WO2022182190A1

WO2022182190A1 - System and method for determining sync capacity and source capacity

Info

Publication number: WO2022182190A1
Application number: PCT/KR2022/002772
Authority: WO
Inventors: 조진형
Original assignee: 농업회사법인 아이오크롭스 주식회사
Priority date: 2021-02-26
Filing date: 2022-02-25
Publication date: 2022-09-01

Abstract

The present invention according to an embodiment is a method for controlling a greenhouse environment, the method comprising the steps of: acquiring at least one image of a crop; extracting a first feature, a second feature, and a third feature included in the at least one acquired image; calculating a first parameter on the basis of at least one feature among the first to third features; calculating a second parameter on the basis of at least one feature among the first to third features; calculating a third parameter on the basis of the first and second parameters; and controlling the greenhouse environment on the basis of the third parameter.

Description

A system and method for determining sink capacity and source capacity.

The present invention relates to a system and method for determining sink capacity and source capacity of a crop, and more particularly, to a system and method for determining a sink and source of a crop based on fruit information and leaf information of a crop in a crop image.

A smart farm is an intelligent farm created by combining information and communication technology with farming technology.

Among smart farms, a greenhouse is a space equipped with a system that can provide a uniform environment in relation to crop growth. In order for such a greenhouse to provide a uniform environment, a plurality of sensors required for environmental control are required.

A plurality of sensors provided in the greenhouse include a temperature sensor, a CO2 concentration sensor, and the like, and the plurality of sensors can measure the environment in the greenhouse. The greenhouse is controlled to provide a uniform environment inside the greenhouse through the measured values measured through the sensor.

Among the values measured through the sensor, there are values related to the sink and source capacity based on the photosynthetic amount and respiration amount of the crop itself, and these values are limited to the CO2 concentration and temperature in the greenhouse. In addition, since a complex process is required to determine the sink and source capacity of crops, there is a problem in that time and cost are consumed.

Therefore, it is necessary to determine the sink and source capacity of the crop in a more convenient way, and a method is needed to determine the sink and source capacity of the crop differently according to the growth of the crop.

One object of the present invention is to provide a system and method for determining the sink capacity and source capacity of crops in a more convenient way for the user.

Another object of the present invention is to provide a system and method for determining a sink capacity by using different weights according to the size of fruit.

Another object of the present invention is to provide a system and method for determining a sink capacity by using different weights according to the height at which fruits are located.

Another object of the present invention is to provide a system and method for determining the sink capacity according to the height of the leaves of a crop.

According to an embodiment, a method for determining a sink capacity of a crop in a crop image includes: receiving a crop image including fruits and leaves; obtaining fruit information including a location, size, and maturity level of a fruit based on the crop image; and calculating a sink capacity of a crop based on the fruit information. wherein the calculating of the sink capacity comprises: a total sink capacity of the crop based on a first sink capacity for a first fruit having a first size and a second sink capacity for a second fruit having a second size is determined, the first sink capacity of the first fruit is calculated based on a weight of a first size corresponding to the first size, and the second sink capacity of the second fruit corresponds to the second size. It is calculated based on a second size weight, wherein the first size is greater than the second size, and the first size weight is greater than the second size weight.

A system for determining a sink capacity and a source capacity of a crop according to an embodiment includes: a measuring device for acquiring a crop image including fruits and leaves of a crop; and a server for calculating a sink capacity and a source capacity of the crop based on fruit information and leaf information calculated based on the crop image. wherein the fruit information includes at least one of location information, size information, and ripeness information of the fruit, and the sink capacity includes a location weight corresponding to the location information of the fruit and a location weight corresponding to the size information. It is calculated based on the size weight and the maturity weight corresponding to the maturity level information.

The method for controlling a greenhouse environment according to an embodiment includes acquiring at least one image of a crop, extracting a first feature, a second feature, and a third feature included in the acquired at least one image, the first calculating a first parameter based on at least one of the first to third characteristics, calculating a second parameter based on at least one of the first through third characteristics, and the first and second parameters Calculating a third parameter based on , calculating a second sub-parameter based on the second characteristic, and calculating the first parameter based on at least the first sub-parameter and the second sub-parameter, wherein the second parameter The calculating comprises calculating a third sub-parameter based on the third characteristic and calculating the second parameter based on at least the third sub-parameter, and based on the third parameter, the greenhouse The controlling of the environment may include comparing the third parameter with a reference value, and at least one included in the first group greenhouse control operation or the second group greenhouse control operation based on the comparison result of the third parameter and the reference value. and performing a control operation of , wherein the first characteristic is a characteristic corresponding to a flower of a crop, the second characteristic is a characteristic corresponding to a fruit of a crop, and the third characteristic is a characteristic corresponding to a leaf of a crop may be characteristic.

According to the present invention, the user can be provided with the sink capacity and the source capacity of the crop based on the crop image rather than a complicated process.

According to the present invention, the user can calculate the sink capacity of the crop through different weights according to the size of the fruit.

According to the present invention, it is possible to provide a smart farm system in which crops can grow effectively by calculating and controlling the sink capacity of crops through different weights according to the height at which the fruits are located to the user.

1 is a schematic diagram of a greenhouse system according to an embodiment.

2 is a block diagram illustrating a measuring device according to an exemplary embodiment.

3 is a block diagram illustrating a server according to an embodiment.

4 is a block diagram of a system for measuring sink capacity and source capacity according to an embodiment.

5 is a flowchart illustrating a method for measuring sink capacity and source capacity according to an embodiment.

6 is a view of a crop image processing process according to an embodiment.

7 is a flowchart illustrating a method of calculating a sink capacity according to an embodiment.

8 is a view for explaining a method of specifying fruits and leaves of a crop in a measuring device according to an exemplary embodiment.

9 is a flowchart illustrating a method of calculating a sink capacity and a source capacity based on leaf information according to an embodiment.

10 to 12 are diagrams for explaining a reference position according to an embodiment.

13 is a flowchart illustrating a method of calculating total sink capacity and source capacity according to an embodiment.

14 is a view of a crop image according to an embodiment.

15 is a flowchart illustrating a method of calculating a crop source capacity and a sink capacity according to an embodiment.

16 is a view for explaining a crop image according to an embodiment.

17 is a diagram for explaining a parameter calculation system for controlling a greenhouse according to an embodiment.

18 is a diagram for explaining a parameter calculation system for controlling a greenhouse according to an embodiment.

The above-mentioned objects, features and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. However, since the present invention may have various changes and may have various embodiments, specific embodiments will be exemplified in the drawings and described in detail below.

In the drawings, the thicknesses of layers and regions are exaggerated for clarity, and also that an element or layer is “on” or “on” another component or layer. What is referred to includes all cases in which another layer or other component is interposed in the middle as well as directly on top of another component or layer. Throughout the specification, like reference numerals refer to like elements in principle. In addition, components having the same function within the scope of the same idea shown in the drawings of each embodiment will be described using the same reference numerals.

If it is determined that a detailed description of a known function or configuration related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. In addition, numbers (eg, first, second, etc.) used in the description process of the present specification are merely identification symbols for distinguishing one component from other components.

In addition, the suffixes "module" and "part" for the components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have distinct meanings or roles by themselves.

According to an embodiment, a method for determining a sink capacity of a crop in a crop image includes: receiving a crop image including fruits and leaves; obtaining fruit information including a location, size, and maturity level of a fruit based on the crop image; and calculating a sink capacity of a crop based on the fruit information. wherein the calculating of the sink capacity comprises: a total sink capacity of the crop based on a first sink capacity for a first fruit having a first size and a second sink capacity for a second fruit having a second size is determined, the first sink capacity of the first fruit is calculated based on a weight of a first size corresponding to the first size, and the second sink capacity of the second fruit corresponds to the second size. It is calculated based on a second size weight, wherein the first size is greater than the second size, and the first size weight is smaller than the second size weight.

In the calculating of the sink capacity, the total sink capacity is determined by further considering a third sink capacity for a third fruit having a first location and a fourth sink capacity for a fourth fruit having a second location, and The third sink capacity of three fruits is calculated based on a third position weight corresponding to the first position, and the fourth sink capacity of the fourth fruits is calculated based on a fourth position weight corresponding to the second position. , the third position may be higher than the fourth position, and the third position weight may be greater than the fourth position weight.

obtaining leaf information about the leaf based on the crop image; may further include.

In the calculating of the sink capacity, the total sink capacity is determined by further considering a fifth sink capacity based on the leaf information, and the fifth sink capacity is calculated by assigning a fifth weight to some leaves of the crop. can

The fifth sink capacity may be calculated for leaves positioned below a predetermined reference position from the ground of the crop.

The step of receiving the crop image including the fruit and leaves may further include specifying at least one crop from the crop image, and specifying one of a plurality of fruits and one of a plurality of leaves included in the crop. can

The maturity information may be determined based on color information based on the RGB Color Histogram of the crop image.

The ripening level information may be aging level information determined based on the number of red pixels in the RGB Color Histogram.

The maturity weight includes a first maturity weight and a second maturity weight, wherein the first maturity weight corresponds to the first maturity information and the second maturity weight corresponds to the second maturity information; , when the first ripeness information has a greater number of red pixels than the second ripeness information, the first ripeness weight may be greater than the second ripeness weight.

The crop image may include a growth point of the crop, and the location information may be distance information based on the growth point.

the location information includes first location information and second location information, the location weight includes a first location weight and a second location weight, the first location information and the first location weight correspond to each other; The second location information and the second location weight may correspond to each other, and when the first location information is greater than the second location information, the first location weight may be smaller than the second location weight.

The measuring device may generate a panoramic crop image in which at least two or more crop images are connected, and the at least two or more crop images may each be an image of a crop.

The server divides a crop area and a target area for the crop image, calculates a green area for the target area, the server applies a weight for each target area to the target area, and the weight for each target area Total source capacity and sink capacity may be calculated based on the information on the target area and the green area.

The server may extract key point information from the crop image, and calculate a crop source capacity and sink capacity based on the key point information and the total source capacity and sink capacity.

