KR20220142956A - Method for predicting growth quantity of paprika - Google Patents

Abstract

The present invention relates to a method for predicting a growth amount of a paprika and more specifically, by using a node number or fruiting number of the paprika, the present invention enables to predict the growth amount of the paprika without resorting to a destructive method. In addition, the present invention relates to the method for predicting the growth amount of the paprika located in a closed system, wherein by using an effective cumulative temperature of the paprika located in the closed system, the growth amount of the paprika existing in the closed system can easily be predicted. The method comprises: a measurement step; and a calculation step.

Description

Method for predicting growth quantity of paprika

The present invention relates to a method for predicting paprika growth.

Smart farm is a technology that creates an environment suitable for crop production environment by collecting weather environment and crop growth information inside and outside the greenhouse and controlling the greenhouse driving device. Smart farm is spotlighted as a core field of the 4th industrial revolution as a cloud-based Internet of Things (IoT) device for increasing crop productivity, a device for collecting crop growth information using sensors, and a convergence technology to data analysis service.

Paprika is a major domestic facility and vegetable item, and it reacts very sensitively to temperature, and irregular productivity deviations occur depending on temperature and light conditions, making it difficult to control the supply and demand of paprika. In order to increase the productivity of paprika, it is necessary to provide an environment suitable for the crop, and big data processing and processing that can diagnose and predict the current state of the weather environment, crop growth information, and control device information generated in the smart farm. The need for technology has increased.

Growth model is an analysis tool between factors of big data collected from smart farms, and it is a technology that can be applied as a decision-making tool for smart farms by analyzing vast amounts of data. do. Smart farm decision-making consists of providing appropriate environmental factors by diagnosing the current crop status based on crop growth information and predicting future growth.

On the other hand, the technology for quantifying and predicting crop growth information for stable production through optimization of crop growth control according to environmental changes of paprika with a long cultivation period is paprika growth prediction technology according to light, temperature, and CO ₂ concentration. However, periodic growth and environmental information collection through the destructive investigation method of crops must be preceded, and the process is very complicated.

However, there is no particular known technique for estimating growth information such as leaf area index using a simple growth index of paprika.

Korean Patent Publication No. 2020-0081583

An object of the present invention is to provide a method for predicting the growth of paprika.

An object of the present invention is to provide a method for predicting the growth of paprika located in a closed system.

1. Measuring the average number of nodes per stem (X1) or the number of fruits per unit area (1 m ² ) (X2) from the paprika individual; and

From at least one of the following Equations 1 to 5, the expected leaf area index (Y1), the estimated leaf growth per unit area (1 m ² ) (Y2), the estimated leaf growth per unit area (1 m ² ) (Y3), and the expected unit area (1 m) ² ) A method of predicting the growth of paprika, comprising: calculating the amount of growth per stem (Y4) or the amount of fruit growth (Y5) per expected unit area (1 m ² ):

[Equation 1]

(In Equation 1, X1 is the average number of nodes per stem of the individual, and Y1 is the expected leaf area index)

[Equation 2]

(In Equation 2, X1 is the average number of nodes per stem of the individual, and Y2 is the expected leaf growth per unit area (1m ² ))

[Equation 3]

(in Equation 3, X1 is the average number of nodes per stem of the individual, and Y3 is the amount of petiole growth per estimated unit area (1m ² ))

[Equation 4]

(In Equation 4, X1 is the average number of nodes per stem of the individual, and Y4 is the amount of stem growth per expected unit area (1m ² ))

[Equation 5]

(In Equation 5, X2 is the number of fruits per unit area (1 m ² ) of the individual, and Y5 is the expected amount of fruit growth per unit area (1 m ² )).

2. Calculating the effective integrated temperature (X3) of the paprika individual located in the closed system; and

Calculating the average number of nodes (Y6) per expected stem of a paprika individual from Equation 6 below; a method of predicting the growth of paprika located in a closed system comprising:

[Equation 6]

(In Equation 6, X3 is the effective integrated temperature for the total growth period after planting of the individual, and Y6 is the average number of nodes per expected stem).

