A multi-parameter study on the effects of different hormones on hybrid seed cultivation of Rhododendron

Abstract

The invention discloses a multi-parameter research method for the influence of different hormones in the cultivation of azalea hybrid seeds, performs data collection operation on plants located in a cultivation room, and uses a processing system to perform data processing operation, and the invention relates to the technical field of plant cultivation research. The multi-parameter research method for the influence of different hormones in the cultivation of azalea hybrid seeds performs data collection operation on plants located in a cultivation room, performs preliminary processing on the collected video data to form image data, determines various characteristic data from the beginning flowering period to the end flowering period of the plant according to the analysis of the image data, combines the characteristic data and realizes the judgment and evaluation operation, and then selects the optimal hormone ratio according to the data tracing, so that the evaluation operation of the plants under the influence of hormones can be realized by combining the influence results of multiple types of parameters, the efficiency of data processing is improved, and the optimal hormone ratio is selected.

Description

Technical Field

[0001] The invention relates to the technical field of plant cultivation research, in particular to a multi-parameter research method for the influence of different hormones in the cultivation of azalea hybrid seeds. Background Art

[0002] The reference patent name is: A digital agricultural planting system and method based on Internet of Things technology (patent publication number: CN111507857A, patent publication date: 2020-08-07), including an agricultural product management module, a data acquisition and transmission module, a data management module and a client. Through the data acquisition and transmission system, the farmland base environmental index data is continuously collected for real-time monitoring and preservation. At the same time, the data collected by the farmland base monitoring is used for image recognition to construct a three-dimensional model of the base crop environment, import AR augmented reality scenes, and create a digital management system to enable users to virtually experience the entire process of crop planting online. Agricultural technicians only need to read environmental data and video information from the network to scientifically arrange agricultural production. The system sets up robots to accurately remotely control agricultural facilities according to the various indicators of crop growth to realize intelligent agricultural production, save manpower, reduce human errors, and enable users to remotely control the farmland planting environment online, accurately manage crops, and ensure food safety.

[0003] Based on the description in the above-mentioned document, in order to improve the sustained flowering period of azalea and the corresponding quality of azalea, in the existing azalea cultivation process, different hormones are used to cultivate azalea cultivated plants and realize research operations. However, it is often too simple to judge the influence of hormones on cultivation by a certain data, and it is necessary to continuously observe and complete the judgment operation through personnel, which makes the research efficiency low. For this reason, the present invention provides a multi-parameter research method for the influence of different hormones in the cultivation of azalea hybrid seeds. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a multi-parameter research method for the effects of different hormones on the cultivation of azalea hybrid seeds, which solves the problem that in the existing azalea cultivation process, different hormones are used to cultivate azalea cultivated plants and realize research operations, while the influence of hormones on cultivation is often judged too simply by a certain data, and the judgment operation needs to be completed by continuous observation by personnel, resulting in low research efficiency.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: a multi-parameter research method of the effects of different hormones on the cultivation of azalea hybrid seeds, specifically comprising the following steps:

[0006] A1. Mixing multiple hormones by setting different concentrations or ratio parameters to form multiple hormone sprays, and then spraying the same batch of azalea hybrid plants cultivated in the same environment, and ensuring that the number of spraying, dosage, and time are the same;

[0007] A2. Collecting data from the plants in the cultivation room and processing the data using a processing system;

[0008] The collected video data is processed preliminarily to form image data, and the characteristic data of the plant from the beginning to the end of flowering period is determined based on the analysis of the image data. The characteristic data are combined and judged and evaluated, and then the optimal hormone ratio is selected based on data tracing;

[0009] A3. The selected results are transmitted and displayed through the display interface, and a small-scale cultivation operation is performed based on the selected results to confirm the results.

