MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection

TL;DR

MobileMold is a comprehensive smartphone-based microscopy dataset designed for food mold detection, enabling high-accuracy classification and detection of mold using deep learning models in real-world conditions.

Contribution

The paper introduces MobileMold, an open dataset with nearly 5,000 images, and establishes baseline deep learning models for mold detection and food classification.

Findings

Achieved near-ceiling accuracy of 99.54% in mold detection.

Validated the dataset's utility for food spoilage detection.

Provided saliency explanations for model predictions.

Abstract

Smartphone clip-on microscopes turn everyday devices into low-cost, portable imaging systems that can even reveal fungal structures at the microscopic level, enabling mold inspection beyond unaided visual checks. In this paper, we introduce MobileMold, an open smartphone-based microscopy dataset for food mold detection and food classification. MobileMold contains 4,941 handheld microscopy images spanning 11 food types, 4 smartphones, 3 microscopes, and diverse real-world conditions. Beyond the dataset release, we establish baselines for (i) mold detection and (ii) food-type classification, including a multi-task setting that predicts both attributes. Across multiple pretrained deep learning architectures and augmentation strategies, we obtain near-ceiling performance (accuracy = 0.9954, F1 = 0.9954, MCC = 0.9907), validating the utility of our dataset for detecting food spoilage. To increase transparency, we complement our evaluation with saliency-based visual explanations highlighting mold regions associated with the model's predictions. MobileMold aims to contribute to research on accessible food-safety sensing, mobile imaging, and exploring the potential of smartphones enhanced with attachments.