Fish-NET: Advancing Aquaculture Management through AI-Enhanced Fish Monitoring and Tracking

Joshua SalakoFoluso OjoOlushina Olawale Awe

DOI 10.7160/aol.2024.160209
No 2/2024, June
pp. 121-131

Salako, J., Ojo, F. and Awe, O. O. (2024) "Fish-NET: Advancing Aquaculture Management through AI-Enhanced Fish Monitoring and Tracking", AGRIS on-line Papers in Economics and Informatics, Vol. 16, No. 2, pp. 121-131. ISSN 1804-1930. DOI 10.7160/aol.2024.160209.

Abstract

This study seeks to enhance aquaculture and fishery management using artificial intelligence, focusing on Nigerian catfish farming. The methodology encompasses a sequence of steps from data collection to validation. A dataset, primarily composed of aerial imagery from catfish ponds and supplemented with additional data from the internet, formed the foundation of this research. By leveraging computer vision and deep learning techniques, the data were processed to assess the potential of the three distinct cutting-edge object detection models. Based on various evaluation metrics to gauge their effectiveness in fish detection tasks, the Faster R-CNN emerged as the optimal model, boasting a superior balance of precision and recall. This model was subsequently integrated with an object-tracking model and deployed as an application, yielding promising results in terms of fish detection and tracking. The findings in this study suggest that AI-driven tools can automate monitoring processes, significantly increasing accuracy and efficiency in resource utilization.

Keywords

Aquaculture, fish detection, AI fish tracking, Nigerian catfish farming, sustainability.

