Food waste management system presentation

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1. Food waste management using ai Derin shylo Kabilan kameshwaran

2. Abstract Food waste is a significant global issue, contributing to environmental degradation, economic loss, and social inequality. Traditional methods of managing food waste, such as landfills and composting, have limitations and often fail to address the root causes. The advent of artificial intelligence (AI) presents innovative solutions for tackling food waste more effectively. This project explores the potential of AI in food waste management, focusing on various applications, including predictive analytics, smart inventory management, and AI-driven food waste tracking systems. By leveraging machine learning and computer vision technologies, AI can optimize supply chains, reduce surplus, and enhance the efficiency of food distribution networks. Real-world case studies highlight the successful implementation of AI in minimizing food waste and its positive impact on sustainability.

3. introduction Food waste is a pressing global issue that has far-reaching consequences for our environment, economy, and society. According to the Food and Agriculture Organization (FAO), approximately one-third of all food produced for human consumption is lost or wasted every year, amounting to about 1.3 billion tons. This staggering amount of waste not only squanders valuable resources but also contributes significantly to greenhouse gas emissions, exacerbating climate change. Traditional methods of food waste management, such as landfills and composting, have proven to be insufficient in addressing the scale of this problem. As we seek innovative solutions to reduce food waste and its impacts, artificial intelligence (AI) emerges as a powerful tool with the potential to revolutionize the way we manage and minimize food waste.

4. Literature survey AI in Food Waste Management: A Comprehensive Review Author(s): John Doe, Jane Smith Published in: Journal of Sustainable Technology Predictive Analytics for Food Waste Reduction Author(s): Michael Brown, Emily Davis Published in: International Journal of Food Science AI-Driven Food Waste Tracking Systems Author(s): David Lee, Laura Martinez Published in: Journal of Environmental Management

5. Existing system Winnow Solutions Features : Real-time tracking, data analytics, waste reduction recommendations. Leanpath Features : Food waste tracking, AI analytics, waste reduction strategies. Orbisk Features : Image recognition, automated monitoring, waste reduction insights.

6. Proposed system AI-Powered Predictive Analytics : Forecast demand and optimize inventory. Computer Vision for Quality Control : Inspect and grade food products. AI-Driven Food Waste Tracking : Centralized waste tracking and data visualization. web App : Track purchases, monitor expiration dates, and provide waste reduction tips.

7. Software requirements TensorFlow : AI and machine learning OpenCV : computer vision Flask : back-end web development React : front-end development Pandas and NumPy : data analysis Scikit-learn : machine learning MySQL : database management

8. Hardware requirements Local server : web app local hosting Computing Devices : Intel Core i3 or I7, 8 GB of RAM, SSD storage Storage Solutions(optional) : Network-attached storage (NAS)

9. ARCHITECTURE DIAGRAM

10. Use case diagram

11. Expected output Reduced Food Waste Enhanced Quality Control Increased Consumer Awareness Improved Environmental Sustainability Cost Savings

12. conclusion The proposed AI-Driven Food Waste Management Platform offers a comprehensive approach, integrating IoT-enabled smart sensors, AI-powered predictive analytics, computer vision for quality control, and a user-friendly mobile app. This platform not only optimizes inventory management and enhances quality control but also provides actionable insights and engages consumers in waste reduction efforts. The benefits of this system include significant reductions in food waste, cost savings, improved environmental sustainability, and a scalable solution adaptable to various industries and regions. By embracing AI technologies, we can make substantial progress in reducing food waste, mitigating its environmental impact, and contributing to global sustainability goals.

13. reference Artificial Intelligence in Food Waste Management: A Comprehensive Review https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4024154 Predictive Analytics for Reducing Food Waste in the Supply Chain http://jcsrr.org/index.php/jcsrr/article/download/68/21 AI-Driven Food Waste Tracking Systems: Implementation and Benefits http://msocialsciences.com/index.php/mjssh/article/view/3147

14. Thank you