Akademik Veri Yönetim Sistemi
BIOLOGY-BASEL, cilt.15, sa.1, 2026 (SCI-Expanded, Scopus)
Microalgae are an evolutionarily ancient and morphologically diverse group of photosynthetic eukaryotes, with taxonomic resolution complicated by environmentally driven phenotypic plasticity. This study merges deep learning and explainable artificial intelligence (XAI) to establish a transparent, reliable, and biologically meaningful framework for green microalgae (Chlorophyta) classification. Microscope images from three morphologically distinct algal species-Desmodesmus flavescens, Desmodesmus subspicatus, and Tetradesmus dimorphus representing the genera Desmodesmus and Tetradesmus within Chlorophyta-were analyzed using twelve convolutional neural networks, including EfficientNet-B0-B7, DenseNet201, NASNetLarge, Xception, and ResNet152V2. A curated dataset comprising 3624 microscopic images from three Chlorophyta species was used, split into training, validation, and test subsets. All models were trained using standardized preprocessing and data augmentation procedures, including grayscale conversion, CLAHE-based contrast enhancement, rotation, flipping, and brightness normalization. The model's performance was assessed using accuracy and loss metrics on independent test datasets, while interpretability was evaluated through saliency maps and Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations. ResNet152V2 achieved the highest overall performance among all evaluated architectures, outperforming EfficientNet variants, NASNetLarge, and Xception in terms of macro F1-score. Visualization analysis showed that both Grad-CAM and saliency mapping consistently highlighted biologically relevant regions-including cell walls, surface ornamentation, and colony structures-confirming that the models relied on taxonomically meaningful features rather than background artifacts. The findings indicate that the integration of deep learning and XAI can attain consistently high test accuracy for microalgal species, even with constrained datasets. This approach enables automated taxonomy and supports biodiversity monitoring, ecological assessment, biomass optimization, and biodiesel production by integrating interpretability with high predictive accuracy.