Binary Image Classification of Water Samples Using Convolutional Neural Networks and Transfer Learning for Environmental Monitoring

Water pollution poses a critical threat to both public health and environmental sustainability, while conventional testing remains costly, slow, and dependent on specialized laboratories. This study introduces a deep learning-based framework for rapid water quality assessment using Convolutional Neural Networks (CNNs). A custom dataset, supplemented by the Kaggle “Clean or Dirty Water Images” collection, was pre-processed with normalization and augmentation techniques to improve generalization. Two models were evaluated: a custom CNN and EfficientNetB0 (transfer learning). The Custom CNN achieved 67% accuracy, showing strong precision for polluted water samples but weaker recall. In contrast, EfficientNetB0 achieved 58% accuracy yet produced a higher ROC-AUC score (0.63 vs. 0.37), reflecting stronger discriminative ability despite less consistent classification. A comparative analysis confirmed that the Custom CNN better captured dataset-specific features, whereas EfficientNetB0 demonstrated potential for scalability with larger and more balanced data. These findings underscore the feasibility of image-based monitoring as a low-cost, non-invasive, and scalable solution for water quality detection. Furthermore, integrating the proposed framework into drones, IoT devices, and smart city infrastructures could enable real-time, automated identification of contaminated water sources, supporting sustainable resource management and early intervention. This work establishes a foundation for applying deep learning to environmental monitoring, bridging the gap between laboratory-based testing and intelligent field-level solutions.