[0010] Preferably, the data collection operation for the plants in the cultivation room in A2 is:

[0011] a21. Using a high-definition camera installed in the cultivation room, setting a moving track for collecting videos of the plants, and controlling the high-definition camera to collect data on the surface of the plants;

[0012] a22. Video recording starts from the first application of hormone spray and ends after all flowering periods;

[0013] a23. The collected data is then transmitted to the storage module of the processing system for storage and matched with the research data of the corresponding plants.

[0014] Preferably, the operation of performing preliminary processing on the collected video data to form image data in A2 is:

[0015] B1. Divide the recorded time into multiple segments of data video, and implement parallel processing of the video through the processing system;

[0016] B2. Set a frame number cycle, segment the video according to the set frame number cycle from the beginning of the video data, use the end of each frame number cycle as the corresponding image data, and perform a screening operation on the image data, and then sort and classify the screened image data according to the acquisition time period to form an image data set;

[0017] B3. Select an image data from the image data set, use the pixels of the selected image data as reference values, and convert the other image data into reference values ​​of the selected image data.

[0018] Preferably, the steps of performing the screening operation on the image data in B2 are:

[0019] b21, by comparing the first image data and the second image data of the same plant;

[0020] b22, aligning the diagonal points of the image data, performing preliminary segmentation on the first image data located at the upper layer, retaining the data containing plant features, removing the background data, performing feature matching between the plant feature data and the lower-layer image data, and determining whether the plant features in the first image data and the plant features in the lower-layer image data produce distinguishing features after growth;

[0021] b23. If distinguishing features are generated between the plant features in the comparison of b22, both the primary image data and the secondary image data are retained; if no distinguishing features are generated, the primary image data is removed and the secondary image data is retained;

[0022] b24, and the next comparison is to perform operations b22 and b23 on the secondary image data and the subsequent adjacent image data until all image data are screened.

[0023] Preferably, the operation of determining various characteristic data of the plant from the beginning to the end of the flowering period based on the analysis of the image data in A2 is:

[0024] C1. Grayscale the image data in the image data set, and then divide the features in the image data;

[0025] C2, and determine the flowering period parameter data of the beginning flowering period, the peak flowering period and the end flowering period of the corresponding plants on a daily basis, and compare them with the corresponding flowering period parameters in the historical data;

[0026] C3. Count the number of flowers during the flowering period;

[0027] C4. Identify the diameter of flowers with complete flower features in the image data during the flowering period, and calculate the average flower diameter of a single plant.

[0028] Preferably, the operation of performing feature division in C1 is:

[0029] c11. Determine a complete acquisition operation time period, and grayscale the image data within the time period, and determine different features based on the grayscale value changes on the image;

[0030] c12. Extracting plant-related parameter feature data from the database and matching them with the determined different features, and marking the feature meanings of the matched features.

[0031] Preferably, the operation of determining the flowering period parameter data in C2 is:

[0032] c21, extracting the image data where the flower features appear for the first time in the time period sequence, and judging the subsequent adjacent image data;

[0033] c22, if there is a flower feature in the subsequent adjacent image data, if so, determine whether the flower feature in the adjacent image data is the same as the flower feature in the previous image data, if it is the same feature, continue to determine the subsequent adjacent image data, if not, extract the subsequent image data where the flower feature appears and repeat operations c21 to c22;

[0034] c23, the determined number of flower features is counted and marked as M, and the number threshold is extracted from the historical data and compared with the flower feature number M, and the number thresholds are the beginning flowering period threshold N1, the full flowering period threshold N2 and the end flowering period threshold N3, and N1<N3<N2;

[0035] If M≥N1, it means that the plant has officially entered the initial flowering period;

[0036] If M ≥ N2, it means that the plant has officially entered the flowering period;

[0037] After the peak flowering period, if M≤N3, it means that the plant has officially entered the final flowering period;

[0038] c24. Then determine the time parameter from the start of recording to the beginning of flowering period for extraction, and the time parameter of the peak flowering period is the time from the peak flowering period to the end flowering period, and the time parameter of the end flowering period is the time from the start of recording to the end flowering period.