References

  1. Abedeen, I., Rahman, M. A., Prottyasha, F. Z., Ahmed, T., Chowdhury, T. M. and Shatabda, S. (2023) "FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs", Scientific Data, Vol. 10, No. 1, p. 521. ISSN 2052-4463. DOI 10.1038/s41597-023-02432-4.
  2. An, D., Hao, J., Wei, Y., Wang, Y. and Yu, X. (2021) "Application of computer vision in fish intelligent feeding system—A review", Aquaculture Research, Vol. 52, No. 2, pp. 423-437. ISSN 1365-2109. DOI 10.1111/are.14907.
  3. Chan, S. N., Fan, Y. W. and Yao, X. H. (2022) "Mapping of coastal surface chlorophyll-a concentration by multispectral reflectance measurement from unmanned aerial vehicles", Journal of Hydro-Environment Research, Vol. 44, pp. 88-101. ISSN 1876-4444. DOI 10.1016/j.jher.2022.08.003.
  4. Chen, Q., Liu, Z., Zhang, Y., Fu, K., Zhao, Q. and Du, H. (2021) "RGB-D salient object detection via 3D convolutional neural networks", Proceedings of the AAAI conference on artificial intelligence, Vol. 35, No. 2, pp. 1063-1071. ISSN 2374-3468. DOI 10.1609/aaai.v35i2.16191.
  5. Dixit, S., Kumar, A., Srinivasan, K., Vincent, P. D. R. and Krishnan, N. R. (2023) "Advancing genome editing with artificial intelligence: Opportunities, challenges, and future directions", Frontiers in Bioengineering and Biotechnology, Vol. 11. ISSN 2296-4185. DOI 10.3389/fbioe.2023.1335901.
  6. Dupont, C., Cousin, P. and Dupont, S. (2018) "IoT for Aquaculture 4.0 Smart and easy-to-deploy real-time water monitoring with IoT", 22018 Global Internet of Things Summit (GIoTS), Bilbao, Spain, 2018. pp. 1-5. DOI 10.1109/GIOTS.2018.8534581.
  7. Emmanuel, O., Chinenye, A., Oluwatobi, A. and Kolawole, P. (2014) "Review of aquaculture production and management in Nigeria", American Journal of Experimental Agriculture, Vol. 4, No. 10, p. 1137. ISSN 2231-0606. DOI 10.9734/AJEA/2014/8082.
  8. Everingham, M., Van Gool, L., Williams, C. K., Winn, J. and Zisserman, A. (2010) "The Pascal Visual Object Classes (VOC) Challenge", International Journal of Computer Vision, Vol. 88, pp. 303-338. ISSN 1573-1405. DOI 10.1007/s11263-009-0275-4.
  9. Føre, M., Frank, K., Norton, T., Svendsen, E., Alfredsen, J. A., Dempster, T., Eguiraun, H., Watson, W., Stahl, A., Sunde, L. M. and Schellewald, C. (2018) "Precision fish farming: A new framework to improve production in aquaculture", Biosystems Engineering, Vol. 173, pp. 176-193. ISSN 1537-5129. DOI 10.1016/j.biosystemseng.2017.10.014.
  10. Gunda, N. S. K., Gautam, S. H., and Mitra, S. K. (2019) "Editors' Choice—Artificial Intelligence Based Mobile Application for Water Quality Monitoring", Journal of The Electrochemical Society, Vol. 166, No. 9. ISSN 1945-7111. DOI 10.1149/2.0081909jes.
  11. Harshith, D. G., Surve, S., Prasad, S. S., Ganesh, B. V. and Thomas, K. A. (2023) "Remote Aquaculture Monitoring with Image Processing [ML] and AI", 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1-4. ISSN 2831-4344. DOI 10.1109/BioSMART58455.2023.10162001.
  12. Iyang, N. F., Effiong, U. E. and Okon, J. I. (2020) "Nigeria diversification agenda and economic growth: The role of agriculture", Social Science and Management International Journal, Vol. 1, No. 2. pp. 36-51. ISSN 2583-9853.
  13. Javaid, M., Haleem, A., Khan, I. H. and Suman, R. (2022) "Understanding the potential applications of artificial intelligence in agriculture sector", Advanced Agrochem, Vol. 2, No. 1, pp. 15-30. ISSN 2773-2371. DOI 10.1016/j.aac.2022.10.001.
  14. Jia, B. B. and Zhang, M. L. (2020) "Multi-dimensional classification via kNN feature augmentation", Pattern Recognition, Vol. 33, No. 1, pp. 3975-3982, ISSN 2374-3468. DOI 10.1609/aaai.v33i01.33013975.
  15. Kacprzyk, J. (2009) "Fuzzy Sets Theory, Foundations of", Encyclopedia of Complexity and Systems Science, pp. 4059-4080. ISBN 978-0-387-30440-3. DOI 10.1007/978-0-387-30440-3_238.
  16. Kaidarova, A., Geraldi, N. R., Wilson, R. P., Kosel, J., Meekan, M. G., Eguíluz, V. M., Hussain, M. M., Shamim, A., Liao, H., Srivastava, M. and Saha, S. S. (2023) "Wearable sensors for monitoring marine environments and their inhabitants", Nature Biotechnology, Vol. 41, No. 9, pp. 1208-1220. ISSN 1546-1696. DOI 10.1038/s41587-023-01827-3.
  17. Katampe, B. (2016) "Overview of aquaculture in Nigeria: prospects and challenges", Seminar Presentation presented to: 5th Postgraduate Research Symposium, Moulton College, Northampton, 15 December 2016. Available: http://nectar.northampton.ac.uk/id/eprint/9310.
  18. Khurshid, H., Mumtaz, R., Alvi, N., Haque, A., Mumtaz, S., Shafait, F., Ahmed, S., Malik, M. I., and Dengel, A. (2022) "Bacterial prediction using internet of things (IoT) and machine learning", Environmental Monitoring and Assessment, Vol. 194, No. 2. ISSN 1573-2959. DOI 10.1007/s10661-021-09698-4.
  19. Lim, L. W. K. (2023) "Implementation of Artificial Intelligence in Aquaculture and Fisheries: Deep Learning, Machine Vision, Big Data, Internet of Things, Robots and Beyond", Journal of Computational and Cognitive Engineering, Vol. 3 No. 2, pp. 112-118. ISSN 2810-9503. DOI 10.47852/bonviewJCCE3202803.
  20. Lin, T. Y., Goyal, P., Girshick, R., He, K. and Dollár, P. (2017) "Focal loss for dense object detection", Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988. E-ISSN 2380-7504. DOI 10.1109/ICCV.2017.324.
  21. Lu, H. Y., Cheng, C. Y., Cheng, S. C., Cheng, Y. H., Lo, W. C., Jiang, W. L., Nan, F. H., Chang, S. H. and Ubina, N. A. (2022) "A low-cost AI buoy system for monitoring water quality at offshore aquaculture cages", Sensors, Vol. 22, No. 11. ISSN 1424-8220. DOI 10.3390/s22114078.
