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Application of Hyperspectral and Deep Learning in Farmland Soil Microplastic Detection by Wenjie Ai; Guanglong Chen; Xuejun Yue; Jun Wang is a Environmental Science article available to read on EtoBox.
What is Application of Hyperspectral and Deep Learning in Farmland Soil Microplastic Detection about?
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 traditi
Who reads Application of Hyperspectral and Deep Learning in Farmland Soil Microplastic Detection?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Wenjie Ai; Guanglong Chen; Xuejun Yue; Jun Wang
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Environmental Science (Physical Sciences)