3. In the above 2, the effective integrated temperature (X3) is calculated from the following Equation 7, paprika growth prediction method:

[Equation 7]

는 상기 개체의 정식(定植) 이후 d일째 생육일에 측정된 닫힌 계의 일 평균 기온이고, n은 상기 개체의 정식 이후 총 생육일수임).(of Equation 7,

is the average daily temperature of the closed system measured on the d-day growing day after planting of the individual, and n is the total number of growing days after planting of the individual).

4. In the above 2, by substituting the calculated average number of nodes per stem (Y6) into X1 of any one of Equations 1 to 4 below, the expected leaf area index (Y1), and the estimated leaf area per unit area (1 m ² ) A method of predicting the growth of paprika located in a closed system further comprising the step of calculating the growth (Y2), the growth amount of the petiole per unit area (1m ² ) (Y3), or the growth amount of the stem per expected unit area (1 m ² ) (Y4); :

[Equation 1]

(In Equation 1, X1 is the average number of nodes per stem of the individual, and Y1 is the expected leaf area index)

[Equation 2]

(In Equation 2, X1 is the average number of nodes per stem of the individual, and Y2 is the expected leaf growth per unit area (1m ² ))

[Equation 3]

(in Equation 3, X1 is the average number of nodes per stem of the individual, and Y3 is the amount of petiole growth per estimated unit area (1m ² ))

[Equation 4]

(In Equation 4, X1 is the average number of nodes per stem of the individual, and Y4 is the amount of stem growth per expected unit area (1m ² )).

The paprika growth rate prediction method of the present invention can predict the paprika growth rate without a destructive method by using the number of nodes or fruiting numbers of paprika.

In addition, the method for predicting the growth of paprika located in a closed system of the present invention can easily predict the growth of paprika existing in a closed system by using the effective integrated temperature of paprika located in a closed system such as a greenhouse.

1 to 3 show the average value of the average number of nodes per stem and the average value of the leaf area index of paprika individuals according to the number of growing days of paprika for each test cycle.
4 is a function formula for calculating the leaf area index (Y1) per unit area (1m ² ) of paprika using the average number of nodes (X1) per stem of paprika, which is derived from the data of FIGS. 1 to 3 above. it has been shown
5 to 7 show the average value of the average number of nodes per stem and the average value of the leaf growth per unit area of paprika individuals according to the number of growing days of paprika for each test cycle.
8 is a function formula for calculating the leaf growth amount (Y2) per unit area (1 m ² ) of paprika using the average number of nodes (X1) per stem of paprika, which is derived from the data of FIGS. 5 to 7 . it has been shown
9 to 11 show the average value of the average number of nodes per stem of paprika individuals and the average value of the growth of petioles per unit area according to the number of growing days of paprika for each test cycle.
12 is a function formula for calculating the leaf growth amount (Y3) per unit area (1 m ² ) of paprika using the average number of nodes (X1) per stem of paprika, which is derived from the data of FIGS. 9 to 11 . it has been shown
13 to 15 show the average value of the average number of nodes per stem and the average value of the stem growth amount per unit area of paprika individuals according to the number of growing days of paprika for each test cycle.
16 is a function formula for calculating the stem growth amount (Y4) per unit area (1 m ² ) of paprika using the average number of nodes (X1) per stem of paprika, which is derived from the data of FIGS. 13 to 15 . it has been shown
17 to 19 show the average value of the number of fruits per unit area and the average value of the amount of fruit growth per unit area of paprika individuals according to the number of growing days of paprika for each test cycle.
20 is a diagram derived from the data of FIGS. 17 to 19, and using the number of fruits (number of fruits) (X2) per unit area (1 m ² ) of paprika, the amount of fruit growth per unit area (1 m ² ) of paprika ( The function formula for calculating Y5) is shown.
21 to 23 show the average value of the effective integrated temperature of paprika individuals and the average value of the average number of nodes per stem according to the number of growing days of paprika for each test cycle.
FIG. 24 shows a function formula for calculating the average number of nodes (Y6) per stem of paprika using the effective integrated temperature (X3), which is derived from the data of FIGS. 21 to 23 .
FIG. 25 is a result of using the number of nodes calculated through the function equation of FIG. 24 and the function equation of FIG. 4, and predicts the leaf area index of paprika using the number of nodes according to the average temperature of the greenhouse.

