Extracting statistically significant behaviour from fish tracking data with and without large dataset cleaning

Beyan, Çiğdem; Katsageorgiou, Vasiliki-Maria; Fisher, Robert B.

doi:10.1049/iet-cvi.2016.0462

Cited by 24 publications

(8 citation statements)

References 47 publications

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“…The experimental platform is generally composed of a computer, a camera and an aquarium, as shown in Figure 10. DL has shown good results in the fish behaviour recognition and analysis, which can verify certain laws, for example, the higher the water temperature is, the faster the fish swim 175 . Large‐scale losses of fisheries are often caused by the disease of individual fish, and mass death of fish is often accompanied by abnormal behaviour in the early stage of fish disease 176 .…”

Section: Applicationsmentioning

confidence: 87%

“…DL has shown good results in the fish behaviour recognition and analysis, which can verify certain laws, for example, the higher the water temperature is, the faster the fish swim. 175 Large-scale losses of fisheries are often caused by the disease of individual fish, and mass death of fish is often accompanied by abnormal behaviour in the early stage of fish disease. 176 DL networks, combined with the Directed Cycle Graph (DCG) and Dynamic Time Warping (DTW), can effectively detect the abnormal behaviour of fish to determine the cause of diseases or death.…”

Section: Behaviour Analysismentioning

confidence: 99%

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Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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The ocean is an important ecosystem, and aquatic animals play an important role in the biological world, especially in aquaculture. How to accurately and intelligently recognise and detect aquatic animals is one of the urgent problems in the field of underwater biological detection. The wide applications of artificial intelligence (AI), especially deep learning (DL), provide new opportunities and challenges for the efficient and intelligent exploration of aquatic animals. DL has been widely used in the visual recognition and detection of terrestrial animals, but it is in the early stages of use for aquatic animals due to the complexity of underwater environment and the difficulty of data acquisition. Here, this article reviews the current application status of DL for aquatic animals, potential challenges and future directions. The key advances of DL algorithms applied to the visual recognition and detection of aquatic animals are generalised, including datasets, algorithms and performance. The applications of DL are summarised in aquatic animals, including image detection, video detection, species classification, biomass estimation, behaviour analysis and food safety. Furthermore, the challenges are summed up and classified in the object recognition and detection domain for aquatic animals. Finally, further research direction is discussed and the conclusions are drawn. The key advances of DL in the recognition and detection of aquatic animals will help to further excavate and extend the application of DL in the field of marine biological exploration.

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Section: Applicationsmentioning

confidence: 87%

Section: Behaviour Analysismentioning

confidence: 99%

Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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“…needs the support of video-based approaches. For instance, Beyan et al [43] demonstrated through analysis of 12,247 videos that, as the water temperature increases, the swimming speed of fish increases. Xu et al [44] analyzed the abnormal behavior trajectory, movement volume, and movement speed of sturgeon, bass, and crucian carp in different stages of acute ammonia nitrogen stress-recovery experiments through video monitoring, in order to alert whether ammonia nitrogen in aquaculture water was abnormal.…”

Section: Limitation and Future Workmentioning

confidence: 99%

Identification of Fish Hunger Degree with Deformable Attention Transformer

Wu,

Xu,

et al. 2024

JMSE

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Feeding is a critical process in aquaculture, as it has a direct impact on the quantity and quality of fish. With advances in convolutional neural network (CNN) and vision transformer (ViT), intelligent feeding has been widely adopted in aquaculture, as the real-time monitoring of fish behavior can lead to better feeding decisions. However, existing models still have the problem of insufficient accuracy in the fish behavior-recognition task. In this study, the largemouth bass (Micropterus salmoides) was selected as the research subject, and three categories (weakly, moderately, and strongly hungry) were defined. We applied the deformable attention to the vision transformer (DeformAtt-ViT) to identify the fish hunger degree. The deformable attention module was extremely powerful in feature extraction because it improved the fixed geometric structure of the receptive fields with data-dependent sparse attention, thereby guiding the model to focus on more important regions. In the experiment, the proposed DeformAtt-ViT was compared with the state-of-the-art transformers. Among them, DeformAtt-ViT achieved optimal performance in terms of accuracy, F1-score, recall, and precision at 95.50%, 94.13%, 95.87%, and 92.45%, respectively. Moreover, a comparative evaluation between DeformAtt-ViT and CNNs was conducted, and DeformAtt-ViT still dominated the others. We further visualized the important pixels that contributed the most to the classification result, enabling the interpretability of the model. As a prerequisite for determining the feed time, the proposed DeformAtt-ViT could identify the aggregation level of the fish and then trigger the feeding machine to be turned on. Also, the feeding machine will stop working when the aggregation disappears. Conclusively, this study was of great significance, as it explored the field of intelligent feeding in aquaculture, enabling precise feeding at a proper time.

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“…The raw data might not be consistent all the time, they might contain empty values or incorrect values, and sometimes they even contain data that are irrelevant from an analysis perspective. These inconsistencies in the data could result in a disputed outcome that might include the failure of the entire model that is built [22]. As a result, the dataset needs to be reconsidered, after refining the data it contains-after removing unnecessary information, which could be overwhelming, and cleaning data by removing disparities.…”

Section: Preprocessing and Cleaning Datamentioning

confidence: 99%

“…Support Vector Regression (SVR) model is an advancement to the conventional Support Vector Machines (SVM)-which are widely used for solving the problems that are of classification categories [22]. The aim of SVR is to obtain a hyperplane-after boosting the dimensions of the dataset-and organize them into distinct classes.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Rainfall Forecasting Using Machine Learning Algorithms for Localized Events

Ganapathy¹,

Srinivasan²,

Datta³

et al. 2022

Computers, Materials &Amp; Continua

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A substantial amount of the Indian economy depends solely on agriculture. Rainfall, on the other hand, plays a significant role in agriculturewhile an adequate amount of rainfall can be considered as a blessing, if the amount is inordinate or scant, it can ruin the entire hard work of the farmers. In this work, the rainfall dataset of the Vellore region, of Tamil Nadu, India, in the years 2021 and 2022 is forecasted using several machine learning algorithms. Feature engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the data. On removal of autocorrelation, the data could be used for performing operations on the time-series data, which otherwise could only be performed on any other regular regression data. The work uses forecasting techniques like the AutoRegessive Integrated Moving Average (ARIMA) and exponential smoothening, and then the time-series data is further worked on using Long Short Term Memory (LSTM). Later, regression techniques are used by manipulating the dataset. The work is benchmarked with several evaluation metrics on a test dataset, where XGBoost Regression technique outperformed the test. The uniqueness of this work is that it forecasts the daily rainfall for the year 2021 and 2022 in Vellore region. This work can be extended in the future to predict rainfall over a bigger region based on previously recorded time-series data, which can help the farmers and common people to plan accordingly and take precautionary measures.

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Extracting statistically significant behaviour from fish tracking data with and without large dataset cleaning

Cited by 24 publications

References 47 publications

Deep learning for visual recognition and detection of aquatic animals: A review

Deep learning for visual recognition and detection of aquatic animals: A review

Identification of Fish Hunger Degree with Deformable Attention Transformer

Rainfall Forecasting Using Machine Learning Algorithms for Localized Events

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