Prediction of Plant Uptake and Translocation of Engineered Metallic Nanoparticles by Machine Learning

Machine learning was applied to predict the plant uptake and transport of engineered nanoparticles (ENPs). A back propagation neural network (BPNN) was used to predict the root concentration factor (RCF) and translocation factor (TF) of ENPs from their essential physicochemical properties (e.g., composition and size) and key external factors (e.g., exposure time and plant species). The relative importance of input variables was determined by sensitivity analysis, and gene-expression programming (GEP) was used to generate predictive equations. The BPNN model satisfactorily predicted the RCF and TF in both hydroponic and soil systems, with an R-2 higher than 0.8 for all simulations. Inclusion of the initial ENP concentration as an input variable further improved the accuracy of the BPNN for soil systems. Sensitivity analysis indicated that the composition of ENPs (e.g., metals vs metal oxides) is a major factor affecting RCF and TF values in a hydroponic system. However, the soil organic matter and clay contents are more dominant in a soil system. The GEP model (R-2 = 0.8088 and 0.8959 for RCF and TF values) generated more accurate predictive equations than the conventional regression model (R-2 = 0.5549 and 0.6664 for RCF and TF values) in a hydroponic system, which could guide the sustainable design of ENPs for agricultural applications.

Automated summary

Machine learning was utilized to predict plant uptake and transport of engineered nanoparticles, with different factors affecting predictive accuracy. The GEP model generated more accurate predictive equations in a hydroponic system compared to the conventional regression model, guiding sustainable design in agricultural applications.