[0039] Preferably, the operation of counting the number of C3 flowers is:

[0040] c31, collecting image data a few days after entering the blooming period, and then extracting flower features in the image data;

[0041] c32. Perform a division operation on the flower features according to the closed outer boundary lines connected to the petals to determine a flower feature, and then continue to divide the flower features outside the flower feature boundary line to determine the closed boundary line, thereby achieving statistics on the number of flower features and determining the number of flowers.

[0042] Preferably, the calculation steps of the average flower diameter in C4 are:

[0043] c41, based on the complete flower feature determined in c32, any point in the complete flower feature area is selected as the center point;

[0044] c42, then establish a ray from the center point to the boundary of the flower feature, and establish rays at intervals of 15° around the center point in a circular shape, determine the intersection of the ray and the flower feature as P, and by combining the distance Q from the starting point to the intersection point P n , and add the distance from the starting point to the intersection point P and the distance from the reverse extension end to the characteristic boundary of the flower, and compare the various distances to obtain the longest flower diameter;

[0045] c43, and the calculation formula for the longest flower diameter is:

[0046] When n ≥ 12, L max =Q n +Q n-12 ;

[0047] When n<12, L max =Q n +Q n+12 ;

[0048] Among them, L max is the longest flower diameter, Q n is the distance from the nth ray established from the starting point to the characteristic boundary of the flower, Q n-12 and Q n+12 They are all the distances from the starting point to the reverse extension line of the nth ray to the characteristic boundary of the flower;

[0049] c44, and the calculation formula for the average flower diameter is:

[0050] L 平 =[(L max )1+(L max )2+…+(L max ) t ] / t;

[0051] t is the number of flower features corresponding to a single plant, L 平 is the average flower diameter, (L max ) t is the longest floral diameter of the t-th floral feature.

[0052] Preferably, the A2 combines the feature data and implements the judgment and evaluation operation as follows:

[0053] D1, by assigning weights to the results from C2 to C4, the best evaluated plant is calculated;

[0054] D2. Count the number of days from the beginning to the end of flowering for each plant and rank them as R u , and assign a weight value k1 to calculate the flowering period parameter evaluation value, count the number of flowers on each plant and rank them as S u, and assign a weight value k2 to calculate the flower quantity evaluation value, and count the average flower diameter of each plant and rank it as J u , and assign a weight value k3 to realize the calculation of the average flower diameter evaluation value, and k1<k3<k2;

[0055] D3. The formula for calculating the optimal evaluation value is:

[0056] W max =k1×R u +k2×S u +k3×J u ;

[0057] W max It is the maximum value of the evaluation value of a single plant after statistics.

[0058] The present invention provides a multi-parameter research method for the influence of different hormones in the cultivation of azalea hybrid seeds. Compared with the prior art, it has the following beneficial effects:

[0059] 1. The multi-parameter research method of the influence of different hormones in the cultivation of azalea hybrid seeds performs data collection operations on plants in the cultivation room, performs preliminary processing on the collected video data to form image data, determines various characteristic data of the plants from the beginning to the end of the flowering period based on the analysis of the image data, combines the characteristic data and implements judgment and evaluation operations, and then selects the optimal hormone ratio based on data tracing, thereby combining the influence results of multiple types of parameters to implement evaluation operations on plants under the influence of hormones, improve the efficiency of data processing, and select the optimal hormone ratio.

[0060] 2. The multi-parameter research method of the influence of different hormones on the cultivation of azalea hybrid seeds grayscales the image data in the image data set, and then divides the various features in the image data to realize the combination of flowering period parameters, flower number parameters during the peak flowering period, and average flower diameter parameters of plants during the peak flowering period, and compares and ranks them with plants under other hormone ratios. The combined evaluation values ​​are ranked and the evaluation value with the highest score is selected as the optimal plant. The process is processed by the processing system, which can not only solve the roughness of personnel observation, but also realize more accurate judgment and processing of data, and improve the robustness of data research results.

