Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Can, Erkan(1)
• Can, Şafak Seyhaneyildiz(1)
• Cetin, A. Kadri(1)
• Ertargin, Merve(1)
• Pezzolesi, Laura(1)
• Pistocchi, Rossella(1)
• Simsek, Gokce Kendirlioglu(1)
• Yildirim, Ozal(1)
• Yilmaz, Kadir(1)

• MakaleAçık Erişim2025Scopus SJRQ2/r/munzur/986
  Species classification of microalgae using a CNN-based deep learning approach under optimal cultivation conditions

  Simsek, Gokce Kendirlioglu; Ertargin, Merve; Pezzolesi, Laura; Pistocchi, Rossella; Yildirim, Ozal; Cetin, A. Kadri

  Microalgae possess significant potential in a wide range of applications due to their valuable bioactive compounds. They are utilized in biofuel production to reduce dependence on fossil fuels, in wastewater treatment for the biological removal of heavy metals and pollutants, in carbon capture for mitigating climate change, and in the pharmaceutical and nutraceutical industries as supplements. As their applications expand, the accurate and efficient identification of microalgal species becomes increasingly important. Traditional classification methods are time-consuming and rely heavily on expert knowledge. The main aim of this study is to develop a reliable, fast, and expert-independent deep learning-based approach for the classification of microalgae species using microscopic images. In this context, deep learning techniques, specifically convolutional neural network (CNN)-based models were employed to classify microalgal species. Four widely used pre-trained CNN architectures (ResNet152, DenseNet201, MobileNetV2, and EfficientNetB0), along with a custom-designed CNN, were implemented. The models were trained and tested on a labeled dataset consisting of microscopic images of Chlorella vulgaris, Scenedesmus acutus, and Haematococcus pluvialis. The classification models achieved accuracy rates ranging from 96.87 % (Custom CNN) to 100 % (DenseNet201), demonstrating the potential of CNN-based approaches in automating and improving microalgae species identification.

  AtıfScopus5·WoS5·OpenAlex5

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• MakaleAçık Erişim2024Scopus SJRQ3/r/munzur/1585
  Lipid content and wastewater treatment potential of Chlorella vulgaris and Scenedesmus obliquus isolated from Uzuncaylr Dam Lake

  Can, Şafak Seyhaneyildiz; Can, Erkan; Yilmaz, Kadir

  Production of microalgae for biodiesel, one of the alternative renewable energy sources, is costly due to nutrient media. Industrial and domestic wastewater contains nitrogen, phosphorus and other nutrients that are the primary food source for algae. Releasing this environmentally harmful effluent into receiving bodies of water, such as the sea or a freshwater reservoir, without prior treatment causes serious problems. Combining microalgae cultivation with wastewater treatment is a promising strategy for improving wastewater and reducing the cost of nutrient media required for algae production. In this study, wastewater obtained from a wastewater treatment facility was diluted with clean water to 0.25%, 50%, and 75% concentrations, and Chlorella vulgaris and Scenedesmus obliquus were cultured in these nutrient media for 20 days. As a result, microalgae increased their biomass and lipid content, while consuming nitrite, nitrate, phosphate, and ammonium from the wastewater as nutrients.

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  AtıfScopus3·WoS2·OpenAlex2

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