KR20140077513A - System and method for crops information management of greenhouse using image - Google Patents

System and method for crops information management of greenhouse using image Download PDF

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KR20140077513A
KR20140077513A KR1020120146417A KR20120146417A KR20140077513A KR 20140077513 A KR20140077513 A KR 20140077513A KR 1020120146417 A KR1020120146417 A KR 1020120146417A KR 20120146417 A KR20120146417 A KR 20120146417A KR 20140077513 A KR20140077513 A KR 20140077513A
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문애경
김규형
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한국전자통신연구원
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Abstract

The present invention relates to a system for managing information on crops in a greenhouse using images and a method thereof. The system for managing information on crops in a greenhouse using images includes: a reference crop unit which includes reference crops that are the same crops as grouped crops, and is positioned separately from the grouped crops; an image sensor unit which includes one or two or more image photographing devices, and photographs the reference crops using the image photographing devices to create crop image information; and a management server unit which grasps growth conditions of the reference crops by analyzing the created crop image information, estimates growth conditions of the grouped crops, based on the grasped growth conditions of the reference crops, and predicts the growth and yield of the grouped crops, based on the estimated growth conditions of the grouped crops.

Description

TECHNICAL FIELD The present invention relates to a greenhouse information management system and a method thereof,

The present invention relates to a greenhouse automatic control system, and more particularly, to a greenhouse information management system and method for an automatic control system suitable for a greenhouse environment for implementing an agricultural-IT environment.

Crops grown in greenhouses can be affected by temperature, humidity, radiation and atmospheric conditions, which can affect crop growth and growth rates, including crop size, growth rate, yield and taste. The environmental information of the greenhouse and the information on the growth status of the crops can be used to predict the yield. The predicted yield can be used to predict the price of the crop and the import / export amount of the crop. Environmental control of the greenhouse is controlled at different times depending on the stage of growth, growth condition, pest incidence, etc. Therefore, the growth information of the crop in the greenhouse has the greatest influence on the control level. Therefore, in order to control the greenhouse environment automatically, it is necessary to control the temperature and humidity of the greenhouse in addition to the weather conditions such as temperature, humidity, and leaf temperature, leaf humidity, plant length, leaf area, Crop growth information should be able to be collected, and these crop growth information should be automatically fed back to the main control computer for environmental control. In this case, unlike the existing image recognition, the image recognition section is designated to improve the accuracy of the image recognition. It is stored in the database when the image of the crop is transferred to the environmental control system, and the growth information of the crop, which is recognized through image processing module such as the plant length, leaf area, bar length, fruit color and fruit number, is also stored in the database. Depending on the size of the greenhouse and the number of crops in the greenhouse, it is possible to predict crop growth and yield yield models.

In general, it has been used as a method for observing crop growth and growth rate, which is dependent on the naked eye which is directly observed by the human eye. Although it is essential to accurately measure the growth of crops in mountainous hillsides in order to more precisely estimate the crop growth and yield according to the meteorological changes, there is no means to accurately measure it every moment, Because of the inconvenience, there are difficulties in observing and storing accurate crop growth data.

In addition, observing the growth status of cultivated crops by human observation has the problem that the inconsistency of observing time and frequency of observation are limited, , It takes a lot of time to measure crop growth information, and it is inconvenient to exchange information with researchers or the Agricultural Development Administration. As a result, there is a difficulty in collecting sufficient data and using it as research data.

A problem to be solved by the present invention is to provide a greenhouse crop information management system and method for capturing crop growing information for building a more precise and convenient greenhouse automatic control system.

The greenhouse crop information management system according to the present invention is a greenhouse crop information management system that includes a reference crop, which is located inside a greenhouse and is the same crop as the crops, a reference crop part located separately from the clustered crop, An image sensor unit for photographing a reference crop using a video image pickup apparatus to generate crop image information, and analyzing the generated crop image information to grasp the growth state of the reference crop, And a management server unit for estimating the growth state of the clusters based on the estimated growth state of the clusters and estimating the growth and the yield of the clusters based on the estimated growth state of the clusters.

