WO2020116775A1 - Dispositif d'analyse de productivité et de maladie agricole utilisant des mégadonnées, et procédé d'analyse de productivité et de maladie l'utilisant - Google Patents

Dispositif d'analyse de productivité et de maladie agricole utilisant des mégadonnées, et procédé d'analyse de productivité et de maladie l'utilisant Download PDF

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Publication number
WO2020116775A1
WO2020116775A1 PCT/KR2019/013921 KR2019013921W WO2020116775A1 WO 2020116775 A1 WO2020116775 A1 WO 2020116775A1 KR 2019013921 W KR2019013921 W KR 2019013921W WO 2020116775 A1 WO2020116775 A1 WO 2020116775A1
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Prior art keywords
farm
data
productivity
disease
collected
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PCT/KR2019/013921
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English (en)
Korean (ko)
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정진해
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주식회사 한밭아이오티
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Publication of WO2020116775A1 publication Critical patent/WO2020116775A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

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  • the present invention relates to a farm productivity and disease analysis device using big data and a productivity and disease analysis method using the same, and more specifically, predicts productivity by collecting farm data in real time by a data collection unit installed in various places on the farm. , Analyze, and predict the disease based on the pictures of livestock photographed through a camera installed on the farm. It relates to a productivity analysis device and a productivity analysis method using the same.
  • producers or producers of agricultural and livestock products want to more accurately and dynamically check the agricultural and industrial data, which is data on agricultural and livestock products.
  • livestock farms that raise other livestock such as chickens, ducks, pigs, dogs, cows, horses, and the like, are subject to biological changes, such as physiological changes, disease outbreaks, and emergency reversion to livestock that are raised or propagated It is important to identify the back in a timely manner and take necessary measures.
  • the prior art has a problem in that since it is necessary to attach a biological device to each of a large number of livestock, an economic burden is applied, and since it is necessary to collect and analyze the biological change data in a batch, it is impossible to immediately identify the biological change and immediately act on it. .
  • the bio-change data is sent to a specialized institution, and the bio-change can be grasped by receiving the analyzed data from the professional institution, there is an inefficient problem that cannot be immediately identified. .
  • the present invention has been devised to solve the above-mentioned problems, and a conventional user has to attach a biological device to each of the livestock, so an economic burden is applied, and the biological change or disease data must be collectively collected and analyzed.
  • the purpose is to solve the problem that it is not possible to immediately identify and change the disease or change the body immediately.
  • a farm server that is installed on a farm for raising livestock and analyzes productivity and disease, and stores at least one installed input unit and information on the farm, and predicts and analyzes the productivity of the farm and the disease of livestock in the farm.
  • a cloud server that collects, classifies, and stores information from the farm server.
  • the farm server is characterized in that it comprises a data collection unit for collecting data captured in the farm and images captured through the input unit and an analysis unit for analyzing and processing data collected from the data collection unit.
  • the analysis unit compares and analyzes an image captured through the input unit with a pre-stored picture.
  • the data is collected in real time.
  • Installed in a farm for raising livestock in a method of analyzing productivity and disease, collecting data in a farm in real time, storing data in a farm collected in real time on a farm server, and collecting farm data It is characterized in that it comprises the step of collecting and sorting in the cloud server, comparing and analyzing images or videos collected through the input unit with pre-stored photos, to grasp farm productivity or to analyze diseases.
  • the present invention collects data in real time and analyzes it, so that it is possible to immediately grasp the surrounding environment, to grasp an instant change in the body, and to immediately recognize whether or not a disease is infected.
  • FIG. 1 is a view showing a big data construction according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating data collection according to an embodiment of the present invention.
  • FIG. 3 is a diagram illustrating data analysis according to an embodiment of the present invention.
  • FIG. 4 is a view showing a disease identification method according to an embodiment of the present invention.
  • FIG. 5 is a view showing a disease identification method according to an embodiment of the present invention.
  • the present invention relates to an apparatus and method for analyzing and predicting productivity and disease of a farm using big data, and an apparatus and method for analyzing productivity and predicting disease based on data information collected from a farm and providing it to a user will be.
  • Production environment, breeding environment, farm management, automation machinery, etc. which are information necessary for the entire process from birth to shipment of livestock, are collected as data information, and the collected information is converted into big data. Analyze, predict, and provide information on productivity, disease, etc., and provide productivity analysis and disease analysis results to users through varieties, breeding facilities, regions, environments, feed, and drugs through big data collected for each farm. .
