ADI, Moët Hennessy, and UC Davis Develop AI-Based Wine Quality Detection Technology
Published2026.10.15 11:26
이번 기술은 MIT의 초기 연구를 바탕으로, ADI의 센싱 및 머신러닝 플랫폼을 포도 재배·양조 분야에 적용한 것이다.
모엣 헤네시의 로베르-장 드 보귀에 리서치 센터(Robert-Jean de Vogüé Research Center) 연구진은 FMA 관련 화학 특성을 인식하도록 머신러닝 알고리즘을 학습시키기 위해 자체 샘플 라이브러리를 구축했다.
이 시스템은 전통적 방식으로 결함이 감지되기 훨씬 이전 단계에서 FMA 위험 샘플을 식별하는 데 성공했다고 밝혔다.ADI의 플랫폼은 광범위한 화학 정보 스펙트럼을 분석하고 AI가 주요 신호를 추출하는 방식으로 작동한다.
사전 정의된 조건만을 탐색하는 것이 아니라, 화학 데이터를 지속적으로 학습해 생물학적 활동과 품질 리스크를 파악하는 구조다.
참여 기관들은 △포도나무 질병 조기 감지 △토양 평가 △기후 변화·산불 관련 결함 식별 등으로 플랫폼 활용을 확대할 수 있을 것으로 전망했다.이번 협력에는 ADI 헬스 솔루션(ADI Health Solutions) 사업 부문이 참여했다.
ADI는 이 기술을 향후 광범위한 산업용 애플리케이션으로 확대 제공할 계획이며, 모엣 헤네시와 UC 데이비스는 공유 참조 라이브러리를 더 넓은 포도·와인 산업에 활용하는 방안을 모색 중이라고 밝혔다.
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