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지도학습 알고리즘 기반 3D 노지 작물 구분 모델 개발
정영준 ( Jeong Young-joon ),이종혁 ( Lee Jong-hyuk ),이상익 ( Lee Sang-ik ),오부영 ( Oh Bu-yeong ),( Ahmed Fawzy ),서병훈 ( Seo Byung-hun ),김동수 ( Kim Dong-su ),서예진 ( Seo Ye-jin ),최원 ( Choi Won ) 한국농공학회 2022 한국농공학회논문집 Vol.64 No.1
3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.