The image sensor unit can generate crop image information including a three-dimensional image through two or more image capturing apparatuses as well as photographing through a single image capturing apparatus. The image sensor unit further includes illumination for photographing a reference crop, and the illumination can be adjusted to obtain an optimal image corresponding to the brightness of the inside of the greenhouse or the light from outside the greenhouse.

The management server unit analyzes the generated crop image information through at least one image processing technique among image processing techniques including color correction, image identification, and image recognition to determine the growth state of the reference crop. The management server part stores expert knowledge data on the clustered crops, and estimates the growth and yield of the clustered crops in consideration of the growth state of the estimated clustered crops and the expert knowledge data. In addition, the management server can estimate the growth state of the clustered crops based on the similarity between the growth period of the reference crop and the growth period of the clustered crops.

In the method for managing greenhouse crop information according to the present invention, first, the reference crop, which is the same crop as the clustered crop, is located separately from the crops clustered in the greenhouse to generate crop image information. Based on the generated crop image information, the growth state of the reference crop is grasped, and the growth state of the clustered crop is estimated based on the detected growth state of the reference crop. Next, the growth and yield of the clusters are predicted based on the growth state of the estimated clustered crops.

The system and method for collecting greenhouse information using images according to the present invention can control the greenhouse environment by measuring the status of crops from the beginning of harvest to the harvest time without any significant influence on time, It is possible to predict high quality crops and yields.

1 is a configuration diagram illustrating an embodiment of a greenhouse crop information management system 100 according to the present invention.
2 is a detailed view showing an embodiment of the management server unit 130 of the greenhouse crop information management system 100 according to the present invention.
FIG. 3 is a flowchart illustrating a method for managing greenhouse crop information through an image according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The terms used in the present specification are terms selected in consideration of the functions and effects in the embodiments, and the meaning of the terms may vary depending on the intention of the user or the operator or industry custom. Therefore, the meaning of the term used in the following embodiments is based on the defined definition when specifically stated in this specification, and unless otherwise stated, it should be interpreted in a sense generally recognized by those skilled in the art.

1 is a configuration diagram illustrating an embodiment of a greenhouse crop information management system 100 according to the present invention.

Referring to FIG. 1, a greenhouse crop information management system 100 according to an exemplary embodiment of the present invention includes an image sensor unit 110, a management server unit 130, and a reference crop unit 150.

The image sensor unit 110 includes one or more image photographing apparatuses that can collect image information by capturing crops inside the greenhouse and inside the greenhouse. The image sensor unit 110 may be configured through a single image capturing device and may be configured in a stereoscopic form using two image capturing devices or in a multiview form using a plurality of image capturing devices . 3D stereoscopic images can be acquired through stereo or multi-view.

The image sensor unit 110 may include illumination for capturing crops. The illumination of the image sensor unit 110 is adjusted so as to obtain an optimal image based on the light inside the greenhouse or the light outside the greenhouse. In the case of crops clustered together, it is difficult to obtain correct crop image information due to differences in brightness or contrast. The image sensor unit 110 can increase the accuracy of image recognition by controlling the illumination.

 In the case of clustered crops, the spacing between the crops is narrow, so that the leaves or crops overlap each other. If such overlap occurs, it is difficult to accurately recognize the state of the crop through the image sensor unit 110. [ Therefore, the image sensor unit 110 solves this problem by capturing the reference crop of the reference crop unit 150 and collecting crop image information.

The image sensor unit 110 picks up crops and collects crop image information. The image information of the crop can include information on the length of the crop (the size of the crop on the surface), the leaf area, the spacing (the length between nodes), the color of the fruit, and the number of fruits. Leaf area and fruit color can be measured by using stereo vision technology using two imaging devices. The image sensor unit 110 captures reference crops located inside the greenhouse to collect crop image information. The reference crop is located in the reference crop section 170, and a detailed description of the reference crop will be described in the reference crop section 170 described later. The crop image information may further include image information obtained by photographing the crops.

