{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T03:30:26Z","timestamp":1791775826817,"version":"4.3.4"},"reference-count":57,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["IITP-2021-0-02051"],"award-info":[{"award-number":["IITP-2021-0-02051"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information & communications Technology Planning & Evaluation","award":["IITP-2024-RS-2023-00255968"],"award-info":[{"award-number":["IITP-2024-RS-2023-00255968"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Fish species recognition is essential for ecological studies, fishery management, and marine biology. Accurate detection and categorization are critical for preserving biodiversity, allowing scientists to track species distribution, identify invasive species, and analyze the effects of environmental changes. The fish sector is essential to any country's food and agriculture. Identification of species by the morphology process is both inaccurate and costly. However, the manual process of measuring important details like species identification, length, and quantity is difficult to capture, which shows the need for automation. The merging of automated systems and artificial intelligence has revolutionized this industry. Recent advancements in image detection systems based on machine learning and deep learning have been explored across various domains. Yet, applying state-of-the-art deep model Convolutional Neural Networks (CNNs) to identify the fish species\u2019 complexity of season and location, and limited public datasets pose a challenge for the detection. Machine learning and deep learning use artificial neural networks to simulate how humans think and learn, efficiently automating similar monitoring applications such as species identification on land and in water. You Only Look Once (YOLO) is a state-of-the-art method for object detection based on deep learning. The goal of this study is to develop a deep learning system for recognizing fish species using the YOLO paradigm. The Fish-Pak dataset, which includes information on tropical fish farming in Pakistan, consists of 915 images against 6 targeted classes, freely available at the Mendeley data source. To ensure the suggested YOLO architecture's improved performance on the Fish-Pak data collection, we will conduct an experimental comparison with other versions of YOLO v3 and V4. The total accuracy of fish species identification using the proposed methods is 99%, with an mAP of 99.65%, top performance results as compared to existing literature.<\/jats:p>","DOI":"10.1371\/journal.pone.0342901","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T18:30:31Z","timestamp":1770921031000},"page":"e0342901","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["A multi-class framework for fish species classification using deep learning technique"],"prefix":"10.1371","volume":"21","author":[{"given":"Zain","family":"Farooq","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Ramzan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9827-5023","authenticated-orcid":true,"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Attique","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae-Sun","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anam","family":"Naz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"issue":"5","key":"pone.0342901.ref001","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1007\/s10462-021-10102-3","article-title":"Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish","volume":"55","author":"D Li","year":"2022","journal-title":"Artif Intell Rev"},{"key":"pone.0342901.ref002","doi-asserted-by":"crossref","DOI":"10.1016\/j.fishres.2023.106790","article-title":"Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review","volume":"266","author":"A Yassir","year":"2023","journal-title":"Fisheries Research"},{"key":"pone.0342901.ref003","first-page":"1829","article-title":"Machine learning in marine ecology: an overview of techniques and applications","volume-title":"ICES Journal of Marine Science","author":"P Rubbens","year":"2023"},{"issue":"2","key":"pone.0342901.ref004","doi-asserted-by":"crossref","DOI":"10.1111\/jwas.13051","article-title":"Cutting\u2010edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges","volume":"55","author":"SI Islam","year":"2024","journal-title":"J World Aquaculture Soc"},{"issue":"2","key":"pone.0342901.ref005","first-page":"188","article-title":"Fish species recognition using transfer learning techniques","volume":"7","author":"JS Murugaiyan","year":"2021","journal-title":"Int J Adv Intell Informatics"},{"issue":"1","key":"pone.0342901.ref006","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1093\/icesjms\/fsx109","article-title":"Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data","volume":"75","author":"SA Siddiqui","year":"2017","journal-title":"ICES Journal of Marine Science"},{"issue":"10","key":"pone.0342901.ref007","doi-asserted-by":"crossref","first-page":"372","DOI":"10.3390\/fishes9100372","article-title":"Systematic