  • the livestock described in the general specification of the present invention describes chicken as an example, but is not limited thereto, and can be applied to various livestock.
  • the farm productivity and disease analysis device using big data is installed on a farm for raising livestock, and is a device for analyzing productivity and disease, and is largely composed of an input unit, a farm server, and a cloud server.
  • At least one input unit is installed in the farm, and is provided to photograph an image of a livestock or to photograph an environment of a farm.
  • the input unit includes not only image information in the farm, but also environment information, user input information, and the like. The use of the environment of the livestock or farm photographed by the input unit will be described later.
  • FIG. 1 is a view showing a big data construction according to an embodiment of the present invention. This will be described in more detail with reference to FIG. 1.
  • the farm server itself is capable of analyzing productivity and predicting diseases. That is, the farm server provided in each farm collects and stores the data of each farm, and the information of each farm is stored in a cloud server and constructed as one big data. The information stored in the cloud server is provided to the user.
  • FIG. 2 is a diagram illustrating data collection according to an embodiment of the present invention.
  • the input unit may be a camera or the like.
  • the information is transmitted to the data collection program of the data collector, and the date and time are input together.
  • the input data changes compared to previously stored data, raw data is collected, necessary data is extracted, and processing data is stored.
  • the stored processing data is transmitted to the farm server.
  • the sensor, sorter, input unit, camera, and specification manager in the farm will perform monitoring again.
  • FIG. 3 is a diagram illustrating data analysis according to an embodiment of the present invention. It will be described in more detail with reference to FIG. 3.
  • a device for analyzing productivity and disease may include an input unit, a farm server, a cloud server, and a user.
  • the input unit includes data such as a camera, a microphone and a sensor, equipment, specifications, breeding, and production, and also includes a user input unit directly input by a user.
  • image and image data acquired through the input unit is image converted.
  • the converted image is stored in a storage and classified.
  • the criteria to be classified are classified by a behavior pattern or an image form.
  • the converted image analyzes disease and behavior patterns.
  • the voice data obtained through the microphone is first, noise is removed.
  • the voice data from which the noise has been removed is also stored in the storage and classified. Based on the classified data, disease and voice patterns are analyzed.
  • Information input by information such as user input, sensors, devices, specifications, breeding, production, etc. is data input through clinical observation, and the data analyzes the association of diseases.
  • the analyzed data is stored in the farm server. All of the analyzed data is collected, and the collected data is stored in a cloud server, thereby providing information to the user.
  • the cloud server stores each data in various categories such as history, region, breed, feed, drug, and farm.
  • farm A acquires data of farm A, wherein the data is obtained from an input unit, a microphone, clinical observation, a specification device, and a sensor.
  • the input unit and the microphone are devices such as cameras, CCTVs, smart phones, and microphones installed in the farm, and the clinical observations are observation data such as mortality, culling, movement, shipping, weight, feed, drugs, drinking water, facilities, and the like.
  • the specification device is a device such as feed measurement, negative measurement, egg screening, egg screening, and weight measuring device
  • the sensor is a device for measuring temperature, humidity, carbon dioxide, ammonia, wind speed, sound pressure, intrusion detection, power failure, and the like.
  • Data obtained from the input unit, microphone, clinical observation, specification device, and sensor are moved to the storage and classified.
  • the classified data is analyzed, and the data is divided into basic data, learning data, and data through learning, and based on this, disease or productivity can be predicted and disease or productivity can be analyzed.
  • the farm server can collect all data such as egg selection information, farm breeding information, environmental information, feed bin management, water volume management, and disaster management occurring in the farm. Collected on the farm server, and connected to the cloud server, collects data from each farm server into one to predict and analyze productivity and disease.
  • the farm server provided in each farm collects and stores the data of each farm, and the information of each farm is stored in a cloud server and constructed as one big data. The information stored in the cloud server is provided to the user.
  • the cloud server includes all data and history, region, breed, feed, drug, farm, such as egg selection information, farm breeding information, environmental information, feed bin management, water volume management, disaster management, etc.
  • Data is stored in various classifications such as stars, and the farm server provided in each farm is connected to a cloud server to analyze productivity and predict disease and data in each farm server.
  • productivity and disease data for each farm on a cloud server, analyzes and predicts various productivity analysis, such as farm, breed, family, feed, and drug, and correlates disease to provide it to users.
  • farm productivity for example, environment, breeding, specifications, management, etc., and provides it to users.
  • the farm server is characterized in that it further comprises a data collection unit for collecting data captured in the farm and images captured through the input unit and an analysis unit for analyzing and processing data collected from the data collection unit.