The image sensor unit 110 can photograph crops in the greenhouse at predetermined time intervals or at any time according to a predetermined schedule. When a specific control command is received from the management server unit 130, Crops can be photographed. The image sensor unit 110 may acquire not only the reference crop of the reference crop unit 150 but also the cropped crops according to the received control command to obtain more crop image information.

The image sensor unit 110 captures a reference crop and transmits the collected crop image information to the management server unit 130.

The reference crop section 150 is a crop for comparison with the crop community located inside the greenhouse. Since the crop cluster is composed of two or more crops, the crops may compete with each other, and differences in growth and growth may occur between the respective crops due to differences in nutrient supply and light absorption rate. That is, in the case of crops belonging to a crop community, the sizes and colors of the respective crops may be different from each other, and may be different from the reference crops. In addition, since the distance between them is close to each other, the leaves or the crop itself can be photographed in the image capturing process. For example, when leaves overlap each other, color, shape and pattern of each other are similar to each other, so that it is difficult to recognize the exact state of the crop in the image captured by the image sensor unit 110.

For this reason, reference crops can be used to determine the growth status of the crops in order to better understand the crop growth status. Although reference crops are not associated with clustered crops, they can grow in a natural environment within the same greenhouse, resulting in a much similar growth condition to the clustered crops. On the other hand, only one or a few crops can be placed with sufficient spacing compared to the clustered crops where yields or space must be taken into account. Therefore, when crop image information is obtained through the image sensor unit 110, the state of the crop can be accurately recognized, and the grown state of the recognized crop is relatively similar to the clustered crop.

For example, when determining crop yields, you can calculate the yield of the crops by multiplying the yield of the reference crop by the number of crops that are clustered. In addition, when it is not easy to grasp the exact status of each crop belonging to the crop community, it is difficult to grasp the precise state of each crop. The comparison of the crop growth of the reference crop with the similarity of the growth condition of the crop community, I can predict growth and yield.

The management server unit 130 stores the crop image information received from the image sensor unit 110. The image information of the received crop image is analyzed by image processing technology such as color correction, image identification and recognition, and the detailed information of the present crop such as the current crop height, leaf area, fruit color, bar length and fruit number . When analyzing based on clustered crop information contained in the crop image information, the details of the crop include details of the crop in the crop community. If the analysis is based on the reference crop information contained in the crop image information, the details of the crop include details of the reference crop.

 It is not easy to understand the growth status of crops only by crop image information of clusters of crops because clusters of crops may cause overlapping of leaves or overlapping of crops in each crop. Therefore, it is possible to grasp the growth state of the crop using the crop image information obtained by photographing the reference crop located in the reference cropping unit 150.

The management server unit 130 can analyze the crop image information acquired from the reference crop of the reference crop unit 150 through the image sensor unit 110 to grasp the current growth state of the reference crop. The management server unit 130 may determine that the current crop of the reference crop is the same as the current crop of the crop. Or the management server unit 130 compares the crop image information of the clustered crop with the crop image information of the reference crop to determine the similarity and then applies the similarity to the current growth state of the reference crop to estimate the current growth state of the clustered crop can do.

For example, the number of crops in a cluster can be estimated by multiplying the current number of crops in the reference crop by the number of crops in the clustered crop. Or considering the similarity between the reference crop and the clustered crops, it is considered that the clustered crops are compared with the reference crops to have a certain number of errors compared to the reference crops. Considering this fact, applying the similarity to the fruit number of the reference crops, Can be estimated.

The management server unit 130 predicts the growth and yield of the clustered crops by using detailed information about the crops stored in advance, detailed information about the reference crops, and detailed information about the clustered crops. Professional information on crops represents expert knowledge and data on the crops. The management server unit 130 can grasp the current crop growth and state based on the crop image information. The management server unit 130 predicts the growth and yield of the crop using information on the growth and condition of the current crop and the expert knowledge / data about the crop, and calculates the growth and yield of the crop And generates and provides the user with crop estimation information.

