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    • Data and code from: AI-based image profiling and detection for the beetle byte quintet using Vision Transformer (ViT) in advanced stored product infestation monitoring

    Metadata Updated: December 2, 2025

    Managing beetles that infest stored products is crucial for reducing losses in harvest supply chains and improving food security and safety. Successful pest management programs require effective and timely monitoring programs, but traditional methods for detecting pests are time- and labor-intensive and require taxonomic expertise. New, automated methods using computer vision have the potential to improve accuracy and speed of detection, but often struggle to differentiate between beetle species, which tend to be small and morphologically similar. Our research centers on five economically significant beetle species, referred to as the 'Beetle Byte Quintet,' and proposes a novel methodology leveraging Vision Transformers (ViT) to enhance the precision and robustness of their classification. The method involves using an image profiling technique to capture morphological characteristics like body shape, color and exoskeleton structures that are key for distinguishing between species. By utilizing this species profiling, the ViT model achieved an accuracy rate of over 99.34% during training and 96.57% during testing. These findings highlight the model’s ability to generalize and maintain precision with new unseen data surpassing traditional computer vision algorithms significantly. The integration of ViT can help enable real time monitoring and is adaptable to a range of pest monitoring solutions for large scale storage settings which addresses the complexities of environments. This AI driven approach not only simplifies species identification but also promotes accurate and targeted pest control practices leading to reduced economic losses and improved food security.

    A subsample of images used in the model are included here for Rhyzopertha dominica (lesser grain borer), Sitophilus zeamais (maize weevil), Tribolium castaneum (red flour beetle), Cryptolestes ferrugineus (rusty grain beetle), and Oryzaephilus surinamensis (sawtoothed grain beetle). Custom MatLab code and a data descriptor README are also included.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: Creative Commons CCZero

    Downloads & Resources

    Dates

    Metadata Created Date September 2, 2025
    Metadata Updated Date December 2, 2025
    Data Update Frequency irregular

    Metadata Source

    Harvested from USDA JSON

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date September 2, 2025
    Metadata Updated Date December 2, 2025
    Publisher Agricultural Research Service
    Maintainer
    Identifier 10.15482/USDA.ADC/29251787.v1
    Data Last Modified 2025-11-20
    Public Access Level public
    Data Update Frequency irregular
    Bureau Code 005:18
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id 1d2064f1-e6e6-4eda-b0f8-e9bfffabb769
    Harvest Source Id d3fafa34-0cb9-48f1-ab1d-5b5fdc783806
    Harvest Source Title USDA JSON
    License https://creativecommons.org/publicdomain/zero/1.0/
    Program Code 005:040
    Source Datajson Identifier True
    Source Hash dd114aec824933f88bc7fb6828b507608e1601c1fa1fdf0dbc3016d9ed1f4caf
    Source Schema Version 1.1
    Temporal 2024-04-29/2025-03-04

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