KR20230128219A - Artificial intelligence service to determine the quality of agricultural products - Google Patents
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Abstract
본 발명은 농산물 품질 판별 인공지능 서비스에 관한 것으로, 더 상세히는 농산물의 이미지 데이터를 분석하여 흑점, 멍, 열과 등을 추출하여 품질을 판별하는 농산물 품질 판별 인공지능 서비스에 관한 것이다.The present invention relates to an artificial intelligence service for determining the quality of agricultural products, and more particularly, to an artificial intelligence service for determining the quality of agricultural products by analyzing image data of agricultural products and extracting black spots, bruises, heat fruits, etc.
Description
본 발명은 농산물 품질 판별 인공지능 서비스에 관한 것으로, 더 상세히는 농산물의 이미지 데이터를 분석하여 흑점, 멍, 열과 등을 추출하여 품질을 판별하는 농산물 품질 판별 인공지능 서비스에 관한 것이다.The present invention relates to an artificial intelligence service for determining the quality of agricultural products, and more particularly, to an artificial intelligence service for determining the quality of agricultural products by analyzing image data of agricultural products and extracting black spots, bruises, heat fruits, etc.
한국형 지속가능한 농업 달성을 위해 농림축산식품부, 과학기술정보통신부 및 농촌진흥청이 공동으로 추진하는 스마트팜다부처패키지혁신기술개발사업단이 설립되었다. 특히 인공지능과 빅데이터 융합 스마트 솔루션 및 차세대 융복합·원천기술 개발을 목적으로 스마트팜 연구개발 사업을 진행하고 있다.In order to achieve Korean-style sustainable agriculture, the Ministry of Agriculture, Food and Rural Affairs, the Ministry of Science and ICT, and the Rural Development Administration jointly promoted the Smart Farm Multi-Ministerial Package Innovation Technology Development Project Group. In particular, it is conducting a smart farm research and development project for the purpose of developing artificial intelligence and big data convergence smart solutions and next-generation convergence and source technologies.
과학기술정보통신부는 4차 산업혁명의 혜택을 농어촌으로 확산하기 위한 2021년도 ‘지능형 마을(스마트빌리지) 사업’을 진행하였으며 전남 장성군은 ‘인공지능 기반 옐로우시티 주민행복 소득형 마을(빌리지)’라는 주제로 지역민의 소득 증대를 목표로 참여하고 있다.The Ministry of Science and ICT conducted the 2021 'Intelligent Village (Smart Village) Project' to spread the benefits of the 4th Industrial Revolution to rural areas, and Jangseong-gun, Jeollanam-do, launched the 'Artificial Intelligence-based Yellow City Resident Happy Income Village (Village)'. The theme is to participate with the goal of increasing the income of local residents.
이러한 사업의 하나로 생산물의 품질을 자동을 분석할 수 있는 시스템의 요구의 증대되고 있으며 이러한 배경에서 인위적으로 찍은 이미지가 아닌 실시간 자동선별기 속 농산물의 이미지를 인식하여 불량품/ 양품을 구별하며 양품의 등급을 나눌 수 있는 AI 서비스가 필요하다고 할 수 있다.As one of these projects, the demand for a system that can automatically analyze the quality of products is increasing. Against this background, images of agricultural products in real-time automatic sorting machines are recognized instead of artificially taken images to distinguish defective products from good products and to classify good products. It can be said that there is a need for AI services that can be shared.
본 발명은 딥러닝 기반의 경험적 피드백을 통해서 다양한 예외상황 및 결함에 강인하며 간단한 설정으로 판별 모델의 변경 및 적용 가능한 스마트팜 전용 인공지능 이미지 검사용 팜별 서비스를 제공하고 클라우드 기반으로 학습 모델을 공유할 수 있는 농산물 품질 판별 인공지능 서비스를 제공한다The present invention is robust to various exceptions and defects through deep learning-based empirical feedback, provides farm-specific services for artificial intelligence image inspection exclusively for smart farms that can change and apply discrimination models with simple settings, and can share learning models based on the cloud. Provides artificial intelligence services that can determine the quality of agricultural products
상기의 과제를 해결하기 위한 본 발명의 농산물 품질 판별 인공지능 서비스은 자동선별기의 카메라부터 농산물의 이미지를 획득하는 데이터 획득부; 이미지로부터 농산물의 객체를 추출하는 객체추출부; 추출한 객체로부터 불량요소(흑점, 멍, 열과 등)을 검출하는 불량검출부; 불량의 판별한 농산물을 분리하기 위해 분리기 값을 전송하는 피드백부; 분석 결과를 시각화하는 시각화부; 학습 모델과 데이터를 클라우드로 공유하는 공유부를 포함한다.The artificial intelligence service for determining the quality of agricultural products of the present invention for solving the above problems includes a data acquisition unit that acquires images of agricultural products from a camera of an automatic sorter; An object extraction unit for extracting an object of agricultural products from an image; a defect detection unit that detects defect elements (black spots, bruises, heat, etc.) from the extracted objects; A feedback unit for transmitting a separator value to separate the agricultural products determined to be defective; Visualization unit for visualizing the analysis result; It includes a sharing unit that shares learning models and data to the cloud.