[0061] 3. The multi-parameter research method of the influence of different hormones in the cultivation of azalea hybrid seeds is carried out by randomly selecting a point in the complete flower characteristic area as the center point, and then establishing rays from the center point to the boundary of the flower characteristic, and establishing rays every 15° in a circular shape with the center point to determine the intersection of the ray and the flower characteristic, and by combining the distance from the starting point to the intersection, and adding the distance from the starting point to the reverse extension end of the intersection to the boundary of the flower characteristic, and comparing the various distances to obtain the longest flower diameter, so as to more quickly calculate the flower diameter, and combine the characteristics of multiple complete flowers on a single plant to realize the calculation of the average value of the flower characteristics after the diameter measurement, thereby improving the fault tolerance of the flower diameter data extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is an operation flow chart of the parameter research method of the present invention;

[0063] Figure 2 It is an operational flow chart of data processing of the present invention;

[0064] Figure 3 This is an operational flow chart of image data analysis according to the present invention. DETAILED DESCRIPTION

[0065] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] See also Figure 1-Figure 3 , the present invention provides two technical solutions:

[0067] Embodiment 1: A multi-parameter research method for the influence of different hormones on the cultivation of azalea hybrid seeds, comprising the following steps:

[0068] A1. Mixing multiple hormones by setting different concentrations or ratio parameters to form multiple hormone sprays, and then spraying the same batch of azalea hybrid plants cultivated in the same environment, and ensuring that the number of spraying, dosage, and time are the same;

[0069] A2. Collecting data from the plants in the cultivation room and processing the data using a processing system;

[0070] The collected video data is processed preliminarily to form image data, and the characteristic data of the plant from the beginning to the end of flowering period is determined based on the analysis of the image data. The characteristic data are combined and judged and evaluated, and then the optimal hormone ratio is selected based on data tracing;

[0071] A3. The selected results are transmitted and displayed through the display interface, and a small-scale cultivation operation is performed based on the selected results to confirm the results.

[0072] By performing data collection operations on the plants in the cultivation room, the collected video data is preliminarily processed to form image data, and the characteristic data of the plants from the beginning to the end of the flowering period are determined based on the analysis of the image data. The characteristic data are combined and judgment and evaluation operations are performed, and then the optimal hormone ratio is selected based on data tracing. In this way, the influence results of multiple types of parameters can be combined to realize the evaluation operation of plants under the influence of hormones, improve the efficiency of data processing, and select the optimal hormone ratio.

[0073] Among them, the optimal hormone ratio is selected based on data tracing, which is to know the corresponding plant through the optimal evaluation value result, and then trace the specific values ​​and conditions based on the hormone spray and other parameters of the plant at that time, so as to understand and select the optimal hormone ratio;

[0074] Then, by selecting the optimal hormone ratio, another batch of azalea hybrid plants cultivated in the same environment were cultivated, and the subsequent growth results were statistically verified.

[0075] In the embodiment of the present invention, the data collection operation for the plants in the cultivation room in A2 is as follows:

[0076] a21. Using a high-definition camera installed in the cultivation room, setting a moving track for collecting videos of the plants, and controlling the high-definition camera to collect data on the surface of the plants;

[0077] a22. Video recording starts from the first application of hormone spray and ends after all flowering periods;

[0078] a23. The collected data is then transmitted to the storage module of the processing system for storage and matched with the research data of the corresponding plants.

[0079] In the embodiment of the present invention, the operation of performing preliminary processing on the collected video data to form image data in A2 is:

[0080] B1. Divide the recorded time into multiple segments of data video, and implement parallel processing of the video through the processing system;

[0081] B2. Set a frame number cycle, segment the video according to the set frame number cycle from the beginning of the video data, use the end of each frame number cycle as the corresponding image data, and perform a screening operation on the image data, and then sort and classify the screened image data according to the acquisition time period to form an image data set;

[0082] B3. Select an image data from the image data set, use the pixels of the selected image data as reference values, and convert the other image data into reference values ​​of the selected image data.

