Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization

Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic species in water samples are often time-consuming, require intensive manual effort, are prone to subjective errors, and can lead to delays in necessary water management interventions. This emphasizes the pressing need for a quick and precise automated method. Both aquatic and terrestrial environments include cyanobacteria, and under some circumstances, poisonous cyanobacteria can grow in large numbers and form harmful blooms called harmful cyanobacterial blooms (Cyano-HABs). In addition, cyanoHABs cause hypoxia, ecological imbalances, the generation of toxins, and other detrimental phenomena that put people, animals, and plants in danger of illness. Climate change is expected to cause these situations to increase in frequency and globally. This study presents a novel approach for the automatic detection of harmful cyanobacteria genera by utilizing a newly introduced and publicly available dataset, TCB-DS. In the initial stage, discriminative features are extracted using two powerful deep Convolutional Neural Network (CNN) models: ShuffleNet and ResNet-50. Subsequently, feature fusion is applied to the extracted features to enhance the representation. Then, to select the most relevant features, feature selection is performed using the Artemisinin Optimization (AO) algorithm, a robust meta-heuristic algorithm inspired by the mechanisms of malaria treatment and recently proposed in 2024. This step aims to reduce feature redundancy and improve the overall efficiency of the model. In classifying microscopic images of cyanobacteria species with the proposed method, GoogleNet, MobileNetV2, EfficientNetb0, DarkNet53, ShuffleNet, and ResNet101 models were used. Among these, the proposed method obtained the highest accuracy, with a mean accuracy of 97.471% and max accuracy of 97.683%. Since these results are the highest accuracy values obtained in the TCB-DS dataset, our proposed method significantly improves water quality monitoring in our world.