FOOD WASTE MANAGEMENT STRATEGY WITH GREEN AND DIGITAL TECHNOLOGY

Abstract

Food waste presents a critical global challenge that impacts environmental sustainability, food security, and economic stability. Traditional waste management approaches often lack the agility and scalability required to address the growing volume of food waste, particularly in urban environments. This paper explores an integrated strategy combining green and digital technologies—specifically Artificial Intelligence (AI) and the Internet of Things (IoT)—to improve the classification, sorting, and conversion of food waste into useful resources. Through a comparative analysis of five core techniques—computer vision, sensor-based monitoring, sensor fusion with machine learning, object detection, and rule-based systems—this study evaluates the strengths and limitations of each approach in real-world applications. The proposed model supports smart sorting for food donation, animal feed, composting, and maggot farming (BSF), offering a scalable solution that aligns with SDG 11, 12, and 13 as well as Indonesia’s circular economy goals. Results highlight the importance of multi-sensor data integration and AI-based classification in optimizing food waste management while addressing social, environmental, and economic impacts. The study concludes by recommending a collaborative, adaptive framework that enhances sustainability through technological innovation and stakeholder engagement.