For example, by comparing the similarity of the crop growth of the reference crop with the growth status of the crop community, it is possible to determine the current state of the crop and the number of fruits, and compare the current state of growth and fruit number with the expert knowledge / Crop growth, harvest time or fruit number can be predicted.

The management server unit 130 may control the image sensor unit 110 by transmitting a control signal to the image sensor unit 110. The control signal may include an instruction to perform imaging at a constant cycle, or may include an instruction to perform imaging in accordance with a user ' s specific instruction.

2 is a detailed view showing an embodiment of the management server unit 130 of the greenhouse crop information management system 100 according to the present invention.

2, the management server unit 130 of the greenhouse crop information management system 100 according to the present invention includes an information storage unit 131, an information processing unit 132, a control unit 133, and a communication unit 134 ).

The information storage unit 131 stores the crop image information received from the image sensor unit 110. The image sensor unit 110 transmits the crop image information captured by the image capturing apparatus to the management server unit 130, The information storage unit 131 stores the received crop image information to form a database.

The information storage unit 131 stores expert information on the crop. Professional information on crops includes expertise and data on each crop. Expert information on crops is used as data for predicting the current crop status and yield from collected crop image information.

The information storage unit 131 transmits the received crop image information and the stored crop information to the information processing unit 132.

The information processing unit 132 grasps the current crop growth state based on the crop image information received from the information storage unit 131 and the expert information on the crop. The information processing unit 132 analyzes the crop image information by applying an image processing technique such as color correction, image identification, and recognition to the received crop image information to determine the current crop height, leaf area, fruit color, Generate details of the current crop. In the present invention, not only clustered crops but also reference crops are separately arranged to collect crop image information, so that the problem of overlapping each crop can be prevented in advance. Therefore, the information processing unit 132 can analyze the image information of the reference crops rather than directly analyze the image information of the clustered crops, thereby more accurately grasping the crop growth state.

The information processing unit 132 analyzes the image information of the reference crop and estimates the growth state of the clustered crop based on the detected growth state of the reference crop. First, it is assumed that the growth status of the reference crop is similar to the average growth status of the clustered crop, and the growth status of the reference crop can be estimated as the growth status of the whole crop of the clustered crop. Another method is to estimate the growth status of the clustered crops by applying similarity to the growth status of the reference crops, taking into account the similarity between the reference crops and the clustered crops. The degree of similarity can be input by a user or an expert, and can be determined by comparing the image information of the reference crop and the image information of the clustered crop. The degree of similarity can be determined by comparing the growth period of the reference crop and the growth period of the clustered crop.

The information processing unit 132 can analyze the current state of the crop by comparing the detailed information of the crop with the specialized information about the crop. Information on crop growth, leaf area, and fruit color, as well as leaf area and fruit color of the crops included in the specialized information on the crops can be combined to confirm the accurate growth and health status of the crops. In this case, details of the current crop can be used, as well as details of past crops previously collected and stored.

The information processing unit 132 predicts future growth and yield of the crop based on the detailed information of the crop and the specialized information about the crop. The information processing unit 132 generates information on the growth and state of the current crop, Estimates the growth and yield of the crop using the expert knowledge / data on the crop, generates crop estimation information including information on the growth and yield of the crop, and provides the generated information to the user. For example, comparing the date of planting of the crop and the growth status of the current crop with the growth status of the general crop, the better the result, the higher the yield and the more specific the yield based on the expertise. The information processing unit 132 generates crop estimation information including the predicted yield and the crop growth state, and transmits the generated crop estimation information to the communication unit 134.

The control unit 133 generates a control signal based on the control request received from the communication unit 134, and transmits the generated control signal to the image sensor unit 110. The control signal includes a control command for the image sensor unit, and the control command may include an instruction to perform imaging at a predetermined period, or may include an instruction to perform imaging according to a user's specific instruction.

