한국농림기상학회지 (Korean Journal of Agricultural and Forest Meteorology)

  • 제25권1호
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  • Pages.17-27
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  • 2023
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  • 1229-5671(pISSN)
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  • 2288-1859(eISSN)
한국농림기상학회 (Korean Society of Agricultural and Forest Meteorology)
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딥러닝 기반 옥수수 포장의 잡초 면적 평가

Deep Learning Approaches for Accurate Weed Area Assessment in Maize Fields

  • 박혁진 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 권동원 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 상완규 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 반호영 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 장성율 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 백재경 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 이윤호 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 임우진 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 서명철 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 조정일 (농촌진흥청 국립식량과학원 작물재배생리과)
  • Hyeok-jin Bak (National Institute of Crop Science, Rural Development Administration) ;
  • Dongwon Kwon (National Institute of Crop Science, Rural Development Administration) ;
  • Wan-Gyu Sang (National Institute of Crop Science, Rural Development Administration) ;
  • Ho-young Ban (National Institute of Crop Science, Rural Development Administration) ;
  • Sungyul Chang (National Institute of Crop Science, Rural Development Administration) ;
  • Jae-Kyeong Baek (National Institute of Crop Science, Rural Development Administration) ;
  • Yun-Ho Lee (National Institute of Crop Science, Rural Development Administration) ;
  • Woo-jin Im (National Institute of Crop Science, Rural Development Administration) ;
  • Myung-chul Seo (National Institute of Crop Science, Rural Development Administration) ;
  • Jung-Il Cho
  • 투고 : 2023.02.16
  • 심사 : 2023.03.27
  • 발행 : 2023.03.30
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초록

포장에서 잡초의 발생은 농작물의 생산량을 크게 떨어트리는 원인 중 하나이고 SSWM을 기반으로 잡초를 변량 방제하기 위해서 잡초의 발생 위치, 밀도 그리고 이를 정량화하는 것은 필수적이다. 본 연구에서는 2020년의 국립식량과학원에서 잡초 피해를 입은 옥수수 포장의 영상데이터를 무인항공기를 활용해서 수집하였고 이를 배경과 옥수수로 분리하여 딥러닝 기반 영상 분할 모델 제작을 위한 학습데이터를 획득하였다. DeepLabV3+, U-Net, Linknet, FPN의 4가지의 영상 분할 네트워크들의 옥수수의 검출 정확도를 평가하기 위해 픽셀정확도, mIOU, 정밀도, 재현성의 지표를 활용해서 정확도를 검증하였다. 검증 결과 DeepLabV3+ 모델이 0.76으로 가장 높은 mIOU를 나타냈고, 해당 모델과 식물체의 녹색 영역과 배경을 분리하는 지수인 ExGR을 활용해서 잡초의 면적을 정량화, 시각화하였다. 이러한 연구의 결과는 무인항공기로 촬영된 영상을 활용해서 넓은 면적의 옥수수 포장에서 빠르게 잡초의 위치와 밀도를 특정하고 정량화하는 것으로 잡초의 밀도에 따른 제초제의 변량 방제를 위한 의사결정에 도움이 될 것으로 기대한다.

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

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과제정보

본 논문은 농촌진흥청 공동연구사업(과제번호: PJ0151012022)의 지원에 의해 이루어진 것임.

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