减少HORECA部门的食物浪费,使用基于AI的废物追踪设备
Evangelia G Sigala1, Paula Gerwin2, Christina Chroni1
1School of Environment, Geography and Applied Economics, Harokopio University, 17671, Athens, Greece.
Waste management (New York, N.Y.)
|March 2, 2025
概括
一个人工智能驱动的食品废弃物跟踪系统显著减少了23-51%的HORECA企业的废物. 这项技术提高了员工的意识,降低了食品成本,在各种环境中被证明是有效的.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 食品科学 食品科学 食品科学
背景情况:
- 食品浪费在HORECA行业是一个重要的环境和经济问题.
- 现有的追踪食物浪费的方法往往是人工和劳动密集型的.
- 过度生产和消费者剩余的食物是造成可避免的食物浪费的主要原因.
研究的目的:
- 评估基于人工智能的自动废物跟踪系统对减少食物浪费的有效性.
- 评估系统对员工意识和纠正措施实施的影响.
- 确定干预在不同HORECA环境中的可转移性和可持续性.
主要方法:
- 实施人工智能驱动的废物跟踪设备,利用计算机视觉和深度学习.
- 在餐馆,酒店和餐饮服务中实时称重和光学分离食品废物.
- 基线数据收集和对食品废弃物数量和成本的干预后分析.
主要成果:
- 大多数机构的食物浪费减少率从23%到51%不等,瑞士一家酒店的增幅显著.
- 该系统成功地提高了员工的意识,导致了有针对性的纠正措施.
- 与基线相比,每餐食物浪费成本下降了高达39%.
- 蔬菜和预制食品被确定为主要的废物贡献者.
结论:
- 基于人工智能的自动废物跟踪系统是减少HORECA行业食品浪费的有效工具.
- 这项干预表明了在各种酒店环境中具有可转移性和长期可持续性.
- 来自该系统的数据驱动洞察力有助于大大减少食品制备和过度生产浪费.
- 进一步的研究应将消费者层面的干预措施与废物跟踪技术相结合,以提高影响力.
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