Integration of smart sensors and phytoremediation for real-time pollution monitoring and ecological restoration in agricultural waste management

Abstract

Global climate change and ecological degradation highlight the urgency of dealing with agricultural waste and ecological restoration. Traditional pollutant monitoring and ecological restoration methods face challenges in accuracy and adaptability, especially when dealing with complex environmental data. This paper proposes the Bio-DANN model, which combines biogeochemical models and deep learning techniques to improve the accuracy of pollutant monitoring and ecological restoration prediction. The model uses deep neural networks (DNNs) and attention mechanisms to process multidimensional environmental data in various agricultural and ecological scenarios in real time. Experimental results based on Open Soil Data and NEON datasets show that Bio-DANN performs well in pollutant prediction, with mean square errors (MSE) of 0.012 and 0.018, root mean square errors (RMSE) of 0.109 and 0.134, and accuracy of 0.92 and 0.90, respectively. In terms of ecological restoration assessment, Bio-DANN achieved ΔF and PIPGR of 0.15 and 18%, and 0.20 and 22%, respectively, and H’ values of 1.5 and 1.7, which are better than other models. Bio-DANN provides a promising technical solution for environmental protection, resource recovery and sustainable agriculture, especially showing significant potential in pollutant monitoring, soil health assessment and ecological restoration evaluation.