Your browser doesn't support javascript.
loading
Evaluation of disease outbreak in terms of physico-chemical characteristics and heavy metal load of water in a fish farm with machine learning techniques.
Yilmaz, Mesut; Çakir, Mustafa; Oral, Mükerrem Atalay; Kazanci, Hüseyin Özgür; Oral, Okan.
Afiliação
  • Yilmaz M; Akdeniz University, Faculty of Fisheries, Antalya, Türkiye.
  • Çakir M; Iskenderun Technical University, Iskenderun Vocational School of Higher Education, Iskenderun, Hatay, Türkiye.
  • Oral MA; Akdeniz University, Elmali Vocational School of Higher Education, Antalya, Türkiye.
  • Kazanci HÖ; Akdeniz University, Faculty of Engineering, Antalya, Türkiye.
  • Oral O; Akdeniz University, Faculty of Engineering, Antalya, Türkiye.
Saudi J Biol Sci ; 30(4): 103625, 2023 Apr.
Article em En | MEDLINE | ID: mdl-37008282
ABSTRACT
Diseases are quite common in fish farms because of changes in physico-chemical characteristics in the aquatic environment, and operational concerns, i.e., overstocking and feeding issues. In the present study, potential factors (water physico-chemical characteristics and heavy metal load) on the disease-causing state of the pathogenic bacteria Lactococcus garvieae and Vagococcus sp. were examined with machine learning techniques in a trout farm. Recording of physico-chemical characteristics of the water, fish sampling and bacteria identification were carried out at bimonthly intervals. A dataset was generated from the physico-chemical characteristics of the water and the occurrence of bacteria in the trout samples. The eXtreme Gradient Boosting (XGBoost) algorithm was used to determine the most important independent variables within the generated dataset. The most important seven features affecting bacteria occurrence were determined. The model creation process continued with these seven features. Three well-known machine learning techniques (Support Vector Machine, Logistic Regression and Naïve Bayes) were used to model the dataset. Consequently, all the three models have produced comparable results, and Support Vector Machine (93.3% accuracy) had the highest accuracy. Monitoring changes in the aquaculture environment and detecting situations causing significant losses through machine learning techniques have a great potential to support sustainable production.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article