A method for predicting the lifespan of Dendrobium species based on a univariate linear regression equation and its application.

Abstract

A method for predicting the lifespan of Dendrobium species based on a univariate linear regression equation and its application are disclosed. The method involves using a push-pull force meter with a 0.5 mm diameter needle to penetrate the petals of sample flowers and measuring the penetration force. The determination of the penetration force characterizes the mechanical support strength of the petals to explore the correlation formula between flower lifespan and the method. Alternatively, the method involves measuring the dry weight per unit area of ​​the lateral petals using a leaf area meter and an oven. The determination of the dry weight per unit area characterizes the biomass. Simultaneously, the determination of flower area and dry weight per unit area allows for the calculation of the dry weight per unit area to explore the correlation formula between flower lifespan and the method. By comparing correlation indices, the optimal formula is derived. This invention can shorten the time for flower lifespan detection, reduce the workload of monitoring, and improve the efficiency and accuracy of experiments. Furthermore, the operation process is simple and easy to understand, with low cost. By measuring the dry weight per unit area of ​​the petals, flower lifespan can be predicted efficiently and accurately.

Description

Technical Field

[0001] This application relates to the prediction of flower lifespan, specifically a method for predicting the lifespan of Dendrobium species based on a univariate linear regression equation. Background Technology

[0002] Current techniques for monitoring flower lifespan involve first attaching tags to flower buds, then starting monitoring from the day the flower fully opens, with observations every 3 days. Monitoring ends when the lip closes, or when yellow spots of aging appear on the lip, petals, and sepals. Flower lifespan is recorded in days. Additionally, for indeterminate inflorescence types, 3-10 plants are selected, with 2 flowers from each plant observed, ensuring the lifespan of 6-20 flowers is monitored. For plants with determinate inflorescence types or single-flowered plants, 6-20 plants are observed, ensuring the lifespan of 6-20 flowers is monitored.

[0003] Currently, during flower life monitoring, it's inevitable that tags will fall off when staff hang them on the flower buds. Furthermore, monitoring is conducted every three days starting from the day the flower fully opens. Since many flowers open at different times, and human judgment can influence the timing of opening, this not only increases the workload for staff but also leads to significant errors in the experimental results. In addition, external environmental factors (such as high temperatures, heavy rain, and pests) can affect petal closure and age spots, thus impacting flower life and affecting the results, thereby reducing the efficiency and accuracy of the experiment.

[0004] Previous studies have investigated parameters influencing flower lifespan. For example, average temperature during the flowering period has a high predictive power for flower lifespan, followed by the number of pollen cells per flower; calyx length is positively correlated with flower lifespan; genome size is positively correlated with flower lifespan, while flower lifespan is negatively correlated with the size of the lip epidermal cells, and flower lifespan is also correlated with anatomical characteristics. However, due to the cumbersome and costly experimental procedures, it is difficult to quickly predict flower lifespan based on these findings. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the lifespan of Dendrobium based on a univariate linear regression equation. By performing correlation analysis on data of flower lifespan and puncture force, as well as flower lifespan and dry weight of flowers per unit area, this invention provides a method for predicting the lifespan of Dendrobium flowers that can shorten the monitoring time, reduce the monitoring workload, and improve the efficiency and accuracy of experiments. The method makes the flower lifespan prediction process simple, easy to understand, and low in cost.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for predicting the lifespan of Dendrobium based on a univariate linear regression equation is characterized in that: the flower lifespan is the total number of days from the day the flower fully opens until any of the following states occur: closure of the lip petal and the appearance of yellow spots of aging on the sepals, lateral petals, lip petal, and sepals. The prediction method specifically includes the following steps:

[0008] S1 Selecting and labeling plants: Select more than 20 species of Dendrobium orchids and label them for data collection. Data collection includes collecting data on the dry weight of flowers per unit area and puncture force of the labeled plants, as well as collecting data on the natural lifespan of flowers through artificial monitoring.

[0009] S2 Data Collection of Flower Dry Weight and Puncture Force per Unit Area: The puncture force of the sepals, lateral petals, and lip of the plant under test was measured using a push-pull force meter (accuracy 0.001N, HADPI, Leqing, China). The flower dry weight per unit area of ​​the sepals, lateral petals, and lip was measured using measuring instruments including a leaf area meter, an oven, and an electronic scale. Three sets of puncture force data and three sets of flower dry weight per unit area of ​​the sepals, lateral petals, and lip were obtained. The plants of the species for which the flower dry weight and puncture force data were collected were then subjected to artificial lifespan monitoring. Several flowers were randomly selected from each plant for natural lifespan testing, and the average value was taken and the data was recorded. The resulting seven sets of data were stored in an Excel file.

