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

TL;DR

This paper introduces MobileMold, an open smartphone-based microscopy dataset for food mold detection and food classification, and establishes baselines for (i) mold detection and (ii) food-type classification, including a multi-task setting that predicts both attributes.

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.

Figures (6)

• Figure 1: Effective field of view comparison using millimeter paper. Both images are cropped according to the preprocessing pipeline and show an effective FOV of $11\text{mm}\times 11\text{mm}$.
• Figure 2: Typical data acquisition setup mimicking consumer use. A smartphone equipped with the Apexel $100\times$ attachment is held manually over a carrot sample.
• Figure 3: MobileMold per-food distribution of mold vs. no mold.
• Figure 4: Samples of MobileMold.
• Figure 5: Saliency maps of MobileNet for 3 random samples of the test set with mold.