따라서, 본 발명의 농산물 품질 판별 인공지능 서비스는 농산물의 크기 선별, 품질 판독 등을 수행해 축적된 선별 데이터를 통해 품질 개선 효과를 얻을 수 있으며 인건비 감소를 통한 가격 경쟁력 확보가 가능하고 다품종 소량 생산에 능동적으로 대처할 수 있으며, 농가소득 창출에도 크게 기여할 것으로 기대된다.Therefore, the artificial intelligence service for determining the quality of agricultural products of the present invention can obtain the quality improvement effect through the selection data accumulated by performing size selection and quality reading of agricultural products, secure price competitiveness through labor cost reduction, and actively promote multi-variety small quantity production. It is expected to contribute greatly to the generation of farm household income.
도 1은 본 발명의 농산물 품질 판별 인공지능 서비스의 구성을 도시한 것이다.
도 2는 본 발명의 농산물 품질 판별 인공지능 서비스의 품질 자동 판별 서비스의 방법을 도시한 것이다.
도 3은 본 발명의 농산물 품질 판별 인공지능 서비스의 학습 모델과 데이터를 공유하는 방법을 도시한 것이다.1 shows the configuration of an artificial intelligence service for determining the quality of agricultural products of the present invention.
2 illustrates a method of automatic quality determination service of the artificial intelligence service for determining quality of agricultural products according to the present invention.
3 illustrates a method of sharing data with a learning model of an artificial intelligence service for determining agricultural product quality according to the present invention.
이상의 본 발명의 목적들, 다른 목적들, 특징들 및 이점들은 첨부된 도면과 관련된 이하의 바람직한 실시예들을 통해서 쉽게 이해될 것이다. 그러나 본 발명은 여기서 설명되는 실시예들에 한정되지 않고 다른 형태로 구체화될 수도 있다. 오히려, 여기서 소개되는 실시예들은 개시된 내용이 철저하고 완전해질 수 있도록 그리고 당업자에게 본 발명의 사상이 충분히 전달될 수 있도록 하기 위해 제공되는 것이다.The above objects, other objects, features and advantages of the present invention will be easily understood through the following preferred embodiments in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided so that the disclosed content will be thorough and complete and the spirit of the present invention will be sufficiently conveyed to those skilled in the art.
본 명세서에서, 단수형은 문구에서 특별히 언급하지 않는 한 복수형도 포함한다. 명세서에서 사용되는 '포함한다' 및/또는 '포함하는'은 언급된 구성요소는 하나 이상의 다른 구성요소의 존재 또는 추가를 배제하지 않는다.In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. The terms 'comprise' and/or 'comprising' used in the specification do not exclude the presence or addition of one or more other elements.
이하, 첨부된 도면을 참조하여 본 발명의 바람직한 실시예에 따른 농산물 품질 판별 인공지능 서비스를 상세히 설명한다. 아래의 특정 실시예를 기술하는데 있어서 여러 가지의 특정적인 내용들은 발명을 더 구체적으로 설명하고 이해를 돕기 위해 작성되었다. 하지만 본 발명을 이해할 수 있을 정도로 이 분야의 지식을 갖고 있는 독자는 이러한 여러 가지의 특정적인 내용들이 없어도 사용될 수 있다는 것을 인지할 수 있다. 어떤 경우에는 발명을 기술하는 데 있어서 흔히 알려졌으면서 발명과 크게 관련 없는 부분들은 본 발명을 설명하는 데 있어 혼돈을 막기 위해 기술하지 않음을 미리 언급해 둔다.Hereinafter, an artificial intelligence service for determining the quality of agricultural products according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings. In describing specific embodiments below, various specific contents are prepared to more specifically describe the invention and aid understanding. However, readers who have knowledge in this field to the extent that they can understand the present invention can recognize that it can be used without these various specific details. In some cases, it is mentioned in advance that parts that are commonly known in describing the invention and are not greatly related to the invention are not described to prevent confusion in describing the present invention.
