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

2026-03-02

AI Summary

This study addresses the absence of publicly available datasets for mold detection in food using smartphone-based microscopes, a gap that has hindered the development of low-cost, portable food safety technologies. To bridge this gap, the authors present the first open dataset tailored to real-world scenarios, comprising 4,941 handheld microscopic images spanning 11 food categories, captured with four smartphone models and three microscope types. The dataset supports dual tasks: mold detection and food classification. Leveraging pre-trained models, multi-strategy data augmentation, and saliency visualization, the proposed multi-task learning baseline achieves exceptional performance on both tasks, with an accuracy of 0.9954, F1-score of 0.9954, and Matthews Correlation Coefficient (MCC) of 0.9907, thereby demonstrating the dataset’s effectiveness and practical utility.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Object Detection & Categorization
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

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.

Problem

• food mold detection
• smartphone microscopy
• food classification
• mobile imaging
• food safety

Innovation

• smartphone microscopy
• food mold detection
• mobile imaging
• multi-task learning
• explainable AI