review of multi-species models in fisheries: key features and current trends","volume":"9","author":"P Couve","year":"2024","journal-title":"Fishes"},{"issue":"1","key":"pone.0342901.ref008","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1111\/raq.12464","article-title":"Deep learning for smart fish farming: applications, opportunities and challenges","volume":"13","author":"X Yang","year":"2020","journal-title":"Reviews in Aquaculture"},{"issue":"4","key":"pone.0342901.ref009","doi-asserted-by":"crossref","first-page":"01","DOI":"10.17762\/ijritcc.v10i4.5524","article-title":"A Study on Fish Classification Techniques using Convolutional Neural Networks on Highly Challenged Underwater Images","volume":"10","author":"S Malla","year":"2022","journal-title":"IJRITCC"},{"issue":"3","key":"pone.0342901.ref010","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1177\/1070496520925878","article-title":"Potential development contribution of fisheries reform: evidence from Pakistan","volume":"29","author":"DJ Kaczan","year":"2020","journal-title":"J Environ Dev"},{"key":"pone.0342901.ref011","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1201\/9781003097921-6","article-title":"Sustainable development of marine fisheries in Pakistan","volume-title":"Sustainable entrepreneurship, renewable energy-based projects, and digitalization","author":"A Baset","year":"2020"},{"key":"pone.0342901.ref012","first-page":"40","article-title":"Underwater Object Detection model based on YOLOv3 architecture using Deep Neural Networks.","author":"P Athira.","year":"2021","journal-title":"2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)"},{"key":"pone.0342901.ref013","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.neuroscience.2024.12.013","article-title":"Anxiety in aquatics: Leveraging machine learning models to predict adult zebrafish behavior","volume":"565","author":"V Srivastava","year":"2025","journal-title":"Neuroscience"},{"key":"pone.0342901.ref014","doi-asserted-by":"crossref","first-page":"109090","DOI":"10.1016\/j.compag.2024.109090","article-title":"Agricultural object detection with You Only Look Once (YOLO) algorithm: A bibliometric and systematic literature review","volume":"223","author":"CM Badgujar","year":"2024","journal-title":"Comput Electron Agric"},{"issue":"1","key":"pone.0342901.ref015","first-page":"117","article-title":"Enforcing UNCLOS in Pakistan\u2019s Exclusive Economic Zone: A Case Study of Fisheries Management","volume":"3","author":"M Khan","year":"2025","journal-title":"Dialogue Social Science Review"},{"issue":"5","key":"pone.0342901.ref016","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1016\/j.jksuci.2020.07.005","article-title":"A survey on fish classification techniques","volume":"34","author":"MK Alsmadi","year":"2022","journal-title":"Journal of King Saud University - Computer and Information Sciences"},{"key":"pone.0342901.ref017","doi-asserted-by":"crossref","DOI":"10.3389\/fmars.2021.823173","article-title":"Automated detection, classification and counting of fish in fish passages with deep learning","volume":"8","author":"V Kandimalla","year":"2022","journal-title":"Front Mar Sci"},{"issue":"9","key":"pone.0342901.ref018","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1002\/lom3.10113","article-title":"Fish species classification in unconstrained underwater environments based on deep learning","volume":"14","author":"A Salman","year":"2016","journal-title":"Limnol Oceanogr Methods"},{"issue":"9","key":"pone.0342901.ref019","doi-asserted-by":"crossref","DOI":"10.3390\/s21092999","article-title":"Semi-automated data processing and semi-supervised machine learning for the detection and classification of water-column fish schools and gas seeps with a multibeam echosounder","volume":"21","author":"A Minelli","year":"2021","journal-title":"Sensors"},{"issue":"22","key":"pone.0342901.ref020","doi-asserted-by":"crossref","DOI":"10.3390\/s23229062","article-title":"A novel machine-learning framework based on a hierarchy of dispute models for the identification of fish species using multi-mode spectroscopy","volume":"23","author":"M Sueker","year":"2023","journal-title":"Sensors"},{"key":"pone.0342901.ref021","doi-asserted-by":"crossref","DOI":"10.1109\/MERCon55799.2022.9906206","article-title":"Performance comparison of sea fish species classification using hybrid and supervised machine learning algorithms.","author":"LI Mampitiya","year":"2022","journal-title":"MERCon 2022 - Moratuwa Engineering Research Conference, Proceedings"},{"issue":"21","key":"pone.0342901.ref022","article-title":"A scalable open-source framework for machine learning-based image collection, annotation and classification: A case study for automatic fish species identification","volume":"14","author":"CNS Silva","year":"2022","journal-title":"Sustainability"},{"issue":"16","key":"pone.0342901.ref023","doi-asserted-by":"crossref","DOI":"10.3390\/rs15163952","article-title":"Classification of fish species using multispectral data from a low-cost camera and machine learning","volume":"15","author":"F Monteiro","year":"2023","journal-title":"Remote Sens"},{"key":"pone.0342901.ref024","doi-asserted-by":"crossref","unstructured":"Rauf HT, Ikram M, Lali U, Zahoor S, Zakir S, Shah H. Visual features based automated identification of fish species using deep convolutional neural networks. 