<b>TgFC: </b><b><i>Tectona grandis</i></b><b> </b><b>Fungal Community Dataset</b>
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The <b>TgFC dataset</b> comprises <b>5,236 annotated microscopic images</b> (640×640 resolution) of fungal spores from diseased <i>Tectona grandis</i> (teak) leaves, collected in Bangladesh (Sylhet and Rangamati). The images represent three fungal taxa:<b><i>Olivea tectonae</i></b><b> (2,219 images)</b><b><i>Neopestalotiopsis</i></b><b> sp. (1,688 images)</b><b><i>Colletotrichum siamense</i></b><b> (1,329 images)</b>Spores were imaged at 40× magnification using a Zeiss Primostar 3 microscope. Wet mount preparations were made without staining to preserve field morphology. All images were manually annotated with bounding boxes using LabelImg for object detection, based on species-specific spore morphology.The dataset is structured for YOLO-based object detection and includes separate folders for training (80%), validation (10%), and testing (10%)—each containing <code>images/</code> and <code>labels/</code> subdirectories. No image preprocessing was performed, allowing flexible adaptation to custom pipelines.The dataset is annotated in YOLO format for object detection but can be adapted for various computer vision and image analysis tasks, including traditional machine learning, deep learning, classification, and segmentation workflows.<b>Code & Validation: </b><i>here</i><b>Applications and Impact</b><b>:</b>Enables automated spore identification from foliar samples, reducing manual diagnostic workloadSupports airborne spore detection and spore quantification in environmental samples for outbreak predictionIntegrates with real-time disease monitoring systems for early intervention in agricultureFacilitates training and benchmarking of deep learning models (e.g., YOLO, CNNs) for fungal detectionSupports transfer learning across fungal species and imaging conditionsProvides a foundation for developing portable, field-ready diagnostic toolsApplicable beyond teak, as the included fungi are common in other tropical and subtropical ecosystemsOpen-access format encourages future expansion and collaborative contributions of more annotated images<br><b>If you use this dataset or validation scripts, please cite:</b>The TgFC dataset paper is Under Review <i>(Journal: Scientific Data)</i>Cite the Dataset. DOI: 10.6084/m9.figshare.28855910<b>For questions or collaborations, please contact:</b>Maaz Ahmed – ahmedmaaz.maaz@gmail.comSyeda Munjiba Islam - islammunjiba@gmail.comMd. Faysal Ahamed - faysal.ahamed@ece.ruet.ac.bd
