{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:48:45Z","timestamp":1787312925362,"version":"build-2736575974"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T00:00:00Z","timestamp":1769212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"General Scientific Research Projects of Liaoning Province Science and Technology Joint Plan Project","award":["2024JH2\/102600083"],"award-info":[{"award-number":["2024JH2\/102600083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network\u2019s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model.<\/jats:p>","DOI":"10.3390\/s26030791","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T16:13:48Z","timestamp":1769444028000},"page":"791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["YOLO-Shrimp: A Lightweight Detection Model for Shrimp Feed Residues Fusing Multi-Attention Features"],"prefix":"10.3390","volume":"26","author":[{"given":"Tianwen","family":"Hou","sequence":"first","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinying","family":"Miao","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9686-6654","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"He","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-7180","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Dalian Ocean University, Dalian 116023, China"},{"name":"Liaoning Provincial Key Laboratory Marine Informat Technology, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s11157-013-9311-5","article-title":"Impacts of shrimp farming on the coastal environment of Bangladesh and approach for management","volume":"12","author":"Hossain","year":"2013","journal-title":"Rev. Environ. Sci. Bio\/Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Macusi, E.D., Estor, D.E.P., Borazon, E.Q., Clapano, M.B., and Santos, M.D. (2022). Environmental and socioeconomic impacts of shrimp farming in the Philippines: A critical analysis using PRISMA. Sustainability, 14.","DOI":"10.20944\/preprints202201.0220.v2"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"740178","DOI":"10.1016\/j.aquaculture.2023.740178","article-title":"Is economies of scale driving the development in shrimp farming from Penaeus monodon to Litopenaeus vannamei? The case of Indonesia","volume":"579","author":"Asmild","year":"2024","journal-title":"Aquaculture"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"740867","DOI":"10.1016\/j.aquaculture.2024.740867","article-title":"Climatic events and disease occurrence in intensive Litopenaeus vannamei shrimp farming in the Mekong area of Vietnam","volume":"587","author":"Le","year":"2024","journal-title":"Aquaculture"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1111\/raq.12659","article-title":"Harnessing the potentialities of probiotics, prebiotics, synbiotics, paraprobiotics, and postbiotics for shrimp farming","volume":"14","author":"Goh","year":"2022","journal-title":"Rev. Aquac."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Emerenciano, M.G., Miranda-Baeza, A., Mart\u00ednez-Porchas, M., Poli, M.A., and Vieira, F.D.N. (2021). Biofloc technology (BFT) in shrimp farming: Past and present shaping the future. Front. Mar. Sci., 8.","DOI":"10.3389\/fmars.2021.813091"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.aej.2025.04.075","article-title":"LAF: Enhancing person re-identification via Latent-Assisted Feature Fusion","volume":"127","author":"Li","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106082","DOI":"10.1016\/j.ocecoaman.2022.106082","article-title":"Mangrove-shrimp farming: A triple-win approach for communities in the Mekong River Delta","volume":"221","author":"Nguyen","year":"2022","journal-title":"Ocean Coast. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100895","DOI":"10.1016\/j.aqrep.2021.100895","article-title":"Livelihood status of coastal shrimp farmers in Bangladesh: Comparison before and during COVID-19","volume":"21","author":"Rahman","year":"2021","journal-title":"Aquac. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.eap.2021.09.015","article-title":"What motivates farmers to accept good aquaculture practices in development policy? Results from choice experiment surveys with small-scale shrimp farmers in Vietnam","volume":"72","author":"Phong","year":"2021","journal-title":"Econ. Anal. Policy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1007\/s10499-023-01066-y","article-title":"Ecological and economic analysis for different shrimp farming models in China","volume":"31","author":"Wang","year":"2023","journal-title":"Aquac. Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, M., Tao, Z., Lin, S., and Feng, K. (2024). Mix-net: Hybrid attention\/diversity network for person re-identification. Electronics, 13.","DOI":"10.3390\/electronics13051001"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/13657305.2021.1947414","article-title":"Assessment of the impacts of social capital on the profit of shrimp farming production in the Mekong Delta, Vietnam","volume":"26","author":"Duy","year":"2022","journal-title":"Aquac. Econ. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13007-025-01324-5","article-title":"Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds","volume":"21","author":"Li","year":"2025","journal-title":"Plant Methods"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"26877","DOI":"10.1109\/JSEN.2024.3418618","article-title":"Automated defect detection for mass-produced electronic components based on YOLO object detection models","volume":"24","author":"Mao","year":"2024","journal-title":"IEEE Sensors J."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 11\u201317). Oriented R-CNN for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo algorithm developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_20","first-page":"13467","article-title":"Towards large-scale small object detection: Survey and benchmarks","volume":"45","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"127447","DOI":"10.1016\/j.eswa.2025.127447","article-title":"SO-YOLOv8: A novel deep learning-based approach for small object detection with YOLO beyond COCO","volume":"280","author":"Giri","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hussain, M. (2023). YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines, 11.","DOI":"10.3390\/machines11070677"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","article-title":"Object detection using YOLO: Challenges, architectural successors, datasets and applications","volume":"82","author":"Diwan","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"42816","DOI":"10.1109\/ACCESS.2024.3378568","article-title":"Yolov1 to v8: Unveiling each variant\u2013a comprehensive review of yolo","volume":"12","author":"Hussain","year":"2024","journal-title":"IEEE Access"},{"key":"ref_25","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (October, January 29). