ArticleScientific reports2022
An affordable and easy-to-use tool for automatic fish length and weight estimation in mariculture.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Genetic Parameter Estimation of Growth Traits and Their Implications for Selective Breeding in Small Yellow Croaker (Larimichthys Polyactis).Marine biotechnology (New York, N.Y.) · 2026Article
- Nonintrusive and Effective Volume Reconstruction Model of Swimming Sturgeon Based on RGB-D Sensor.Sensors (Basel, Switzerland) · 2024Article
- Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand.Food chemistry: X · 2024Review
- Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning.Animals : an open access journal from MDPI · 2024Article
- Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species.Biomolecules · 2023Review
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Authors and funding
6 authors.
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Abstract
Common aquaculture practices involve measuring fish biometrics at different growth stages, which is crucial for feeding regime management and for improving farmed fish welfare. Fish measurements are usually carried out manually on individual fish. However, this process is laborious, time-consuming, and stressful to the fish. Therefore, the development of fast, precise, low cost and indirect measurement would be of great interest to the aquaculture sector. In this study, we explore a promising way to take fish measurements in a non-invasive approach through computer vision. Images captured by a stereoscopic camera are used by Artificial Intelligence algorithms in conjunction with computer vision to automatically obtain an accurate estimation of the characteristics of fish, such as body length and weight. We describe the development of a computer vision system for automated recognition of body traits through image processing and linear models for the measurement of fish length and prediction of body weight. The measurements are obtained through a relatively low-cost prototype consisting of a smart buoy equipped with stereo cameras, tested in a commercial mariculture cage in the Mediterranean Sea. Our findings suggest that this method can successfully estimate fish biometric parameters, with a mean error of ± 1.15 cm.
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