periods.

[0096] like Figure 3 As shown, in one or more embodiments, preferably, the dividing of plant growth periods and extracting first matching data according to the focus points of different growth periods specifically includes:

[0097] S301, dividing the plant growth process into a seedling stage, a mature stage, a flowering stage and a senescence stage;

[0098] S302, setting the focus area in the seedling stage to leaves;

[0099] S303, setting the mature stage focus area to the neck;

[0100] S304, setting the flowering period focus area to flowers;

[0101] S305, setting the aging period focus area to fruit;

[0102] S306, extracting clear images with focus points corresponding to different periods and saving them as high-quality information images;

[0103] S307. Mark all high-quality information images with corresponding time periods, and store the corresponding data as first matching data.

[0104] In an embodiment of the present invention, the plant growth process is divided into seedling stage, mature stage, flowering stage and senescence stage; the focus area is set for each stage, focusing on leaves in the seedling stage, necks in the mature stage, flowers in the flowering stage, and fruits in the senescence stage; clear images with focus points corresponding to different periods are extracted and saved as high-quality information images, and stored in the corresponding period, and all matching data are extracted as the first matching data.

[0105] Figure 4 It is a flowchart of obtaining second matching data by screening according to the first matching data in a method for analyzing plant high-definition video image comparison big data according to an embodiment of the present invention.

[0106] like Figure 4 As shown, in one or more embodiments, preferably, the step of screening the first matching data to obtain the second matching data specifically includes:

[0107] S401, presetting the corresponding matching graphs of leaves, necks, fruits and flowers;

[0108] S402: Intelligently identify all first matching data, and if it is determined that there are matching graphs that are identical or highly similar, then they are considered to be second matching data.

[0109] In the embodiment of the present invention, the focus area in the seedling stage is set as leaves; the focus area in the mature stage is set as the neck; the focus area in the flowering stage is set as flowers; and the focus area in the senescence stage is set as fruits; therefore, in each focus area, the shape of the corresponding matching matching data has a certain probability, and the corresponding matching graphs of leaves, necks, fruits and flowers are pre-set. If it is determined through intelligent recognition that there are identical or highly similar matching graphs, it is considered that the corresponding second matching data is further screened out in the first matching data, and the second matching data is the most direct data for analyzing the current plant status.

[0110] Figure 5It is a flowchart of extracting and analyzing the size of the second matching data in a big data analysis method for comparing plant high-definition video images in one embodiment of the present invention to generate real-time size data information.

[0111] like Figure 5 As shown, in one or more embodiments, preferably, the extracting and analyzing the size of the second matching data to generate real-time size data information specifically includes:

[0112] S501, performing size analysis on the second matching data to generate a total area of ​​a focus area;

[0113] S502, extracting the total area growth rate using the first calculation formula;

[0114] S503, taking the total area of ​​the concerned region and the total area size growth rate as real-time size data information;

[0115] The first calculation formula is:

[0116] C=(A + -A - )÷(T + -T - )

[0117] Among them, C is the growth rate of total area, A + is the total area of ​​the region of interest at the current acquisition time in the second matching data, A - is the total area of ​​the region of interest at the last acquisition time in the second matching data, T + is the current acquisition time of the second matching data, T - is the collection time of the last moment in the second matching data.

[0118] In an embodiment of the present invention, data analysis is performed on all the second matching data. First, a time series analysis is performed. In the same stage, the acquisition time of two adjacent images is analyzed, and the total area size of the focus area corresponding to each acquisition time is extracted. The size change rate is calculated based on the extracted total area size at the current moment and the first calculation formula, and the saturation change rate and color saturation are used as real-time size data information.

[0119] Figure 6 It is a flow chart of extracting and analyzing the color of the second matching data to generate real-time color data information in a big data analysis method for comparing high-definition plant video images according to an embodiment of the present invention.

[0120] like Figure 6As shown, in one or more embodiments, preferably, the color extraction and analysis of the second matching data to generate real-time color data information specifically includes:

[0121] S601, performing color saturation analysis on the second matching data to generate an average saturation of a focus area;

[0122] S602, extracting the saturation growth rate using a second calculation formula;

[0123] S603, taking the saturation growth rate and the average saturation together as real-time color data information;

[0124] The second calculation formula is:

[0125] Z=(B + -B - )÷(T + -T-)

[0126] Among them, Z is the saturation growth rate, B + is the saturation of the acquisition time at the current moment in the second matching data, B- is the saturation of the acquisition time at the previous moment in the second matching data, T + is the current acquisition time of the second matching data, T - is the collection time of the last moment in the second matching data.

[0127] In an embodiment of the present invention, data analysis is performed on all the second matching data. First, a time series analysis is performed. In the same stage, the acquisition time of two adjacent images is analyzed, and the color saturation of the focus area corresponding to each acquisition time is extracted. The saturation change rate is calculated based on the extracted color saturation at the current moment and the second calculation formula, and the saturation change rate and color saturation are used as real-time color data information.