[0010] S3 Data Processing: The seven sets of data obtained in step S2 are processed. Correlation analysis is performed on the data regarding flower lifespan and puncture force, as well as flower lifespan and dry weight per unit area, resulting in six linear function formulas and the corresponding correlation index R for each linear regression equation. 2 Compare 6 groups of R 2 Value, select the one with the largest R 2 The linear function formula of the value is used as a formula for predicting the lifespan of Dendrobium flowers.

[0011] More preferably, step S2, in which the push-pull force gauge measures the puncture force of the sepals, lateral petals, and lip of the plant to be measured, specifically includes the following steps:

[0012] S21 Preparation: Organize the collected Dendrobium genus samples, select 3 flowers of similar size and shape, remove the side petals of the 3 flowers, remove one side petal from each flower, and mark them 1-3. Then place them in a pre-prepared petri dish or beaker containing water to rehydrate.

[0013] S22 Puncture Measurement and Data Recording: The puncture force of the three lateral lobes was measured sequentially. After wiping away the impurities and moisture on the surface of the lateral lobes after rehydration, the lateral lobes were placed horizontally on the round hole magnet of the push-pull force gauge, with the middle position between the lateral lobe boundary and the midrib, and between the lateral lobe tip and the base, ensuring that the 0.5 mm diameter puncture needle avoided the main vein and important secondary veins in the middle of the lateral lobes when it fell. The maximum force required for the measurement is the lateral lobe puncture force Fp, kN / m. The data were recorded sequentially and the average value was taken. S21-S22 were repeated to collect puncture force data for the sepals and lip.

[0014] Measuring the stem weight per unit area of ​​sepals, lateral petals, and lip petals using measuring instruments specifically includes the following steps:

[0015] S23 Preparation: Arrange the flowers whose puncture force data has been measured according to their original sequence numbers;

[0016] S24. Measurement and recording of dry weight per unit area of ​​flowers: The petal area of ​​each lateral petal was measured sequentially using a Li-Cor 3000A leaf area meter. After measuring the petal area, the sample of each lateral petal was placed in an envelope according to its serial number, labeled, and placed in a 70℃ oven. After 48 hours, the dry weight of each lateral petal was measured. The dry weight of the flower was divided by the flower area to obtain the dry weight per unit area of ​​flowers (gm²). -2 After recording the data sequentially, the average value was taken. S23-S24 were repeated to collect the dry weight of the sepals and lip petals per unit area.

[0017] Furthermore, the data processing includes the following steps:

[0018] S31, Data Organization: Open the Excel spreadsheet and enter the flower lifespan, sepal, lateral petal, and lip puncture force data, and sepal, lateral petal, and lip unit area flower dry weight data into the blank Excel sheet, for a total of 7 columns of data;

[0019] S32, the linear function formulas were derived: Correlation analysis was performed on the data of flower lifespan and puncture force, as well as the data of flower lifespan and dry weight of flowers per unit area, to calculate six sets of linear function formulas and the corresponding correlation index R. 2 Select the index R with the highest correlation. 2 The formula for a linear function is the optimal formula;

[0020] S33, Predicting Flower Lifespan: Measure the dry weight or puncture force of the flower per unit area, substitute the data into the optimal formula in S32 to calculate the lifespan of the Dendrobium flower to be tested.

[0021] Furthermore, the data processing involves using Excel in conjunction with R language software to read data and writing code to create graphs.

[0022] More preferably, 23 plants from the genus *Dendrobium* (Orchidaceae) were selected, and the optimal formula obtained through data processing was a univariate linear regression equation for the relationship between the dry weight of the flower per unit area of ​​the lateral petals and the flower's lifespan: y = 0.5806x + 4.034, R0. 2 =0.78.

[0023] The application of the Dendrobium lifespan prediction method based on the univariate linear regression equation is characterized by: measuring the dry weight of the flower per unit area of ​​the lateral petals x and plugging it into the optimal formula to calculate the value of the flower lifespan y.