The present invention provides a method for predicting paprika growth.

Paprika growth prediction method of the present invention comprises the steps of measuring the average number of nodes per stem (X1) or the number of fruits per unit area (1m ² ) (X2) from the paprika individual; and

From at least one of the following Equations 1 to 5, the expected leaf area index (Y1), the estimated leaf growth per unit area (1 m ² ) (Y2), the estimated leaf growth per unit area (1 m ² ) (Y3), and the expected unit area (1 m) ² ) calculating the amount of growth per stem (Y4) or the amount of fruit growth (Y5) per expected unit area (1 m ² ); includes:

[Equation 1]

(In Equation 1, X1 is the average number of nodes per stem of the individual, and Y1 is the expected leaf area index)

[Equation 2]

(In Equation 2, X1 is the average number of nodes per stem of the individual, and Y2 is the expected leaf growth per unit area (1m ² ))

[Equation 3]

(in Equation 3, X1 is the average number of nodes per stem of the individual, and Y3 is the amount of petiole growth per estimated unit area (1m ² ))

[Equation 4]

(In Equation 4, X1 is the average number of nodes per stem of the individual, and Y4 is the amount of stem growth per expected unit area (1m ² ))

[Equation 5]

(In Equation 5, X2 is the number of fruits per unit area (1 m ² ) of the individual, and Y5 is the expected amount of fruit growth per unit area (1 m ² )).

The present inventors found that there is a correlation between the number of nodes or fruitings of paprika and the growth of paprika, and by regression analysis, an equation capable of calculating the growth of paprika from the number of nodes or number of fruitings was derived.

Specifically, the present inventors determined that the average number of nodes per stem of paprika and the leaf area index of paprika, the amount of leaf growth per unit area (1 m ² ), the amount of petiole growth per unit area (1 m ² ), and the amount of stem growth per unit area (1 m ² ), respectively A correlation was found and analyzed by exponential linear regression to derive an expression that could calculate the leaf area index, leaf growth, leaf growth, or stem growth of paprika from the number of nodes. In addition, the present inventors found a correlation between the number of fruits per unit area (1 m ² ) of paprika and the amount of fruit growth per unit area (1 m ² ) of paprika. A possible expression was derived.

The number of nodes of paprika means the average number of nodes per stem included in the paprika individual, and includes nodes below the branch of the cultivated stem. The average number of nodes per stem of paprika means the average value of the number of nodes included in each stem included in the paprika individual. For example, if an individual (tree) of paprika contains a total of n stems and a total of m nodes, the average number of nodes per stem of the individual is m/n.

The number of fruits of paprika refers to the number of fruits per unit area (1 m ² ) of the cultivated ground area of paprika, and includes immature fruits with a size of 10 mm or more and no coloring of more than 70%.

The leaf area index of paprika refers to the ratio of the leaf area (m ² ) to the unit area (1 m ² ) of the cultivated ground area of paprika. The leaf, petiole, stem, and fruit of paprika correspond to the organs of paprika, respectively, and the expected leaf growth, expected petiole growth, expected stem growth, and expected fruit growth are the ratio of each organ per unit area (1 m ² ) of paprika cultivation ground area. It means dry weight (g).

Since paprika is a crop with a long growing period, it is necessary to optimize the growth environment by predicting the growth of the crop in order to secure a stable yield. The number of nodes or fruiting numbers of paprika is a growth indicator that can be measured without destructive methods. Therefore, if the growth rate is predicted using the number of nodes or number of fruits of paprika, the variability of the individual can be reduced without damaging the paprika. Accordingly, the growth rate of paprika can be predicted more stably and repeatedly.

The present invention provides a method for predicting the growth of paprika located in a closed system.

The method for predicting the growth of paprika located in a closed system of the present invention comprises the steps of calculating an effective integrated temperature (X3) of paprika individuals located in a closed system; and

Calculating the average number of nodes (Y6) per expected stem of the paprika individual from Equation 6 below; includes:

[Equation 6]

(In Equation 6, X3 is the effective integrated temperature for the total growth period after planting of the individual, and Y6 is the average number of nodes per expected stem).

