IMAGE PROCESSING METHOD TO DETECT THE POSITION OF VANNAMEI SHRIMP IN MUDDY WATERS
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Ahmadi, K., & Salari, E. (2017). Single image super resolution using evolutionary sparse coding technique. IET Image Processing, 11(1), 13–21. https://doi.org/10.1049/iet-ipr.2016.0273
Awalludin, E. A., Arsad, T. N. T., & Wan Yussof, W. N. J. H. (2020). A Review on Image Processing Techniques for Fisheries Application. Journal of Physics: Conference Series, 1529(5). https://doi.org/10.1088/1742-6596/1529/5/052031
Awalludin, E. A., Mat Yaziz, M. Y., Abdul Rahman, N. R., Yussof, W. N. J. H. W., Hitam, M. S., & T Arsad, T. N. (2019). Combination of Canny Edge Detection and Blob Processing Techniques for Shrimp Larvae Counting. Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019, 308–313. https://doi.org/10.1109/ICSIPA45851.2019.8977746
Badgujar, C. M., Poulose, A., & Gan, H. (2024). Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review. Computers and Electronics in Agriculture, 223(January), 109090. https://doi.org/10.1016/j.compag.2024.109090
Chirdchoo, N., & Cheunta, W. (2019). Detection of shrimp feed with computer vision. Interdisciplinary Research Review, 14(5), 13–17.
Hashisho, Y., Dolereit, T., Segelken-Voigt, A., Bochert, R., & Vahl, M. (2021). AI-assisted automated pipeline for length estimation, visual assessment of the digestive tract and counting of shrimp in aquaculture production. VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 4(Visigrapp), 710–716. https://doi.org/10.5220/0010342007100716
Humayun, M. F., Nasir, F. A., Bhatti, F. A., Tahir, M., & Khurshid, K. (2024). YOLO-OSD: Optimized Ship Detection and Localization in Multiresolution SAR Satellite Images Using a Hybrid Data-Model Centric Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 5345–5363. https://doi.org/10.1109/JSTARS.2024.3365807
Iber, B. T., & Kasan, N. A. (2021). Recent advances in Shrimp aquaculture wastewater management. Heliyon, 7(11), e08283. https://doi.org/10.1016/j.heliyon.2021.e08283
Khai, T. H., Abdullah, S. N. H. S., Hasan, M. K., & Tarmizi, A. (2022). Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network. Water (Switzerland), 14(2), 1–23. https://doi.org/10.3390/w14020222
Lai, P. C., Lin, H. Y., Lin, J. Y., Hsu, H. C., Chu, Y. N., Liou, C. H., & Kuo, Y. F. (2022). Automatic measuring shrimp body length using CNN and an underwater imaging system. Biosystems Engineering, 221(1), 224–235. https://doi.org/10.1016/j.biosystemseng.2022.07.006
Liang, Q., Liu, G., Luan, Y., Niu, J., Li, Y., Chen, H., Liu, Y., & Zhu, S. (2025). Impact of Feeding Frequency on Growth Performance and Antioxidant Capacity of Litopenaeus vannamei in Recirculating Aquaculture Systems. Animals, 15(2). https://doi.org/10.3390/ani15020192
Liu, D., Xu, B., Cheng, Y., Chen, H., Dou, Y., Bi, H., & Zhao, Y. (2023). Shrimpseed-Net: Counting of Shrimp Seed Using Deep Learning on Smartphones for Aquaculture. IEEE Access, 11(July), 85441–85450. https://doi.org/10.1109/ACCESS.2023.3302249
Mahari, W. A. W., Waiho, K., Fazhan, H., Azwar, E., Shu-Chien, A. C., Hersi, M. A., Kasan, N. A., ... Lam, S. S. (2024). Emerging paradigms in sustainable shellfish aquaculture: Microalgae and biofloc technologies for wastewater treatment. Aquaculture, 587(October 2023), 740835. https://doi.org/10.1016/j.aquaculture.2024.740835
Na nakorn, A., Chevakidagarn, P., & Danteravanich, S. (2017). Environmental impact of white shrimp culture during 2012–2013 at Bandon Bay, Surat Thani Province: A case study investigating farm size. Agriculture and Natural Resources, 51(2), 109–116. https://doi.org/10.1016/j.anres.2016.08.007
Pan, P. M., Li, J. P., Lv, G. L., Yang, H., Zhu, S. M., & Lou, J. Z. (2009). Prediction of shelled shrimp weight by machine vision. Journal of Zhejiang University: Science B, 10(8), 589–594. https://doi.org/10.1631/jzus.B0820364
Ragab, M. G., Abdulkadir, S. J., Muneer, A., Alqushaibi, A., Sumiea, E. H., Qureshi, R., Al-Selwi, S. M., & Alhussian, H. (2024). A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023). IEEE Access, 12(February), 57815–57836. https://doi.org/10.1109/ACCESS.2024.3386826
Saleh, A., Hasan, M. M., Raadsma, H. W., Khatkar, M. S., Jerry, D. R., & Rahimi Azghadi, M. (2024). Prawn morphometrics and weight estimation from images using deep learning for landmark localization. Aquacultural Engineering, 106(July 2023), 102391. https://doi.org/10.1016/j.aquaeng.2024.102391
Setiawan, A., Hadiyanto, H., & Widodo, C. E. (2022). Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach. Revue d’Intelligence Artificielle, 36(6), 905–912. https://doi.org/10.18280/ria.360611
Taukhid, I., Trijuno, D. D., Karim, M. Y., Syah, R., & Makmur. (2022). Effect of Lens Aperture for Analysis of Bubble Image Size Microbubble Generator Aeration System. IOP Conference Series: Earth and Environmental Science, 1030(1), 012011. https://doi.org/10.1088/1755-1315/1030/1/012011
Xue, S., Ding, J., Li, J., Jiang, Z., Fang, J., Zhao, F., & Mao, Y. (2021). Effects of live, artificial and mixed feeds on the growth and energy budget of Penaeus vannamei. Aquaculture Reports, 19(February), 100634. https://doi.org/10.1016/j.aqrep.2021.100634
Yang, B., & Chen, X. (2024). Face Recognition Implementation Based on Image Processing Techniques. Proceedings of 2024 2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024, 1105–1109. https://doi.org/10.1109/SPIC62469.2024.10691607
Zainuddin, Z., Tuluran, J., Achmad, A., Areni, I. S., & Tahir, Z. (2022). The Waste Detection System of Shrimp Feeding with a Waterproof Camera using Yolo Algorithm. Journal of Physics: Conference Series, 2312(1). https://doi.org/10.1088/1742-6596/2312/1/012083
Zhang, L., Zhou, X., Li, B., Zhang, H., & Duan, Q. (2022). Automatic shrimp counting method using local images and lightweight YOLOv4. Biosystems Engineering, 220, 39–54. https://doi.org/10.1016/j.biosystemseng.2022.05.011
Zhou, C., Yang, G., Sun, L., Wang, S., Song, W., & Guo, J. (2024). Counting, locating, and sizing of shrimp larvae based on density map regression. Aquaculture International, 32(3), 3147–3168. https://doi.org/10.1007/s10499-023-01316-z
DOI: http://dx.doi.org/10.15578/iaj.20.2.2025.145-156

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