[0024] Compared with the prior art, the present invention has the following features and beneficial effects:

[0025] This invention discloses a rapid method for predicting the lifespan of Dendrobium flowers. The determination of puncture force characterizes the mechanical support strength of petals to explore the relationship between flower lifespan and puncture force; the determination of dry weight characterizes biomass to explore the relationship between flower lifespan and dry weight; and the determination of flower area and dry weight calculates the dry weight per unit area to explore the relationship between flower lifespan and dry weight. By measuring the puncture force, area, and dry weight parameters of the sepals, lateral petals, and lip of the flower, and through subsequent data processing, a correlation formula is derived. Based on this formula, flower lifespan can be quickly predicted, shortening the monitoring time and avoiding the influence of environmental factors when monitoring flower lifespan outdoors, thereby improving the efficiency and accuracy of the experiment. Furthermore, the operation process is simple, easy to understand, and low in cost. Attached Figure Description

[0026] Figure 1 This is a diagram illustrating the data arrangement used in this application for data processing in an Excel spreadsheet.

[0027] Figure 2 The six scatter plots obtained during the specific implementation process are illustrated below.

[0028] Figure 3 A graph of flower lifespan and puncture force plotted using R software;

[0029] Figure 4 A graph showing the relationship between flower lifespan and dry weight of flowers per unit area, plotted using R software.

[0030] Figure 5 A scatter plot showing the correlation between flower lifespan and lateral petal puncture force, generated using R software.

[0031] Figure 6 This is a code diagram for plotting using the R language software. Detailed Implementation

[0032] To make the technical means, innovative features, objectives and effects of this invention easier to understand, the invention will be further described below.

[0033] The embodiments described herein are specific implementations of the present invention, used to illustrate the concept of the invention, and are illustrative and exemplary, and should not be construed as limiting the implementation or scope of the invention. In addition to the embodiments described herein, those skilled in the art can employ other obvious technical solutions based on the content disclosed in the claims and specification of this application. These technical solutions include those that make any obvious substitutions and modifications to the embodiments described herein.

[0034] This application estimates the corresponding characteristics of the population based on the statistical characteristics of sample data. Based on the scatter plot drawn from multiple samples, the linear regression equation can be determined using the data in the scatter plot. The unknown quantity can be inferred from the known quantity based on this equation, providing an important method for estimation and prediction.

[0035] Specifically, this belongs to a method for predicting the lifespan of Dendrobium based on a univariate linear regression equation. The flower lifespan is the total number of days from the day the flower fully opens until any of the following states occur: closure of the lip, or the appearance of yellow spots of aging on the sepals, lateral petals, lip, and sepals. The prediction method specifically includes the following steps:

[0036] S1 Selecting and labeling plants: Select more than 20 species of Dendrobium orchids and label them for data collection. Data collection includes collecting data on the dry weight of flowers per unit area and puncture force of the labeled plants, as well as collecting data on the natural lifespan of flowers through artificial monitoring.

[0037] S2 Data Collection of Flower Dry Weight and Puncture Force per Unit Area: The puncture force of the sepals, lateral petals, and lip of the plant under test was measured using a push-pull force meter. The flower dry weight per unit area of ​​the sepals, lateral petals, and lip was also measured using instruments including a leaf area meter, an oven, and an electronic scale. Three sets of puncture force data and three sets of flower dry weight per unit area data for the sepals, lateral petals, and lip were obtained. Artificial lifespan monitoring was conducted on the plants of the species for which flower dry weight and puncture force data were collected. Several flowers were randomly selected from each plant for natural lifespan monitoring, and the average value was recorded. The seven sets of data were stored in an Excel file.

[0038] S3 Data Processing: The 7 sets of data obtained in step S2 are processed to derive 6 sets of linear function formulas and the correlation index R corresponding to each linear regression equation. 2 Compare 6 groups of R 2 Value, select the one with the largest R 2 The linear function formula of the value is used as a formula for predicting the lifespan of Dendrobium flowers.

[0039] Step S2, which involves measuring the puncture force of the sepals, lateral petals, and lip of the plant under test using a push-pull force gauge, specifically includes the following steps:

[0040] S21 Preparation: Organize the collected Dendrobium orchid samples and select 3 flowers of similar size and shape. Remove the side petals from the 3 flowers, removing one side petal from each flower and marking them with 1-3. Then place them in a pre-prepared petri dish or beaker containing water to rehydrate. If they are not rehydrated, the side petals will lose water too quickly, which will affect the accuracy of data measurement. The purpose of rehydration is to achieve water balance.

