{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:28:37Z","timestamp":1772724517956,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T00:00:00Z","timestamp":1742083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017170","name":"Thailand Science Research and Innovation (TSRI)","doi-asserted-by":"publisher","award":["2567A12512056"],"award-info":[{"award-number":["2567A12512056"]}],"id":[{"id":"10.13039\/501100017170","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Automating fish freshness assessment is crucial for ensuring quality control and operational efficiency in large-scale fish processing. This study evaluates deep learning models for classifying the freshness of Barbonymus gonionotus (Silver Barb) and optimizing their deployment in an automated fish quality sorting system. Three lightweight deep learning architectures, MobileNetV2, MobileNetV3, and EfficientNet Lite2, were analyzed across 18 different configurations, varying model size (Small, Medium, Large) and preprocessing methods (With and Without Preprocessing). A dataset comprising 1200 images, categorized into three freshness levels, was collected from the Lam Pao Dam in Thailand. To enhance classification performance, You Only Look Once version 8 (YOLOv8) was utilized for object detection and image preprocessing. The models were evaluated based on classification accuracy, inference speed, and computational efficiency, with Technique for Order Preference by Similarity to Ideal Solution with Linear Programming (TOPSIS-LP) applied as a multi-criteria decision-making approach. The results indicated that the MobileNetV3 model with a large parameter size and preprocessing (M2-PL-P) achieved the highest closeness coefficient (CC) score, with an accuracy of 98.33% and an inference speed of 6.95 frames per second (fps). This study establishes a structured framework for integrating AI-driven fish quality assessment into fishery-based community enterprises, improving productivity and reducing reliance on manual sorting processes.<\/jats:p>","DOI":"10.3390\/computers14030105","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T04:29:28Z","timestamp":1742185768000},"page":"105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluating Optimal Deep Learning Models for Freshness Assessment of Silver Barb Through Technique for Order Preference by Similarity to Ideal Solution with Linear Programming"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6884-3889","authenticated-orcid":false,"given":"Atchara","family":"Choompol","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7179-7510","authenticated-orcid":false,"given":"Sarayut","family":"Gonwirat","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7292-8647","authenticated-orcid":false,"given":"Narong","family":"Wichapa","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"given":"Anucha","family":"Sriburum","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1296-4953","authenticated-orcid":false,"given":"Sarayut","family":"Thitapars","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2117-8514","authenticated-orcid":false,"given":"Thanakorn","family":"Yarnguy","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"given":"Noppakun","family":"Thongmual","sequence":"additional","affiliation":[{"name":"Department of Sciences and Mathematics, Faculty of Technology and Healthy of Science, Kalasin University, Kalasin 46000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9553-4795","authenticated-orcid":false,"given":"Waraporn","family":"Warorot","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0876-3629","authenticated-orcid":false,"given":"Kiatipong","family":"Charoenjit","sequence":"additional","affiliation":[{"name":"Department of Food Technology, Faculty of Agricultural Technology, Kalasin University, Kalasin 46000, Thailand"}]},{"given":"Ronnachai","family":"Sangmuenmao","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,16]]},"reference":[{"key":"ref_1","first-page":"e882","article-title":"A Novel TOPSIS Linear Programming Model Based on the Taguchi Method for Solving the Multi-Response Optimization Problems: A Case Study of a Fish Scale Scraping Machine","volume":"23","author":"Sriburum","year":"2023","journal-title":"Eng. 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