  22. Mandal, A. and Ghosh, A. R. (2023) "Role of artificial intelligence (AI) in fish growth and health status monitoring: a review on sustainable aquaculture", Aquaculture International, Vol. 32, pp. 1-30. ISSN 2455-8400. DOI 10.1007/s10499-023-01297-z.
  23. Martín, F. F., Rodríguez, A. V., de Quiros, L. B., Martínez, A. L. and Postolache, O. (2022) "An Underwater Radio-Frequency IoT System for the Identification of Fish", 2022 International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI), pp. 127-131. DOI 10.1109/issi55442.2022.9963314..
  24. Mei, Y., Sun, B., Li, D., Yu, H., Qin, H., Liu, H., Yan, N. and Chen, Y. (2022) "Recent advances of target tracking applications in aquaculture with emphasis on fish", Computers and Electronics in Agriculture, Vol. 201, p. 107335. ISSN 1872-7107. DOI 10.1016/j.compag.2022.107335.
  25. Mercaldo, F., Brunese, L., Martinelli, F., Santone, A. and Cesarelli, M. (2023) "Object Detection for Brain Cancer Detection and Localization", Applied Sciences, Vol. 13, No. 16, p. 9158. ISSN 2523-3971. DOI 10.3390/app13169158.
  26. Patel, N., Patel, S., Parekh, P. and Shah, M. (2022) "Advancing Aquaculture with Artificial Intelligence", In "Agricultural Biotechnology", 1st ed., pp. 189-213. ISBN 9781003268468. DOI 10.1201/9781003268468-10.
  27. Patrício, D. I. and Rieder, R. (2018) "Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review", Computers and Electronics in Agriculture, Vol. 153, pp. 69-81. ISSN 0168-1699. DOI 10.1016/j.compag.2018.08.001.
  28. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016) "You only look once: Unified, real-time object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788. E-ISSN 1063-6919. DOI 10.1109/CVPR.2016.91.
  29. Ren, S., He, K., Girshick, R. and Sun, J. (2015) "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", Advances in Neural Information Processing Systems, Vol. 28, No. 6. ISSN 1049-5258. DOI 10.1109/TPAMI.2016.2577031.
  30. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I. and Savarese, S. (2019) "Generalized intersection over union: A metric and a loss for bounding box regression", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658-666. DOI 10.1109/CVPR.2019.00075.
  31. Rodriguez, A., Rico-Diaz, A. J., Rabunal, J. R., Puertas, J. and Pena, L. (2009) "Fish Monitoring and Sizing Using Computer Vision", In: Ferrández V. J., Álvarez-Sánchez, J., de la Paz L. F., Toledo-Moreo, F. and Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science, Vol. 9108, Springer, Cham, pp. 419-428. ISBN 978-3-319-18832-4. DOI 10.1007/978-3-319-18833-1_44.
  32. Saeed, R., Zhang, L., Cai, Z., Ajmal, M., Zhang, X., Akhter, M., Hu, J. and Fu, Z. (2022) "Multisensor monitoring and water quality prediction for live ornamental fish transportation based on artificial neural network", Aquaculture Research, Vol. 53, No. 7, pp. 2833-2850. ISSN 1365-2109. DOI 10.1111/are.15799.
  33. Shreesha, S., Manohara, P. M., Verma, U. and Pai, R. M. (2020) "Computer Vision Based Fish Tracking and Behaviour Detection System", 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 252-257, IEEE. DOI 10.1109/DISCOVER50404.2020.9278101.
  34. Shu, L., Ludwig, A. and Peng, Z. (2020) "Standards for Methods Utilizing Environmental DNA for Detection of Fish Species", Genes, Vol. 11, No. 3, p. 296. ISSN 2073-4425. DOI 10.3390/genes11030296.
  35. Sung, M., Yu, S. C. and Girdhar, Y. (2017) "Vision based real-time fish detection using convolutional neural network", OCEANS 2017-Aberdeen, IEEE. pp. 1-6. DOI 10.1109/OCEANSE.2017.8084889.
  36. Ubina, N. A., Lan, H. Y., Cheng, S. C., Chang, C. C., Lin, S. S., Zhang, K. X., Lu, H. Y., Cheng, C. Y. and Hsieh, Y. Z. (2023) "Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT)", Smart Agricultural Technology, Vol. 5, p. 100285. ISSN 2772-3755. DOI 10.1016/j.atech.2023.100285.
  37. Udeogu, C. U., Nwakanma, C. I., Ayoade, I. A., Amadi, C. S. and Eze, U. F. (2023) "Agro-vision IoT-enabled Crop Pest Recognition System based on VGG-16", 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), IEEE. pp. 1-5. DOI 10.1109/ICMEAS58693.2023.10429830.
  38. Wu, Y., Duan, Y., Wei, Y., An, D. and Liu, J. (2022) "Application of intelligent and unmanned equipment in aquaculture: A review", Computers and Electronics in Agriculture, Vol. 199, p. 107201. ISSN 1872-7107. DOI 10.1016/j.compag.2022.107201.
  39. Yang, L., Liu, Y., Yu, H., Fang, X., Song, L., Li, D. and Chen, Y. (2021) "Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: A review", Archives of Computational Methods in Engineering, Vol. 28, pp. 2785-2816. ISSN 1753-5131. DOI 10.1007/s11831-020-09486-2.
  40. Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S. and Zhou, C. (2021) "Deep learning for smart fish farming: applications, opportunities and challenges", Reviews in Aquaculture, Vol. 13, No. 1, pp. 66-90. ISSN 1753-5131. DOI 10.1111/raq.12464.
  41. Yang, Y., Elsinghorst, R., Martinez, J. J., Hou, H., Lu, J. and Deng, Z. D. (2022) "A real-time underwater acoustic telemetry receiver with edge computing for studying fish behavior and environmental sensing", IEEE Internet of Things Journal, Vol. 9, No. 18, pp. 17821-17831. ISSN 2327-4662. DOI 10.1109/JIOT.2022.3164092.
  42. Zhao, S., Zhang, S., Liu, J., Wang, H., Zhu, J., Li, D. and Zhao, R. (2021) "Application of machine learning in intelligent fish aquaculture: A review", Aquaculture, Vol. 540, p. 736724. ISSN 1873-5622. DOI 10.1016/j.aquaculture.2021.736724.

Full paper

  Full paper (.pdf, 1.01 MB).