[0041] S22 Puncture Measurement and Data Recording: The puncture force of the three lateral lobes was measured sequentially. After rehydration, the impurities and moisture on the surface of the lateral lobes were wiped away. The lateral lobes were placed horizontally on the round hole magnet of the push-pull force gauge at the midpoint between the lateral lobe boundary and the midrib, as well as between the lateral lobe tip and the base. To reduce data error and affect the measurement results, the 0.5 mm diameter puncture needle should avoid the main rib and important secondary ribs in the middle of the lateral lobes when it falls. The maximum force required for the measurement is the lateral lobe puncture force Fp, kN / m. The data were recorded sequentially and the average value was taken. S21-S22 were repeated to collect puncture force data for the sepals and lip.

[0042] Measuring the stem weight per unit area of ​​the sepals, lateral petals, and lip petals using measuring instruments specifically includes the following steps:

[0043] S23 Preparation: Arrange the flowers whose puncture force data has been measured according to their original sequence numbers;

[0044] S24. Measurement and recording of dry weight per unit area of ​​flowers: The petal area of ​​the lateral petals was measured sequentially using a Li-Cor 3000A leaf area meter. After measuring the petal area, the sample of each lateral petal was placed in an envelope according to its serial number, marked, and placed in a 70℃ oven. After 48 hours, the dry weight of the lateral petals was measured sequentially, and the data were recorded and averaged. S23-S24 were repeated to collect data on the dry weight per unit area of ​​flowers for the sepals and lip.

[0045] Method 1, using Excel for data processing, includes the following steps:

[0046] S31, Data Processing: Open the Excel spreadsheet and enter the flower lifespan, puncture force data of sepals, lateral petals, and lip petals, and the dry weight per unit area of ​​sepals, lateral petals, and lip petals into the blank Excel sheet. In the specific implementation of this application, 23 species of the genus *Dendrobium* in the Orchidaceae family were collected as samples for monitoring flower lifespan, and their puncture force and dry weight per unit area were measured respectively; for example... Figure 1 As shown, there are a total of 7 columns of data;

[0047] S32, the linear function formulas were derived: Correlation analysis was performed on the data of flower lifespan and puncture force, as well as the data of flower lifespan and dry weight of flowers per unit area, to calculate six sets of linear function formulas and the corresponding correlation index R. 2Select the index R with the highest correlation. 2 The formula for a linear function is the optimal formula;

[0048] Excel steps: Select the flower lifespan and the dry weight of flowers per unit area in a single column. Select "Insert" from the top menu bar, click "Chart," and then select "Scatter." A scatter chart will appear. Select the scatter chart, right-click, and choose "Add Trendline" from the options. Then, set the trendline format by selecting "Show Equations" and "Show R-squared Values" to obtain the correlation index R. 2 To create a scatter plot of a linear function, repeat the above steps, selecting 5 columns of data each time, and finally produce the following result: Figure 2 The six scatter plots shown represent the puncture force: lateral flap ( =0.494) > sepals ( =0.423) > Lip ( =0.234); while the dry weight of flowers per unit area: side petals ( =0.78) > sepals ( =0.651) > Lip ( =0.266); Comparison of the results revealed a correlation between the dry weight per unit area of ​​the lateral petals and the flower's lifespan ( =0.78) is the highest, and it best reflects the characteristics of the population with the characteristics of the sample, that is, y=0.5806x+4.034 is the optimal formula;

[0049] S33, Predicting Flower Lifespan: Measure the dry weight or puncture force of the flower per unit area, substitute the data into the optimal formula in S32 to calculate the lifespan of the Dendrobium flower to be tested.

[0050] Method two involves using an Excel spreadsheet combined with R language software to read data and write code to create graphs. Specifically:

[0051] 1. Similarly, organize the data in an Excel spreadsheet, listing the data on flower lifespan and puncture resistance, and the data on flower lifespan and dry weight of flowers per unit area, as follows: Figure 3 and 4 Two tables;

[0052] 2. Import the data from Excel into R software, then run the script and write the code within it. After the code is written, run the script to generate the output as shown below. Figure 5 The scatter plot image shown; the code required for the correlation analysis between lateral petal puncture force and flower life is as follows: Figure 6 As shown, based on Figure 6 For other groups of one legend, you need to select data to transform the x-axis. The transformation code is as follows:

[0053] 1. Select Figure 3(In actual use, the data on flower lifespan and puncture force is named Sheet3 in the Excel spreadsheet:)

