Real-time freshness prediction for Apples and Lettuces using imaging recognition and advanced algorithms in a user-friendly mobile application

Abstract

Over recent decades, consumer expectations for food quality and freshness have steadily increased. To meet these standards, fresh fruits and fresh-cut vegetables in supermarkets and other commercial outlets undergo rigorous sorting processes. Quality assessments typically focus on visible characteristics such as color, ripeness, shape uniformity, defect-free skin and flesh, and texture features like firmness, toughness, and tenderness. To automate real-time quality assurance of perishable agricultural products, we have developed a user-friendly smartphone application that enables freshness assessment of apples and lettuces using RGB data at multiple stages of the supply chain. This app utilizes image recognition technology, allowing for precise freshness assessment and estimated product lifespan. Nine deep algorithms were compared in the research for image classification including Vision Transformer (ViT), Swin Transformer, Residual Networks (ResNet), EfficientNet, ConvNeXt, DeiT, MobileNetV3, MaxViT, and TNT (Transformer in Transformer). The comparison considered three metrics, including accuracy ( %), parameters (millions), and inference time (ms). Based on the findings, the MobileNetV3 was identified as the optimal deep learning architecture for the apple and lettuce classification because it maintained a good compromise between classification accuracy and mobile device resource constraints - (99.95 % and 2.5 ms for apple; 99.17 % and 2.5 million for lettuce). Such advancements offer valuable insights for policymakers, farmers, and stakeholders in making more informed decisions, thus supporting sustainable agricultural practices and improving food security across supply chains.