Automatic mass estimation of Jade perch Scortum barcoo by computer vision |
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| Affiliation: | 1. Division of Measure, Model & Manage Bioresponses (M3-BIORES), KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium;2. Division of Gene Technology, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium;1. Department of Bio-industrial Mechatronics Engineering, University of Chung-Hsing, Taichung, Taiwan, ROC;2. Department of Biomechatronic Engineering, University of Chiayi, Chiayi, Taiwan, ROC;1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;3. National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China;1. Institute of Control Systems and Industrial Computing (AI2), Universitat Politècnica de València (UPV), Camino de Vera (s/n), 46022 Valencia, Spain;2. Institut d’Investigació per a la Gestió Integrada de Zones Costaneres (IGIC), Universitat Politècnica de València (UPV), Camino de Vera (s/n), 46022 Valencia, Spain |
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| Abstract: | The aim of this study was to test and evaluate a 2D computer vision technique that estimates the mass of Jade perch Scortum barcoo swimming freely in a tank of a recirculation aquaculture system. The first step of this study, which is described in this paper, was to build up a relationship between the fish shape and its mass in order to be able to estimate the mass of the fish by vision techniques. A set of 120 images of fish outside the water was captured and different features were extracted by using computer vision techniques. Regression analysis was used on the training dataset in order to generate the best model that estimated accurately the mass of the fish. Single-factor regression equation using the area of the fish without considering the fin tail proved adequate for measuring the mass of Jade perch S. barcoo and revealed a coefficient of determination (R2) of 0.99. When applied to the evaluation dataset, the mean relative error was 6 ± 3% compared to the value measured by a weighing scale. This suggests that the calculated model can be used in a second step to estimate the biomass of fish moving freely in a tank without causing any stress or damage to the fish. |
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| Keywords: | Aquaculture Image processing Mass estimation |
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