A closed system refers to a physical system that only exchanges energy without exchanging matter with the outside. The closed system may be a closed cultivation facility, for example, a greenhouse such as a plastic greenhouse or a glass greenhouse.

The present inventors found that there is a correlation between the effective cumulative temperature of paprika located in a greenhouse (that is, a closed system) and the average number of nodes per stem of paprika, and calculated the number of paprika nodes from the effective integrated temperature of paprika by regression analysis. A possible expression was derived.

Specifically, the present inventors found that there is a correlation between the effective integrated temperature of paprika calculated by considering the average temperature and the reference temperature (10°C) of the greenhouse and the average number of nodes per stem of paprika, and analyzed by a linear regression type to analyze the greenhouse An equation for calculating the average number of nodes per stem of paprika was derived from the effective cumulative temperature of paprika located in

The effective integrated temperature (℃·day) is to indicate the amount of heat required for the growth of crops, and is the sum of the daily effective temperature calculated as “daily average temperature-standard temperature” over the number of growing days. In the present invention, in order to predict the average number of nodes per stem of paprika in the greenhouse, the accumulated temperature calculated using the preset average temperature of the greenhouse and the reference temperature of 10°C was used.

The effective integrated temperature (X3) may be calculated from the following Equation 7:

[Equation 7]

는 상기 개체의 정식(定植) 이후 d일째 생육일에 측정된 닫힌 계의 일 평균 기온이고, n은 상기 개체의 정식 이후 총 생육일수임).(of Equation 7,

is the average daily temperature of the closed system measured on the d-day growing day after planting of the individual, and n is the total number of growing days after planting of the individual).

In the method for predicting the growth of paprika located in a closed system of the present invention, the expected leaf area index (Y1), the expected unit area by substituting the calculated average number of nodes per stem (Y6) into X1 of any one of Equations 1 to 4 below (1 m ² ) Calculating the amount of leaf growth per unit area (Y2), the expected unit area (1 m ² ), the leaf growth amount (Y3) per unit area (1 m 2 ), or the stem growth amount (Y4) per expected unit area (1 m ² ); may further include:

[Equation 1]

(In Equation 1, X1 is the average number of nodes per stem of the individual, and Y1 is the expected leaf area index)

[Equation 2]

(In Equation 2, X1 is the average number of nodes per stem of the individual, and Y2 is the expected leaf growth per unit area (1m ² ))

[Equation 3]

(in Equation 3, X1 is the average number of nodes per stem of the individual, and Y3 is the amount of petiole growth per estimated unit area (1m ² ))

[Equation 4]

(In Equation 4, X1 is the average number of nodes per stem of the individual, and Y4 is the amount of stem growth per expected unit area (1m ² ))

The average number of nodes per expected stem Y6 means the average number of nodes per stem calculated through Equation 6 using the effective integrated temperature.

According to the method of the present invention, the average number of nodes per stem of a paprika individual can be predicted from the effective integrated temperature of paprika located in a closed system through Equation 6 above. In addition, from the predicted average number of nodes per stem, the leaf area index of paprika, the leaf growth per unit area (1 m ² ), the leaf growth amount per unit area (1 m ² ), or the stem per unit area (1 m ² ) through Equations 1 to 4 As the growth rate can be predicted, it can be a great help in managing the cultivation of paprika according to changes in the surrounding environment.

Hereinafter, examples will be given to describe the present invention in detail.

Example

<Example 1>

Estimation of paprika growth rate using non-destructive growth indicators

The present inventors used the number of nodes and fruiting numbers of paprika to determine the leaf area index (m ² /m ² ) (leaf area index, LAI), the leaf growth amount (g/m ² ), the petiole growth amount (g/m ² ), and the stem growth amount (g/m ² ), a growth model for predicting fruit growth (g/m ² ) was developed.

A total of 13 tests were performed to develop a growth model, and data were measured multiple times according to the number of days of growth of paprika for each run. And after measuring each value using 6 to 10 paprika trees for each cycle, the average value was used.

