A Deep Learning-Based System for Transparent Dry Fish Markets: Fostering Fair Trade and Sustainable Economics

DOI

10.2991/978-2-38476-581-2_3How to use a DOI?

Keywords

Transfer Learning; Dry Fish Classification; Fair Trade; Market Transparency; Consumer Protection; Sustainable Economy

Abstract

The dried fish market: a central economic source for coastal communities in Bangladesh, threatened by the substitution of species, misdescription and low product quality. All these associations have grave implications for the long-term survival of the industry. This work grants a novel image-based platform to provide transparency and fairness in such a legacy market. Our approach is based on transfer learning, a powerful method for models to leverage knowledge from one task to another. We fine-tuned pre-trained deep learning models, MobileNetV3-Small, ResNet50, Vision Transformer (ViT) and ConvNeXt-Tiny, for two different purposes of diagnosing dry fish species and fair price range prediction, using 1251 images augmented to 6,255 dataset images characterized into 7 diverse classes. The models were well adjusted, and they verified good discriminative powers. The performance of the lightweight MobileNetV3-Small model with an accuracy of 96.83%, established the practicability of the proposed approach in a resource-limited setting. However, the ConvNeXt-Tiny model outperformed the other models with an accuracy of 99.63% on this task, which shows the high quality of our framework. Building on this great performance, we’ve built an API where users can upload a photo of a dry fish to identify the classes and get a fair price range. This could also help to avoid illegal trade, contribute to market transparency and offer consumers and sellers proper information, enabling fair competition and ultimately more sustainable and resilient markets going forward.

Copyright

© 2026 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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TY  - CONF
AU  - Aishwarya Debnath Ayshi
AU  - Shourav Dey
PY  - 2026
DA  - 2026/05/30
TI  - A Deep Learning-Based System for Transparent Dry Fish Markets: Fostering Fair Trade and Sustainable Economics
BT  - Proceedings of the International Conference on Challenges and Trends in Arts and Social Sciences (ICCTASS 2025)
PB  - Atlantis Press
SP  - 16
EP  - 29
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-581-2_3
DO  - 10.2991/978-2-38476-581-2_3
ID  - Ayshi2026
ER  -