도 1은 본 발명의 농산물 품질 판별 인공지능 서비스의 구성도로 자동선별기(100)의 카메라(110)부터 컨베이어밸트에서 회전하면서 이동하고 있는 농산물(111)의 이미지 프레임들(112)를 획득하는 데이터 획득부(113); 획득한 이미지로부터 농산물의 객체(114)를 추출하는 객체추출부(115); 클라우드(130)에 저장된 학습모델(116)을 다운로드하여 추출한 객체로부터 불량요소(흑점, 멍, 열과 등)(117)를 검출하는 불량검출부(118); 불량의 판별한 농산물을 분리(120)하기 위해 분리기 값을 전송하는 피드백부(121); 학습 모델과 결과 데이터를 클라우드로 공유하는 공유부(130); 결과 데이터를 활용하여 시각화하는 시각화부(140)를 포함한다.1 is a configuration diagram of an artificial intelligence service for determining the quality of agricultural products according to the present invention. Data acquisition for obtaining image frames 112 of agricultural products 111 moving while rotating on a conveyor belt from a camera 110 of an automatic sorter 100 section 113; An object extraction unit 115 for extracting an object 114 of agricultural products from the obtained image; a defect detection unit 118 that downloads the learning model 116 stored in the cloud 130 and detects a defect element (black spot, bruise, heat, etc.) 117 from the extracted object; A feedback unit 121 for transmitting a separator value in order to separate 120 agricultural products determined to be defective; Sharing unit 130 for sharing the learning model and result data to the cloud; and a visualization unit 140 that visualizes the resulting data.
도 2는 본 발명의 농산물 품질 판별 인공지능 서비스의 품질 자동 판별 서비스의 방법으로 이미지 데이터를 수집(200)하여 농산물의 종류별로 라벨링하는 단계(210); 이미지 수의 수를 증가시키기 위한 이미지 증식단계(220); Yolo 모델을 활용하여 객체 탐지 모델을 학습하는 단계(230); 자동선별기의 컨베이터벨트에서 회전하고 있는 농산물(240)의 이미지를 획득하고(241) 학습된 모델에 적용하여 농산물 객체를 탐지하는 단계(242); 수집된 이미지들(200)에서 불량영역 이미지를 분리하여(250) 저장하고(251) 불량이미지를 증가시키기 위해 증식을 수행한 후에(252) 불량영역 탐지 모델을 학습하여 저장하고(253) 학습된 모델을 이용하여 탐재된 농산물 객체에서 불량 영역이 있는지 판별하는 단계(254); 만약 불량이 있다고 판단되면(260) 불량품을 분리하는 단계(261); 정상품이라고 판단되면 정상품을 따로 분리하는 단계(262); 처리 결과를 데이터베이스에 저장하는 단계(270)를 포함한다.2 is a step 210 of collecting (200) image data and labeling by type of agricultural products by the method of the automatic quality determination service of the artificial intelligence service for determining the quality of agricultural products according to the present invention; image augmentation step 220 to increase the number of images; Learning an object detection model using the Yolo model (230); Obtaining an image of agricultural products 240 rotating on the conveyor belt of the automatic sorter (241) and detecting agricultural products objects by applying them to the learned model (242); Defective area images are separated from the collected images 200 (250) and stored (251), and after propagation is performed to increase the defective images (252), a defective area detection model is learned and stored (253), and the learned Determining whether there is a defective area in the detected agricultural product object using the model (254); If it is determined that there is a defect (260), separating the defective product (261); If it is determined that the product is normal, separating the normal product separately (262); and storing the processing results in a database (270).
도 3은 본 발명의 농산물 품질 판별 인공지능 서비스의 학습 모델과 데이터를 공유하는 방법으로 사용자(300)가 웹이나 모바일 장치(310)를 통해 가입 후 농산물 판별 솔루션(모델, 데이터)를 구입하면(301) 클라우드(320)에 있는 판별 솔루션이 다운로드되고(321) 다운로드된 솔루션을 이용해서 선별기(330)를 제어하며(340) 판별 결과 데이터는 데이터베이스 저장되고(350) 사용자는 저장된 데이터를 시각화하여 확인할 수 있고(360) 클라우드(320)에 결과 데이터를 업로드(370)하여 공유할 수 있는 기능을 제공한다.3 is a method of sharing a learning model and data of an artificial intelligence service for determining agricultural product quality according to the present invention, when a user 300 purchases an agricultural product discrimination solution (model, data) after subscribing through the web or mobile device 310 ( 301) The discrimination solution in the cloud 320 is downloaded (321), the sorter 330 is controlled using the downloaded solution (340), and the discrimination result data is stored in the database (350), and the user visualizes and checks the stored data. It provides a function of uploading (370) the resulting data to the cloud (320) and sharing it (360).