2019.","DOI":"10.1016\/j.compag.2019.105075"},{"key":"pone.0342901.ref025","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ecoinf.2018.12.007","article-title":"Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks","volume":"50","author":"S Aronica","year":"2019","journal-title":"Ecol Inform"},{"issue":"4","key":"pone.0342901.ref026","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1093\/icesjms\/fsaa052","article-title":"Automated classification of schools of the silver cyprinid Rastrineobola argentea in Lake Victoria acoustic survey data using random forests","volume":"77","author":"R Proud","year":"2020","journal-title":"ICES Journal of Marine Science"},{"issue":"4","key":"pone.0342901.ref027","article-title":"Automated identification of morphological characteristics of three Thunnus species based on different machine learning algorithms","volume":"8","author":"L Ou","year":"2023","journal-title":"Fishes"},{"issue":"8","key":"pone.0342901.ref028","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1139\/cjfas-2022-0270","article-title":"Automatic classification of the phenotype textures of three Thunnus species based on the machine learning SVM algorithm","volume":"80","author":"L Ou","year":"2023","journal-title":"Can J Fish Aquat Sci"},{"issue":"3","key":"pone.0342901.ref029","doi-asserted-by":"crossref","first-page":"144","DOI":"10.24843\/LKJITI.2020.v11.i03.p03","article-title":"Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset","volume":"11","author":"Y Adiwinata","year":"2020","journal-title":"LKJITI"},{"issue":"12","key":"pone.0342901.ref030","article-title":"Intelligent measurement of morphological characteristics of fish using improved u-net","volume":"10","author":"C Yu","year":"2021","journal-title":"Electronics"},{"key":"pone.0342901.ref031","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2020.101088","article-title":"Fish detection and species classification in underwater environments using deep learning with temporal information","volume":"57","author":"A Jalal","year":"2020","journal-title":"Ecol Inform"},{"key":"pone.0342901.ref032","first-page":"39","article-title":"Deep Learning-Based Fish Classification with Data Augmentation.","author":"Y-W Wu","year":"2023","journal-title":"2023 IEEE International Conference on Marine Artificial Intelligence and Law (ICMAIL)"},{"key":"pone.0342901.ref033","first-page":"1","article-title":"Marine Fish Species Classification Using Transfer Learning and Residual Network.","author":"BD Satoto","year":"2024","journal-title":"Institute of Electrical and Electronics Engineers (IEEE)"},{"key":"pone.0342901.ref034","unstructured":"Amity University, Institute of Electrical and Electronics Engineers, Uttar Pradesh Section, Institute of Electrical and Electronics Engineers, Amity University, School of Engineering and Technology. In: Amity School of Engineering and Technology, Noida, India, 2019."},{"issue":"1","key":"pone.0342901.ref035","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1093\/icesjms\/fsy147","article-title":"Fish species identification using a convolutional neural network trained on synthetic data","volume":"76","author":"V Allken","year":"2018","journal-title":"ICES Journal of Marine Science"},{"issue":"2","key":"pone.0342901.ref036","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s11277-019-06634-1","article-title":"Automatic fish species classification using deep convolutional neural networks","volume":"116","author":"MA Iqbal","year":"2021","journal-title":"Wireless Personal Communications"},{"issue":"8","key":"pone.0342901.ref037","doi-asserted-by":"crossref","DOI":"10.22214\/ijraset.2023.54965","article-title":"Deep blue discoveries: advancements in fish detection and species taxonomy using deep learning in the subaquatic realm","volume":"11","author":"S Swetha","year":"2023","journal-title":"Int J Res Appl Sci Eng Technol"},{"issue":"8","key":"pone.0342901.ref038","article-title":"Targeted data augmentation and hierarchical classification with deep learning for fish species identification in underwater images","volume":"8","author":"A Tamou","year":"2022","journal-title":"J Imaging"},{"issue":"1","key":"pone.0342901.ref039","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4173\/mic.2021.1.1","article-title":"NorFisk: fish image dataset from Norwegian fish farms for species recognition using deep neural networks","volume":"42","author":"AM Crescitelli","year":"2021","journal-title":"MIC"},{"key":"pone.0342901.ref040","doi-asserted-by":"crossref","first-page":"46723","DOI":"10.1109\/ACCESS.2020.2978912","article-title":"Efficient Foreign Object Detection Between PSDs and