Transfer learning method for plastic pollution evaluation in soil using NIR sensor

Abstract

Plastic debris are ubiquitous in soil and bring severe threatening to environment and ecosystem. It is of great significance to extensively investigate the plastic pollution level in soil. An ultra-portable Near-infrared (NIR) sensor was used to detect plastic pollution level in soil. Soil samples were collected from three different regions and artificially polluted in two degrees (10–1.5% and 0.7–0.15%). Here, instead of constructing detection models for specific soil region, transfer learning approaches were explored to build classification model which could evaluate plastic pollution level in different soil regions simultaneously. The transfer learning algorithms, Manifold Embedded Distribution Alignment (MEDA) and Transfer Component Analysis (TCA), were employed for transfer learning model construction. Supporting Vector Machine (SVM) models were calibrated for transferability analysis and comparison. MEDA transferable models achieved the average classification accuracy of 97.78% in source soil regions and 79.52% in target soil regions. The average accuracy of TCA based models and conventional SVM models were equivalent to each other and lower than MEDA models. Besides, the average running time of MEDA method (0.70 s) was much lower than TCA based method (21.90 s) and conventional SVM models (41.38 s). Overall, the results indicated that transfer learning approaches especially MEDA method could work in a more efficient manner than that of conventional multivariate analysis. The ultra-portable NIR sensor in combination with MEDA transfer learning algorithm as modelling method was a promising solution for low-cost and efficient field detection of plastic contaminated level in soil.