Applications of data mining and machine learning framework in aquaculture and fisheries: A review

Gladju, J.; Kamalam, Biju Sam; Kanagaraj, A.

doi:10.1016/j.atech.2022.100061

Cited by 65 publications

(32 citation statements)

References 90 publications

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“…By integrating this data, AI algorithms can provide more accurate and comprehensive insights to farmers on how different environmental factors can impact the health and growth of the fish ( Niloofar et al, 2021 ). This can help farmers make more informed decisions on how to optimize their fish farm operations and prevent potential problems that may arise due to changes in temperature or other environmental factors ( Gladju et al, 2022 ). Considering the findings of these studies, it can be inferred that AI algorithms can provide valuable insights to fish farmers.…”

Section: Potential Applications Of Artificial Intelligence In Aquacul...mentioning

confidence: 99%

“…In the realm of aquaculture, AI facilitates the analysis of data derived from sensors and cameras to monitor fish behaviour, detect signs of disease or stress, employ automatic sensors for measuring fish length and weight, and optimize feeding regimes ( Barreto et al, 2022 , Føre et al, 2018 , Tonachella et al, 2022 ). By analyzing this data using AI algorithms, researchers can develop predictive models that can identify the early signs of disease or stress in fish ( Gladju et al, 2022 ). Sharma and Kumar (2021) emphasize the role of integrated sensors, biosensors, and AI in minimizing the reliance on antibiotics and other medications.…”

Section: Introductionmentioning

confidence: 99%

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Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

Ashraf Rather,

Ahmad,

Shah

et al. 2024

Food Chemistry: X

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Section: Potential Applications Of Artificial Intelligence In Aquacul...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

Ashraf Rather,

Ahmad,

Shah

et al. 2024

Food Chemistry: X

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“…The application of machine-learning algorithms to fishing area prediction is appropriate for improving estimation accuracy [37,38]. Machine learning is an effective approach to evaluating relations between the potential fishing area and corresponding environmental parameters.…”

Section: Ensemble Modelmentioning

confidence: 99%

Fishing Area Prediction Using Scene-Based Ensemble Models

Alfatinah

Chu

Tatas

et al. 2023

JMSE

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This study utilized Chlorophyll-a, sea surface temperature (SST), and sea surface height (SSH) as the environmental variables to identify skipjack tuna catch hotspots. This study conducted statistical methods (decision tree, DT, and generalized linear model, GLM) as ensemble models that were employed for predicting skipjack area for each time slice. Using spatial historical data, each model was trained for one of the ensemble model sets. For prediction, the correlations of historical and new inputs were applied to select the predictive model. Using the scene-based model with the highest input correlation, this study further identified the fishing area of skipjack tuna in every case whether the alterations in their environment affected their abundance or not. Overall, the performance achieved over 83% for correlation coefficients (CC) based on the accuracy assessment. This study concluded that DT appears to perform better than GLM in predicting skipjack tuna fishing areas. Moreover, the most influential environmental variable in model construction was sea surface temperature (SST), indicating that the presence of skipjack tuna was primarily influenced by regional temperature.

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“…Phase 2, which is addressed in the present review, deals with the handling and interpretation of the large amount of data generated by automatic monitoring. However, the automatic interpretation of the data demands the application of machine learning, artificial intelligence (AI) algorithms, and decision support systems, whose addressal is outside the scope of the present work (see reviews by [ 7 , 15 , 16 , 17 , 18 , 19 ]). Phase 3 also benefits from AI algorithms and decision support systems, and both Phases 3 and 4 (implementation) will most likely ultimately rest on the farmer’s experience, and it will, in all probability, be the farmer, and not an automated system, who will make the ultimate decisions.…”

Section: Introductionmentioning

confidence: 99%

Entropy and Fractal Techniques for Monitoring Fish Behaviour and Welfare in Aquacultural Precision Fish Farming—A Review

Eguiraun

Martı́nez

2023

Entropy

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In a non-linear system, such as a biological system, the change of the output (e.g., behaviour) is not proportional to the change of the input (e.g., exposure to stressors). In addition, biological systems also change over time, i.e., they are dynamic. Non-linear dynamical analyses of biological systems have revealed hidden structures and patterns of behaviour that are not discernible by classical methods. Entropy analyses can quantify their degree of predictability and the directionality of individual interactions, while fractal dimension (FD) analyses can expose patterns of behaviour within apparently random ones. The incorporation of these techniques into the architecture of precision fish farming (PFF) and intelligent aquaculture (IA) is becoming increasingly necessary to understand and predict the evolution of the status of farmed fish. This review summarizes recent works on the application of entropy and FD techniques to selected individual and collective fish behaviours influenced by the number of fish, tagging, pain, preying/feed search, fear/anxiety (and its modulation) and positive emotional contagion (the social contagion of positive emotions). Furthermore, it presents an investigation of collective and individual interactions in shoals, an exposure of the dynamics of inter-individual relationships and hierarchies, and the identification of individuals in groups. While most of the works have been carried out using model species, we believe that they have clear applications in PFF. The review ends by describing some of the major challenges in the field, two of which are, unsurprisingly, the acquisition of high-quality, reliable raw data and the construction of large, reliable databases of non-linear behavioural data for different species and farming conditions.

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Applications of data mining and machine learning framework in aquaculture and fisheries: A review

Cited by 65 publications

References 90 publications

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

Fishing Area Prediction Using Scene-Based Ensemble Models

Entropy and Fractal Techniques for Monitoring Fish Behaviour and Welfare in Aquacultural Precision Fish Farming—A Review

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