{"updated":"2025-01-19T14:50:11.517671+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219605","sets":["6164:6165:6640:11008"]},"path":["11008"],"owner":"44499","recid":"219605","title":["圃場画像による植物病害自動検出に向けた鉢花領域の分割"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-06"},"_buckets":{"deposit":"5d932d92-ae95-4624-91f4-3d9128c295be"},"_deposit":{"id":"219605","pid":{"type":"depid","value":"219605","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"圃場画像による植物病害自動検出に向けた鉢花領域の分割","author_link":["572923","572926","572924","572925"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"圃場画像による植物病害自動検出に向けた鉢花領域の分割"}]},"item_type_id":"18","publish_date":"2022-07-06","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"公立はこだて未来大学"},{"subitem_text_value":"京都橘大学"},{"subitem_text_value":"公立はこだて未来大学"},{"subitem_text_value":"公立はこだて未来大学"}]},"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/219605/files/IPSJ-DICOMO2022031.pdf","label":"IPSJ-DICOMO2022031.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2022031.pdf","filesize":[{"value":"1.9 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a7ed543a-9737-4314-be57-516d187a1323","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"藤井, 駿"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 嘉隆"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"稲村, 浩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"石田, 繁巳"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"農業生産量において植物病害の影響は大きく，植物病害は発見次第速やかに対処し除去等の処置を行う必要がある．従来手法では病害検出精度を高める試みはなされているものの，病害検出のために大量の画像データ収集および監視機器の設置・管理，病害画像抽出作業等のコストが大きい．そこで，本研究では圃場全体の画像のみを利用し，個々の作物にあたる画像を画像処理を用いて分割して切り取る手法を提案し，切り取った画像に対して病害識別を行うことで病害検出の精度向上を試みる．評価実験の結果，作物の画像は CNN による作物部分の分割と比較して約 7 ポイント高い 71.8% の F 値で 1 株の鉢花をトリミングできていることを確認した．病害検出に関しては，物体検出アルゴリズム YOLOV5 を圃場全体の画像に対して適用した場合と比較して約 23 ポイント高い 23.8% の F 値で病害であることを識別できることを確認した．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"226","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散，協調とモバイルシンポジウム2022論文集"}],"bibliographicPageStart":"221","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:19:40.529824+00:00","id":219605,"links":{}}