人工知能を活用して,持続可能な高品質の食品生産を強化する:現状,課題,将来の方向性
Kalliopi V Dalakleidi1, Violeta Pemaj2, John Kapolos2
1Department of Informatics, Athens University of Economics and Business, Athens, Greece.
Advances in food and nutrition research
|February 14, 2026
まとめ
人工知能 (AI) は,農場からフォークまで多様なデータを分析することによって,食品科学を変革しています. AIベースの食品科学および栄養システム (AIFNS) は,倫理的で安全なグローバルな適用のためにさらなる研究を必要とします.
科学分野:
- 食品科学 食品科学について
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- データサイエンス データサイエンス
背景:
- 現代の食品科学は,ますます人工知能 (AI) を使用して異質なマルチモダルデータを分析しています.
- どこにでも存在するセンサー,カメラ,モバイルアプリは,農業,食品生産,加工,輸送,栄養モニタリングなどで膨大なデータセットを生成しています.
- これらのデータセットには,テーブル,画像,テキスト,オミック,センサー,ソーシャルメディア,およびトランザクションデータが含まれます.
研究 の 目的:
- 人工知能とマルチモダルデータ分析によってもたらされる食品科学の根本的な変化を強調する.
- AIベースの食品科学および栄養システム (AIFNS) が使用するパイプラインと戦略を概説します.
- これらのシステムの倫理的,透明,包括的,安全な運用に関するさらなる研究の必要性を強調する.
主な方法:
- 人工知能を用いた異質なマルチモダルデータの分析.
- AIベースの食品科学および栄養システム (AIFNS) の適用.
- データの事前処理,セグメンテーション,次元の減少,分類,クラスタリング,データ融合戦略を含むパイプラインを使用します.
主要な成果:
- AIは,食品サプライチェーン全体で生成された膨大なマルチモダルデータの分析を可能にします.
- AIベースの食品科学および栄養システム (AIFNS) は,既知のデータ分析技術を採用しています.
- データ融合戦略は,予測タスクのために複数のデータソースが利用可能な場合に使用できます.
結論:
- AIとマルチモダルデータ分析は,食品科学における重要な変化を表しています.
- AIベースの食品科学および栄養システム (AIFNS) は,これらのデータを活用するために不可欠です.
- 倫理的で,透明で,包括的で,安全な国境を越えた運用は,将来のAIFNSの開発に不可欠です.
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