[0083] In the embodiment of the present invention, the steps of performing the screening operation on the image data in B2 are:

[0084] b21, by comparing the first image data and the second image data of the same plant;

[0085] b22, aligning the diagonal points of the image data, performing preliminary segmentation on the first image data located at the upper layer, retaining the data containing plant features, removing the background data, performing feature matching between the plant feature data and the lower-layer image data, and determining whether the plant features in the first image data and the plant features in the lower-layer image data produce distinguishing features after growth;

[0086] b23. If distinguishing features are generated between the plant features in the comparison of b22, both the primary image data and the secondary image data are retained; if no distinguishing features are generated, the primary image data is removed and the secondary image data is retained;

[0087] b24, and the next comparison is to perform operations b22 and b23 on the secondary image data and the subsequent adjacent image data until all image data are screened.

[0088] In the embodiment of the present invention, the operation of determining various characteristic data of the plant from the beginning flowering period to the end flowering period based on the analysis of the image data in A2 is:

[0089] C1. Grayscale the image data in the image data set, and then divide the features in the image data;

[0090] C2, and determine the flowering period parameter data of the beginning flowering period, the peak flowering period and the end flowering period of the corresponding plants on a daily basis, and compare them with the corresponding flowering period parameters in the historical data;

[0091] C3. Count the number of flowers during the flowering period;

[0092] C4. Identify the diameter of flowers with complete flower features in the image data during the flowering period, and calculate the average flower diameter of a single plant.

[0093] By graying the image data in the image data set and then dividing the various features in the image data, the flowering period parameters, the number of flowers during the peak flowering period and the average flower diameter parameters of the plants during the peak flowering period are combined, and the ranking is compared with the plants under other hormone ratios. The combined evaluation values ​​are ranked and the highest score is selected as the optimal plant. The process is processed by the processing system, which can not only solve the roughness of personnel observation, but also achieve more accurate judgment and processing of data, thereby improving the robustness of data research results.

[0094] In the embodiment of the present invention, the operation of performing feature division in C1 is:

[0095] c11. Determine a complete acquisition operation time period, and grayscale the image data within the time period, and determine different features based on the grayscale value changes on the image;

[0096] c12. Extracting plant-related parameter feature data from the database and matching them with the determined different features, and marking the feature meanings of the matched features.

[0097] In the embodiment of the present invention, the operation of determining the flowering period parameter data in C2 is:

[0098] c21, extracting the image data where the flower features appear for the first time in the time period sequence, and judging the subsequent adjacent image data;

[0099] c22, if there is a flower feature in the subsequent adjacent image data, if so, determine whether the flower feature in the adjacent image data is the same as the flower feature in the previous image data, if it is the same feature, continue to determine the subsequent adjacent image data, if not, extract the subsequent image data where the flower feature appears and repeat operations c21 to c22;

[0100] c23, the determined number of flower features is counted and marked as M, and the number threshold is extracted from the historical data and compared with the flower feature number M, and the number thresholds are the beginning flowering period threshold N1, the full flowering period threshold N2 and the end flowering period threshold N3, and N1<N3<N2;

[0101] If M≥N1, it means that the plant has officially entered the initial flowering period;

[0102] If M ≥ N2, it means that the plant has officially entered the flowering period;

[0103] After the peak flowering period, if M≤N3, it means that the plant has officially entered the final flowering period;

[0104] c24. Then determine the time parameter from the start of recording to the beginning of flowering period for extraction, and the time parameter of the peak flowering period is the time from the peak flowering period to the end flowering period, and the time parameter of the end flowering period is the time from the start of recording to the end flowering period.

[0105] In the embodiment of the present invention, the operation of counting the number of C3 flowers is as follows:

[0106] c31, collecting image data a few days after entering the blooming period, and then extracting flower features in the image data;

[0107] c32. Perform a division operation on the flower features according to the closed outer boundary lines connected to the petals to determine a flower feature, and then continue to divide the flower features outside the flower feature boundary line to determine the closed boundary line, thereby achieving statistics on the number of flower features and determining the number of flowers.