The communication unit 134 transmits the crop image information and the crop estimation information received from the information processing unit 132 to the user. The control unit 133 transmits the control request received from the user to the control unit 133.

FIG. 3 is a flowchart illustrating a method for managing greenhouse crop information through an image according to an embodiment of the present invention.

Referring to FIG. 3, in the method of managing greenhouse crop information through an image according to an embodiment of the present invention, first, a reference crop is photographed to generate crop image information (301). The greenhouse crop information management system according to the present invention includes a clustered crop planted with a crop to be cultivated and a reference crop for improving the efficiency and accuracy of the image recognition process. Clustered crops may overlap each other in the process of acquiring image information through image capture because the interval between them is narrow. Because the same crop is planted, it is not easy to accurately recognize the target crops because they are similar in color, shape and pattern. Thus, one or a small number of reference crops can be used to determine the current growth state of the crop.

For this reason, reference crops can be used to determine the growth status of the crops in order to better understand the crop growth status. Although reference crops are not associated with clustered crops, they can grow in a natural environment within the same greenhouse, resulting in a much similar growth condition to the clustered crops. On the other hand, only one or a few crops can be placed with sufficient spacing compared to the clustered crops where yields or space must be taken into account. Therefore, when crop image information is acquired through image capture, the state of the crop can be correctly recognized, and the growth state of the recognized crop can be inferred to be relatively similar to the clustered crop.

The image capturing device for capturing reference crops can be configured as a single image capturing device, and can be photographed in stereo format or multi-view format through two or more image capturing devices, or captured in 3D stereoscopic image to collect crop image information can do. In addition, it can acquire the optimal image considering the brightness inside the greenhouse and the light coming from the outside of the greenhouse by adjusting the own lighting.

The generation of the crop image information by capturing the crop can be performed by capturing the image at a predetermined cycle according to the predetermined value or continuously capturing it continuously. The crop image information is generated by taking the image at an arbitrary time according to the user's request or command can do.

Next, the current crop state of the reference crop is analyzed (302) by analyzing the generated crop image information. By applying image processing technology such as color correction, image identification and recognition to the crop image information, the crop image information is analyzed and the details of the present crop such as the crop height, leaf area, fruit color, bar length, . It is not easy to understand the growth status of crops only by crop image information of clusters of crops because clusters of crops may cause overlapping of leaves or overlapping of crops in each crop. Therefore, the growth status of the crop can be grasped by using the crop image information obtained by photographing the reference crop.

Based on the current growth state of the identified reference crop, the current growth state of the clusters is estimated (303). The greenhouse environment system based on the image according to the present invention estimates the growth state of the crops collected through reference crops. Two methods can be used as a method for estimating the growth state of the clusters through reference to the growth state of the reference crops.

The first is to assess that the current crop status of the reference crop is the same as the average crop status of the clustered crop. Reference crops are grown in the same natural environment within the same greenhouse as the clustered crops, receiving the same water, fertilizer and energy. Therefore, it is assumed that the growth status of the reference crops is almost similar to the average growth status of the clustered crops. For example, the fruit number of a reference crop can be estimated by multiplying the number of crops in the clustered crop by the number of crops in the reference crop.

The second is to estimate the growth status of the clustered crops, taking into account the similarity between the reference crops and the clustered crops. The degree of similarity between reference crops and clustered crops can be input by a user or a specialist, or the similarity can be determined by comparing the reference crops with the image information of the clustered crops. For example, if the clustered crops are compared with the reference crops in terms of similarity, the number of fruits can be estimated by applying the similarity to the fruit number of the reference crops.

Next, the growth and yield of the crop is predicted 304 based on the current growth state of the crop and the specialized information about the crop stored in advance. Based on the estimated current clustered crop growth status, the growth or yield of the future clustered crops can be predicted. Estimate the growth and yield of the clustered crops, taking into account the expert knowledge / data associated with the crop in question and the estimated current crop growth status. For example, expert knowledge / data can be applied to the current state of crop growth to predict crop growth, harvest timing, or fruit number.