According to an embodiment, a method for controlling a smart farm based on a sink capacity and a source capacity of a crop may include: receiving an image of a crop including fruits and leaves; obtaining fruit information including a location, size, and maturity level of a fruit based on the crop image; obtaining leaf information including a leaf of a fruit based on the crop image; calculating a sink capacity of a crop based on the fruit information and the leaf information; calculating a source capacity of a crop based on the leaf information; and controlling the smart farm based on the sink capacity and the source capacity. includes

The controlling of the smart farm may include adjusting the temperature in the smart farm to a preset value when the sink capacity is greater than the source capacity.

The controlling of the smart farm may include controlling the temperature in the smart farm to increase when the sink capacity is greater than the source capacity.

The step of controlling the smart farm may include controlling the temperature in the smart farm to increase so that the deviation from the current temperature of the smart farm is larger when the difference between the sink capacity and the sink capacity is large. .

The leaf information may include a plurality of leaf information, a sink capacity may be calculated based on at least some of the leaves among the plurality of leaves, and a source capacity may be calculated based on a remaining part of the plurality of leaves.

The calculating of the sink capacity of the crop may include calculating the sink capacity based on leaves positioned below a reference position in a total height from the ground of the crop.

The reference position may be changed according to a change in the total height of the crop.

The reference position may be maintained at a constant position even if the total height of the crop is changed.

The step of controlling the smart farm may output a message to remove leaves based on the sink capacity and the source capacity.

The controlling of the smart farm may output a message for removing leaves located below the reference position based on the sink capacity and the source capacity.

The controlling of the smart farm may output a message to remove more leaves as the sink capacity is greater than the source capacity and the difference between the sink capacity and the source capacity is greater.

The controlling of the smart farm may be a step of outputting a message instructing harvesting of at least some of the fruits of the crop based on the sink capacity and the source capacity.

The controlling of the smart farm may be a step of outputting a message designating at least one of a location and number of fruits to be harvested according to a deviation between the sink capacity and the source capacity when the sink capacity is greater than the source capacity.

A smart farm control system according to an embodiment includes: a measuring device for acquiring a crop image including fruits and leaves of a crop; and a server for calculating a sink capacity and a source capacity of the crop based on information calculated based on the fruit information and leaf information. includes

The crop image is a crop image including at least one crop, the server distinguishes a crop area and a non-crop area with respect to the crop image, and extracts a target area from the crop area, and the server is located in the target area. Based on the weight of the target area for , the total crop sink capacity and source capacity may be calculated.

The measuring device may recognize a crop feature point in the crop image.

The crop characteristic point may include the growth point of the crop.

The server may calculate a crop sink capacity and a source capacity based on the number of the crop feature points.

The server may output a control message to harvest at least one or more fruits based on the fruit information.

The server may output a control message to remove at least one or more leaves based on the leaf information.

Here, when the third parameter is equal to or greater than the reference value, at least one control operation included in the second group greenhouse control operation is performed, and when the third parameter is less than the reference value, it is included in the first group greenhouse control operation At least one control operation may be performed.

Here, the first weight for calculating the first sub-parameter may be greater than the second weight for calculating the second sub-parameter.

Here, the second weight for calculating the second sub-parameter may be changed based on the magnitude of the second feature.

Here, the second weight for calculating the second sub-parameter may increase as the size of the second feature decreases.

Here, the first group greenhouse control operation is an operation of changing the standard of fruit removal to remove more fruits in order to reduce the number of remaining fruits per one room, and at least one fruit to reduce the number of remaining fruits per one room. The action of deciding to remove Changing the standard of leaf removal so that there are 3 leaves in the , controlling the greenhouse to reduce the temperature and humidity deviation to induce vegetative growth by giving less stress to the crop, and reducing the stress on the crop to induce vegetative growth. The operation of controlling the greenhouse to reduce the deviation, the operation of controlling the greenhouse so that the average temperature of the greenhouse is lowered to reduce the respiration rate of crops, the operation of transmitting the guide message for the above-mentioned control, the operation of outputting a notification for the above-mentioned control It may include at least one of the operations.

Here, the second group greenhouse control operation is an operation of changing the standard of fruit removal so as to remove fewer fruits in order to increase the number of remaining fruits per one flower room, and a leaf so that the number of leaves to be left on a new side branch is reduced at the upper end. Changing the standard of removal, changing the standard of leaf removal so that 0 to 1 leaves are left on the new geodesic in the upper part control operation, controlling the greenhouse to increase the moisture content deviation in order to induce reproductive growth by applying more stress to the crops; It may include at least one of an operation of transmitting a guide message for , and an operation of outputting a notification for the aforementioned control.

Here, the first parameter may be a parameter corresponding to the sink capacity, and the second parameter may be a parameter corresponding to the source capacity.

The method for controlling a greenhouse environment according to an embodiment includes acquiring at least one image of a crop, extracting a first feature, a second feature, and a third feature included in the acquired at least one image, the first to calculating a first parameter based on at least one characteristic of a third characteristic, calculating a second parameter based on at least one characteristic of the first to third characteristics, and the first parameter and the second characteristic A method comprising controlling a greenhouse environment based on a parameter, wherein the calculating of the first parameter includes calculating a first sub-parameter based on the first characteristic, and a second sub-parameter based on the second characteristic. and calculating the first parameter based on at least the first sub-parameter and the second sub-parameter, wherein the calculating of the second parameter comprises: calculating a sub-parameter and calculating the second parameter based on at least the third sub-parameter, wherein controlling the greenhouse environment based on the first parameter and the second parameter comprises the first performing a first group greenhouse control operation when the parameter is greater than the second parameter and performing a second group greenhouse control operation when the second parameter is greater than the first parameter, the first characteristic may be a characteristic corresponding to a flower of a crop, the second characteristic may be a characteristic corresponding to a fruit of a crop, and the third characteristic may be a characteristic corresponding to a leaf of a crop.

Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. However, the spirit of the present invention is not limited to the presented embodiment, and those skilled in the art who understand the spirit of the present invention may add, change, delete, etc. other elements within the scope of the same spirit, through addition, change, deletion, etc. Other embodiments included within the scope of the invention may be easily suggested, but this will also be included within the scope of the invention.

1 is a schematic diagram of a greenhouse system according to an embodiment.

Referring to FIG. 1 , a greenhouse system 1 according to an embodiment may include a greenhouse 2 , a measurement device 20 , and a server 30 .

The greenhouse 2 may be a space in which crops are grown. The greenhouse 2 may be a space in which a system capable of measuring basic information inside the greenhouse, such as humidity and temperature inside the greenhouse for the growth of crops, is built. In addition, the greenhouse 2 may be a space in which a system capable of controlling the inside of the greenhouse is built based on the measured internal basic information.

In the drawings and detailed description, the greenhouse 2 has been described as an example, but the greenhouse 2 may be a smart farm. That is, the greenhouse system 1 according to the embodiment may be applied to a space such as an open field rather than a closed space such as the greenhouse 2 .

The crop 10 may be a plant capable of growing in the greenhouse 2 . The crop 10 may be a plant grown in soil or a plant grown through a nutrient solution. For example, the crop 10 may be a tomato, paprika, or the like.

The crops 10 may be arranged along rows parallel to each other in the greenhouse 2 . That is, the crop 10 may be classified according to heat and grown in a greenhouse.

The crop 10 may include a first row crop 11 , a second row crop 12 , and a third row crop 13 . The crops 11 in the first row may be disposed and grown in the first row, the crops 12 in the second row may be disposed and grown in the second row, and the crops 13 in the third row may be disposed and grown in the third row can be

Crops 10 may be grown at regular intervals in each row. The crop 10 may move at regular intervals within the row as it grows. The crop 10 may move at regular intervals within a row along the bait line.

The measuring device 20 is movable within the greenhouse 2 . The measuring device 20 can move along between rows in which crops are grown. The measuring device 20 may move between the first row crop 11 and the second row crop 12 , and between the second row crop 12 and the third row crop 13 . The measuring device 20 may move through a rail installed between the crops in the first row 11 and the crops 12 in the second row, and between the crops in the second row 12 and the crop 13 in the third row, or without rails. It can move while maintaining a certain distance between crops.

The measuring device 20 may acquire an image of a crop including fruits and leaves in the greenhouse 2 . The measuring device 20 may acquire a crop image while moving in the greenhouse 2 . The measuring device 20 moves between the crops in the first row 11 and the crops in the second row 12 and between the crops in the second row 12 and the crop 13 in the third row, and crops images including fruits and leaves. can be obtained. The measuring device 20 may acquire a crop image while maintaining a constant speed, and may acquire a crop image at a fixed position.

The measuring device 20 may acquire crop information included in the crop image based on the crop image. The crop information may include at least one of a position, size, maturity, and leaf position of a fruit. The position of the fruit may be a height at which the fruit is located from the ground, and the position of the leaf may also be a height at which the leaf is located from the ground.

The measuring device 20 may transmit the crop information to the server 30 .

The server 30 may determine the growth information of the crop based on the crop information. The server 30 may determine the growth information of crops by applying different weights to the crop information. The weight may be determined according to the size and position of the fruit included in the crop, and the position of the leaf included in the crop.

The growth information may include at least one of a sink capacity and a source capacity of a crop. The source capacity may mean a capacity at which essential nutrients are obtained, and the sink capacity may mean a capacity at which nutrients are used.

In addition, the sink capacity may mean a capacity difference between the respiratory rate and the photosynthetic amount when the crop's respiration rate is greater than the photosynthetic amount by comparing the crop's respiration rate and the photosynthetic amount. The source capacity compares the photosynthetic amount and the respiration rate of the crop, and when the photosynthetic amount of the crop is greater than the respiration rate, it may mean a difference capacity between the photosynthetic amount and the respiration rate.

The server 30 may control the greenhouse 2 based on the growth information. The greenhouse control may include at least one of a determination to harvest fruit of a crop, a determination to remove leaves of a crop, and a determination to control the temperature in the greenhouse 2 based on the growth information.

