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Diagnosis of the Rice Lodging for the UAV Image using Vision Transformer

Vision Transformer를 이용한 UAV 영상의 벼 도복 영역 진단

  • 명현정 (한국전자기술연구원 IT응용연구센터) ;
  • 김서정 (한국전자기술연구원 IT응용연구센터) ;
  • 최강인 (한국전자기술연구원 IT응용연구센터) ;
  • 김동훈 (한국전자기술연구원 IT응용연구센터) ;
  • 이광형 (한국전자기술연구원 IT응용연구센터) ;
  • 안형근 (국립종자원 국제종자생명교육센터) ;
  • 정성환 (한국전자기술연구원 IT응용연구센터) ;
  • 김병준 (한국전자기술연구원 IT응용연구센터)
  • Received : 2023.07.26
  • Accepted : 2023.09.12
  • Published : 2023.10.31
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Abstract

The main factor affecting the decline in rice yield is damage caused by localized heavy rains or typhoons. The method of analyzing the rice lodging area is difficult to obtain objective results based on visual inspection and judgment based on field surveys visiting the affected area. it requires a lot of time and money. In this paper, we propose the method of estimation and diagnosis for rice lodging areas using a Vision Transformer-based Segformer for RGB images, which are captured by unmanned aerial vehicles. The proposed method estimates the lodging, normal, and background area using the Segformer model, and the lodging rate is diagnosed through the rice field inspection criteria in the seed industry Act. The diagnosis result can be used to find the distribution of the rice lodging areas, to show the trend of lodging, and to use the quality management of certified seed in government. The proposed method of rice lodging area estimation shows 98.33% of mean accuracy and 96.79% of mIoU.

쌀 수확량 감소에 크게 영향을 주는 것은 집중호우나 태풍에 의한 도복 피해이다. 도복 피해 면적 산정 방법은 직접 피해 지역을 방문하는 현장 조사를 기반으로 육안 검사 및 판단하여 객관적인 결과 획득이 어렵고 많은 시간과 비용이 요구된다. 본 논문에서는 무인 항공기로 촬영된 RGB 영상을 Vision Transformer 기반 Segformer을 활용한 벼 도복 영역 추정 및 진단을 제안한다. 제안된 방법은 도복, 정상, 그리고 배경 영역을 추정하고 종자관리요강 내 벼 포장 검사를 통해 도복률을 진단한다. 진단된 결과를 통해 벼 도복 피해 분포를 관찰할 수 있게 하며, 정부 보급종 포장 검사에 활용할 수 있다. 본 연구의 벼 도복 영역 추정 성능은 평균 정확도 98.33%와 mIoU 96.79%의 성능을 나타내었다.

Keywords

Acknowledgement

본 논문은 농림축산식품부의 재원으로 농림식품기술기획평가원의 디지털육종전환기술개발사업의 지원을 받아연구되었음 (No. 322065-3).

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