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. Proceedings of the European Conference on Computer Vision (ECCV), Milan, Italy."},{"key":"ref_26","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_27","unstructured":"Kotthapalli, M., Ravipati, D., and Bhatia, R. (2025). YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sinha, D., and El-Sharkawy, M. (2019, January 10\u201312). Thin mobilenet: An enhanced mobilenet architecture. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON47517.2019.8993089"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201322). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, J., and Wang, L. (2018, January 25\u201327). Lightweight network research based on deep learning: A review. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8483963"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wan, L. (2026, January 22\u201324). Research on the Construction of Fine-Grained Recognition Model of Genuine Medicinal Materials Images Based on GhostNet. Proceedings of the 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL), Changsha, China.","DOI":"10.1109\/CVIDL65390.2025.11085498"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1080\/21550085.2023.2272237","article-title":"The Compound Injustice of the EU Carbon Border Adjustment Mechanism (CBAM)","volume":"28","author":"Corvino","year":"2025","journal-title":"Ethics Policy Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11227-025-07075-3","article-title":"Sim-ConvFormer: A lightweight fault diagnosis framework incorporating SimAM and external attention","volume":"81","author":"Gao","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.64032\/mca.v29i1.249","article-title":"Improving YOLOv8 Deep leaning model in rice disease detection by using Wise-IoU loss function","volume":"29","author":"Trinh","year":"2025","journal-title":"J. Meas. Control Autom."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6516","DOI":"10.1109\/TMM.2025.3586127","article-title":"Beyond 3D: Generic IoU for 3D Object Detection","volume":"27","author":"Lun","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ma, M., Zhang, X., Bin, W., Wang, Q., and Che, K. (2025, January 10\u201312). Charging Out Rectangle: Quadrangle IoU for Scene Text Detection under Balanced Supervision. Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering, Heifei, China.","DOI":"10.1145\/3727648.3727743"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"128686","DOI":"10.1016\/j.eswa.2025.128686","article-title":"UCIW-YOLO: Multi-category and high-precision obstacle detection model for agricultural machinery in unstructured farmland environments","volume":"294","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Che, J. (2025, January 28\u201330). Research on an Improved Algorithm for Small Object Detection in Remote Sensing Ground Images Based on SW-YOLOv9. Proceedings of the 2025 44th Chinese Control Conference (CCC), Chongqing, China.","DOI":"10.23919\/CCC64809.2025.11179621"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"22129","DOI":"10.1007\/s00521-024-10877-w","article-title":"PreSA: An intelligent blockchain-based platform for monitoring and predicting water quality for smart aquaculture","volume":"37","author":"Hachicha","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102584","DOI":"10.1016\/j.aquaeng.2025.102584","article-title":"Smart Technologies in Aquaculture: An Integrated IoT, AI, and Blockchain Framework for Sustainable Growth","volume":"111","author":"Chandran","year":"2025","journal-title":"Aquac. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s10499-025-02028-2","article-title":"Drivers and barriers to climate-smart aquaculture adoption: Insights from Mwanza and Mara Regions, Tanzania","volume":"33","author":"Mdoe","year":"2025","journal-title":"Aquac. Int."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tina, F.W., Afsarimanesh, N., Nag, A., and Alahi, M.E.E. (2025). Integrating AIoT technologies in aquaculture: A systematic review. Future Internet, 17.","DOI":"10.3390\/fi17050199"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102637","DOI":"10.1016\/j.aquaeng.2025.102637","article-title":"A review of generative AI in aquaculture: Applications, case studies and challenges for smart and sustainable farming","volume":"112","author":"Akram","year":"2025","journal-title":"Aquac. Eng."},{"key":"ref_44","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 17\u201318). Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s11760-021-02024-y","article-title":"Leaf-based disease detection in bell pepper plant using YOLO v5","volume":"16","author":"Mathew","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Qadri, S.A.A., Huang, N.F., Wani, T.M., and Bhat, S.A. (2023, January 25\u201326). Plant disease detection and segmentation using end-to-end yolov8: A comprehensive approach. Proceedings of the 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.","DOI":"10.1109\/ICCSCE58721.2023.10237169"},{"key":"ref_48","first-page":"107984","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sekar, K., Dheepa, T., Sheethal, R., Suvarna Smita, R., and Teja, V.D. (2025, January 24\u201325). Efficient Object Detection on Low-Resource Devices Using Lightweight MobileNet-SSD. Proceedings of the 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN), Bidar, India.","DOI":"10.1109\/ICISCN64258.2025.10934442"},{"key":"ref_50","unstructured":"Li, C., Zhao, R., Wang, Z., Xu, H., and Zhu, X. (March, January 25). Remdet: Rethinking efficient model design for uav object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/791\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:10:19Z","timestamp":1770268219000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,24]]},"references-count":50,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26030791"],"URL":"https:\/\/doi.org\/10.3390\/s26030791","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,24]]}}}