[0054] mydata=read_xlsx("D:\\1.Paper\\MyRProject\\Flower\\Flowerlongevity.xlsx",sheet="Sheet3",col_names = T)

[0055] head(mydata)

[0056] With the y-axis unchanged, the puncture force data for the sepals along the x-axis are as follows:

[0057] yy=mydata$Floral.longevity

[0058] xx=mydata$sepal

[0059] With the y-axis unchanged, the puncture force data of the lateral flap on the x-axis (and) Figure 6 Consistent):

[0060] yy=mydata$Floral.longevity

[0061] xx=mydata$petal

[0062] With the y-axis unchanged, the puncture force data for the labial flap along the x-axis are as follows:

[0063] yy=mydata$Floral.longevity

[0064] xx=mydata$labellum

[0065] The x and y coordinates of the sepals, lateral petals, and lip are all the same:

[0066] my.ylab="lifespan of the flower (days)"

[0067] my.xlab = expression("Punishment force (kN m"^-1)")

[0068] 2. Select Figure 4 (In actual use, the Excel spreadsheet is named Sheet4) File data on flower lifespan and puncture force:

[0069] mydata=read_xlsx("D:\\1.Paper\\MyRProject\\Flower\\Flowerlongevity.xlsx",sheet="Sheet4",col_names = T)

[0070] head(mydata)

[0071] With the y-axis unchanged, the puncture force data for the sepals along the x-axis are as follows:

[0072] yy=mydata$Floral.longevity

[0073] xx=mydata$sepal

[0074] With the y-axis unchanged, the puncture force data for the lateral flap along the x-axis are as follows:

[0075] yy=mydata$Floral.longevity

[0076] xx=mydata$petal

[0077] With the y-axis unchanged, the puncture force data for the labial flap along the x-axis are as follows:

[0078] yy=mydata$Floral.longevity

[0079] xx=mydata$labellum

[0080] The x and y coordinates of the sepals, lateral petals, and lip are all the same:

[0081] my.ylab="lifespan of the flower (days)"

[0082] my.xlab = expression("Dried flower weight per unit area (gm"^-2*")")

[0083] The R language software used was RStudio, and the six sets of univariate linear regression equations obtained were the same as those generated using Excel alone.

[0084] After obtaining the optimal formula, the dry weight of Dendrobium cat's eye flowers per unit area is 32 (gm). -2 If x=32 is plugged into the formula (y=0.5806X+4.034 R2=0.78), the lifespan of the flower can be predicted as y=22 days.