[0108] In the embodiment of the present invention, the steps for calculating the average flower diameter in C4 are:

[0109] c41, based on the complete flower feature determined in c32, any point in the complete flower feature area is selected as the center point;

[0110] c42, then establish a ray from the center point to the boundary of the flower feature, and establish rays at intervals of 15° around the center point in a circular shape, determine the intersection of the ray and the flower feature as P, and by combining the distance Q from the starting point to the intersection point P n , and add the distance from the starting point to the intersection point P and the distance from the reverse extension end to the flower feature boundary, and compare the various distances to obtain the longest flower diameter;

[0111] c43, and the calculation formula for the longest flower diameter is:

[0112] When n ≥ 12, L max =Q n +Q n-12 ;

[0113] When n<12, L max =Q n +Q n+12 ;

[0114] Among them, L max is the longest flower diameter, Q n is the distance from the nth ray established from the starting point to the characteristic boundary of the flower, Q n-12 and Q n+12 They are all the distances from the starting point to the reverse extension line of the nth ray to the characteristic boundary of the flower;

[0115] c44, and the calculation formula for the average flower diameter is:

[0116] L平 =[(L max )1+(L max )2+…+(L max ) t ] / t;

[0117] t is the number of flower features corresponding to a single plant, L 平 is the average flower diameter, (L max ) t is the longest floral diameter of the t-th floral feature.

[0118] By taking any point in the complete flower feature area as the center point, and then establishing rays from the center point to the boundary of the flower feature, and establishing rays every 15° in a circular shape with the center point, the intersection of the ray and the flower feature is determined, and by combining the distance from the starting point to the intersection, and adding the distance from the starting point to the reverse extension end of the intersection to the flower feature boundary, and comparing each distance to obtain the longest flower diameter, the flower diameter can be calculated more quickly, and the average value is calculated by combining the complete flower features of multiple plants on a single plant, thereby improving the fault tolerance of the flower diameter data extraction.

[0119] In the embodiment of the present invention, A2 combines the feature data and implements the judgment and evaluation operation as follows:

[0120] D1, by assigning weights to the results from C2 to C4, the best evaluated plant is calculated;

[0121] D2. Count the number of days from the beginning to the end of flowering for each plant and rank them as R u , and assign a weight value k1 to calculate the flowering period parameter evaluation value, count the number of flowers on each plant and rank them as S u , and assign a weight value k2 to calculate the flower quantity evaluation value, and count the average flower diameter of each plant and rank it as J u , and assign a weight value k3 to realize the calculation of the average flower diameter evaluation value, and k1<k3<k2;

[0122] D3. The formula for calculating the optimal evaluation value is:

[0123] W max =k1×R u +k2×S u +k3×J u ;

[0124] W max It is the maximum value of the evaluation value of a single plant after statistics.

[0125] Embodiment 2: Compared with Embodiment 1, the difference is that the specific test operation of the multiple hormone sprays in the present invention A1 is as follows:

[0126] Gibberellic acid, ethephon, butyric acid hydrazide and 2,4-D were prepared into solutions of different concentrations, and water was used as a control instead of the solution. The dewdrop azalea plants were sprayed by automatic spraying equipment. During the spraying, the solution was evenly sprayed on the upper and lower surfaces of the leaves. The spraying time was 9:00-11:00 in the morning. The experiment set up three groups of spraying 1 time, 3 times and 5 times, and each time was 2 days apart. The beginning flowering period, the peak flowering period and the end flowering period of each treatment plant were recorded.

