1Jesus M. Lindo, Jr., 2Arnel C. Fajardo
1,2College of Computing Studies, Information and Communications Technology,Isabela State University Cauayan Campus Philippines
DOI : https://doi.org/10.47191/ijmra/v7-i04-03Google Scholar Download Pdf
ABSTRACT:
The success of fish farming will depend on improved feed management and lower operating costs, which are essential factors in facilitating an efficient food allocation to the fish. Various automatic fish feeders were used to feed the fish at set intervals, it consists of a mechanical and electrical system to form a device and execute a programmed method, instead of manually feeding the fish by hand. Many methods are effective at evaluating and quantifying fish feeding intensity but are mostly done on the movement and behavior of the fish. However, recognition accuracy is affected due to water quality and the overlapping of fish. To solve this problem, in this study, the author will be focusing on the model in counting the fish pellets, it will capture the image, and count the pellets and the results will be a novel method as a basis for releasing pellets in laying the logical foundation in creating a modern real-time smart fish feeder. The model produced a significant result that detected small objects like fish pellet and count that has gathered a minimal loss in terms of classification, localization, regularization, and normalization.
KEYWORDS:Machine Learning, IoT, CNN, Deep Learning, Fish Feeder, Smart Aquaculture
REFERENCES1) De Verdal, H.; Komen, H.; Quillet, E.; Chatain, B.; Allal, F.; Benzie, J.A.H.; Vandeputte, M. Improving Feed Efficiency in Fish Using Selective Breeding: A Review. 2018, 10, 833–851.
2) Hu, W.-C.; Wu, H.-T.; Zhang, Y.-F.; Zhang, S.-H.; Lo, C.-H. Shrimp Recognition Using ShrimpNet Based on Convolutional Neural Network. J. Ambient Intelligence Humanized Computing 2020.
3) Liu, Z.; Li, X.; Fan, L.; Lu, H.; Liu, L.; Liu, Y. Measuring Feeding Activity of Fish in RAS Using Computer Vision. Aquacultural Engineering 2014, 60, 20–27
4) Wang, T.; Xu, X.; Wang, C.; Li, Z.; Li, D. From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production. Agriculture 2021, 11, 145.
5) Akbar, M.O.; Shahbaz Khan, M.S.; Ali, M.J.; Hussain, A.; Qaiser, G.; Pasha, M.; Pasha, U.; Missen, M.S.; Akhtar, N. IoT for Development of Smart Dairy Farming. J. Food Qual. 2020, 2020, 1212805
6) Zhou, C.; Xu, D.; Chen, L.; Zhang, S.; Sun, C.; Yang, X.; Wang, Y. Evaluation of Fish Feeding Intensity in Aquaculture Using a Convolutional Neural Network and Machine Vision. Aquaculture 2019, 507, 457–465.
7) Måløy, H.; Aamodt, A.; Misimi, E. A Spatio-Temporal Recurrent Network for Salmon Feeding Action Recognition from Underwater Videos in Aquaculture. Computer. Electronics. Agriculture. 2019, 167, 105087.
8) Li, Daoliang; Wang, Zhenhu; Wu, Suyuan; Miao, Zheng; Du, Ling Duan, Yanqing. Automatic recognition methods of fish feeding behavior in aquaculture: A review. Aquaculture. 2020, 528, 735508.
9) Zhou, C., Zhang, B., Lin, K., Xu, D., Chen, C., Yang, X., Sun, C., 2017. Near-infrared imaging to quantify the feeding behavior of fish in aquaculture. Computers and Electronic in Agriculture. 2017. 135, 233–241
10) Ye, Z.Y., Zhao, J., Han, Z.Y., Zhu, S.M., Li, J.P., Lu, H.D., Ruan, Y.J., 2016. Behavioral characteristics and statistics-based imaging techniques in the assessment and optimization of tilapia feeding in a recirculating aquaculture system. Trans. ASABE 59 (1), 345–355
11) Bradley, D.; Merrifield, M.; Miller, K.M.; Lomonico, S.; Wilson, J. R.; Gleason, M.G. Opportunities to Improve Fisheries Management through Innovative Technology and Advanced Data Systems. Fish and Fisheries. 2019, 20, 564–583.
12) Schneider, S.; Taylor, G.W.; Linquist, S.; Kremer, S.C. Past, Present and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data. Methods Ecology and Evolution. 2019, 10, 461–470.
13) Rauf, H.T.; Lali, M.I.U.; Zahoor, S.; Shah, S.Z.H.; Rehman, A.U.; Bukhari, S.A.C. Visual Features Based Automated Identification of Fish Species Using Deep Convolutional Neural Networks. Computers and Electronic in Agriculture 2019, 167, 105075.
14) Zhou, C.; Zhang, B.; Lin, K.; Xu, D.; Chen, C.; Yang, X.; Sun, C. Near-Infrared Imaging to Quantify the Feeding Behavior of Fish in Aquaculture. Computers and Electronic in Agriculture. 2017, 135, 233–241
15) Zhou, C.; Lin, K.; Xu, D.; Chen, L.; Guo, Q.; Sun, C.; Yang, X. Near Infrared Computer Vision and Neuro-Fuzzy Model-Based Feeding Decision System for Fish in Aquaculture. Computers and Electronic in Agriculture. 2018, 146, 114–124.
Volume 07 Issue 04 April 2024
There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.
Our Services and Policies
Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected.
The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.
International Journal of Multidisciplinary Research and Analysis will publish 12 monthly online issues per year,IJMRA publishes articles as soon as the final copy-edited version is approved. IJMRA publishes articles and review papers of all subjects area.
Open access is a mechanism by which research outputs are distributed online, Hybrid open access journals, contain a mixture of open access articles and closed access articles.
International Journal of Multidisciplinary Research and Analysis initiate a call for research paper for Volume 07 Issue 11 (November 2024).
PUBLICATION DATES:
1) Last Date of Submission : 26 November 2024 .
2) Article published within a week.
3) Submit Article : editor@ijmra.in or Online
Why with us
1 : IJMRA only accepts original and high quality research and technical papers.
2 : Paper will publish immediately in current issue after registration.
3 : Authors can download their full papers at any time with digital certificate.
The Editors reserve the right to reject papers without sending them out for review.
Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected. The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.