文献
J-GLOBAL ID:202102259251924538
整理番号:21A0519120
リンゴ園における無人地上車両の適用に対する低解像度画像を用いたバイナリピクセル定量化に基づく機械学習特徴抽出【JST・京大機械翻訳】
Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards
著者 (4件):
Lyu Hong-Kun
(Division of Electronics and Information System, ICT Research Institute, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea)
,
Yun Sanghun
(Division of Electronics and Information System, ICT Research Institute, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea)
,
Choi Byeongdae
(Division of Electronics and Information System, ICT Research Institute, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea)
,
Choi Byeongdae
(Department of Interdisciplinary Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea)
資料名:
Agronomy (Web)
(Agronomy (Web))
巻:
10
号:
12
ページ:
1926
発行年:
2020年
JST資料番号:
U7129A
ISSN:
2073-4395
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
スイス (CHE)
言語:
英語 (EN)