In addition, for example, the greenhouse control may include a change in fruit removal criteria, a change in the number of fruits to be left per one flower room, a change in the number of leaves to be left on a side branch, a change in temperature and humidity deviation, a change in moisture content deviation, a change in the greenhouse It may include at least one of controlling the temperature.

The following is a detailed description of the measuring device 20 .

Referring to FIG. 2 , the measurement apparatus 20 according to an embodiment may include an image acquisition unit 21 , a control unit 22 , and a storage unit 23 .

The image acquisition unit 21 may be implemented as at least one camera.

The image acquisition unit 21 may acquire an image. The image acquisition unit 21 may acquire a plurality of images. The image may be an image including at least one of fruits and leaves of crops grown in a greenhouse. The image may be an image including a plurality of crops grown in a greenhouse, or the image may be an image including an inducer line installed for each of the plurality of crops.

The image acquisition unit 21 may transmit the acquired image to the control unit 22 or the storage unit 23 .

The controller 22 may determine crop information based on the image. The crop information may include at least one of a position, size, maturity, and leaf position of a fruit. The crop information may include location information of a fruit included in the crop, information on the size of the fruit, information on the maturity level of the fruit, and location information of a leaf included in the crop. The location information of the fruit and the location information of the leaves are information on the vertical height of the fruit from the ground, information on the vertical distance spaced apart from the specific location of the ceiling in the greenhouse, It may be information about distance.

The control unit 22 may transmit the crop information to the storage unit 23 or the server 30 .

The storage unit 23 may store the image acquired by the image acquisition unit 21 . The storage unit 23 may store crop information determined by the control unit 22 . The storage unit 23 may transmit the crop information to the server 30 .

Hereinafter, a detailed description of the server will be provided.

3 is a block diagram illustrating a server according to an embodiment.

Referring to FIG. 3 , the server 30 may include a control unit 31 and a storage unit 32 .

The control unit 31 may determine the growth information of the crop based on the crop information received from the measuring device 20 . The control unit 31 may determine the growth information of the crop by using at least one weight for the crop information.

The growth information of the crop may include at least one of a sink capacity and a source capacity of the crop.

The control unit 31 may determine the growth information of the crop by using different weights for the crop information. For example, the controller 31 may determine the growth information of the crop based on different weights according to the fruit size of the crop among the crop information.

Alternatively, the control unit 31 may determine the growth information of the crop according to the position of the leaf among the crop information. For example, the controller 31 may determine that at least one leaf is a sink capacity or a source capacity according to a location based on location information of the leaf from the ground.

The control unit 31 may determine information about the control inside the greenhouse based on the growth information. The information on the control inside the greenhouse may be information about controlling the temperature inside the greenhouse by comparing the sink capacity and the source capacity. Alternatively, the information on the control inside the greenhouse may be information on the determination to harvest the fruit of the crop by comparing the sink capacity and the source capacity, and information about the determination to remove the leaves of the crop by comparing the sink capacity and the source capacity may be

The control unit 31 may transmit growth information to the storage unit 32 . The control unit 31 may transmit information related to the control of the inside of the greenhouse to the storage unit 32 .

The storage unit 32 may store the growth information. The storage unit 32 may store information about the control inside the greenhouse. The storage unit 32 may transmit the growth information and information on the control inside the greenhouse to the control unit 31 .

Hereinafter, a specific method for determining the growth information of a crop based on the aforementioned fruit information will be described in more detail with reference to FIG. 4 .

4 and 5 are a block diagram of a system for measuring sink capacity and source capacity and a flowchart of a method for measuring sink capacity and source capacity according to an embodiment.

Referring to FIG. 4 , the measuring device 20 may acquire an image 100 , and extract fruit information 210 and leaf information 220 based on the acquired image 100 .

The image 100 may be an image including at least one fruit. Alternatively, the image 100 may be an image of a crop including at least one fruit and at least one leaf.

The image 100 may be a plurality of images related to one crop. For example, the image 100 may be a panoramic image in which several images corresponding to one crop are stitched together. The process of concatenating a plurality of images and processing them into one image will be described later with reference to FIG. 6 .

The leaf may refer to all the leaves formed on the stem extending from the largest original branch to the side branch in the crop. Alternatively, the leaf may refer to one of a plurality of leaves formed on the stem extending from the side branch.

The image 100 may include at least one crop or an image including only one crop.

The image 100 may be an image acquired by the measuring device in a specific area in the greenhouse. The image 100 may be an image including a cluster area of crops in a greenhouse.

The image 100 may be an image processed through image processing. For example, image processing may be a process of generating a plurality of images into one panoramic image. More specifically, image processing may be a process of concatenating a plurality of images of one crop to create one image corresponding to the crop.

The measuring device 20 may extract fruit information 210 and leaf information 220 about a crop based on the image.

Fruit information 210 and leaf information 220 about a crop based on the image may be extracted from the server 30 . In this case, the measuring device 20 transmits the image to the server 30 , and the server 30 may extract fruit information 210 and leaf information 220 about the crop based on the received image.

Here, a learned neural network is installed in the measuring device 20 or the server 30, and fruit information 210 and leaf information 220 about a crop may be extracted based on the image by the learned neural network. .

The fruit information 210 may be information including at least one of the location, size, and maturity of the fruit included in the crop image.

The information on the location of the fruit may be information on the location of one fruit included in the crop with respect to one crop included in the crop image. More specifically, it may be information about the height of the fruit separated from the ground, information about the distance away from the ceiling in the greenhouse, or information about the distance away from the growing point of the crop.

The information on the size of the fruit may be information on the size of one fruit included in the crop for one crop included in the crop image. More specifically, it may be information about the relative size of the fruit compared with other fruits, or information about the absolute size of the fruit.

The information on the maturity level of the fruit may be information on the maturity level of one fruit included in the crop for one crop included in the crop image. More specifically, with respect to the ripening degree of the fruit, it may be relative maturity information with other fruits or absolute ripeness information.

The leaf information 220 may be information about a position of a leaf included in a crop image. The information about the position of the leaf may be information about the height at which one leaf included in the crop is spaced apart from the ground, information about the distance away from the ceiling in the greenhouse, or information about the distance away from the growth point of the crop. have.

The measuring device 20 may transmit fruit information 210 and leaf information 220 about the crop to the server 30 .

Referring to FIG. 4 , the server 30 may calculate the sink capacity 310 and the source capacity 320 based on the fruit information 210 and the leaf information 220 . In addition, the server 30 may control the greenhouse 400 based on the sink capacity 310 and the source capacity 320 .

The server 30 may calculate the sink capacity of the crop based on the fruit information 210 and the leaf information 220 .

The server 30 may calculate the sink capacity of the crop based on at least one of the location, size, and maturity of the fruit among the fruit information. The server 30 may calculate the sink capacity by using different weights for the error information.

The server 30 may calculate the sink capacity by using the leaf information. The server 30 may determine the sink capacity based on the position of the leaf among the leaf information. A detailed method for the server 30 to calculate the sink capacity will be described later with reference to FIGS. 7 and 8 .

The server 30 may calculate the source capacity by utilizing the leaf information. The server 30 may calculate the source capacity based on leaf position information among the leaf information. The server 30 may calculate the source capacity of the crop by using different weights for the leaf position information among the leaf information.

The method of calculating the sink capacity and the source capacity with reference to FIG. 5 will be described. The method for calculating the sink capacity and the source capacity includes the steps of receiving, by the measuring device, a crop image including fruits and leaves of the crop (S101); extracting, by the measuring device, fruit information and leaf information based on the crop image (S102); determining, by the server, a sink capacity and a source capacity of a crop based on the fruit information and the leaf information (S103); and the server controlling the greenhouse based on the sink capacity and the source capacity (S104).

The method for calculating the sink capacity and the source capacity includes the steps of: a measuring device receiving a crop image including fruits and leaves of a crop (S101), the measuring device extracting fruit information and leaf information based on the crop image The detailed description of the step (S102), the server determining the sink capacity and the source capacity of the crop based on the fruit information and the leaf information (S103), is the same as the description of the sink capacity and source capacity measurement system described above. Duplicate bars will be omitted.

The server may control the greenhouse based on the sink capacity and the source capacity.

The meaning that the server controls the greenhouse based on the sink capacity and the source capacity means an operation of directly changing at least some of parameters for the greenhouse, an operation of transmitting a command for controlling the greenhouse, and an operation of controlling the greenhouse. It may include an operation of transmitting a guide message for controlling the . Hereinafter, for convenience of description, one operation among the above-described operations will be described as an example.

The server according to an embodiment may change the error removal criterion based on the sink capacity and the source capacity.

For example, when the sink capacity is greater than the source capacity, the server according to an embodiment may change the criteria for removing fruits so that more fruits are removed in order to reduce the number of fruits remaining per room, but is not limited thereto. does not

Also, for example, when the sink capacity is greater than the source capacity, the server according to an embodiment may determine to remove at least one fruit in order to reduce the number of remaining fruits per one room, but is not limited thereto.

Also, for example, when the source capacity is greater than the sink capacity, the server according to an embodiment may change the criteria for removing fruits so that fewer fruits are removed in order to increase the number of fruits remaining per one room. not limited

Also, the server according to an embodiment may change the leaf removal criterion based on the sink capacity and the source capacity.

For example, when the sink capacity is larger than the source capacity, the server according to an embodiment sets the standard of leaf removal so that the number of leaves to be left on a new branch at the upper end increases in order to further increase leaves that can become sources in the future. It can be changed, but is not limited thereto.

For a more specific example, the server according to an embodiment may change the leaf removal criteria so that, when the sink capacity is greater than the source capacity, 2 to 3 leaves are left on the newly created geodesic at the upper end, but is not limited thereto. does not

Also, for example, when the source capacity is greater than the sink capacity, the server according to an exemplary embodiment may change the leaf removal criterion so that the number of leaves to be left on the newly created geodesic at the upper end decreases, but is not limited thereto.

