{"created":"2025-01-19T01:30:48.196064+00:00","updated":"2025-01-19T10:59:28.259518+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230814","sets":["6504:11436:11444"]},"path":["11444"],"owner":"44499","recid":"230814","title":["機械学習を用いた工場野菜の収穫量予測モデルの開発"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"f7790acf-58f0-4014-8fc4-816e2131f2ba"},"_deposit":{"id":"230814","pid":{"type":"depid","value":"230814","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を用いた工場野菜の収穫量予測モデルの開発","author_link":["622411","622412","622410","622413","622409"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いた工場野菜の収穫量予測モデルの開発"}]},"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":"豊橋技科大"},{"subitem_text_value":"豊橋技科大"},{"subitem_text_value":"豊橋技科大"},{"subitem_text_value":"豊橋技科大"},{"subitem_text_value":"豊橋技科大"}]},"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/230814/files/IPSJ-Z85-7ZG-05.pdf","label":"IPSJ-Z85-7ZG-05.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-7ZG-05.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"26e0afc6-2faa-4f18-9f72-83a761ea9862","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":"多田, 桃大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"細田, 侑也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"後藤, 仁志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"戸田, 清太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高山, 弘太郎"}],"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":"生育環境を管理してコンテナ内で野菜を栽培する植物工場では，管理システムを運用するために費用が発生する．本研究では，運用計画を最適化するために幼苗期の生育データに基づいて工場野菜であるレタスの収穫量を予測する手法を提案する．提案法では，工場野菜の上部のカメラから株ごとに投影面積とエッジ長を生育データとして毎日計測する．そして，曲線あてはめで計測した生育データから収穫段階での生育データを予測する．最後に，予測した生育データを説明変数とする回帰モデルで出荷量を推定する．植物工場での実験を通して，栽培10日目までの生育データを用いて20日目の重量を相対誤差20%以下で提案法は予測することを示す．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"556","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"555","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230814,"links":{}}