Metro Doors via Deep Neural Networks","volume":"8","author":"Y Dai","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"pone.0342901.ref041","doi-asserted-by":"crossref","first-page":"753","DOI":"10.3390\/make4030036","article-title":"Live fish species classification in underwater images by using convolutional neural networks based on incremental learning with knowledge distillation loss","volume":"4","author":"A Tamou","year":"2022","journal-title":"Mach Learn Knowl Extr"},{"key":"pone.0342901.ref042","doi-asserted-by":"crossref","DOI":"10.3389\/fmars.2022.944582","article-title":"Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning","volume":"9","author":"D Marrable","year":"2022","journal-title":"Frontiers in Marine Science"},{"issue":"2","key":"pone.0342901.ref043","first-page":"106","article-title":"Efficient data acquisition and CNN design for fish species classification in inland waters","volume":"18","author":"JH Park","year":"2020","journal-title":"Journal of Information and Communication Convergence Engineering"},{"issue":"21","key":"pone.0342901.ref044","doi-asserted-by":"crossref","DOI":"10.3390\/s23218909","article-title":"A spatial-spectral classification method based on deep learning for controlling pelagic fish landings in Chile","volume":"23","author":"JE Pezoa","year":"2023","journal-title":"Sensors (Basel)"},{"issue":"21","key":"pone.0342901.ref045","doi-asserted-by":"crossref","DOI":"10.3390\/s22218268","article-title":"Class-aware fish species recognition using deep learning for an imbalanced dataset","volume":"22","author":"SY Alaba","year":"2022","journal-title":"Sensors"},{"key":"pone.0342901.ref046","article-title":"YOLOv3: An Incremental Improvement","author":"J Redmon","year":"2018"},{"key":"pone.0342901.ref047","article-title":"Fish species classification using deep learning and appearance-based feature extraction","volume":"20","author":"JMJ Mol","year":"2024","journal-title":"J Electrical Systems"},{"key":"pone.0342901.ref048","doi-asserted-by":"crossref","DOI":"10.3389\/fcomp.2024.1326452","article-title":"Automatic labeling of fish species using deep learning across different classification strategies","volume":"6","author":"J Jare\u00f1o","year":"2024","journal-title":"Front Comput Sci"},{"issue":"2","key":"pone.0342901.ref049","doi-asserted-by":"crossref","first-page":"154","DOI":"10.3390\/info16020154","article-title":"CNN-Based Optimization for Fish Species Classification: Tackling Environmental Variability, Class Imbalance, and Real-Time Constraints","volume":"16","author":"A Mohammadisabet","year":"2025","journal-title":"Information"},{"issue":"12","key":"pone.0342901.ref050","doi-asserted-by":"crossref","DOI":"10.1002\/eng2.12992","article-title":"FishAI \u00a0: Automated hierarchical marine fish image classification with vision transformer","volume":"6","author":"C Yang","year":"2024","journal-title":"Engineering Reports"},{"key":"pone.0342901.ref051","article-title":"Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images","author":"KS Mehrab","year":"2024"},{"key":"pone.0342901.ref052","article-title":"Lightweight fish classification model for sustainable marine management: Indonesian case","author":"F Kurniawan","year":"2024"},{"key":"pone.0342901.ref053","article-title":"FisHook -- An Optimized Approach to Marine Specie Classification using MobileNetV2","author":"K Dey","year":"2023"},{"key":"pone.0342901.ref054","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2015.10.122","article-title":"DeepFish: Accurate underwater live fish recognition with a deep architecture","volume":"187","author":"H Qin","year":"2016","journal-title":"Neurocomputing"},{"key":"pone.0342901.ref055","article-title":"Computer vision neural network using YOLOv4 for underwater fish video detection in Roatan, Honduras.","author":"DAJ Nixon","year":"2021","journal-title":"Proceedings of the 2021 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2021"},{"key":"pone.0342901.ref056","doi-asserted-by":"crossref","unstructured":"Shah SZH, Rauf HT, IkramUllah M, Bukhari SAC, Khalid MS, Farooq M. Fish-Pak: Fish Species Dataset from Pakistan for Visual Features Based Classification. 2019;3.","DOI":"10.1016\/j.dib.2019.104565"},{"issue":"2","key":"pone.0342901.ref057","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s11277-019-06634-1","article-title":"Automatic Fish Species Classification Using Deep Convolutional Neural Networks","volume":"116","author":"MA Iqbal","year":"2019","journal-title":"Wireless Pers Commun"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0342901","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T18:30:44Z","timestamp":1770921044000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0342901"}},"subtitle":[],"editor":[{"given":"Ankit","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,12]]},"references-count":57,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,12]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0342901","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,12]]}}}