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 PDFInfo
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Abstract
Description
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
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
The
The
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
The
The
The
The
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
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
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
The
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
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
2 is a detailed view showing an embodiment of the
2, the
The
The
The
The
The
The
The
The
The
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 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:
Wherein the image sensor unit comprises:
And generating crop image information including a three-dimensional image through at least two image capturing apparatuses.
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 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 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 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.
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:
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.
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.
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.
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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Cited By (14)
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CN107272778A (en) * | 2017-06-26 | 2017-10-20 | 芜湖纯元光电设备技术有限公司 | A kind of robot control system(RCS) of greenhouse with remote monitoring |
WO2018057799A1 (en) * | 2016-09-21 | 2018-03-29 | iUNU, LLC | Horticultural care tracking, validation and verification |
WO2018207989A1 (en) * | 2017-05-10 | 2018-11-15 | 안주형 | Crop harvesting and management system using automatic control equipment |
US10339380B2 (en) | 2016-09-21 | 2019-07-02 | Iunu, Inc. | Hi-fidelity computer object recognition based horticultural feedback loop |
CN110197308A (en) * | 2019-06-05 | 2019-09-03 | 黑龙江省七星农场 | A kind of crop monitoring system and method for agriculture Internet of Things |
WO2019245122A1 (en) * | 2018-06-21 | 2019-12-26 | 주식회사 에스에스엘 | System for monitoring, in real time, growth state of crop in greenhouse on basis of iot |
US10635274B2 (en) | 2016-09-21 | 2020-04-28 | Iunu, Inc. | Horticultural care tracking, validation and verification |
US10791037B2 (en) | 2016-09-21 | 2020-09-29 | Iunu, Inc. | Reliable transfer of numerous geographically distributed large files to a centralized store |
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US11062516B2 (en) | 2018-02-07 | 2021-07-13 | Iunu, Inc. | Augmented reality based horticultural care tracking |
KR102334681B1 (en) * | 2021-01-19 | 2021-12-06 | 허영훈 | Smart farm control device using mesh-type network and method of operating the same |
US11244398B2 (en) | 2016-09-21 | 2022-02-08 | Iunu, Inc. | Plant provenance and data products from computer object recognition driven tracking |
US11538099B2 (en) | 2016-09-21 | 2022-12-27 | Iunu, Inc. | Online data market for automated plant growth input curve scripts |
US11720980B2 (en) | 2020-03-25 | 2023-08-08 | Iunu, Inc. | Crowdsourced informatics for horticultural workflow and exchange |
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2012
- 2012-12-14 KR KR1020120146417A patent/KR20140077513A/en not_active Application Discontinuation
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US10635274B2 (en) | 2016-09-21 | 2020-04-28 | Iunu, Inc. | Horticultural care tracking, validation and verification |
US11244398B2 (en) | 2016-09-21 | 2022-02-08 | Iunu, Inc. | Plant provenance and data products from computer object recognition driven tracking |
US10339380B2 (en) | 2016-09-21 | 2019-07-02 | Iunu, Inc. | Hi-fidelity computer object recognition based horticultural feedback loop |
US10791037B2 (en) | 2016-09-21 | 2020-09-29 | Iunu, Inc. | Reliable transfer of numerous geographically distributed large files to a centralized store |
US11538099B2 (en) | 2016-09-21 | 2022-12-27 | Iunu, Inc. | Online data market for automated plant growth input curve scripts |
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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 |
US11347384B2 (en) | 2016-09-21 | 2022-05-31 | Iunu, Inc. | Horticultural care tracking, validation and verification |
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WO2018207989A1 (en) * | 2017-05-10 | 2018-11-15 | 안주형 | Crop harvesting and management system using automatic control equipment |
CN107272778A (en) * | 2017-06-26 | 2017-10-20 | 芜湖纯元光电设备技术有限公司 | A kind of robot control system(RCS) of greenhouse with remote monitoring |
US11062516B2 (en) | 2018-02-07 | 2021-07-13 | Iunu, Inc. | Augmented reality based horticultural care tracking |
US11804016B2 (en) | 2018-02-07 | 2023-10-31 | Iunu, Inc. | Augmented reality based horticultural care tracking |
WO2019245122A1 (en) * | 2018-06-21 | 2019-12-26 | 주식회사 에스에스엘 | System for monitoring, in real time, growth state of crop in greenhouse on basis of iot |
CN110197308A (en) * | 2019-06-05 | 2019-09-03 | 黑龙江省七星农场 | A kind of crop monitoring system and method for agriculture Internet of Things |
US11720980B2 (en) | 2020-03-25 | 2023-08-08 | Iunu, Inc. | Crowdsourced informatics for horticultural workflow and exchange |
KR102273531B1 (en) * | 2021-01-06 | 2021-07-05 | 김민성 | System and method for circulation cultivation based on hydrogen fuel cell |
KR102334681B1 (en) * | 2021-01-19 | 2021-12-06 | 허영훈 | Smart farm control device using mesh-type network and method of operating the same |
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