{"created":"2025-01-19T01:39:25.290985+00:00","updated":"2025-01-19T09:01:39.910207+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00237026","sets":["6504:11678:11684"]},"path":["11684"],"owner":"44499","recid":"237026","title":["大豆の遺伝特性と環境特性に基づくTemporal Random Forestを利用した混合モデルによる収量予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"3caaa267-9da2-4f42-8e0a-c65e58944426"},"_deposit":{"id":"237026","pid":{"type":"depid","value":"237026","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"大豆の遺伝特性と環境特性に基づくTemporal Random Forestを利用した混合モデルによる収量予測","author_link":["648124","648125"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"大豆の遺伝特性と環境特性に基づくTemporal Random Forestを利用した混合モデルによる収量予測"}]},"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":"2024-03-01","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":"神戸大"},{"subitem_text_value":"神戸大"}]},"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/237026/files/IPSJ-Z86-2ZL-01.pdf","label":"IPSJ-Z86-2ZL-01.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-04"}],"format":"application/pdf","filename":"IPSJ-Z86-2ZL-01.pdf","filesize":[{"value":"329.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"2c32d970-24be-4e05-8c6a-7b3faa59a5c7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大橋, 真琴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大川, 剛直"}],"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":"近年,農業の分野では科学技術を導入するスマートアグリが進められている.スマートアグリの一環として,作物の収量予測がある.収量を予測することで,人員確保や価格の調整が可能になる.我々の研究グループでは,日本の食文化を支える大豆に着目し,その収量予測を行う. 　本研究では,収量に影響を及ぼす特徴を,品種などの遺伝特性と気象条件などの環境特性の2つに分けて考える.遺伝特性によりデータをグループ化し,グループ毎に環境要因を利用した予測モデルを構築し,混合させる.予測には環境特性の時系列性からTemporal Random Forestを利用した.この混合モデルにより,収量予測精度の改善が見られた.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"838","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"837","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":237026,"links":{}}