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    • Data and Code from: Smart vision-based monitoring system for AI-driven moth population estimation using camera-equipped trap imaging

    Metadata Updated: September 2, 2025

    Real-time, image-based monitoring for stored product insect pests could increase timely treatments and protection for postharvest products throughout the supply chain. Artificial intelligence (AI) and machine learning can provide the models necessary for accurate identification and population-counting within a trap-based system. This study presents the development of a smart vision-based monitoring system for moth population estimation using sticky traps with automated camera imaging. The proposed system integrates advanced image processing techniques with a Convolutional Neural Network (CNN) to accurately detect and classify moths and non-moth insects captured on sticky traps. Sticky traps, widely employed in integrated pest management (IPM) systems, often require manual inspection, which is labor-intensive and prone to human error. To address this, the developed system automates the detection process, reducing reliance on manual counting while improving classification precision. The dataset, consisting of 1,739 high-resolution images, was divided into training and testing subsets with a 70–30% split. Each image was preprocessed and annotated with ground-truth labels for accurate performance evaluation. The model demonstrated a high overall classification accuracy of 95.8%, with precision, recall, and F1-scores consistently exceeding 90%. These results highlight the effectiveness of the CNN in managing complex scenarios such as insect overlap, varied environmental conditions, and trap orientations, offering a scalable and efficient solution for real-time insect population monitoring in storage environments. The findings suggest that the proposed system provides a reliable and automated alternative for pest management, significantly reducing labor and enhancing decision-making in storage facilities and postharvest agriculture. In addition, field validation demonstrated the system’s feasibility in real-world storage environments, offering an effective and scalable alternative to traditional inspection practices while minimizing labor and enhancing precision pest control decisions.This dataset features a subset of the images captured every hour in a sticky trap baited with a Plodia interpunctella pheromone lure. The images were processed for classificaiton of Indian meal moths and for population counting over time. A read me on image file naming convention, meta data, and conversion code for MatLab are included in the data files.This research used resources provided by the SCINet project and/or the AI Center of Excellence of the USDA Agricultural Research Service, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D.

    Access & Use Information

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

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    Dates

    Metadata Created Date September 2, 2025
    Metadata Updated Date September 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 September 2, 2025
    Publisher Agricultural Research Service
    Maintainer
    Identifier 10.15482/USDA.ADC/29452583.v1
    Data Last Modified 2025-08-22
    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 02b78c97-d18c-42b5-910f-2e6210c66e32
    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 20942d1d690289368ea9475faeb683090924e7987f8a370fd0236d1276ca380a
    Source Schema Version 1.1
    Temporal 2023-07-01/2023-10-29

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