{"created":"2025-01-19T01:30:46.778317+00:00","updated":"2025-01-19T10:59:49.080296+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230799","sets":["6504:11436:11444"]},"path":["11444"],"owner":"44499","recid":"230799","title":["植生指標を用いたドローンモニタリングデータの時系列クラスタリングによる小麦の生育分析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"191ac34d-47b2-4ef5-a543-b190ac11892e"},"_deposit":{"id":"230799","pid":{"type":"depid","value":"230799","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"植生指標を用いたドローンモニタリングデータの時系列クラスタリングによる小麦の生育分析","author_link":["622356","622355","622354","622357"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"植生指標を用いたドローンモニタリングデータの時系列クラスタリングによる小麦の生育分析"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンピュータと人間社会","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岩手県大"},{"subitem_text_value":"岩手県大"},{"subitem_text_value":"岩手大"},{"subitem_text_value":"岩手県大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/230799/files/IPSJ-Z85-5ZG-06.pdf","label":"IPSJ-Z85-5ZG-06.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-5ZG-06.pdf","filesize":[{"value":"496.0 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"0cb2a580-6c63-4508-812b-5067db2724f0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木, 壮"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"伊藤, 太一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"梅木, 紳太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"南野, 謙一"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"農作業評価は収穫後にしかできず,収量分析しても個々の作業の正否は不明である.そこで,ドローン圃場モニタリングと機械学習により農作業が小麦の生育に与える影響を分析し,農作業評価の可視化を試みた.モニタリングは植物の日光反射率から生育予測をするもので,本研究では複数領域の反射率から3つの植生指標を取得した.植生指標はそれぞれ植生が分かるNDVI,ストレス診断ができるNDRE,適正施肥量がわかるCCCIで,さらにNDREを標準化したsNDREを用いることで,より明確なストレス診断ができた.追肥量比較実験をした岩手の圃場を対象にクラスタリングをし,追肥作業を植生・ストレスの点から評価できた.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"526","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"525","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230799,"links":{}}