For a more specific example, the server according to an embodiment may change the leaf removal criteria so that, when the source capacity is greater than the sink capacity, 0 to 1 leaves are left on a new geodesic at the upper end, but is not limited thereto. does not

In addition, the server according to an embodiment may control the greenhouse so that a temperature and humidity deviation is adjusted based on the sink capacity and the source capacity.

For example, when the sink capacity is greater than the source capacity, the server according to an embodiment may control the greenhouse to reduce the temperature and humidity deviation in order to induce vegetative growth by applying less stress to the crop, but is not limited thereto.

Also, for example, when the source capacity is greater than the sink capacity, the server according to an embodiment may control the greenhouse to increase the temperature and humidity deviation in order to induce reproductive growth by applying more stress to the crop, but is not limited thereto. does not

In addition, the server according to an embodiment may control the greenhouse so that a moisture content deviation is adjusted based on the sink capacity and the source capacity.

For example, when the sink capacity is greater than the source capacity, the server according to an embodiment may control the greenhouse to reduce the moisture content deviation in order to induce vegetative growth by applying less stress to the crop, but is not limited thereto.

Also, for example, when the source capacity is greater than the sink capacity, the server according to an embodiment may control the greenhouse to increase the moisture content deviation in order to induce reproductive growth by applying more stress to the crop, but is not limited thereto. does not

In addition, the server according to an embodiment may control the temperature of the greenhouse based on the sink capacity and the source capacity.

For example, when the sink capacity is greater than the source capacity, the server according to an embodiment may control the greenhouse to lower the average temperature of the greenhouse in order to reduce the respiration rate of crops, but is not limited thereto.

Also, for example, when the source capacity is greater than the sink capacity, the server according to an embodiment may control the greenhouse to increase the average temperature of the greenhouse to increase the respiration rate of crops, but is not limited thereto.

In addition, when the sink capacity is greater than the source capacity, the server may determine to control the temperature, CO2 concentration, and humidity of the greenhouse accordingly. The server may lower the temperature of the greenhouse when the sink capacity is greater than the source capacity.

Alternatively, when the sink capacity is greater than the source capacity, the server may determine to remove any fruit included in the crop in the greenhouse. When the sink capacity is greater than the source capacity, the server may determine that at least one of a plurality of fruits of a specific flower room of a crop in the greenhouse should be removed. When the sink capacity is greater than the source capacity, the server may determine to harvest the removal located below a specific height of the crop. Alternatively, when the sink capacity is greater than the source capacity, the server may determine to remove any leaves included in the crop in the greenhouse. The arbitrary leaf may be a leaf determined by the server based on the height of the region where the leaf is located. The server may output a message based on the determination to remove the fruit and the determination to remove the leaf.

The server may control the greenhouse based on a difference between the sink capacity and the source capacity.

The server may further lower the temperature of the greenhouse as the sink capacity is greater than the source capacity and the difference between the sink capacity and the source capacity is greater. The server may lower the temperature of the greenhouse by the first deviation when the sink capacity is greater than the source capacity by a first capacity difference. The server may lower the temperature of the greenhouse by a second deviation when the sink capacity is greater than the source capacity by a second capacity difference.

In this case, the first capacity difference may be greater than the second capacity difference, and the first deviation may be greater than the second deviation.

Conversely, as the sink capacity is smaller than the source capacity and the difference between the sink capacity and the source capacity is greater, the temperature of the greenhouse may be further increased. The server may increase the temperature of the greenhouse by a third deviation when the sink capacity is smaller than the source capacity by a third capacity difference. The server may increase the temperature of the greenhouse by a fourth deviation when the sink capacity is smaller than the source capacity by a fourth capacity difference.

In this case, the third capacity difference may be greater than the fourth capacity difference, and the third deviation may be greater than the fourth deviation.

The server may output a message to harvest more fruits as the sink capacity is greater than the source capacity and the difference between the sink capacity and the source capacity is greater. The server may output a message to harvest a number of fruits when the sink capacity is greater than the source capacity by a first capacity difference, and select b fruits when the sink capacity is greater than the source capacity by a second capacity difference You can print a message telling you to harvest.

In this case, the first capacity difference may be greater than the second capacity difference, and a may be greater than b.

In addition, as the sink capacity is greater than the source capacity and the difference between the sink capacity and the source capacity is greater, the server may harvest more fruits by designating a location of harvest target fruits.

When the sink capacity is greater than the source capacity by a first capacity difference, the server may output a message to harvest fruit with a height of h1 or less, and when the sink capacity is larger than the source capacity by a second capacity difference, a message to harvest fruits with a height of h2 or less can be printed out. In this case, h1 may be greater than h2. That is, the server can designate the number of harvest target fruits by designating the location of the harvest target fruit as a range, so that the sink capacity of the crop can be reduced as a result.

The server may output a message to remove more lower lobes as the sink capacity is greater than the source capacity and the difference between the sink capacity and the source capacity is greater.

The server may output a message to remove c leaves when the sink capacity is greater than the source capacity by a first capacity difference, and remove d leaves when the sink capacity is greater than the source capacity by a second capacity difference You can print a message asking you to remove it.

In this case, the first capacity difference may be greater than the second capacity difference, and c may be greater than d.

In addition, the server may remove more leaves by designating a height of leaves to be removed as the sink capacity is greater than the source capacity and the difference between the sink capacity and the source capacity is greater.

When the sink capacity is greater than the source capacity by a first capacity difference, the server may output a message to remove leaves with a height of h3 or less, and when the sink capacity is larger than the source capacity by a second capacity difference, a message to remove leaves with a height of h4 or less can be printed out. In this case, h3 may be greater than h4. That is, the server may designate the number of leaves to be removed by designating the position of the leaves to be removed as a range, and as a result, the sink capacity of the crop may be lowered.

At this time, the height may be the reference height that is the boundary between the sink and the source of the leaf, or may be a height lower than the reference height. As a result, when the sink capacity is greater than the source capacity, it is possible to increase the yield of crops by removing the leaves consuming nutrients.

Hereinafter, the greenhouse control determined by the server described above corresponds to an example, and the determination to control the greenhouse based on the sink capacity and the source capacity is not interpreted as being limited to an example.

Hereinafter, with reference to FIG. 6 , a process of processing several images corresponding to one crop into one image will be described in detail.

6 is a view of a crop image processing process according to an embodiment.

Referring to FIG. 6 , the measuring device may acquire at least one image. The image may include several images corresponding to one crop. 6 , the measuring device may acquire a first image 101 , a second image 102 , and a third image 103 corresponding to one crop. The first image 101 , the second image 102 , and the third image 103 may correspond to one crop. More specifically, the meaning of corresponding to one crop may mean that the first image 101 , the second image 102 , and the third image 103 include an arbitrary part in one crop.

The control unit in the measuring device may generate one crop image 100 by connecting the first image 101 , the second image 102 , and the third image 103 . The crop image 100 may be an image that corresponds to one crop and includes all crops in one image.

More specifically, the control unit in the measuring device may acquire the first image 101 , the second image 102 , and the third image 103 by tracking a crop in one at a fixed position. Accordingly, the first image 101 , the second image 102 , and the third image 103 are connected to each other to obtain the crop image 100 , which is one panoramic image.

The controller of the measuring device may acquire a panoramic image of the crop by connecting the first image 101 , the second image 102 , and the third image 103 based on the reference line. The baseline may be the original stem of the crop.

The measuring device may calculate fruit information and leaf information by using the crop image 100 . Hereinafter, a method of outputting error information from the measuring device and determining the sink capacity and the source capacity based on the output error information in the measurement device will be described using FIGS. 7 and 8 .

7 is a flowchart of a method of calculating a sink capacity based on fruit according to an exemplary embodiment, and FIG. 8 is a diagram for explaining a method of specifying a fruit of a crop by a measuring device.

Referring to FIG. 7 , the method for calculating the sink capacity includes a step of inputting an image obtained by a measuring device to the controller (S201), a step of the controller specifying crops and fruits based on the image (S202), and the controller The step of outputting the location information on the fruit and transmitting it to the server (S203), the step of the controller outputting the size information of the fruit and sending it to the server (S204), the controller providing the ripeness information about the fruit The step of outputting and delivering to the server (S205), the step of loading the location weight by the server for the location (S206), the step of loading the size weight by the server for the size (S207), the server for the maturity level Loading the maturity weight (S208), the server calculating the sink capacity of a specific fruit based on the location weight, the size weight, and the maturity weight (S209), the server calculating the sink capacity of the crop based on the sink weight It may include outputting (S210), and the server outputting the total sink capacity based on the crop sink capacity (S211).

The controller of the measuring device may acquire an image including fruits and leaves of a crop. (S201)

The image may be acquired through an image acquisition unit in the measurement device. The acquired image may be transmitted to a control unit in the measurement device.

The image may be an image in which a plurality of crops are photographed.

The measuring device may specify crops and fruits based on the images. (S202)

The measuring device may specify one crop with respect to a plurality of crops included in the image and may specify one fruit with respect to a plurality of fruits included in the crop. In addition, one leaf may be specified for a plurality of leaves included in the crop. This specification may utilize an artificial neural network included in the control unit in the measurement device.

The measuring device may specify one crop among a plurality of crops included in the image based on the characteristic points of the crop. The characteristic point may be a growth point of a crop, and the characteristic point may be a traction line for fixing the position of the crop.

The artificial neural network included in the control unit in the measurement device may learn from the image. When an image is input, the learned artificial neural network can extract features from the image or detect a specific object included in the image.

The artificial neural network may include all architectures having a function of learning from an image and extracting a feature from the image. For example, the artificial neural network may include a CNN-based algorithm model.

A method of specifying a fruit and a leaf using an artificial neural network in the measurement device will be described in detail with reference to FIG. 8 .

Referring to FIG. 8 , the artificial neural network may learn from a crop image (a). The crop image (a) may include at least one fruit and at least one leaf. The crop image (a) may be a crop image acquired in real time by the measuring device or a crop image stored in a storage unit.

