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Food waste management system presentation
- 1. FOOD WASTE MANAGEMENT USINGAI DERIN SHYLO KABILAN KAMESHWARAN
- 2. ABSTRACT • FOOD WASTEIS 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 WASTEIS 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 • AIIN 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 • WINNOWSOLUTIONS • 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-POWEREDPREDICTIVE 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 • LOCALSERVER : WEB APP LOCAL HOSTING • COMPUTING DEVICES : INTEL CORE I3 OR I7, 8 GB OF RAM, SSD STORAGE • STORAGE SOLUTIONS(OPTIONAL) : NETWORK-ATTACHED STORAGE (NAS)
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- 11. EXPECTED OUTPUT • REDUCEDFOOD WASTE • ENHANCED QUALITY CONTROL • INCREASED CONSUMER AWARENESS • IMPROVED ENVIRONMENTAL SUSTAINABILITY • COST SAVINGS
- 12. CONCLUSION • THE PROPOSEDAI-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 INTELLIGENCEIN 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
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