[0127] And the combinations of hormone sprays are:

[0128] Group A: ①, 100 mg of gibberellin, 1 kg of aqueous solution; ②, 200 mg of gibberellin, 1 kg of aqueous solution; ③, 400 mg of gibberellin, 1 kg of aqueous solution;

[0129] Group B: ①, 100 mg of ethephon, 1 kg of water solution; ②, 250 mg of ethephon, 1 kg of water solution; ③, 500 mg of ethephon, 1 kg of water solution;

[0130] Group C: Based on Group A and Group B, 100 mg of diaminobutyric acid hydrazide was added to each group for mixing;

[0131] Group D: Based on Group A and Group B, 100 mg of 2,4-D was added to each group for mixing;

[0132] Then, cultivation research was conducted based on the spraying times of 1, 3 and 5 times of the hormone sprays of the above four groups A, B, C and D respectively, and the evaluation values ​​were obtained according to the research results for comparison and selection of the best plants and the corresponding optimal hormone ratios.

[0133] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0134] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0135] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-parameter study method for the effects of different hormones on the cultivation of azalea hybrid seeds, characterized by: The specific steps include: A1. Mixing multiple hormones by setting different concentrations or ratio parameters to form multiple hormone sprays, and then spraying the same batch of azalea hybrid plants cultivated in the same environment, and ensuring that the number of spraying, dosage, and time are the same; A2. Collecting data from the plants in the cultivation room and processing the data using a processing system; The collected video data is processed preliminarily to form image data, and the characteristic data of the plant from the beginning to the end of flowering period is determined based on the analysis of the image data. The characteristic data are combined and judged and evaluated, and then the optimal hormone ratio is selected based on data tracing; A3. The selected results are transmitted and displayed through the display interface, and a small-scale cultivation operation is performed based on the selected results to confirm the results; The operation of determining various characteristic data of the plant from the beginning to the end of the flowering period according to the analysis of the image data in A2 is as follows: C1. Grayscale the image data in the image data set, and then divide the features in the image data; C2, taking each day as a cycle, and comparing it with the corresponding flowering period parameters in historical data, to determine the corresponding beginning flowering period, peak flowering period and end flowering period of the plant; C3. Count the number of flowers during the flowering period; C4, identifying the diameter of flowers with complete flower features in the image data during the flowering period, and calculating the average flower diameter of a single plant; The calculation steps for the average flower diameter in C4 are: c41, based on the complete flower feature determined in c32, any point in the complete flower feature area is selected as the center point; c42, then establish a ray from the center point to the boundary of the flower feature, and establish rays at intervals of 15° around the center point in a circular shape, determine the intersection of the ray and the flower feature as P, and by combining the distance Q from the starting point to the intersection point P n , and add the distance from the starting point to the intersection point P and the distance from the reverse extension end to the flower feature boundary, and compare the various distances to obtain the longest flower diameter; c43, and the calculation formula for the longest flower diameter is: When n ≥ 12, L max =Q n +Q n-12 ; When n<12, L max =Q n +Q n+12 ; Among them, L max = is the longest flower diameter, Q n is the distance from the nth ray established from the starting point to the characteristic boundary of the flower, Q n-12 and Q n+12 They are all the distances from the starting point to the reverse extension line of the nth ray to the characteristic boundary of the flower; c44, and the calculation formula for the average flower diameter is: L 平 =[(L max )1+(L max )2+…+(L max ) t ] / t; t is the number of corresponding floral features on a single plant.

2. The multi-parameter research method for the influence of different hormones on the cultivation of azalea hybrid seeds according to claim 1, characterized in that: The data collection operation for the plants in the cultivation room in A2 is as follows: a21. Using a high-definition camera installed in the cultivation room, setting a moving track for collecting videos of the plants, and controlling the high-definition camera to collect data on the surface of the plants; a22. Video recording starts from the first application of hormone spray and ends after all flowering periods; a23. The collected data is then transmitted to the storage module of the processing system for storage and matched with the research data of the corresponding plants.