The artificial neural network can recognize the fruit by receiving the crop image as shown in (a). The output result of the artificial neural network recognizing the fruit can be confirmed by referring to the crop image (b).

The artificial neural network can display and output the area with fruit in the form of a bounding box, such as the crop image (b). A fruit may be located inside the bounding box, and a fruit information vector may correspond to each of the bounding boxes.

The fruit information vector may include location information, size information, and ripeness information of the specified fruit.

The control unit may output the location information of the fruit specified by the (X,Y) coordinates of the fruit information vector. (S203)

For example, the first bounding box 104 may correspond to the first fruit information vector 105 . The first bounding box 104 and the first error information vector 105 may be result values output through the learned artificial neural network. The first bounding box 104 and the first fruit information vector 105 may correspond to a fruit.

When the fruit information vector 105 has a vector form of (X,Y,L,H), (X,Y) may indicate the position of the bounding box 104 . (X, Y) in the fruit information vector 105 may be coordinates indicating the location of the fruit in the input image. The (X,Y) may be location coordinate information of the most central point of the bounding box 104 or location coordinate information of one vertex of the bounding box.

The controller may output the location information of the fruit in consideration of the fruit information vector 105 and the photographing position of the image. For example, when the height of the photographing position of the image is Y1, the controller may output (X, Y+Y1) as the location information of the fruit.

The photographing position of the image may be calculated based on a specific position of the crop. That is, the photographing position of the image may be calculated based on the growth point of the crop, or may be calculated based on the bottom surface on which the crop is planted. When the image is synthesized into a panoramic image as shown in FIG. 6 , the panoramic image is configured in a form including a specific position of a crop, so a photographing position of the image may be calculated based on the panoramic image.

The controller may output information on the size of the fruit specified by the (L,H) coordinates of the fruit information vector. (S203)

When the fruit information vector 105 has a vector form of (X,Y,L,H), (L,H) may indicate the size of the bounding box 104 . L may represent the length of one horizontal side of the bounding box 104 , and H may represent the length of one vertical side of the bounding box 104 .

The area of the bounding box 104 may be obtained through L and H of the fruit information vector, and the area may correspond to the size of the fruit corresponding to the fruit information vector. For example, the size of the fruit corresponding to the fruit information vector may be calculated according to the area.

The size information may be information regarding an absolute value regarding the size of the specified fruit or a relative value compared with other fruits. More specifically, the size information may be area information about an area occupied by the specified fruit in the crop image. That is, the controller in the measuring device may determine that the larger the area occupied by the specified fruit in the crop image is, the larger the size of the fruit is. Alternatively, the size information may be information on the number of pixels in the area occupied by the specified fruit. For example, the larger the number of pixels in the area occupied by the specified fruit, the larger the size of the corresponding fruit may be determined.

The control unit may output ripeness information of the fruit specified by the fruit information vector. (S203)

When the fruit information vector 105 has a vector form of (X, Y, L, H, 1), 1 excluding (X, Y, L, H) may be information on fruit ripeness.

Information on fruit ripeness may be data classified into "first ripeness", "second ripeness", and "third ripeness". Information on fruit ripeness may be implemented as 0 labeling data when corresponding to "first ripeness". Furthermore, if it corresponds to the "second ripeness", it may be implemented with labeling data of 1. If it corresponds to the "third degree of maturity", it may be implemented with labeling data of 2.

Classifying the ripeness of fruit into three or more classes may be determined according to the color of the fruit corresponding to each class.

For example, the color of the fruit corresponding to the "first ripeness", the color of the fruit corresponding to the "second ripening degree", and the color of the fruit corresponding to the "third ripening degree" are the colors of the fruit after the fruit is ripe. It can be one of several possible colors.

More specifically, the fruit corresponding to the “first degree of maturity” may correspond to a case in which the time is longer based on the time of ripening than the fruit corresponding to the “second degree of maturity”. The fruit corresponding to the “third degree of ripeness” may correspond to a case in which the time has continued longer than the fruit corresponding to the “second degree of ripeness” based on the time of ripening. That is, if the fruit corresponding to the "first ripening degree", the fruit corresponding to the "second ripening degree", and the "fruit corresponding to the third ripening degree" are the same, it corresponds to the "first ripening degree" A fruit that corresponds to the "second ripening degree". It may be an older case based on the time of fruiting in the order of the fruit corresponding to the "third degree of ripeness".

The ripeness information may be information about an absolute value regarding the ripening degree of the specified fruit or a relative value compared with other fruits. More specifically, the ripeness information may be determined based on color information of an area occupied by the specified fruit. For example, the server may determine the first degree of ripeness, the second degree of ripeness, and the third degree of ripeness by comparing the color of the region occupied by the specified fruit with the reference color.

Alternatively, a method of plotting the area occupied by the specified fruit as an RGB (Red, Green, Blue) color histogram may be used. More specifically, the server may graph the area occupied by the specified fruit as an RGB color histogram, and calculate the number of pixels having a red color in the area. Based on the calculated number of red color pixels, the first ripening level, the second ripening level, and the third ripening level may be determined. For example, the server may determine that the greater the number of Red Color pixels, the greater the maturity level.

Accordingly, when the fruit information vector 105 is (X, Y, L, H, 1), the fruit corresponding to the fruit information vector 105 has a fruit ripening degree of 1 and a second ripening degree corresponding to the labeling data. It can mean to have

The server may load the weights based on the error information received from the measurement device. The fruit information may include at least one of location information, size information, and ripeness information of the fruit.

The server may load the location weight based on the location information of the specified error. (S206)

The server may set the position weight based on the position of the region where the fruit is located in the crop.

The location information of the fruit determined by the measuring device may be first location information or second location information. If the location information of the fruit determined by the measuring device is the first location information, the server may load the first location weight, and if the location information is the second location information, the server may load the second location weight.

If the first location information is greater than the second location information when the location information is information on the height from the ground to the area where the specified fruit is located, the first location weight may have a higher value than the second location weight.

When the location information is a distance from an area in which the specified fruit is located to a feature point of a crop, when the first location information is greater than the second location information, the first location weight may be lower than the second location weight. In other words, the server may set a higher weight for the most recently acquired fruit. Since more nutrients can be consumed in the initial stage after the fruit has arrived, a more accurate sink capacity can be calculated by setting different weights according to the height at which the fruit is located.

The server may load the size weight based on the size information of the specified fruit. (S204)

The server may set the size weight based on the size of the fruit.

The fruit size information determined by the measuring device may include first size information and second size information. The server may load the first size weight when the specified fruit has the first size information, and load the second size weight when the specified fruit has the second size information.

When the first size information is larger than the second size information in the case where the size information is area information on an area occupied by the specified fruit in the crop image, the first size weight may be smaller than the second size weight.

or. When the size information is pixel number information for an area occupied by the specified fruit in the crop image, when the first size information is larger than the second size information, the first size weight may be a value smaller than the second size weight .

That is, the server may set a high weight when the size of the fruit is small. Since a smaller fruit size can consume more nutrients for growth, a more accurate sink capacity can be calculated by setting different weights according to the fruit size.

The server may load the ripeness weight based on the maturity information of the specified fruit. (S205)

The server may set the ripening level weight based on the ripening level of the fruit.

For example, the ripening level information of the fruit determined by the measuring device may be the first ripening level information or the second ripening level information. The server may load the first ripeness weight when the ripeness information of the specified fruit is the first ripeness information, and load the second ripeness weight when the ripeness information is the second ripeness information.

The ripeness information is determined based on the color information of the region occupied by the specified fruit, and when the first ripeness information is higher than the second ripeness information, the first maturity level weight is higher than the second ripeness level weight. It can be a high value.

That is, the server can consume more nutrients when the ripeness of the fruit is high compared to when the ripeness of the fruit is low. have.

Alternatively, when the first maturity level information is higher than the second maturity level information, the first maturity level weight may be a value lower than the second maturity level weight value.

Also, the server may load the same weight according to the ripeness of the fruit.

The server may calculate the sink capacity of the specified fruit based on the location weight, the size weight, and the ripeness weight for the specified fruit. (S209)

The server may calculate the sink capacity of the specified fruit by applying the location weight, the size weight, and the maturity weight to the specified fruit at a predetermined ratio. The sink capacity of a specified fruit may represent the nutrients consumed by one specified fruit. The constant ratio may be a ratio arbitrarily set by a user.

The server may calculate the sink capacity for the crop based on the sink weight for each of the fruits. (S210)

The server may calculate a sink capacity for each of a plurality of fruits that have been harvested on a specified crop. The server may calculate the sink capacity for the specified crop by adding up the sink capacities of a plurality of fruits that have been harvested on the specified crop.

The server can calculate the total sink capacity. (S211)

The server may calculate the total sink capacity based on the sink capacity of a plurality of crops. The server can calculate the total sink capacity by adding up the sink capacity of multiple crops.

In this case, the server may determine the same crop included in the plurality of images to prevent overlapping summing of the same crop included in the plurality of images, and may not overlap the sink capacity for the determined same crop.

The total sink capacity may mean the total sink capacity in the greenhouse or may be the total sink capacity for a specific area. For example, when the specific area is a representative area in the greenhouse, the nutrient consumption of all crops in the greenhouse may be calculated by calculating the sink capacity of a plurality of crops located in the specific area.

If a specific crop is a representative crop in the greenhouse, the server may omit the process of calculating the total sink capacity. That is, the server can derive the sink capacity of the entire area in the greenhouse by calculating the sink capacity of one crop having representativeness.

Hereinafter, a method of calculating the sink capacity and the source capacity through the leaf information of the crop will be described.

9 is a flowchart illustrating a method of calculating a sink capacity and a source capacity based on leaf information according to an embodiment. 10 is a diagram for explaining a process of calculating information on leaves of a crop from an image according to an exemplary embodiment.

