Fungus Detection on Baked Breads: A Comparative Approach of Machine Learning and Deep Learning

Swati Shilaskar; Shripad Bhatlawande; Shalmali Bhalerao; Sharayu Chakole; Sandesh Bagmare

doi:10.2991/978-94-6463-831-8_22

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Fungus Detection on Baked Breads: A Comparative Approach of Machine Learning and Deep Learning

Authors

Swati Shilaskar¹^{, *}, Shripad Bhatlawande¹, Shalmali Bhalerao¹, Sharayu Chakole¹, Sandesh Bagmare¹

¹Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, 411037, India

^*Corresponding author. Email: swati.shilaskar@vit.edu

Corresponding Author

Swati Shilaskar

Available Online 31 August 2025.

DOI: 10.2991/978-94-6463-831-8_22 How to use a DOI?
Keywords: Bread Mold Detection; Deep Learning; Machine Learning; UNET Segmentation; Visual aid
Abstract: Toxigenic mold-contaminated food is a serious health concern, especially among the elderly and visually impaired who cannot detect it in its early stages. Bread is highly susceptible to mold and usually does not exhibit visible or olfactory appearance in early stages. This paper describes a accurate method to identify and quantify mold on bread based on image analysis. A special database was prepared, and classical machine learning models such as decision trees and random forests were tried. A UNet-based deep learning model resulted in 95.10% pixel-level mold segmentation and measurement accuracy. The results indicate the superiority of deep learning in early detection of mold compared to traditional methods with a considerable improvement. The process is an improvement in food safety and is particularly useful for vulnerable individuals, and it has potential for real-time food quality monitoring systems.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceeding of the 1st International Conference on Lifespan Innovation (ICLI 2025)
Series: Advances in Health Sciences Research
Publication Date: 31 August 2025
ISBN: 978-94-6463-831-8
ISSN: 2468-5739
DOI: 10.2991/978-94-6463-831-8_22 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Swati Shilaskar
AU  - Shripad Bhatlawande
AU  - Shalmali Bhalerao
AU  - Sharayu Chakole
AU  - Sandesh Bagmare
PY  - 2025
DA  - 2025/08/31
TI  - Fungus Detection on Baked Breads: A Comparative Approach of Machine Learning and Deep Learning
BT  - Proceeding of the 1st International Conference on Lifespan Innovation (ICLI 2025)
PB  - Atlantis Press
SP  - 175
EP  - 183
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-831-8_22
DO  - 10.2991/978-94-6463-831-8_22
ID  - Shilaskar2025
ER  -

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