Claims (3)
자동선별기의 카메라부터 컨베이어밸트에서 회전하면서 이동하고 있는 농산물의 이미지 프레임들를 획득하는 데이터 획득부와;
획득한 이미지로부터 농산물의 객체를 추출하는 객체추출부와;
클라우드에 저장된 학습모델을 다운로드하여 추출한 객체로부터 불량요소를 검출하는 불량검출부와;
불량의 판별한 농산물을 분리하기 위해 분리기 값을 전송하는 피드백부와;
학습 모델과 결과 데이터를 클라우드로 공유하는 공유부와;
결과 데이터를 활용하여 시각화하는 시각화부를 포함하는 구성도.In the configuration diagram of the artificial intelligence service for determining the quality of agricultural products,
a data acquisition unit that acquires image frames of moving agricultural products while rotating on the conveyor belt from the camera of the automatic sorter;
An object extraction unit for extracting an object of agricultural products from the acquired image;
a defect detection unit that downloads the learning model stored in the cloud and detects a defect element from the extracted object;
a feedback unit for transmitting a separator value to separate defective agricultural products;
a sharing unit that shares the learning model and result data to the cloud;
A configuration diagram including a visualization unit that visualizes using result data.
이미지 데이터를 수집(200)하여 농산물의 종류별로 라벨링하는 단계와;
이미지 수의 수를 증가시키기 위한 이미지 증식단계와;
Yolo 모델을 활용하여 객체 탐지 모델을 학습하는 단계와;
자동선별기의 컨베이터벨트에서 회전하고 있는 농산물의 이미지를 획득하고 학습된 모델에 적용하여 농산물 객체를 탐지하는 단계와;
수집된 이미지들에서 불량영역 이미지를 분리하여 저장하고 불량이미지를 증가시키기 위해 증식을 수행한 후에 불량영역 탐지 모델을 학습하여 저장하고 학습된 모델을 이용하여 탐재된 농산물 객체에서 불량 영역이 있는지 판별하는 단계와;
만약 불량이 있다고 판단되면 불량품을 분리하는 단계와;
정상품이라고 판단되면 정상품을 따로 분리하는 단계와;
처리 결과를 데이터베이스에 저장하는 단계를 포함하는 품질 자동 판별 서비스의 방법.In the method of automatic quality determination service of agricultural product quality determination artificial intelligence service,
Collecting image data (200) and labeling each type of agricultural product;
an image propagation step for increasing the number of images;
learning an object detection model using the Yolo model;
Acquiring images of agricultural products rotating on the conveyor belt of the automatic sorter and applying them to the learned model to detect agricultural objects;
After separating and storing the defective area image from the collected images and performing proliferation to increase the defective image, the defective area detection model is learned and stored, and using the learned model to determine whether there is a defective area in the detected agricultural product object step;
If it is determined that there is a defect, separating the defective product;
Separating the normal product separately if it is determined that the product is normal;
A method of an automatic quality determination service comprising the step of storing a processing result in a database.
사용자가 웹이나 모바일 장치를 통해 가입 후 농산물 판별 솔루션을 구입하는 단계와;
클라우드에 있는 판별 솔루션이 다운로드되는 단계와;
다운로드된 솔루션을 이용해서 선별기를 제어하는 단계와;
판별 결과 데이터는 데이터베이스 저장되는 단계와;
사용자는 저장된 데이터를 시각화하여 확인하는 단계와;
클라우드에 결과 데이터를 업로드하여 공유하는 단계를 포함하는 학습 모델과 데이터를 공유하는 방법.In the method of sharing the learning model and data of the agricultural product quality determination artificial intelligence service,
Purchasing an agricultural product identification solution after a user subscribes through a web or mobile device;
a step in which a determination solution in the cloud is downloaded;
controlling the sorter using the downloaded solution;
The determination result data is stored in a database;
The user visualizes and confirms the stored data;
A method for sharing data with a trained model, including uploading and sharing resulting data to the cloud.
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