Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Classification Upland Crop in Small Scale Agricultural Land

무인항공기와 딥러닝(UNet)을 이용한 소규모 농지의 밭작물 분류

  • Received : 2020.11.24
  • Accepted : 2020.12.06
  • Published : 2020.12.31
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Abstract

In order to increase the food self-sufficiency rate, monitoring and analysis of crop conditions in the cultivated area is important, and the existing measurement methods in which agricultural personnel perform measurement and sampling analysis in the field are time-consuming and labor-intensive for this reason inefficient. In order to overcome this limitation, it is necessary to develop an efficient method for monitoring crop information in a small area where many exist. In this study, RGB images acquired from unmanned aerial vehicles and vegetation index calculated using RGB image were applied as deep learning input data to classify complex upland crops in small farmland. As a result of each input data classification, the classification using RGB images showed an overall accuracy of 80.23% and a Kappa coefficient of 0.65, In the case of using the RGB image and vegetation index, the additional data of 3 vegetation indices (ExG, ExR, VDVI) were total accuracy 89.51%, Kappa coefficient was 0.80, and 6 vegetation indices (ExG, ExR, VDVI, RGRI, NRGDI, ExGR) showed 90.35% and Kappa coefficient of 0.82. As a result, the accuracy of the data to which the vegetation index was added was relatively high compared to the method using only RGB images, and the data to which the vegetation index was added showed a significant improvement in accuracy in classifying complex crops.

경지면적의 작물 상황에 대한 모니터링 및 분석은 식량자급율을 높이기 위한 가장 중요한 요소이지만, 기존의 모니터링 방법은 노동 집약적이며 시간이 많이 들어 식량자급율을 높이기 위한 방안으로 그 활용성이 떨어진다. 이와같은 단점을 극복하기 위하여 국내에 다수 존재하고 있는 소규모 농지에서의 복합 작물 정보를 모니터링 하기위한 효율적인 방법을 개발할 필요가 있다. 본 연구에서는 복합작물의 분류 정확도를 높이기 위하여 무인항공기에서 취득된 RGB영상과 이를 이용한 식생지수를 딥러닝 입력데이터로 적용하고 복합 밭작물을 분류하였다. 각각의 입력데이터 분류 결과 RGB 영상을 이용한 분류는 전체정확도 80.23%, Kappa 계수 0.65가 나타났고, RGB영상과 식생지수를 이용한 방법의 경우 식생지수 3개(ExG,ExR,VDVI) 추가 데이터는 전체정확도 89.51%, Kappa 계수 0.80이며, 식생지수 6개(ExG,ExR,VDVI,RGRI,NGRDI,ExGR)는 90.35%, Kappa 계수 0.82로 분석되었다. 분류결과 RGB영상만을 이용한 방법에 비하여 식생지수를 추가한 결과 값이 비교적 높게 분석되었으며, 복합작물을 분류하는데 있어 식생지수를 추가한 데이터가 더 좋은 결과를 나타내었다.

Keywords

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