3. The multi-parameter research method for the influence of different hormones on the cultivation of azalea hybrid seeds according to claim 1, characterized in that: The operation of performing preliminary processing on the collected video data to form image data in A2 is: B1. Divide the recorded time into multiple segments of data video, and implement parallel processing of the video through the processing system; B2. Set a frame period, segment the video according to the set frame period from the beginning of the video data, use the end of each frame period as the corresponding image data, and perform screening operations on the image data. Then, sort the screened and retained image data according to the acquisition time period to form an image data set.

4. The multi-parameter research method for the influence of different hormones on the cultivation of azalea hybrid seeds according to claim 3, characterized in that: The steps of performing the screening operation on the image data in B2 are: b21, by comparing the first image data and the second image data of the same plant; b22, aligning the diagonal points of the image data, performing preliminary segmentation on the first image data located at the upper layer, retaining the data containing plant features, removing the background data, performing feature matching between the plant feature data and the lower-layer image data, and determining whether the plant features in the first image data and the plant features in the lower-layer image data produce distinguishing features after growth; b23. If distinguishing features are generated between the plant features in the comparison of b22, both the primary image data and the secondary image data are retained; if no distinguishing features are generated, the primary image data is removed and the secondary image data is retained; b24, and the next comparison is to perform operations b22 and b23 on the secondary image data and the subsequent adjacent image data until all image data are screened.

5. The multi-parameter research method for the influence of different hormones on the cultivation of azalea hybrid seeds according to claim 1, characterized in that: The operation of feature division in C1 is: c11. Determine a complete acquisition operation time period, and grayscale the image data within the time period, and determine different features based on the grayscale value changes on the image; c12. Extracting plant-related parameter feature data from the database and matching them with the determined different features, and marking the feature meanings of the matched features.

6. The multi-parameter research method for the influence of different hormones on the cultivation of azalea hybrid seeds according to claim 1, characterized in that: The operation of determining the flowering period parameter data in C2 is: c21, extracting the image data where the flower features appear for the first time in the time period sequence, and judging the subsequent adjacent image data; c22, if there is a flower feature in the subsequent adjacent image data, if so, determine whether the flower feature in the adjacent image data is the same as the flower feature in the previous image data, if it is the same feature, continue to determine the subsequent adjacent image data, if not, extract the subsequent image data where the flower feature appears and repeat operations c21 to c22; c23, the determined number of flower features is counted and marked as M, and the number threshold is extracted from the historical data and compared with the flower feature number M, and the number thresholds are the beginning flowering period threshold N1, the full flowering period threshold N2 and the end flowering period threshold N3, and N1<N3<N2; If M≥N1, it means that the plant has officially entered the initial flowering period; If M ≥ N2, it means that the plant has officially entered the flowering period; After the peak flowering period, if M≤N3, it means that the plant has officially entered the final flowering period; c24. Then determine the time parameter from the start of recording to the beginning of flowering period for extraction, and the time parameter of the peak flowering period is the time from the peak flowering period to the end flowering period, and the time parameter of the end flowering period is the time from the start of recording to the end flowering period.

7. The multi-parameter research method for the influence of different hormones on the cultivation of azalea hybrid seeds according to claim 1, characterized in that: The A2 combines the feature data and performs the judgment and evaluation operation as follows: D1, by assigning weights to the results from C2 to C4, the best evaluated plant is calculated; D2. Count the number of days from the beginning to the end of flowering for each plant and rank them as R u , and assign a weight value k1 to calculate the flowering period parameter evaluation value, count the number of flowers on each plant and rank them as S u , and assign a weight value k2 to calculate the flower quantity evaluation value, and count the average flower diameter of each plant and rank it as J u , and assign a weight value k3 to realize the calculation of the average flower diameter evaluation value, and k1<k3<k2; D3. The formula for calculating the optimal evaluation value is: IN max =k1×R u +k2×S u +k3×J u ; W max is the maximum value of the evaluation value of a single plant after statistics, R u is the ranking of the number of days of flowering of the u-th plant, S u is the ranking of the number of flowers of the u-th plant, J u is the rank of the u-th average flower diameter.

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