Paprika was cultivated for winter crop and high temperature facility. The number of nodes of paprika was visually measured by the number of nodes of the stem, and the nodes below the branch of the cultivated stem were included. And the average number of nodes per stem included in 6 to 10 paprika individuals (trees) randomly selected for each test cycle was obtained, and the average value of the average number of nodes per stem of each individual was used. The number of fruits of paprika was measured by measuring the number of immature fruits with a fruit size of 10 mm or more and not colored more than 70%, and expressed as the number of fruits per unit area (1 m ² ) of paprika cultivation ground area. And the average value of the number of fruits selected for each test cycle was used. The leaf area index of paprika was measured by using a leaf area measuring system (LI-3100C, LI-COR Inc., USA) to measure the leaf area from which the petiole (stalk) was removed, and the average value of the leaf area index of the paprika individuals selected for each test cycle was used. . The leaf, petiole, stem and fruit growth of paprika was measured by measuring the fresh weight on the destructive irradiation day for each experiment, and the dry weight was measured after drying in a dry oven at 70° C. for 72 hours. It was expressed as the amount of growth per unit area (1 m ² ) of the surface area. And the average value of the leaf, petiole, stem and fruit growth of the paprika individuals selected for each test round was used.

The paprika cultivation conditions for each test run are shown in Table 1 below:

kind cultivation facility growing season With or without light source round 1 Sirocco glass greenhouse 2018.08.02~2019.04.01 you round 2 Sirocco glass greenhouse 2018.08.02~2019.04.01 radish round 3 Sirocco glass greenhouse 2018.10.03~2019.04.17 you round 4 Sirocco glass greenhouse 2018.10.03~2019.04.17 radish round 5 Sirocco glass greenhouse 2019.07.17~2020.01.22 you round 6 Cozey glass greenhouse 2019.07.17~2020.01.22 radish round 7 Sirocco glass greenhouse 2019.07.17~2020.01.22 you 8th round Cozey glass greenhouse 2019.07.17~2020.01.22 radish round 9 Sirocco plastic greenhouse 2019.05.21~2019.08.19 you round 10 Sirocco plastic greenhouse 2019.05.21~2019.08.19 you Episode 11 Kessie glass greenhouse 2018.10.16~2019.07.02 radish 12th round Atalante glass greenhouse 2018.10.16~2019.07.02 radish Episode 13 DSP7054 glass greenhouse 2018.10.16~2019.07.02 radish

As will be described later, a function formula was derived by regression analysis of the correlation between the parameters based on the measured paprika node number, leaf area index, leaf growth, leaf growth, and stem growth data. In addition, a functional formula was derived by regression analysis of the correlation between the number of fruits and the amount of fruit growth of paprika.

Specifically, the measured number of nodes, leaf area index, leaf growth, leaf growth, and stem growth data of paprika were analyzed as exponential linear regression type using SPSS software package V27 (SPSS Inc., USA) to derive a functional formula. The measured paprika fruit growth data was analyzed as a linear regression type using SPSS software package V27 (SPSS Inc., USA) to derive a functional formula.

The measured data and the functional formula derived therefrom are as follows.

(1) Paprika leaf area index

1 to 3 show the average value of the average number of nodes per stem and the average value of the leaf area index of paprika individuals according to the number of growing days of paprika for each test cycle. The following Equation 1 is a functional formula derived by regression analysis of the data of FIGS. 1 to 3, and the leaf area index per unit area (1 m ² ) of the cultivated ground area of paprika using the average number of nodes (X1) per stem of paprika ( A function expression for calculating Y1) is shown (FIG. 4).

[Equation 1]

(In Equation 1, X1 is the average node per stem of paprika)

In Equation 1, the independent variable X1 is the average number of nodes per paprika stem, and the dependent variable Y1 is the leaf area index of the paprika.

(2) Paprika leaf growth

5 to 7 show the average value of the average number of nodes per stem and the average value of the leaf growth per unit area of paprika individuals according to the number of growing days of paprika for each test cycle. The following Equation 2 is a functional formula derived by regression analysis of the data of FIGS. 5 to 7, and using the average number of nodes (X1) per stem of paprika, the amount of leaf growth per unit area (1 m ² ) of the cultivated ground area of paprika ( A function expression for calculating Y2) is shown (FIG. 8).

[Equation 2]

(In Equation 2, X1 is the average number of nodes per stem of the subject)

In Equation 2, the independent variable X1 is the average number of nodes per stem of paprika, and the dependent variable Y2 is the amount of leaf growth per unit area (1m ² ) of paprika.

