scholarly journals Tracking and Analysis of the Movement Behavior of European Seabass (Dicentrarchus labrax) in Aquaculture Systems

2021 ◽  
Vol 2 ◽  
Author(s):  
Dimitra G. Georgopoulou ◽  
Orestis Stavrakidis-Zachou ◽  
Nikos Mitrizakis ◽  
Nikos Papandroulakis

Monitoring and understanding fish behavior is crucial for achieving precision in everyday husbandry practices (i.e. for optimizing farm performance), and for improving fish welfare in aquaculture. Various intelligent monitoring and control methods, using mathematical models, acoustic methods and computer vision, have been recently developed for this reason. Here, a tracking algorithm based on computer vision that extracts short trajectories of individual European seabass in both recirculating aquaculture systems and sea cages was developed using videos from network cameras. Using this methodology, parameters such as instantaneous normalized speed, travel direction and preference for the tank surface by European seabass could be quantified. When testing the sensitivity of this algorithm for detecting fish swimming variations under different husbandry scenarios, we found that the algorithm could detect variations in all of the abovementioned parameters and could potentially be a useful tool for monitoring the behavioral state of European seabass.

2000 ◽  
Vol 34 (1) ◽  
pp. 68-78 ◽  
Author(s):  
James M. Ebeling

Intensive recirculating aquaculture systems utilizing water recirculation and pure oxygen injection are examined in terms of the individual unit processes that are required to handle the wastes generated by fish at stocking densities as high as 120‐150 kg/m3. These unit processes include solid waste removal, nitrification of ammonia and nitrite, aeration or oxygenation, carbon dioxide removal, and control and monitoring systems. Overall system integration is reviewed and an example of a research/commercial intensive recirculating system is presented.


Author(s):  
Alexander Alekseevich Nedostup ◽  
Alexey Olegovich Razhev ◽  
Evgeniy Ivanovich Khrustalyov ◽  
Kseniia Andreevna Molchanova

The article highlights the problems of physical modeling the elements of recirculating aquaculture systems (RAS) and open aquaculture cages (OAC) for hydrobionts growing, in particular, the question of substantiating the rules of optical quantities similarity has been raised. Formulation of the problem is based on the assumption that using the computer vision which controls the behavioral reactions of hydrobionts to the growing conditions (e.g. light effect) will make the biotechnological process controllable in RAS and OAC and, as a result, more efficient. Evaluating the light effect on biological objects as to the depth of its penetration into the basins, the degree of its dispersion among the aquatic organisms and other characteristics can become an important element of computer vision. This fact will help to choose the optimal algorithm for the biotechnical process, for example, to calculate the daily feed portion and feeding periods, to define the optimal place for feeding, to determine the appropriate sorting time, the optimal stocking density, etc. There have been proposed the additional similarity scales for optical quantities, methods for their calculation and graphs of their dependences on the geometric scale Cl. However, one should know that achieving the complete similarity is absolutely impossible, no matter how large the list of similarity criteria is.


2003 ◽  
Author(s):  
Eskarne Laizola ◽  
Antonio R. Jimenez ◽  
Fernando Morgado ◽  
Mar Calvache ◽  
Fernando Seco

2020 ◽  
Vol 14 (1) ◽  
pp. 111-118
Author(s):  
Yurley Tatiana Tovar-Martínez ◽  
Arley Bejarano-Martínez ◽  
Andrés Felipe Calvo-Salcedo

Black Sigatoka is one of the main problems that affect the quality and production of the banana crop, it´s because of this, the development of systems to detect diseases, generate an important tool for the monitoring and control carried out by the farmer. The proposed system leverages hardware on mobile devices to implement computer vision techniques to determine the percentage of affected area of the plant. The smartphone is used to acquire data and capture the disease through images. The detection of diseased pixels is then performed through a segmentation algorithm with histogram analysis. A model for the calculation of the affected area is then computed. Finally, the information is presented through the user interface. To validate the proposed method, a database is created with images taken by the application to compare it´s efficiency through the RMS error between manual segmentation and the result of the algorithm. Finally, usability and response time tests are performed.


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