Abstract
Knowing the biomass information of fish in a hatchery is of great importance to determine the food quantity to supply and harvest fish. It is difficult to estimate their number and size due to the area’s high number of fish and conditions. Unlike the traditional method based on the individual manual measurement of each fish, which usually causes stress and damage to the fish. There are less intrusive methods, such as the use of underwater cameras for information gathering. They are becoming more common among researchers. However, this system has a limitation referring to the high cost that its application to multiple cultivation areas could have. Below, we propose a Low-Cost (350 USD) and simple design prototype camera system for underwater monitoring, made with elements that are easy to purchase and implement. Its small size allows for easy installation in the hatchery. It can work as deep as 10 m and has enough autonomy to record more than 8 h of video. An electronic system capable of capturing video based on a StereoPi board and Raspberry pi uses a pair of cameras to form a stereoscopic vision to generate a disparity map. With the collected samples, a data set of images was created. To contact and download the obtained dataset you can go https://compsust.utec.edu.pe/wnina/datasets here.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rosenthal, S., Sandoval, N., Gavidia, C., Tabacchi, L.: Frecuencia de lipidosis hepática en trucha arcoíris (oncorhynchus mykiss) de fase juvenil en una piscigranja de la sierra central del Perú. Revista de Investigaciones Veterinarias del Perú. 24(1), 118–124 (2013)
Rainuzzo, J. Estudio de prospectiva: la cadena de valor de la trucha (2020)
Mallet, D., Pelletier, D.: Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952–2012). Fish. Res. 154, 44–62 (2014)
Delacy, C., Olsen, A., Howey, L., Chapman, D., Brooks, E., Bond, M.: Affordable and accurate stereo-video system for measuring dimensions underwater: a case study using oceanic whitetip sharks Carcharhinus longimanus. Marine Ecol. Prog. Ser. 574, 75–84 (2017,7)
Saleh, A., Sheaves, M., Azghadi, M.: Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey. (2022,3). http://arxiv.org/abs/2203.06951
Towards Underwater Biomass Estimation Using Plenoptic Technology, NTNU (2019)
King, A., George, A., Buckle, D., Novak, P., Fulton, C.: Efficacy of remote underwater video cameras for monitoring tropical wetland fishes. Hydrobiologia. 807, 145–164 (2018)
Shafait, F., et al.: Towards automating underwater measurement of fish length: a comparison of semi-automatic and manual stereo-video measurements. ICES J. Mar. Sci. 74, 1690–1701 (2017)
Weertman, W., Project, B.: Design of a Stereo-RUV for Remote Study of Octopod Behavior and Ecology, MIRE (2018)
Sanchez-Torres, G., Ceballos-Arroyo, A., Robles-Serrano, S.: Automatic measurement of fish weight and size by processing underwater hatchery images. Eng. Lett. 26(4), 461–472 (2018)
Lang-Alvarez, E.: Diseño de un módulo de grabación para un sistema BRUVS personalizado a las necesidades del Centro de Investigación en Ciencias del Mar y Limnología (2021)
Tueller, P., et al.: FishSense Underwater RGBD Imaging for Fish Measurement (2021)
Qin, H., Li, X., Liang, J., Peng, Y., Zhang, C.: DeepFish: Accurate underwater live fish recognition with a deep architecture. Neurocomputing 187, 49–58 (2016)
Boom, B., et al.: A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage. Eco. Inform. 23, 83–97 (2014)
Kavasidis, I., Palazzo, S., Di Salvo, R., Giordano, D., Spampinato, C.: An innovative web-based collaborative platform for video annotation. Multim. Tools Appl.. 70, 413–432 (2014)
Kavasidis, I., Palazzo, S., Di Salvo, R., Giordano, D., Spampinato, C.: A semi-automatic tool for detection and tracking ground truth generation in videos. In: Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in computer vision applications, pp. 1–5 (2012)
Saleh, A., Laradji, I., Konovalov, D., Bradley, M., Vazquez, D., Sheaves, M.: A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Sci. Rep. 10, 1–10 (2020)
McCann, E., Li, L., Pangle, K., Johnson, N., Eickholt, J.: An underwater observation dataset for fish classification and fishery assessment. Sci. Data. 5, 1–8 (2018)
Beijbom, O., Edmunds, P., Kline, D., Mitchell, B., Kriegman, D.: Automated annotation of coral reef survey images. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1170–1177 (2012)
Mouy, X., et al.: FishCam: a low-cost open source autonomous camera for aquatic research. HardwareX (2020)
Pomazov, E.: Hands on: Stereo vision building a depth-sensing camera with beta hardware. IEEE Spectrum 58(2), 14–16 (2021)
Wada, K. Labelme: Image Polygonal Annotation with Python. https://github.com/wkentaro/labelme
Zhun, X., Lyu, S., Wang, X., Wang, Q.: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)
Acknowledgments
This research work is part of the project identified with SP PNIPA-ACU-SIA-00260 CONTRACT N\(^\circ \) 124-2021, which is supported PNIPA and World Bank. The companies that helped were VEOX, AGROPEZ production company and university USALLE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fernandez, A., Fonseca, P., Nina, W. (2023). Design of a Low-Cost RUV Stereo System for Monitoring of a Trout Farm. In: Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 739. Springer, Cham. https://doi.org/10.1007/978-3-031-37963-5_73
Download citation
DOI: https://doi.org/10.1007/978-3-031-37963-5_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37962-8
Online ISBN: 978-3-031-37963-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)