Scholarlink

<p>The minimization of food waste is essential to reduce the environmental and economic implications. Although there have been several sources of food waste, such as commercial dining places, household settings, and hostel messes, the most significant one is hostel messes. The food waste in hostel messes stems from over-preparation, poor food quality, and a lack of student awareness. It is vital to address this issue for sustainable resource use and environmental conservation. The proposed AI-driven IOT approach with a dedicated dashboard performs inventory management, collects real-time waste data through smart dustbins with sensors and cameras, and employs image classifiers to predict waste quantity and categories. YOLOv11 is applied to categorize the waste items in order to detect the most wasted items for menu suggestions, which achieved a precision of 0.919 and a recall of 0.862. The IoT-based module measured the amount of food waste by an individual to nudge them to reduce the waste in the future. The information about the reusable food is updated on the dashboard, where NGOs can send a request to collect the food for consumption. This integrated approach not only minimizes environmental impact but also establishes a scalable model for sustainable food waste management in the institutional settings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Smart AI and IoT-driven Waste Food Management: A Sustainable Approach

  • 期刊论文

  • 发表日期：

    2026年6月3日

查看全文
  • Anshika,
  • Khushi Yadav,
  • Aditya,
  • Sahil Kularia,
  • Palak Mukheja,
  • Manraj Singh,
  • Hardeep Singh,
  • Kanwarpreet Kaur,
  • Neeru Jindal

摘要

The minimization of food waste is essential to reduce the environmental and economic implications. Although there have been several sources of food waste, such as commercial dining places, household settings, and hostel messes, the most significant one is hostel messes. The food waste in hostel messes stems from over-preparation, poor food quality, and a lack of student awareness. It is vital to address this issue for sustainable resource use and environmental conservation. The proposed AI-driven IOT approach with a dedicated dashboard performs inventory management, collects real-time waste data through smart dustbins with sensors and cameras, and employs image classifiers to predict waste quantity and categories. YOLOv11 is applied to categorize the waste items in order to detect the most wasted items for menu suggestions, which achieved a precision of 0.919 and a recall of 0.862. The IoT-based module measured the amount of food waste by an individual to nudge them to reduce the waste in the future. The information about the reusable food is updated on the dashboard, where NGOs can send a request to collect the food for consumption. This integrated approach not only minimizes environmental impact but also establishes a scalable model for sustainable food waste management in the institutional settings.