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Data and Images from: Numerical Signature Dataset of Thoracic and Elytral Fragments from Curculionidae and Tenebrionidae Beetles for AI-Based Species Identification

Published by Agricultural Research Service | Department of Agriculture | Catalog Last Checked: August 01, 2026 at 04:52 AM | Dataset Last Updated: December 19, 2025
This dataset presents curated and annotated 256×256 pixel image fragments of thoracic and elytral regions from six economically significant species within the beetle families Curculionidae and Tenebrionidae. These anatomical fragments were extracted from high-resolution trap images to support numerical signature generation for species-level classification. The dataset enables researchers to develop and validate image-based machine learning pipelines for pest identification in stored product environments. All samples are standardized and include metadata indicating species, anatomical region, and imaging parameters. This work contributes to advancing computational entomology, post-harvest pest management, and real-time automated monitoring systems.Insect pest detection in post-harvest storage environments is crucial for mitigating economic losses and ensuring food security. Traditional identification approaches are labor-intensive and rely on full-body morphological features. This dataset introduces a fragment-based classification framework using annotated thoracic and elytral sections of stored-product beetles. Species included are:Sitophilus zeamaisSitophilus oryzaeSitophilus granariusTribolium castaneumLatheticus oryzaeTribolium confusumFolder abbreviations for the insects are Sitophilus zeamais (mw); Sitophilus oryzae (rw); Sitophilus granarius (ww); Tribolium castaneum (tc); Latheticus oryzae (lo); and Tribolium confusum (tco).Key numerical signature descriptors were computed for each fragment:Skewness – Asymmetry of pixel intensity distributionKurtosis – Sharpness of contour distributionsEntropy – Texture complexityStandard Deviation – Pixel intensity variation

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