Referring to FIG. 9 , the method of calculating the sink capacity and the source capacity based on leaf information of a crop includes the step of inputting an image obtained by a measurement device to the control unit ( S301 ), and the control unit includes the crop and leaf based on the image. step of specifying (S302), the step of the control unit calculating the location information on the leaf (S303), the server comparing the height of the specific leaf with a reference height based on the location information (S304), the specific leaf Calculating the source capacity when the height of is greater than the reference height (S305), calculating the sink capacity when the height of the specific leaf is smaller than the reference height (S306), the sink capacity and the source capacity for the specific leaf It may include calculating the total sink capacity and the source capacity based on the step (S307).

The controller of the measuring device may acquire an image including fruits and leaves of a crop. (S301)

An image as shown in (a) of FIG. 10 may be input to the controller of the measuring device.

The control unit of the measuring device may specify crops and leaves based on the image. (S302)

A process in which the control unit of the measuring device specifies the crop is the same as in FIG. 8 .

The control unit of the measuring device may include a learned artificial neural network, and the learned artificial neural network may recognize a leaf from a crop image. The learned artificial neural network may output a specific leaf in the form as shown in FIG. 10(b).

The learned artificial neural network may display and output the area with leaves in the form of a bounding box. A leaf may be positioned inside the bounding box, and a leaf information vector may be output for each of the bounding boxes. The leaf information vector may be output corresponding to the bounding box.

For example, the second bounding box 106 may correspond to the leaf information vector 107 . The second bounding box 106 and the leaf information vector 107 may be output through the learned artificial neural network.

The controller may output the leaf information vector in the form of (X, Y) coordinates of the leaf information vector. In this case, the leaf information vector may include location information of a specific leaf. (S303)

The leaf information vector may further include size information of a specific leaf.

Since the location information of the leaf is the same as the location information of the fruit described with reference to FIG. 8 , a detailed description thereof will be omitted.

The location information of the specific leaf may be expressed in the form of a bounding box, and one surface of the bounding box may meet an area where a side branch starts from a main branch. That is, the specific leaf may be an area including all portions extending from the main branch.

The server may compare the height of the specified leaf with the reference height. (S304)

The specified leaf height H1 may be a distance from the ground to an area where the leaf is located. The specified leaf height H1 may be a distance between the second bounding box 104 from the ground. Alternatively, the specified leaf height H1 may be the Y coordinate of one point located in the second bounding box 104 . The specified leaf height H1 may be calculated based on the Y value of the leaf information vector. Alternatively, the specified leaf height H1 may be calculated based on the Y value of the center point of the second bounding box.

The height H1 of the specified leaf may be specified as a flower room.

When the crop is a crop that has a flower room and grows, the height of the leaf of the crop may be defined as the flower room. That is, since side branches grow from a plurality of flower rooms as the crop grows, the height of the leaves of the crop may be defined when specifying the number of flower rooms in which the leaves are located.

The reference height Hth may be a value previously stored in the server. The reference height Hth may be a predetermined value or a value that is changed according to the total height of the crop.

The reference height Hth may also be defined as a painting room. That is, the reference height Hth may be defined as, for example, the fifth room.

The server may output a result value by comparing the height of the specified leaf with the reference height.

The server may determine the specified leaf as the source or the sink according to a result of comparing the specified height of the leaf with the reference height. The server may determine the specified leaf as the source if the height of the specified leaf is greater than the reference height, and may determine the specified leaf as the sink if the height of the specified leaf is less than the reference height.

When the height of the leaf and the reference height are specified as a flower room, if the specified leaf flower room has a higher value than the reference flower room, the server may determine the specified leaf as a source, and the specified leaf flower room is If it has a lower value than the reference room, the server may determine the specified leaf as a synchro.

For example, if the reference room is the 5th room and the specified leaf is the 7th room, the server may determine the specified leaf as the source. In addition, when the reference painting room is the 5th painting room and the specified leaf painting room is the 3rd painting room, the server may determine the specified leaf as a synchro.

The server may compare the height of the leaf with the reference height, and calculate the source capacity when the specified leaf is determined as the source. (S305)

The server may assign different source weights according to the specified leaf height. The server may calculate the source capacity of the specified leaf by giving a higher source weight as the height of the leaf increases. In the case of a leaf located at a high position from the ground, the portion covered by other crops is relatively small, so photosynthetic production can be relatively high. The source capacity may be calculated to do so.

The server may assign different source weights according to the leaf size. The server may calculate the source capacity of the specified leaf by giving a higher source weight as the size of the leaf increases. When the size of the leaf is relatively large, the photosynthetic production can be increased, so the source capacity can be calculated to match the production of the crop by giving different source weights according to the size of the leaf.

The server may assign different source weights according to the area occupied by the actual leaf in the area specified as the leaf. For example, by calculating the area occupied by the leaf in the second bounding box 106 specifying the leaf, the actual size of the leaf may be derived and a source weight may be assigned. In this case, the area occupied by the leaf may be calculated by counting the number of green pixels in the second bounding box 106 .

The server may compare the height of the leaf with the reference height, and calculate the sink capacity when it is determined that the specified leaf is the sink. (S306)

The server may give different sink weights according to the height of the leaves. The server may calculate the sink capacity to match the consumption of the crop by giving a sink weight higher as the height of the leaf is lower.

The server may give different sink weights according to the size of the leaf. The server may calculate the sink capacity to match the consumption of crops by giving a higher sink weight as the size of the leaf increases.

The server may give different sink weights according to the area occupied by the actual leaf in the area specified as the leaf.

In addition, the server may determine the specified leaf according to the state of the leaf as the source and may determine it as the sink. The leaves of crops can basically function as a source for generating nutrients, but in the case of leaves that do not produce nutrients properly due to aging, or do not receive sunlight, they may act as a sink rather than a source.

Accordingly, the server may determine whether the specified leaf is a source or a sink based on the pixel values for the leaf.

For example, when the color of the leaf deviates from a specific color reference value, the server may determine the specified leaf as a synchro.

As a more specific example, when the color of the leaf is out of the green reference value, the server may determine the specified leaf as a sync, but is not limited thereto.

Also, for example, when the color of a leaf is included in a specific color reference value range, the server may determine a specific leaf as a synch.

As a more specific example, when the color of the leaf is included within the yellow reference value, the server may determine the specified leaf as a sync, but is not limited thereto.

In addition, when the leaves of a crop are small, they can act as a sink using energy for growth, so the server can determine whether the specified leaf is a source or a sink based on the size of the leaf.

For example, the server may determine the specified leaf as a sink when the leaf size is less than or equal to the reference size, but is not limited thereto. The server may calculate the sink and source capacity of the entire crop. (S307)

The server may calculate the sink capacity of the entire crop based on the sink capacity of the specific leaf, and may calculate the source capacity of the entire crop based on the source capacity of the specific leaf. However, when calculating the sink capacity of the entire crop, the sink capacity of the fruit described in FIG. 7 may be taken into consideration.

In addition, the server may calculate the sink and source capacity of one crop, and calculate the sink capacity and source capacity of all crops in the greenhouse.

Alternatively, the server may use one crop as a representative crop to determine the sink capacity and the source capacity in the greenhouse through one crop.

The server may output a message for greenhouse control based on the source capacity and the sink capacity. When the sink capacity is greater than the source capacity, the server may output a message to remove the leaf at a height lower than the reference height.

The server outputs a message to remove the leaves at a height lower than the reference height, thereby removing the leaves that only consume nutrients, thereby improving the growth of crops and improving the quality of fruits.

The contents described through FIGS. 7 to 9 relate to a method of determining a sink capacity and a sauce capacity based on fruits and leaves among various characteristics included in crops.

However, although not described with reference to FIGS. 7 to 9 , the sink capacity and the source capacity may be determined based on other characteristics included in the crop.

For example, the sink capacity may be determined based on the characteristics of flowers included in the crop, but is not limited thereto.

Also, for example, the sink capacity may be determined based on the characteristics of the peak included in the crop, but is not limited thereto.

In addition, since the contents described with respect to the above-described fruits and leaves may be applied to the detection of the flower or the bud, the overlapping description will be omitted.

In addition, the respective weights and correlations with respect to features such as flowers, buds, fruits, and leaves included in the crop will be described in more detail using FIG. 17 below.

Hereinafter, an example of the reference position will be described in more detail with reference to FIGS. 10 to 12 .

11 and 12 are views for explaining a reference height according to an embodiment.

11 is a view for showing the reference height setting according to the height of the crop.

Referring to FIG. 11 , the server may set different reference positions according to the height of the crop. That is, when the height of the crop is increased, the reference height may also be set high.

For example, when the height of the first crop is the first height 324a, the reference height Hth may be set as the first reference height 324b, and when the height of the second crop is the second height 325a The reference height Hth may be set as the second reference height 325b.

The server can set the height of the crop and the reference height in the same ratio.

For example, the ratio between the first height 324a and the first reference height 324b may be set to be the same as the ratio between the second height 325a and the second reference height 325b. The ratio may be a value predefined in the server.

The height of the crop may be defined as a distance between a part located at the highest position among parts of the crop and the ground. Alternatively, the height of the crop may be defined as the distance between the growth point of the crop and the ground. In addition, the height of the crop may be defined as the distance between the traction line and the ground for guiding the growth of the crop,

In addition, the height of the crop and the reference height can be defined not only by the ground, but also by the distance from the greenhouse ceiling.

12 is a view for showing the reference height setting according to the height of the crop.

Referring to FIG. 12 , the server may set different reference heights according to the height of the crop. However, in this case, the distance between the height of the crop and the reference height may be set to be the same.

For example, when the height of the first crop is the first height 334a, the reference height Hth may be set with reference to the common reference distance 334b, and the height of the second crop is the second height 335a. In the case of , the reference height Hth may be set with reference to the common reference distance 335b.

The common reference distance may be defined as a difference between the height of the crop and the reference height.

That is, the reference height Hth of the first crop may be set to a point spaced apart in the ground direction by a common reference distance 334b based on the first growth point 330 . The reference height Hth of the second crop may be set to a point spaced apart in the ground direction by a common reference distance 335b based on the second growth point 331 .

