Abstract

Food wastage is a pervasive issue in day-to-day life, contributing significantly to environmental and economic challenges. This research introduces a novel Internet of Things (IoT) and Machine Learning (ML)-driven framework aimed at addressing food loss in supply chains and everyday settings through automated fruit quality monitoring and spoilage detection. Utilizing IoT sensors and an ESP32 microcontroller, the system collects real-time environmental data such as temperature, humidity, and gas emissions to classify fruit ripeness stages. By providing timely and accurate predictions, this framework enables individuals, retailers, and supply chain operators to take proactive measures to reduce wastage. Advanced ML models, including Random Forest and CatBoost, ensure exceptional accuracy in identifying ripeness and spoilage. This system not only minimizes human error but also enhances supply chain efficiency and promotes sustainable practices. By automating the monitoring process, this research offers a scalable and practical solution to prevent food waste, ensuring better resource utilization and contributing to global food security. Furthermore, it outlines future applications, including blockchain integration for end-to-end transparency in the food industry.

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