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arXiv · 2603.01944

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

Dinh Nam Pham·Leonard Prokisch·Bennet Meyer·Jonas Thumbs
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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.

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Dinh Nam Pham, Leonard Prokisch, Bennet Meyer, Jonas Thumbs. 2026-03-02. MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection. https://doi.org/10.1145/3793853.3799806

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