The common reference distance may be a value predefined in the server. By setting the height of the crop to be the same between the height of the crop and the reference height, the server can more accurately determine the state of the crop by more accurately specifying the leaves that consume nutrients.

Hereinafter, a method of calculating the total sink capacity and the source capacity from the crop image will be described with reference to FIG. 13 .

13 is a flowchart of a method of calculating total sink capacity and source capacity according to an embodiment, and FIG. 14 is a diagram of a crop image according to an embodiment.

The method of calculating the sink capacity and the source capacity described above relates to a method of calculating the total sink capacity and the source capacity based on the sink capacity and the source capacity for the crop by specifying one crop, but in FIG. 13 , the total sink capacity and the source capacity By calculating the capacity and the source capacity, it is also possible to calculate the sink capacity and the source capacity for one crop based thereon.

Referring to FIG. 13 , the method of calculating the total sink capacity and source capacity includes the steps of acquiring an image by a measuring device (S401), dividing the crop area from the image (S411), and dividing the target area from the crop area (S412), calculating a green area for each target area (S413), applying a weight for each target area (S414), and calculating total source capacity and sink capacity based on the weight (S414) ( S415) may be included.

The step of inputting an image to the measuring device will be omitted since it overlaps with the description described above with reference to FIGS. 5 and 6 .

The measuring device may acquire an image, and the acquired image may be input to the measuring device or a server. (S401)

The image may include at least one or more crops and an image of a greenhouse in which the crops are growing. The image may be acquired at a fixed location each time. Alternatively, the image may be acquired at a fixed height each time.

The measuring device may classify a crop region in the image. (S411)

Alternatively, the server may classify the crop area in the image.

The measuring device may distinguish a non-crop area 410 and a crop area 420 with respect to the crop image 100 . The non-crop area 410 may be an area that does not include a crop in the crop image 100 . The crop area 420 may be an area including crops in the crop image 100 .

The non-crop area 410 may be an image including an inducement line for crop growth in the crop image or a specific buoy corresponding to each crop.

The non-crop area 410 and the crop area 420 may be divided based on color information. The measuring apparatus may classify an area having a relatively large number of green or red colors in the crop image 100 as the crop area 420 , and may classify other areas as the non-crop area 410 .

The measuring device may classify at least one target area with respect to the crop area 420 . (S412)

The target area includes a first target area 421 , a second target area 422 , a third target area 423 , a fourth target area 424 , a fifth target area 425 , and a sixth target area ( 426) may be included.

The first target area 421 , the second target area 422 , the third target area 423 , the fourth target area 424 , the fifth target area 425 , and the sixth target area 426 are They can have the same height as each other. Alternatively, the first target area 421 , the second target area 422 , the third target area 423 , the fourth target area 424 , the fifth target area 425 , and the sixth target area 426 are It may be set to have different heights.

The measuring device may calculate a green area for each target area. (S413)

The green area may be information on the number of green color pixels when the target area is plotted as an RGB color histogram. More specifically, the measuring apparatus may extract color information on pixels for each target region, and calculate information on the number of green color pixels among the color information as information on the green region. The measuring device may transmit information on the green area to the server.

The server may apply a weight for each target area. (S414)

The server may calculate the total source capacity and sink capacity based on the weight for each target area and information on the green area. (S415)

The server may load the weight for each target area. The weight may be different for the target area. For example, the first weight for the first target area, the second weight for the second target area, the third weight for the third target area, the fourth weight for the fourth target area, and the fifth weight for the fifth target area The fifth weight and the sixth weight for the sixth target region may be the same as or different from each other.

Alternatively, the first weight for the first target region, the second weight for the second target region, and the third weight for the third target region may be a weight for calculating the source capacity. The source weight may decrease from the first weight to the third weight. The first target area has a smaller portion that is covered compared to the third area, so it can receive more light. Accordingly, the first weight is given a higher source weight than the third weight to obtain information related to the source capacity of the crop. It can be obtained to be closer to reality.

The fourth weight for the fourth target region, the fifth weight for the fifth target region, and the sixth weight for the sixth target region may be weights for calculating the sink capacity. Also, the sink weight may increase as the fourth to sixth weights increase. Since the sixth area has a larger portion that is covered compared to the fourth area, which contributes less to nutrient production, and thus consumes relatively more nutrients, the sixth weight is given as a sink weight higher than the fourth weight, so that the sink capacity of the crop is reduced. related information can be obtained closer to reality.

In this case, a boundary between the third target area and the fourth target area may be set as a reference height for dividing a source area and a sink area of a crop.

The server may calculate the total source capacity and sink capacity based on the weight for each target area and the information on the green area. More specifically, the server may calculate the total source capacity and sink capacity by multiplying the weight for each target area by the number of pixels in the green area. For example, the server may calculate the sink capacity or the source capacity by multiplying the green area for the first target area by the number of green color pixels and the first weight for the first target area.

Alternatively, when the first to third weights are the weights for calculating the source capacity, the total source capacity may be calculated based on information on the green region of the first to third target regions.

Alternatively, when the fourth to sixth weights are the weights for calculating the sink capacity, the total sink capacity may be calculated based on information on the green region of the fourth to sixth target regions.

Hereinafter, a method of calculating the crop sink capacity and the source capacity based on the total sink capacity and the source capacity will be described with reference to FIGS. 15 and 16 .

15 is a flowchart of a method of calculating a crop source capacity and a sink capacity according to an embodiment, and FIG. 16 is a diagram for explaining a crop image according to an embodiment.

15 , the method for calculating the crop source capacity and the sink capacity includes the steps of inputting an image to a measuring device (S401), classifying a crop area with respect to the image (S411), and a non-crop area other than the crop area. Recognizing crop feature points in the area (S421), classifying a target area in the crop area (S412), calculating a green area for the target area (S413), the server calculates a weight for each target area step of applying (S414), calculating total source capacity and sink capacity using the weight for each target area and the green area (S415), crop source capacity based on the crop feature points and the total source capacity and sink capacity and calculating the sink capacity ( S402 ).

Steps (S411), (S412), (S413), (S414), and (S415) will be omitted since they overlap with the technical contents described above with reference to FIG. 13 .

The measuring device may recognize crop feature points in the non-crop area in the crop image. Referring to FIG. 16 , the crop image 100 may be divided into a non-crop area 410 and a crop area 420 . A method of dividing the non-crop area 410 and the crop area 420 will be omitted as described above with reference to FIG. 13 .

The measuring device may recognize crop feature points in the non-crop area 410 . The crop feature points may be unique information for each crop. For example, the crop characteristic point may be a crop growth point. The growth point may be a branching area in an arbitrary area at the top of the crop, and is not limited thereto.

The crop characteristic points may be specific buoys 411 for each crop. The specific buoy may be a buoy in which, when a crop is grown in a greenhouse, an inducer line that helps the crop grow in the greenhouse is installed on the ceiling of the greenhouse and is hung on each of the attracting line.

The measuring device may recognize the number of the crop feature points. The measuring device may transmit the number of feature points to the server.

The server may calculate the crop sink capacity and source capacity based on the number of feature points and the total sink capacity and source capacity. More specifically, the server may calculate the sink capacity and the source capacity corresponding to one crop by dividing the total sink capacity and the source capacity by the number of the feature points.

Referring to FIG. 17 , the parameter calculation system 1000 for controlling a greenhouse according to an embodiment may extract at least three features from an image of a crop obtained.

In this case, the at least three characteristics may include at least one of a characteristic of a flower of the crop, a characteristic of a peak of the crop, a characteristic of a fruit of the crop, and a characteristic of a leaf of the crop. .

For example, the at least three characteristics may include a first characteristic, a second characteristic and a third characteristic, wherein the first characteristic is a characteristic of a flower of the crop, and the second characteristic is a fruit of the crop. , and the third characteristic may be a characteristic of the leaf of the crop, but is not limited thereto.

Also, according to an embodiment, the parameter calculation system for controlling the greenhouse may acquire sub-parameters by using weights for the extracted features.

For example, when the at least three features include a first feature, a second feature, and a third feature, a first sub-parameter may be calculated based on a first weight for the first feature, and the first sub-parameter may be calculated. The second sub-parameter may be calculated based on the second weight for the two features, and the third sub-parameter may be calculated based on the third weight for the third feature, but is not limited thereto.

In addition, when the first characteristic corresponds to a flower of the crop and the second characteristic corresponds to a fruit of the crop, the first weight may be greater than the second weight, but is not limited thereto. .

In addition, the first to third weights for the first to third features may be associated with at least one of size, shape, location, and presence or absence.

In addition, when the first characteristic is a characteristic corresponding to a flower of the crop and the second characteristic is a characteristic corresponding to a fruit of the crop, the correlation with the size of the first weight depends on the size of the second weight. may be less than the correlation.

For example, when the first characteristic is a characteristic corresponding to a flower of the crop and the second characteristic is a characteristic corresponding to a fruit of the crop, the degree of change of the first weight according to the size of the first characteristic may be smaller than the degree of change of the second weight according to the magnitude of the second feature.

As a more specific example, when the first characteristic is a characteristic corresponding to a flower of the crop and the second characteristic is a characteristic corresponding to a fruit of the crop, the first weight according to the size of the first characteristic is may be the same, and the second weight may be changed according to the size of the second feature, but is not limited thereto.

Also, according to an embodiment, the parameter calculation system for controlling a greenhouse may calculate a first parameter based on the first sub-parameter and the second sub-parameter, and a second parameter based on the third sub-parameter. can be calculated.

In this case, the first parameter may be a parameter corresponding to the above-described sink capacity, and the second parameter may be a parameter corresponding to the above-described source capacity, but is not limited thereto.

In addition, the greenhouse control system (not shown) may control the greenhouse based on parameters calculated from the parameter calculation system for controlling the greenhouse.

For example, the greenhouse control system may control the greenhouse based on the first parameter and the second parameter calculated from the parameter calculation system for controlling the greenhouse.

More specifically, the greenhouse control system may perform at least one operation of a first group greenhouse control operation when the first parameter is greater than the second parameter, and when the second parameter is greater than the first parameter At least one of the second group greenhouse control operations may be performed.