(3) Paprika petiole growth amount

9 to 11 show the average value of the average number of nodes per stem of paprika individuals and the average value of the growth of petioles per unit area according to the number of growing days of paprika for each test cycle. The following Equation 3 is a functional formula derived by regression analysis of the data of FIGS. 9 to 11, and using the average number of nodes (X1) per stem of paprika, the amount of leaf growth per unit area (1 m ² ) of the cultivated ground area of paprika ( A function expression for calculating Y3) is shown (FIG. 12).

[Equation 3]

(in Equation 3, X1 is the average number of nodes per stem of the subject)

In Equation 3, the independent variable X1 is the average number of nodes per paprika stem, and the dependent variable Y3 is the amount of petiole growth per unit area (1m ² ) of paprika.

(4) Paprika stem growth amount

13 to 15 show the average value of the average number of nodes per stem and the average value of the stem growth amount per unit area of paprika individuals according to the number of growing days of paprika for each test cycle. The following Equation 4 is a functional formula derived by regression analysis of the data of FIGS. 13 to 15, and the amount of stem growth per unit area (1m ² ) of the cultivated ground area of paprika using the average number of nodes (X1) per stem of paprika ( A function expression for calculating Y4) is shown (FIG. 16).

[Equation 4]

(in Equation 4, X1 is the average number of nodes per stem of the subject)

In Equation 4, the independent variable X1 is the average number of nodes per paprika stem, and the dependent variable Y4 is the stem growth amount per unit area (1m ² ) of paprika.

(5) Paprika fruit growth

17 to 19 show the average value of the number of fruits per unit area and the average value of the amount of fruit growth per unit area of paprika individuals according to the number of growing days of paprika for each test cycle. The following Equation 5 is a function formula derived by regression analysis of the data of FIGS. 17 to 19, and the number of fruits (number of fruits) per unit area (1 m ² ) of the cultivated ground area of paprika (X2) is used as a cultivation index of paprika. A functional formula for calculating the amount of fruit growth (Y5) per unit area (1 m ² ) of the area is shown ( FIG. 20 ).

[Equation 5]

(In Equation 5, X2 is the number of fruits per unit area (1m ² ) of the subject)

In Equation 5, the independent variable X2 is the number of fruits per unit area (1 m ^{2 ) of paprika, and the dependent variable Y5 is the amount of fruit growth per unit area (1 m 2 )} of paprika.

<Example 2>

Estimation of the growth rate of paprika according to the temperature environment of the facility horticulture smart farm

The present inventors derived a function formula that can calculate the node appearance rate of paprika according to the temperature environment of the smart farm. Through this, the growth rate of paprika can be quantified, and the growth trend of paprika can be visualized in the future by predicting the growth amount of the paprika leaf area index.

Specifically, the present inventors derived a function expression by regression analysis of the correlation between the integration temperature and the number of nodes after the formulation of paprika, and calculated the node appearance rate according to the integration temperature of paprika. The integrated temperature is the sum of the daily effective temperatures during the growing period.

The integration temperature of paprika was calculated using 10℃ as the base temperature (Marcelis et. al., 2006, Acta Horticulturae 718: 121-128), and the appearance rate of nodes was determined per 1℃ of integration temperature. Indicates the speed of node appearance.

The measured paprika integrated temperature and node number data were analyzed in a linear regression type using SPSS software package V27 (SPSS Inc., USA) to derive a functional formula.

The measured data and the functional formula derived therefrom are as follows.

21 to 23 show the average value of the effective integrated temperature of paprika individuals and the average value of the average number of nodes per stem according to the number of growing days of paprika for each test cycle. Equation 6 below is a function expression derived by regression analysis of the data of FIGS. 21 to 24, and shows a function expression for calculating the average number of nodes (Y6) per stem of paprika using the integrated temperature (X3) (FIG. 24) ).

[Equation 6]

(In Equation 6, X3 is the effective integrated temperature (℃·d) for the total growth period after planting of the individual)

In Equation 6, the independent variable X3 is the effective integrated temperature considering the reference temperature, and the dependent variable Y6 is the average number of nodes per stem of paprika.

