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2T2D spectroscopy and deep learning for soil MPs quantification
Summary
Scientists have developed an AI-powered tool that can accurately measure how much microplastic (tiny plastic particles) is hiding in soil, using light-based scanning combined with deep learning. This matters because microplastics in soil can end up in the food we grow and eventually in our bodies, so having a fast, reliable way to detect and measure them is a key step toward understanding—and eventually reducing—our exposure to this pollution.
This dataset is primarily used for quantitative analysis of microplastic content in soil by combining DRSN-SpCA with 2T2D spectra. The code is written in Python, and the data is stored in .h5 format. main.py: This is the main function, which includes data import, model training, and result prediction. model_2T2D.py: The DRSN-SpCA model is encapsulated within it. early_stopping.py: This is the early stopping function. function_Function.py: The functions required by main.py are encapsulated within it. The specific data is available via DOI: 10.17632/zhdxvgzkfk.1 (Date_PE_1), DOI: 10.17632/d2xz4sby82.1 (Date_PE_2), DOI: 10.17632/2jf5gwrrg9.1 (Date_PE_3_4), DOI: 10.17632/ksxndsxdgs.1 (Date_PET_1), DOI: 10.17632/yp5kjgv58s.1 (Date_PET_2), DOI: 10.17632/pybh5kgwz8.1 (Date_PET_3_4), DOI: 10.17632/jdtnfgd5v7.1 (Date_PP_1), DOI: 10.17632/spzk3vstdf.1 (Date_PP_2), DOI: 10.17632/4wy2nrjbgc.1 (Date_PP_3_4), DOI: 10.17632/wv8mn99ccb.1 (Date_PS_1), DOI: 10.17632/4jy8k7j7ph.1 (Date_PS_2), DOI: 10.17632/txrxrpks44.1 (Date_PS_3_4).