Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture

• Published in: Ecological informatics 2023-12, Vol.78, p.102287, Article 102287
• Main Authors: Shreesha, S., Pai, Manohara M M, Pai, Radhika M., Verma, Ujjwal
• Format: Article
• Language: English
• Subjects: Aquaculture; Auto-encoder; Computer vision; Fish behaviour analysis; Outlier detection; Pattern analysis; Water quality monitoring
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2023.102287

•A novel“SSVid-Frantic” dataset to study frantic behaviours of fish Sillago sihama.•Water quality and fish behaviour integrated appraoch to monitor aquaculture.•A novel deep learning-based approach to detect frantic fish behaviour patterns.•Water quality prediction using a learning algorithm to assist fishermen.•Performance evaluation of algorithms on the developed dataset. The United Nations’ Sustainable Development Goals (SDGs) underscore aquaculture as a sustainable practice. However, many countries rely on ineffective conventional methods to monitor the aquaculture. Fish behaviour monitoring systems lack an integrated strategy, often focusing on underwater footage analysis or water quality data. Additionally, limited research exists on using learning algorithms to identify sensor data anomalies, limiting the effectiveness of such systems. This study proposes a comprehensive and effective aquaculture monitoring system to address these issues. The work introduces a novel method integrating fish motion analysis with water quality metrics. The study also presents a learning system that anticipates potential water quality parameters. The proposed model has the potential to significantly enhance aquaculture management procedures and contribute to the UN’s SDGs through its ability to forecast water quality parameters and detect abnormal behaviour without requiring training detection and tracking algorithms. The study employs a large aquarium to capture water quality parameters and fish movement patterns. The study develops an FPGA-based water quality monitoring system and a prediction model to forecast potential water quality metrics. Among multiple LSTM models, the bi-directional LSTM performs well, achieving an RMSE score of 1.01 for water quality prediction. Subsequently, an outlier detection system is applied to identify unusual water quality parameters, triggering the behaviour analysis model. The behaviour analysis model uses an auto-encoder-based reconstruction approach to detect the frantic patterns in fish. The suggested behaviour analysis model has a f1-score of 0.68, demonstrating its reliability in contrast to conventional methods. The developed system is also tested on aquaculture site videos and includes a novel dataset of Sillago sihama with annotations for frantic behaviour patterns. The system works effectively as a decision support system for aquaculture.