FreshSight: An Accessibility-Focused Approach to Produce Freshness and Shelf- Life Detection for Food Safety and Waste Reduction

Abstract

Colour-blind individuals encounter daily challenges, particularly in distinguishing colour-based indicators of food spoilage. This limitation significantly impacts their ability to assess the freshness and safety of fruits and vegetables accurately. Concurrently, global concerns regarding food spoilage have intensified, with millions worldwide affected by foodborne illnesses each year. The modern lifestyle, characterized by its rapid pace and time constraints, exacerbates this issue, often leading to unnoticed spoilage and substantial waste. The resulting annual waste, estimated at one-third of all edible food, imposes significant societal and environmental burdens, underscoring the urgency for effective solutions. FreshSight enables users, including those with colour vision deficiencies, to assess the condition of fruits and vegetables through a Convolutional Neural Network (CNN)- based real-time image analysis engine and an intuitive interface. This system provides immediate visual feedback to help users make informed decisions and avoid the consumption of spoiled produce. It also offers inclusive design features that support individuals with visual impairments. Beyond individual benefits, FreshSight promotes responsible food handling and contributes to the broader goal of sustainable food systems. By combining advanced technology with user-centered design, the solution enhances both safety and accessibility in everyday food-related decisions. In addressing the critical challenges of food safety, inclusivity, and waste reduction, FreshSight aims to support healthier lifestyles and contribute positively to environmental and societal well-being in the modern world.