  • the data collection unit is provided to collect images and environmental information photographed from the input unit, and is provided to collect images photographed through the input unit and to collect various data such as breeding information, feed information, and volume information. .
  • the data collection unit collects data in real time, and in this case, data collected in real time may be data such as feed amount, negative volume, humidity, and temperature, and all the collected data is stored in the farm server.
  • the analysis unit is provided to analyze and process the collected data. For example, it is provided to identify a disease or condition by comparing an image of a livestock photographed through the input unit with a pre-stored photograph.
  • a step of collecting data in a farm in real time is performed, and the size of the collected data in real time is analyzed and accumulated in a server. Then, the accumulated data is compared with pre-stored data.
  • FIG. 4 is a view showing a disease identification method according to an embodiment of the present invention
  • Figure 5 is a view showing a disease identification method according to an embodiment of the present invention. 4 to 5, the disease identification method according to the embodiment will be described in more detail.
  • a behavioral pattern analysis and image analysis are performed on collected images or images of chicks suspected of avian encephalomyelitis.
  • the diagnosis is made with avian encephalomyelitis, and the appropriate prescription, treatment, or treatment is performed.
  • the image or video collected through the input unit is firstly compared and analyzed with the photo stored in the database.
  • the database may be a database provided in a farm server or a cloud server. That is, the disease picture stored in the database and the image captured by the input unit are compared and analyzed.
  • AE avian encephalomyelitis
  • the chicken is clinically observed, and the production of eggs is reduced and the amount of feed intake and the amount of negative water is reduced. That is, based on the data collected on the farm, it is checked whether the chicken has a paralysis symptom, a weakened leg, a slight tremor in the head and neck, or an increased mortality.
  • a collected image or image of an egg having suspected avian adenovirus-egg syndrome is analyzed. Based on the collected images, the images of the eggs stored in the database are compared and analyzed. If the egg syndrome (EDS 76) is suspected, look at the collected database. In the collected database, clinical observations of chickens. Obtain clinical observation and production of eggs. Make sure that the chicken's eyesight is deteriorating and dull, diarrhea occurs, mortality increases, the egg size decreases, the eggs without shells appear, the eggs appear thin, and the color is lost. If the database shows symptoms indicating egg syndrome, it is determined as egg syndrome and informs the user that avian adenovirus-egg syndrome is suspected.
  • EDS 76 egg syndrome
  • the user is suspected of avian adenovirus-egg syndrome, so the veterinarian is notified of the correct diagnosis, and the suspected infection is separated from the chick and poultry, and the confirmed infection and chick and poultry are followed up. It also provides information, as well as information that is transmitted horizontally from other infected children.
  • the analysis unit may use artificial intelligence in comparing and analyzing the image.
  • the analysis unit may train binary classification having 1 or 0 for the normal or abnormal egg.
  • the binary classification has 1 or 0 as the final result, and compares the two input images to determine whether they are the same or different. That is, when an image of an egg is collected by using a binary recipe, compared with an image of an egg in a pre-stored database, a result of 1 is displayed when there is no difference, and a value of 0 is displayed when there is a difference. .
  • the image of the egg in the pre-stored database may be an image of the diseased egg. In the above case, if a value of 1 indicating no difference appears, it can be seen that the current egg is also diseased.
  • a consulting unit may be further provided to assist the user in managing the farm. More specifically, farm information in the database may be provided to a consultant terminal, and consulting information received through the consultant terminal may be provided to a user terminal. The consulting proceeds with consulting on chicken or egg information made through the farm server, but is not limited thereto, and includes all consulting contents that can be provided regarding farm operation, such as a countermeasure against extreme weather. Can be.
  • the consulting unit may structure and provide a common portion of consulting information received from a plurality of consultants to the user. More specifically, when the user is consulted by a plurality of consultants, by highlighting or layering a common part of the consulting information and providing it to the user, the user can more objectively recognize and use the chatting information. There will be.
  • the consulting unit may further include a bulletin board or a community for exchange between users or between the user and the consultant.
  • a bulletin board or a community for exchange between users or between the user and the consultant.
  • the consulting may be performed at a specific time or periodically based on the user's request through the user's terminal.
  • the artificial intelligence system uses an algorithm developed for a specific purpose, such as existing artificial intelligence, and big data related to the specific purpose, and is learned by a human in a laboratory or the like beforehand in the field or in practice. It is not a method to put it in, but it is possible to learn in real time while performing a task as a user next to a user who needs to be assisted (supported) by making a simple pre-learning that is possible without big data and then putting it into the field.