As described above, the system and method for managing greenhouse crop information through the image according to the present invention can control the greenhouse environment by measuring the state of crops from the early stage of cultivation to the harvesting time without any significant influence on time, , Which enables high quality crops and crop yields to be predicted. By applying IT technology to the agricultural sector, it is possible to increase the value added and productivity of agriculture, and it has the effect of reducing labor due to automation.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It is possible.

110: Image sensor unit
130: management server unit
131: Information storage unit
132: Information processor
133:
134:
150: Reference crop section

Claims (11)

A greenhouse environment management system for growing crops clustered in a greenhouse,
A reference crop part located inside the greenhouse and including a reference crop, which is the same crop as the clustered crop, and is located separately from the clustered crop;
An image sensor unit that includes one or more image capturing apparatuses and captures the reference crop using the image capturing apparatus to generate crop image information; And
Analyzing the generated crop image information to grasp a growth state of the reference crop, estimating a growth state of the clustered crop based on the detected growth state of the reference crop, A management server unit for predicting the growth and yield of the clustered crops based on the growth and yield of the crops;
Wherein the greenhouse information management system comprises: The method according to claim 1,
Wherein the image sensor unit comprises:
And generating crop image information including a three-dimensional image through at least two image capturing apparatuses. The method according to claim 1,
Wherein the image sensor unit comprises:
Wherein the illumination is adjusted so as to obtain an optimal image corresponding to the brightness of the inside of the greenhouse or the light from the outside of the greenhouse, Management system. The method according to claim 1,
The management server unit,
And analyzing the generated crop image information through at least one image processing technique among image processing techniques including color correction, image identification and image recognition to grasp the growth state of the reference crop. Information management system. The method according to claim 1,
The management server unit,
And storing the expert knowledge data on the clustered crops and estimating the growth and yield of the clustered crops in consideration of the growth state of the clustered crops and the expert knowledge data. Greenhouse Crop Information Management System. The method according to claim 1,
The management server unit,
Wherein the growth status of the clustered crops is estimated in consideration of a similarity between the growth period of the reference crop and the growth period of the clustered crops. A method for managing a greenhouse environment for cultivating a cluster of crops in a greenhouse,
Generating crop image information by photographing a reference crop, which is located in the greenhouse separately from the clustered crops and is the same crop as the clustered crops;
Determining a growth state of the reference crop based on the generated crop image information;
Estimating a growth state of the clustered crop based on the detected growth state of the reference crop; And
Estimating the growth and yield of the clustered crop based on the estimated growth state of the crop;
The method comprising the steps of: 8. The method of claim 7,
Wherein the step of grasping the growth state of the reference crop based on the generated crop image information comprises:
And analyzing the received crop image information through at least one image processing technique among image processing techniques including color correction, image identification and image recognition to grasp the growth state of the reference crop. Information management method. 8. The method of claim 7,
The step of predicting the growth and yield of the clustered crop based on the estimated growth state of the crop,
Wherein the growth and yield of the clustered crops are predicted based on expert knowledge data about the clustered crops stored in advance. 8. The method of claim 7,
The crop image information is generated by photographing a reference crop, which is located in the greenhouse separately from the clustered crop and is the same crop as the clustered crop,
Wherein the illumination is adjusted so as to obtain an optimal image corresponding to the brightness of the inside of the greenhouse or the light from the outside of the greenhouse, and is photographed. 8. The method of claim 7,
The step of estimating a growth state of the clustered crop based on the detected growth state of the reference crop includes:
Wherein the reference crop is estimated in consideration of a similarity between the growth period of the reference crop and the growth period of the clustered crops.
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US11411841B2 (en) 2016-09-21 2022-08-09 Iunu Inc. Reliable transfer of numerous geographically distributed large files to a centralized store
US11776050B2 (en) 2016-09-21 2023-10-03 Iunu, Inc. Online data market for automated plant growth input curve scripts
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