한국산업정보학회논문지 (Journal of Korea Society of Industrial Information Systems)

한국산업정보학회 (Korea Society of Industrial Information Systems)
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인공지능 기반 콩 생장분석 방법 연구

A Study on the Artificial Intelligence-Based Soybean Growth Analysis Method

  • 전문석 (국립농업과학원 농업공학부) ;
  • 김영태 (국립농업과학원 농업생명자원부) ;
  • 정유석 (국립농업과학원 농업공학부) ;
  • 배효준 (국립농업과학원 농업공학부) ;
  • 이채원 (국립식량과학원 중부작물부) ;
  • 김송림 (국립농업과학원 농업생명자원부) ;
  • 최인찬 (국립농업과학원 농업공학부)
  • 투고 : 2023.09.22
  • 심사 : 2023.10.24
  • 발행 : 2023.10.30
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초록

콩은 세계 5대 식량작물 중 하나로 식물성 단백질의 주요 공급원이다. 작물 특성상 기후변화에 따라 곡물 생산량에 큰 영향을 받기 때문에 국립농업과학원에서는 콩 품종별 생장 분석을 통해 작물표현형 연구를 진행중이다. 콩 품종별 생장 분석을 위한 생장 과정 사진 촬영은 자동화된 시스템으로 이루어지지만 생장 상태를 확인, 기록, 분석하는 과정은 수작업으로 진행되고 있다. 본 논문에서는 이러한 과정을 자동화 할 수 있도록 콩 작물의 영상 데이터에서 콩잎 객체를 검출하는 YOLOv5s 모델과 검출된 콩잎의 전개 여부를 판단하는 합성곱 신경망(Convolution Neural Network; CNN) 모델을 설계, 학습하였다. 두 모델을 결합하고 검출된 콩잎의 좌표데이터로 층을 구분하는 알고리즘을 구현하여 콩 작물의 시계열 데이터를 입력하여 생장을 분석하는 프로그램을 개발하였고, 그 결과 콩 작물의 제2~3복엽까지 생장 시기를 판단할 수 있었다.

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

키워드

과제정보

이 논문은 농촌진흥청 연구사업(과제번호: PJ01486501)의 지원에 의해 이루어진 것임.

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