[0128] Figure 7 It is a flow chart of extracting the optimal state index based on the real-time color data information and the real-time size data information in a big data analysis method for comparing plant high-definition video images in one embodiment of the present invention.

[0129] like Figure 7 As shown, in one or more embodiments, preferably, extracting the optimal state index based on the real-time color data information and the real-time size data information specifically includes:

[0130] S701, extracting the optimal state coefficient using a third calculation formula according to the historical data of the actual evaluation values ​​of some state indexes;

[0131] S702, calculating a state index using a fourth calculation according to the optimal state coefficient;

[0132] The third calculation formula is:

[0133] {k1,k2,k3,k4}=argmin l (Σ|s-k1×A + +k2×C+k3×B + +k4×Z|)

[0134] Among them, {k1, k2, k3, k4} are the optimal state coefficients, s is the actual evaluation value, argmin l To extract Σ(s-k1×A + +k2×C+k3×B + +k4×Z) is the smallest, the corresponding optimal state coefficient function, Σ|s-k1×A + +k2×C+k3×B + +k4×Z| is the absolute value sum of the calculation errors of all actual evaluation values ​​in the historical data;

[0135] The fourth calculation formula is:

[0136] X=k1×A + +k2×C+k3×B + +k4×Z

[0137] Where X is the state index.

[0138] In an embodiment of the present invention, an actual evaluation value of a state index is set in advance, and the actual evaluation value in the historical data is used to learn the optimal coefficients of the real-time size data information and the real-time color data information, thereby realizing the evaluation of the growth state index of the system at the current moment. In this way, the online analysis of the optimal state index can be realized, and subjective questions set by humans can be effectively raised, and adaptive learning can be achieved, with extremely high efficiency.

[0139] According to a second aspect of an embodiment of the present invention, a plant high-definition video image comparison big data analysis system is provided.

[0140] Figure 8 It is a structural diagram of a plant high-definition video image comparison big data analysis system according to an embodiment of the present invention.

[0141] In one or more embodiments, preferably, the plant high-definition video image comparison big data analysis system includes:

[0142] The data acquisition module 801 is used to extract images and generate a plant growth collection database;

[0143] A focus extraction module 802 is used to divide the plant growth period and extract the first matching data according to the focus points in different growth periods;

[0144] A focus region extraction module 803, configured to obtain second matching data by screening the first matching data;

[0145] A size analysis module 804 is used to extract and analyze the size of the second matching data to generate real-time size data information;

[0146] A color analysis module 805, used to extract and analyze the color of the second matching data to generate real-time color data information;

[0147] The state indexing module 806 is used to extract the optimal state index according to the real-time color data information and the real-time size data information.

[0148] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0149] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0150] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9 The electronic device shown is a general plant high-definition video image comparison big data analysis device. Fig. 9 The electronic device 900 includes one or more (only one is shown in the figure) processors 902, a memory 904 and a wireless module 906 coupled to each other. The memory 904 stores a program that can execute the content of the above-mentioned embodiment, and the processor 902 can execute the program stored in the memory 904.

[0151] Among them, the processor 902 may include one or more processing cores. The processor 902 uses various interfaces and lines to connect various parts of the entire electronic device 900, and executes various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets or instruction sets stored in the memory 904, and calling data stored in the memory 904. Optionally, the processor 902 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 902 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and target application program, etc.; the GPU is responsible for rendering and drawing the display content; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 902, and it can be implemented separately through a communication chip.

[0152] The memory 904 may include a random access memory (RAM) or a read-only memory (ROM). The memory 904 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 904 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the following various method embodiments, etc. The data storage area may also store data (such as the aforementioned text document) created by the electronic device 900 during use, etc.

[0153] The wireless module 906 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other equipment, for example, communicate with a base station based on a mobile communication protocol. The wireless module 906 may include various existing circuit elements for performing these functions, for example, an antenna, a radio frequency transceiver, a digital signal processor, an encryption / decryption chip, a user identity module (SIM) card, a memory, etc. The wireless module 906 may communicate with various networks such as the Internet, an intranet, a wireless network, or communicate with other electronic devices through a wireless network. The above-mentioned wireless network may include a cellular telephone network, a wireless local area network, or a metropolitan area network. The above-mentioned wireless network may use various communication standards, protocols, and technologies, including but not limited to WLAN protocols and Bluetooth protocols, and may even include those protocols that have not yet been developed.

[0154] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0155] In the scheme of the present invention, a big data analysis and screening method for plant high-definition videos and pictures focusing on the analysis of plant growth status is provided.

[0156] In the scheme of the present invention, by paying attention to the size, shape and corresponding change rate of the information during the growth process, a rapid analysis result for the plant at different stages is formed.