In this case, the first group greenhouse control operation is an operation of changing the standard of fruit removal to remove more fruits in order to reduce the number of fruits remaining per one flower room, and at least one method to reduce the number of fruits remaining per flower room The action of deciding to remove the fruit, the action of changing the standard of leaf removal so that the number of leaves to be left on the new branch on the upper part increases in order to increase the number of leaves that can be a source in the future, Changing the standard of leaf removal so that only 3 leaves are left, in order to induce vegetative growth by giving less stress to the crop, In order to control the greenhouse to reduce temperature and humidity deviation, to induce vegetative growth by giving less stress to the crop The operation of controlling the greenhouse to reduce the moisture content deviation, the operation of controlling the greenhouse so that the average temperature of the greenhouse is lowered in order to reduce the respiration rate of crops, the operation of transmitting the guide message for the above-described control, and outputting the notification for the above-mentioned control It may include at least one of the operations to be performed.

In addition, the second group greenhouse control operation is an operation of changing the standard of fruit removal so as to remove fewer fruits in order to increase the number of remaining fruits per one flower room, and a leaf so that the number of leaves to be left on a new side branch is reduced at the upper end. Changing the standard of removal, changing the standard of leaf removal so that 0 to 1 leaves are left on the new geodesic in the upper part control operation, controlling the greenhouse to increase the moisture content deviation in order to induce reproductive growth by applying more stress to the crops; It may include at least one of an operation of transmitting a guide message for , and an operation of outputting a notification for the aforementioned control.

Referring to FIG. 18 , since the contents described with reference to FIG. 17 may be applied to the parameter calculation system 1100 for controlling the greenhouse according to an embodiment, the overlapping description will be omitted.

Referring to FIG. 18 , a parameter calculation system 1100 for controlling a greenhouse according to an embodiment may calculate a third parameter based on the first parameter and the second parameter.

For example, the third parameter may be calculated as the first parameter - the second parameter, and the second parameter - may be calculated as the first parameter, etc. It may be calculated by a function to, but is not limited thereto.

For example, the greenhouse control system may control the greenhouse based on the third parameter calculated from the parameter calculation system for controlling the greenhouse.

More specifically, the greenhouse control system may perform at least one operation by selecting a first group greenhouse control operation or a second group greenhouse control operation based on the third parameter.

In this case, since the contents described in FIG. 17 may be applied to the first group greenhouse control operation and the second group greenhouse control operation, overlapping descriptions will be omitted.

Although described above based on the greenhouse according to the present invention, it can be widely applied to smart farms including greenhouses.

In the above, the configuration and features of the present invention have been described based on the embodiments according to the present invention, but the present invention is not limited thereto, and it is understood that various changes or modifications can be made within the spirit and scope of the present invention. It is intended that such changes or modifications will be apparent to those skilled in the art, and therefore fall within the scope of the appended claims.

As described above, in the best mode for carrying out the invention, related matters have been described.

Claims

A method for controlling a greenhouse environment, comprising:

acquiring at least one image of the crop;

extracting a first feature, a second feature, and a third feature included in the at least one acquired image;

calculating a first parameter based on at least one of the first to third characteristics;

calculating a second parameter based on at least one of the first to third characteristics;

calculating a third parameter based on the first and second parameters; and

Including; controlling the greenhouse environment based on the third parameter;

The step of calculating the first parameter is

calculating a first sub-parameter based on the first characteristic;

calculating a second sub-parameter based on the second characteristic; and

calculating the first parameter based on at least the first sub-parameter and the second sub-parameter,

The step of calculating the second parameter is

calculating a third sub-parameter based on the third characteristic; and

calculating the second parameter based on at least the third sub-parameter;

The step of controlling the greenhouse environment based on the third parameter

comparing the third parameter with a reference value; and

performing at least one control operation included in a first group greenhouse control operation or a second group greenhouse control operation based on a comparison result of the third parameter and the reference value;

The first characteristic is a characteristic corresponding to the flower of the crop,

The second characteristic is a characteristic corresponding to the fruit of the crop,

The third characteristic is a characteristic corresponding to the leaves of the crop.

Greenhouse environmental control methods.
The method of claim 1,

When the third parameter is equal to or greater than the reference value, at least one control operation included in the first group greenhouse control operation is performed,

performing at least one control operation included in the second group greenhouse control operation when the third parameter is less than the reference value

Greenhouse environmental control methods.
The method of claim 1,

When the third parameter is equal to or greater than the reference value, at least one control operation included in the second group greenhouse control operation is performed,

performing at least one control operation included in the first group greenhouse control operation when the third parameter is less than the reference value

Greenhouse environmental control method.
The method of claim 1,

A first weight for calculating the first sub-parameter is greater than a second weight for calculating the second sub-parameter

Greenhouse environmental control methods.
The method of claim 1,

The second weight for calculating the second sub-parameter is changed based on the magnitude of the second feature.

Greenhouse environmental control methods.
6. The method of claim 5,

The second weight for calculating the second sub-parameter increases as the size of the second feature decreases.

Greenhouse environmental control methods.
The method of claim 1,

The first group greenhouse control operation is an operation of changing the criterion of fruit removal to remove more fruits in order to reduce the number of remaining fruits per one room, and removing at least one fruit to reduce the number of remaining fruits per one room. In order to increase the number of leaves that can become a source in the future, the operation of changing the standard of leaf removal so that the number of leaves to be left on the new geodesic on the upper part increases, The operation of changing the standard of leaf removal so as to leave a gap, the operation of controlling the greenhouse to reduce the temperature and humidity deviation to induce vegetative growth by giving less stress to the crop, the operation of reducing the moisture content deviation to induce vegetative growth by giving less stress to the crop During the operation of controlling the greenhouse to decrease, the operation of controlling the greenhouse so that the average temperature of the greenhouse is lowered to reduce the respiration rate of crops, the operation of transmitting the guide message for the above-mentioned control, the operation of outputting a notification for the above-mentioned control at least one action

Greenhouse environmental control methods.
The method of claim 1,

The second group greenhouse control operation is an operation of changing the standard of fruit removal so as to remove fewer fruits in order to increase the number of remaining fruits per one flower room, and removing leaves so that the number of leaves to be left on a new side branch is reduced at the upper end. The operation of changing the standard, the operation of changing the standard of leaf removal so that 0 to 1 leaves are left on the new geodesic at the upper part, and controlling the greenhouse to increase the temperature and humidity deviation in order to induce reproductive growth by adding more stress to the crop operation, controlling the greenhouse to increase the moisture content deviation in order to induce reproductive growth by applying more stress to the crops, controlling the greenhouse to increase the average temperature of the greenhouse to increase the respiration rate of crops, for the above-mentioned control comprising at least one of an operation of transmitting a guide message and an operation of outputting a notification for the above-described control

Greenhouse environmental control methods.
The method of claim 1,

The first parameter is a parameter corresponding to a sink capacity, and the second parameter is a parameter corresponding to a source capacity.

Greenhouse environmental control methods.
A method for controlling a greenhouse environment, comprising:

acquiring at least one image of the crop;

extracting a first feature, a second feature, and a third feature included in the at least one acquired image;

calculating a first parameter based on at least one of the first to third characteristics;

calculating a second parameter based on at least one of the first to third characteristics; and

Including; controlling the greenhouse environment based on the first parameter and the second parameter;

The step of calculating the first parameter is

calculating a first sub-parameter based on the first characteristic;

calculating a second sub-parameter based on the second characteristic; and

calculating the first parameter based on at least the first sub-parameter and the second sub-parameter,

The step of calculating the second parameter is

calculating a third sub-parameter based on the third characteristic; and

calculating the second parameter based on at least the third sub-parameter;

Controlling the greenhouse environment based on the first parameter and the second parameter comprises:

performing a first group greenhouse control operation when the first parameter is greater than the second parameter; and

performing a second group greenhouse control operation when the second parameter is greater than the first parameter; including,

The first characteristic is a characteristic corresponding to the flower of the crop,

The second characteristic is a characteristic corresponding to the fruit of a crop,

The third characteristic is a characteristic corresponding to the leaves of the crop.

Greenhouse environmental control methods.
11. The method of claim 10,

The first group greenhouse control operation is an operation of changing the criterion of fruit removal to remove more fruits in order to reduce the number of remaining fruits per one room, and removing at least one fruit to reduce the number of remaining fruits per one room. In order to increase the number of leaves that can become a source in the future, the operation of changing the standard of leaf removal so that the number of leaves to be left on the new geodesic on the upper part increases, The operation of changing the standard of leaf removal so as to leave a gap, the operation of controlling the greenhouse to reduce the temperature and humidity deviation to induce vegetative growth by giving less stress to the crop, the operation of reducing the moisture content deviation to induce vegetative growth by giving less stress to the crop During the operation of controlling the greenhouse to decrease, the operation of controlling the greenhouse so that the average temperature of the greenhouse is lowered to reduce the respiration rate of crops, the operation of transmitting the guide message for the above-mentioned control, the operation of outputting a notification for the above-mentioned control at least one action

Greenhouse environmental control methods.
11. The method of claim 10,

The second group greenhouse control operation is an operation of changing the standard of fruit removal so as to remove fewer fruits in order to increase the number of remaining fruits per one flower room, and removing leaves so that the number of leaves to be left on a new side branch is reduced at the upper end. The operation of changing the standard, the operation of changing the standard of leaf removal so that 0 to 1 leaves are left on the new geodesic at the upper part, and controlling the greenhouse to increase the temperature and humidity deviation in order to induce reproductive growth by adding more stress to the crop operation, controlling the greenhouse to increase the moisture content deviation in order to induce reproductive growth by applying more stress to the crops, controlling the greenhouse to increase the average temperature of the greenhouse to increase the respiration rate of crops, for the above-mentioned control comprising at least one of an operation of transmitting a guide message and an operation of outputting a notification for the above-described control

Greenhouse environmental control methods.