IoT and ML approach for ornamental fish behaviour analysis Article Swipe
K. Suresh Kumar Patro
,
Vinod Kumar Yadav
,
Vidya Shree Bharti
,
Arun Kumar Sharma
,
Arpita Sharma
,
Senthil Kumar Thangavel
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-48057-w
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-48057-w
Ornamental fish keeping is the second most preferred hobby in the world and it provides a great opportunity for entrepreneurship development and income generation. Controlling the environment in ornamental fish farm is a considerable challenge because it is affected by a variety of parameters like water temperature, dissolved oxygen, pH, and disease occurrences. One particular interesting ornamental fish species is goldfish ( Carassius auratus ). Machine learning (ML) and deep learning technique have significant potential in analysing voluminous data collected from fish farm. Through this technique, the fish farmers can get insight on feeding behaviour, fish growth patterns, predict diseases/stress, and environmental factors affecting fish health. The aim of the study is to analyze the behavioural changes in goldfish due to alterations in environmental parameters (water temperature and dissolved oxygen). Decision tree, Naïve Bayes classifier, K-nearest neighbour (KNN), and linear discriminant analysis (LDA) were used to analyse the behavioural change data. To compare the performance between all four classifiers, cross validation and confusion matrix used. The cross-validation error of LDA, Naïve Bayes classification, KNN and decision tree was 19.86, 28.08, 30.14 and 13.78 respectively. Decision tree was proved to be the most accurate and effective classifier. Different temperature and DO range were taken to predict fish behaviour. Some findings are, the behaviour of fish was rest between temperature 37.85 °C and 40.535 °C, erratic when temperature was greater than or equal to 40.535 °C, gasping when temperature was between 37.85 and 40.535 °C and when DO concentration was less than 6.58 mg/L. Blood parameter analysis has been done to validate the change in external behaviours with change in physiological parameters.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-023-48057-w
- https://www.nature.com/articles/s41598-023-48057-w.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389323749
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389323749Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-023-48057-wDigital Object Identifier
- Title
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IoT and ML approach for ornamental fish behaviour analysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-12-05Full publication date if available
- Authors
-
K. Suresh Kumar Patro, Vinod Kumar Yadav, Vidya Shree Bharti, Arun Kumar Sharma, Arpita Sharma, Senthil Kumar ThangavelList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-023-48057-wPublisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-023-48057-w.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-023-48057-w.pdfDirect OA link when available
- Concepts
-
Ornamental plant, Decision tree, Naive Bayes classifier, Linear discriminant analysis, Machine learning, Confusion, Artificial intelligence, Fish
, Hobby, Statistics, Computer science, Fishery, Biology, Mathematics, Ecology, Support vector machine, Psychoanalysis, Law, Political science, Psychology Top concepts (fields/topics) attached by OpenAlex - Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 5Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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