[0157] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0158] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A plant high-definition video image comparison big data analysis method, characterized in that: The method includes: Perform image extraction to generate a plant growth collection database; Divide the plant growth period, and extract the first matching data according to the focus points in different growth periods; Screening the first matching data to obtain second matching data; Extracting and analyzing the size of the second matching data to generate real-time size data information; Performing color extraction and analysis on the second matching data to generate real-time color data information; extracting an optimal state index based on the real-time color data information and the real-time size data information; The step of extracting and analyzing the size of the second matching data to generate real-time size data information specifically includes: Performing size analysis on the second matching data to generate a total area of ​​the region of interest; The first calculation formula is used to extract the total area growth rate; The total area of ​​the concerned region and the total area size growth rate are used as real-time size data information; The first calculation formula is: C=(A + -A - )÷(T + -T - ) Among them, C is the growth rate of total area, A + is the total area of ​​the region of interest at the current acquisition time in the second matching data, A - is the total area of ​​the region of interest at the last acquisition time in the second matching data, T + is the current acquisition time of the second matching data, T - is the collection time of the last moment in the second matching data; The step of extracting and analyzing the color of the second matching data to generate real-time color data information specifically includes: Performing color saturation analysis on the second matching data to generate an average saturation of the focus area; The saturation growth rate is extracted using the second calculation formula; The saturation growth rate and the average saturation are used together as real-time color data information; The second calculation formula is: Z=(B + -B - )÷(T + -T - ) Among them, Z is the saturation growth rate, B + is the saturation of the current acquisition time in the second matching data, B - is the saturation of the acquisition time of the last moment in the second matching data, T + is the current acquisition time of the second matching data, T - is the collection time of the last moment in the second matching data; The step of extracting the optimal state index based on the real-time color data information and the real-time size data information specifically includes: According to the historical data of the actual evaluation values ​​of some state indexes, the optimal state coefficient is extracted by using the third calculation formula; Calculating a state index using a fourth calculation according to the optimal state coefficient; The third calculation formula is: {k1,k2,k3,k4}=argmin l (Σ|s-k1×A + +k2×C+k3×B + +k4×Z|) Among them, {k1, k2, k3, k4} are the optimal state coefficients, k1, k2, k3 and k4 are the first, second, third and fourth coefficients of the optimal state coefficients respectively, s is the actual evaluation value, argmin l To extract Σ(s-k1×A + +k2×C+k3×B + +k4×Z) is the smallest, the corresponding optimal state coefficient function, Σ|s-k1×A + +k2×C+k3×B + +k4×Z| is the absolute value sum of the calculation errors of all actual evaluation values ​​in the historical data; The fourth calculation formula is: X=k1×A + +k2×C+k3×B + +k4×Z Where X is the state index.

2. A method for analyzing plant high-definition video and picture comparison big data as claimed in claim 1, characterized in that: The image extraction to generate the plant growth collection database specifically includes: Set the video acquisition cycle, and collect images of all plant growth videos according to the video acquisition cycle; For data that are images themselves, the images obtained by plant growth video collection are collected and stored together in the plant growth collection database.

3. The method for analyzing plant high-definition video and picture comparison big data according to claim 1, characterized in that: The dividing of plant growth periods and extracting first matching data according to the focus points of different growth periods specifically includes: The plant growth process is divided into seedling stage, maturity stage, flowering stage and senescence stage; Set the focus area for the seedling stage to leaves; Set the maturity focus area to the neck; Set the flowering period focus area to flowers; Set the focus area of ​​senescence period to fruit; Extract clear images with focus points corresponding to different periods and save them as high-quality information images; All high-quality information images are marked with corresponding periods, and the corresponding data are stored as first matching data.

4. The method for analyzing plant high-definition video and picture comparison big data according to claim 1, characterized in that: The step of screening the first matching data to obtain the second matching data specifically includes: The corresponding matching diagrams of leaves, necks, fruits and flowers are preset; All first matching data are intelligently identified, and if matching graphs that are identical or highly similar are found, they are considered to be second matching data.

5. A plant high-definition video and picture comparison big data analysis system, characterized in that: The system is used to implement the method according to any one of claims 1 to 4, and the system comprises: A data acquisition module, used for image extraction to generate a plant growth collection database; A focus extraction module is used to divide the plant growth period and extract the first matching data according to the focus points in different growth periods; A focus region extraction module, configured to obtain second matching data by screening the first matching data; A size analysis module, used for extracting and analyzing the size of the second matching data to generate real-time size data information; A color analysis module, used for extracting and analyzing the color of the second matching data to generate real-time color data information; The state indexing module is used to extract the optimal state index according to the real-time color data information and the real-time size data information.

6. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 4 when executed by a processor.

7. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-4.

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