<Example 3>

Prediction of paprika growth according to the estimated paprika growth rate

According to the greenhouse temperature management standards, the integrated temperature of paprika and the growth rate of paprika change dynamically, and the rate of leaf area expansion varies as the rate of node appearance changes. The leaf area index of paprika affects the amount of transpiration, and as the appropriate level of environmental factors to create an appropriate transpiration environment changes, the nutrient solution supply method to control the water content in the root zone must be changed.

The present inventors predicted the leaf area index of paprika using the number of nodes calculated through Equation 6 of Example 2 and Equation 1 above. 25 is a prediction of the leaf area index of paprika using the node appearance rate according to the average temperature of the greenhouse, and the average temperature after planting in the greenhouse was managed at 20 ° C, 22 ° C, 24 ° C, and 26 ° C, respectively.

The appearance rate of nodes is different depending on the average temperature of the greenhouse, and the time when the leaf area of paprika is sufficiently secured is also different according to the appearance rate of nodes. Thus, decision-making information for greenhouse growth management such as temperature and irrigation can be provided.

Claims (4)

Measuring the average number of nodes per stem (X1) or the number of fruits per unit area (1 m ² ) (X2) from the paprika individual; and
From at least one of the following Equations 1 to 5, the expected leaf area index (Y1), the estimated leaf growth per unit area (1 m ² ) (Y2), the estimated leaf growth per unit area (1 m ² ) (Y3), and the expected unit area (1 m) ² ) A method of predicting the growth of paprika, comprising: calculating the amount of growth per stem (Y4) or the amount of fruit growth (Y5) per expected unit area (1 m ² ):
[Equation 1]

(In Equation 1, X1 is the average number of nodes per stem of the individual, and Y1 is the expected leaf area index)
[Equation 2]

(In Equation 2, X1 is the average number of nodes per stem of the individual, and Y2 is the expected leaf growth per unit area (1m ² ))
[Equation 3]

(in Equation 3, X1 is the average number of nodes per stem of the individual, and Y3 is the amount of petiole growth per estimated unit area (1m ² ))
[Equation 4]

(In Equation 4, X1 is the average number of nodes per stem of the individual, and Y4 is the amount of stem growth per expected unit area (1m ² ))
[Equation 5]

(In Equation 5, X2 is the number of fruits per unit area (1 m ² ) of the individual, and Y5 is the expected amount of fruit growth per unit area (1 m ² )).
Calculating the effective integrated temperature (X3) of the paprika object located in the closed system; and
Calculating the average number of nodes (Y6) per expected stem of a paprika individual from Equation 6 below; a method of predicting the growth of paprika located in a closed system comprising:
[Equation 6]

(In Equation 6, X3 is the effective integrated temperature for the total growth period after planting of the individual, and Y6 is the average number of nodes per expected stem).
The method according to claim 2, wherein the effective integrated temperature (X3) is calculated from the following equation (7), paprika growth prediction method:
[Equation 7]

(of Equation 7,

is the average daily temperature of the closed system measured on the d-day growing day after planting of the individual, and n is the total number of growing days after planting of the individual).
The method according to claim 2, wherein by substituting the calculated average number of nodes per stem (Y6) into X1 of any one of Equations 1 to 4 below, the expected leaf area index (Y1) and the expected leaf growth per unit area (1 m ² ) ( Y2), calculating the leaf growth amount (Y3) per expected unit area (1 m ² ) or the stem growth amount (Y4) per expected unit area (1 m ² ) A method of predicting the growth of paprika located in a closed system further comprising:
[Equation 1]

(In Equation 1, X1 is the average number of nodes per stem of the individual, and Y1 is the expected leaf area index)
[Equation 2]

(In Equation 2, X1 is the average number of nodes per stem of the individual, and Y2 is the expected leaf growth per unit area (1m ² ))
[Equation 3]

(in Equation 3, X1 is the average number of nodes per stem of the individual, and Y3 is the amount of petiole growth per estimated unit area (1m ² ))
[Equation 4]

(In Equation 4, X1 is the average number of nodes per stem of the individual, and Y4 is the amount of stem growth per expected unit area (1m ² )).

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