Advanced Deep Learning Techniques for Assessing Contaminated Irrigation Water and Treated by Solar Powered Carbon Nanostructures Filtration Systems

The increasing reliance on contaminated water for irrigation in water-scarce regions poses significant risks to agricultural productivity, environmental sustainability, and public health. This study explores the integration of advanced deep learning techniques with solar-powered nano filtration systems for the assessment and treatment of contaminated irrigation water. The proposed approach leverages deep neural networks, including convolutional and recurrent architectures, to analyze complex water quality parameters such as heavy metals, pathogens, and chemical pollutants in real time. The solar-powered nano filtration membranes embedded with nanostructured materials enable efficient removal of contaminants while ensuring energy sustainability and cost-effectiveness. Our proposed  deep learning models are trained on multi-source datasets, incorporating physicochemical and biological indicators, to accurately predict contamination levels and filtration efficiency. The system also facilitates automated monitoring and decision-making for irrigation suitability. Experimental results demonstrate improved detection accuracy, enhanced filtration performance, and reduced operational costs compared to conventional methods. This integrated framework offers a scalable and sustainable solution for safe wastewater reuse in agriculture, particularly in resource-constrained environments