[0085] Method Two follows the same principle as Method One. The initial concept for the method presented in this application stems from the following: First, in flower research, detecting flower lifespan has always been a challenge for researchers. Monitoring flower lifespan is not only labor-intensive but also requires timed recording. Furthermore, environmental factors can damage flowers before the monitoring period is complete, introducing numerous uncertainties and errors into the experiment. Second, measuring flower puncture force and dry weight per unit area is a mature and simple experimental method. Third, based on the above two points, we aim to establish a quick way to predict flower lifespan using existing experimental techniques. Specifically, since linear regression equations can estimate population characteristics from a sample, we establish linear regression equations using flower lifespan data, puncture force data, and dry weight per unit area data. By comparing their correlation, a higher correlation indicates a closer approximation to the true value R. 2 It measures the ability of a regression equation to explain the variation in observed data; it is the ratio of the regression sum of squares to the total sum of squares. The closer its value is to 1, the better the model fit. Its range is 0 ≤ 1. ≤1.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the lifespan of Dendrobium flowers based on a univariate linear regression equation, characterized in that: The flower lifespan is defined as the total number of days from the day the flower fully opens until any of the following states appear: aging yellow spots on the sepals, lateral petals, lip, and sepals. Based on the statistical characteristics of the sample data, the corresponding characteristics of the population are estimated. A linear regression equation is determined using the data from a scatter plot drawn from multiple samples. The prediction method specifically includes the following steps: S1 Selecting and labeling plants: Select more than 20 species of Dendrobium orchids and label them for data collection. Data collection includes collecting data on the dry weight of flowers per unit area and puncture force of the labeled plants, as well as collecting data on the natural lifespan of flowers through artificial monitoring. S2 Data Collection of Flower Dry Weight and Puncture Force per Unit Area: The puncture force of the sepals, lateral petals, and lip of the plant under test was measured using a push-pull force meter. The flower dry weight per unit area of ​​the sepals, lateral petals, and lip was measured using measuring instruments including a leaf area meter, an oven, and an electronic scale. Three sets of puncture force data and three sets of flower dry weight per unit area of ​​the sepals, lateral petals, and lip were obtained. The plants of the species for which the flower dry weight and puncture force data were collected were then subjected to artificial lifespan monitoring. Several flowers were randomly selected from each plant for natural lifespan testing. The average value was taken and the data was recorded. The resulting seven sets of data were stored in an Excel file. Step S2, where the push-pull force gauge measures the puncture force of the sepals, lateral petals, and lip of the plant to be measured, specifically includes the following steps: S21 Preparation: Organize the collected Dendrobium genus samples, select 3 flowers of similar size and shape, remove the side petals of the 3 flowers, remove one side petal from each flower, and mark them 1-3. Then place them in a pre-prepared petri dish or beaker containing water to rehydrate. S22 Puncture Measurement and Data Recording: The puncture force of the three lateral lobes was measured sequentially. After wiping away the impurities and moisture on the surface of the lateral lobes after rehydration, the lateral lobes were placed horizontally on the round hole magnet of the push-pull force gauge, with the middle position between the lateral lobe boundary and the midrib, and between the lateral lobe tip and the base, ensuring that the 0.5 mm diameter puncture needle avoided the main vein and important secondary veins in the middle of the lateral lobes when it fell. The maximum force required for the measurement is the lateral lobe puncture force Fp, kN / m. The data were recorded sequentially and the average value was taken. S21-S22 were repeated to collect puncture force data for the sepals and lip. Measuring the stem weight per unit area of ​​sepals, lateral petals, and lip petals using measuring instruments specifically includes the following steps: S23 Preparation: Arrange the flowers whose puncture force data has been measured according to their original sequence numbers; S24. Measurement and recording of dry weight per unit area of ​​flowers: The petal area of ​​each lateral petal was measured sequentially using a Li-Cor 3000A leaf area meter. After measuring the petal area, the sample of each lateral petal was placed in an envelope according to its serial number, labeled, and placed in a 70℃ oven. After 48 hours, the dry weight of each lateral petal was measured. The dry weight of the flower was divided by the flower area to obtain the dry weight per unit area of ​​flowers (gm²). -2 After recording the data sequentially, the average value was taken. S23-S24 were repeated to collect the dry weight of the sepals and lip petals per unit area. S3 Data Processing: The seven sets of data obtained in step S2 are processed. Correlation analysis is performed on the data regarding flower lifespan and puncture force, as well as flower lifespan and dry weight per unit area, resulting in six linear function formulas and the corresponding correlation index R for each linear regression equation. 2 Compare 6 groups of R 2 Value, select the one with the largest R 2 The linear function formula for the value is used as a formula for predicting the lifespan of Dendrobium flowers; The data processing includes the following steps: S31, Data Organization: Open the Excel spreadsheet and enter the flower lifespan, sepal, lateral petal, and lip puncture force data, and sepal, lateral petal, and lip unit area flower dry weight data into the blank Excel sheet, for a total of 7 columns of data; S32, the linear function formulas were derived: Correlation analysis was performed on the data of flower lifespan and puncture force, as well as the data of flower lifespan and dry weight of flowers per unit area, to calculate six sets of linear function formulas and the corresponding correlation index R. 2 Select the index R with the highest correlation. 2 The formula for a linear function is the optimal formula; S33, Predicting flower lifespan: Measure the dry weight of the flower per unit area or the puncture force, and substitute the data into the optimal formula in S32 to calculate the flower lifespan of the Dendrobium species to be tested. Specifically, 23 plants from the genus *Dendrobium* in the Orchidaceae family were selected, and the optimal formula obtained through data processing was a univariate linear regression equation for the relationship between the dry weight of the flower per unit area of ​​the lateral petals and the flower's lifespan: y = 0.5806x + 4.034, R0. 2 =0.

78.

2. The method for predicting the lifespan of Dendrobium flowers based on a univariate linear regression equation as described in claim 1, characterized in that: The data processing involves using Excel and R language software to read data and write code to create graphs.

3. The application of the method for predicting the lifespan of Dendrobium flowers based on a univariate linear regression equation as described in claim 1, characterized in that: The measured dry weight x per unit area of ​​the lateral petals is plugged into the optimal formula to calculate the flower lifespan y.

Images