Application of hyperspectral and deep learning in farmland soil microplastic detection

Abstract

The ecological environment is gravely threatened by the buildup of microplastics (MPs) in soil. Currently, there are no established techniques for detecting MPs in soil. Some of the standard chemical detection methods now in use are time-consuming and cumbersome. This research suggested a method for identifying soil microplastic polymers (MPPs) based on convolutional neural networks (CNN) and hyperspectral imaging (HSI) technologies to address this issue. The categorization model for MPPs on the soil surface was first established by simulating the natural soil environment in the lab. While decision tree (DT) and support vector machine (SVM) models' classification accuracy was 87.9 % and 85.6 %, respectively, that of CNN was 92.6 %. The HIS and CNN model combination produced the best classification results out of all of these models. Secondly, farmland in Guangzhou's Tianhe, Panyu, and Zengcheng districts was sampled for surface soil samples measuring 0–20 cm in order to confirm the model's accuracy in the actual environment. Before data analysis, the physicochemical properties of soil samples were determined by a standardization scheme. MPs in soil samples were extracted by traditional chemical detection method and their chemical properties were obtained as the results of the control group. Then, CNN was applied to hyperspectral data from soil samples collected for MPs detection. Finally, it was demonstrated that the physical and chemical properties of the soil have an impact on the accuracy of the model through the investigation of the physical and chemical characteristics of soil samples from three distinct areas. On the other hand, the results indicated that the suggested technique offers quick and non-destructive results for MPPs detection when comparing the detection results of hyperspectral and conventional chemical methods.