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Abstract

La présente invention concerne un dispositif d'analyse de productivité agricole utilisant des mégadonnées, et un procédé d'analyse de productivité l'utilisant et, plus particulièrement, un dispositif d'analyse de productivité et un procédé d'analyse de productivité l'utilisant, qui collectent des données agricoles en temps réel par des unités de collecte de données installées sur l'ensemble d'une ferme, de façon à prédire et à analyser la productivité, et à prédire et à analyser une maladie sur la base d'une photographie de bétail prise par l'intermédiaire d'une caméra installée dans la ferme.
PCT/KR2019/013921 2018-12-03 2019-10-23 Dispositif d'analyse de productivité et de maladie agricole utilisant des mégadonnées, et procédé d'analyse de productivité et de maladie l'utilisant WO2020116775A1 (fr)

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KR10-2018-0153797 2018-12-03
KR1020180153797A KR102253236B1 (ko) 2018-12-03 2018-12-03 빅데이터를 이용한 농장 질병 분석 장치

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WO2015001538A2 (fr) * 2013-07-05 2015-01-08 Stellapps Technologies Private Limited Système de gestion d'exploitation agricole et de troupeau
JP2017068869A (ja) * 2016-12-27 2017-04-06 三菱電機株式会社 農業用管理システム及び農業用管理システム用管理センタ
KR101832724B1 (ko) * 2016-09-08 2018-04-16 아인정보기술 주식회사 영상이미지를 통한 농작물 생육 진단 시스템 및 방법
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KR20180068347A (ko) * 2016-12-13 2018-06-22 (주)인스페이스 농축산 빅데이터를 이용한 멀티플랫폼 기반 다양한 컨트롤러 가시화 api 제공 시스템.

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KR20130088667A (ko) * 2012-01-31 2013-08-08 (주)아크로엠 가축생산성 향상을 위한 ｕ―ＩＴ기반 원격 화상 진료 장치
KR20140105626A (ko) 2013-02-21 2014-09-02 순천대학교 산학협력단 가축 분만 및 감지 모니터링 시스템과 그 방법
KR20170084834A (ko) * 2016-01-13 2017-07-21 전주기전대학 산학협력단 모바일 클라우드 컴퓨팅 환경하에서 사물인터넷을 이용한 지능형 축사 관리 환경 시스템
KR101866226B1 (ko) * 2016-11-09 2018-07-04 건국대학교 산학협력단 대가축 모니터링 시스템

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Publication number Priority date Publication date Assignee Title
WO2015001538A2 (fr) * 2013-07-05 2015-01-08 Stellapps Technologies Private Limited Système de gestion d'exploitation agricole et de troupeau
KR101832724B1 (ko) * 2016-09-08 2018-04-16 아인정보기술 주식회사 영상이미지를 통한 농작물 생육 진단 시스템 및 방법
KR20180040862A (ko) * 2016-10-13 2018-04-23 (주)농정사이버 클라우드 팜 통합관제시스템
KR20180068347A (ko) * 2016-12-13 2018-06-22 (주)인스페이스 농축산 빅데이터를 이용한 멀티플랫폼 기반 다양한 컨트롤러 가시화 api 제공 시스템.
JP2017068869A (ja) * 2016-12-27 2017-04-06 三菱電機株式会社 農業用管理システム及び農業用管理システム用管理センタ

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