ToxiFruit: A Multi-Class Image Dataset for Formalin Adulteration Detection in Fruits
Authors: Sattar, Abdus, Ridoy, Md. Asif Mahmud, Mahi, Mehraj Hossain
Type: Photo
Published: 2026-07-21
Keywords: Agriculture, Fruit, Chemical Mixed Fruit, Public health, Food Technology

<p class=""><strong>ToxiFruit</strong> is a multi-class image dataset developed to support research in food safety and computer vision, with a particular focus on detecting chemical adulteration in fruits. The dataset comprises <strong>8 classes</strong> across <strong>4 commonly consumed fruit types</strong> (Apple, Malta, Mango, and Banana), where each fruit is categorized into <strong>Fresh</strong> and <strong>Chemically Mixed</strong> conditions.</p>
<p>The dataset contains <strong>7,200 original RGB images</strong> (900 images per class), collected from local markets and domestic orchards in Bangladesh using a smartphone camera under real-world conditions. Images were captured under diverse illumination, background, orientation, and viewing-angle variations to improve dataset diversity and enhance model generalization.</p>
<p>To facilitate machine learning and deep learning research, standard data augmentation techniques—including rotation, horizontal and vertical flipping, zooming, translation, and brightness adjustment—were applied to the original dataset, resulting in <strong>28,800 augmented images</strong> (3,600 images per class).</p>
<h2>Dataset Composition</h2>
<ul>
<li><strong>Fruit Types:</strong> Apple, Malta, Mango, Banana</li>
<li><strong>Classes (8):</strong>
<ul>
<li>Fresh Apple</li>
<li>Mixed Apple</li>
<li>Fresh Malta</li>
<li>Mixed Malta</li>
<li>Fresh Mango</li>
<li>Mixed Mango</li>
<li>Fresh Banana</li>
<li>Mixed Banana</li>
</ul>
</li>
<li><strong>Original Images:</strong> 7,200 (900 per class)</li>
<li><strong>Augmented Images:</strong> 28,800 (3,600 per class)</li>
</ul>
<h2>Collection Locations</h2>
<p>The dataset was collected from four geographically diverse locations in Bangladesh, representing urban, peri-urban, and rural environments. Fruits were sourced from local markets and domestic orchards to capture natural variability in appearance and chemically treated conditions.</p>
<p>Collection sites include:</p>
<ul>
<li><strong>Mirpur-10, Dhaka:</strong> 23.8041° N, 90.3667° E</li>
<li><strong>Daffodil Smart City, Savar:</strong> 23.8738° N, 90.2625° E</li>
<li><strong>Madrasha Para, Joypurhat:</strong> 25.1043° N, 89.0227° E</li>
<li><strong>Nawabganj, Dinajpur:</strong> 25.6337° N, 88.6350° E</li>
</ul>
<h2>Repository Contents</h2>
<p>The Zenodo repository includes:</p>
<ul>
<li><strong>7,200 original high-resolution images</strong></li>
<li><strong>28,800 augmented images</strong></li>
<li><strong>metadata.csv</strong>, providing structured class-level metadata, including:
<ul>
<li>Fruit type</li>
<li>Scientific name</li>
<li>Fresh/Mixed condition</li>
<li>Original and augmented image counts</li>
<li>Image resolution</li>
<li>Camera/device information</li>
<li>Collection locations</li>
<li>Augmentation methods</li>
<li>Repository folder structure</li>
<li>Licensing information</li>
<li>Zenodo repository identifiers</li>
</ul>
</li>
<li>Documentation describing dataset organization, labeling conventions, image acquisition procedures, and augmentation protocols.</li>
</ul>
<h2>Potential Applications</h2>
<ul>
<li>Detection of chemically adulterated fruits using computer vision</li>
<li>Multi-class and binary image classification</li>
<li>Deep learning benchmark development</li>
<li>Transfer learning and explainable AI (XAI)</li>
<li>Food quality inspection and safety monitoring</li>
<li>Precision agriculture and smart farming</li>
<li>Automated fruit quality assessment</li>
<li>Research in food safety, agriculture, and public health</li>
</ul>


<p>This</p>