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    • Data and Code From: AI-Based bread quality assessment using image processing techniques and the developed BQe-CNN

    Metadata Updated: February 12, 2026

    Ensuring consistent bread quality is vital for maintaining industry standards, reducing waste, and keeping consumer satisfaction. Traditional methods of bread quality analysis which rely on manual inspection, are often subjective, time-consuming, and prone to inconsistencies, while modern analysis techniques, though available, tend to be prohibitively expensive. This study introduces an AI-driven approach that leverages advanced image processing techniques to automate and enhance the accuracy of bread quality assessment. By extracting key features such as porosity, texture, and air cell structure, the proposed Bread Quality Enhanced Convolutional Neural Network (BQe-CNN) offers a more precise analysis of bread parameters. The model achieved classification accuracies of 92% for bread colors and 88% for quality levels, significantly outperforming manual methods. By leveraging enhanced layers like residual connections and attention mechanisms, the model efficiently captured fine details in bread images, making it highly effective at detecting subtle variations in texture and air cell distribution. While the model demonstrates high performance in quantitative analysis, it is important to note that artisan scoring—characterized by detailed aesthetic evaluations integral to traditional bread-making—remains a challenging domain for automation. This limitation presents an opportunity to further enhance the model's capabilities by integrating advanced algorithms or hybrid approaches, bridging the gap between precise computational analysis and the specific requirements of artisan scoring. Nevertheless, the BQe-CNN's ability to provide real-time, automated quality control is a dependable and transformative tool, optimizing production, reducing waste, and complementing human expertise in a cost-effective manner. These image processing techniques allow for real-time, automated quality control, optimizing production and reducing waste. This novel approach, rooted in visual analysis of product characteristics, represents a significant leap forward in achieving consistency and scalability in bread quality control for the baking industry.

    Included is a subsample of images of bread of different color and porosity, examples of the processed images, a data descriptor README, metadata for the bread images, porosity values for the bread images, and MatLab code.

    Access & Use Information

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

    Downloads & Resources

    Dates

    Metadata Created Date February 12, 2026
    Metadata Updated Date February 12, 2026
    Data Update Frequency irregular

    Metadata Source

    Harvested from USDA JSON

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date February 12, 2026
    Metadata Updated Date February 12, 2026
    Publisher Agricultural Research Service
    Maintainer
    Identifier 10.15482/USDA.ADC/29251832.v1
    Data Last Modified 2026-02-03
    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 dd496fc1-d76d-4c91-ab28-2c5a3e06b0aa
    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 944e7de58445d7320383ff072a4c65e889f2a837ab8b454eb6cd9f3e6eb70a7e
    Source Schema Version 1.1
    Temporal 2020-07-01/2024-12-30

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