We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Non-destructive detection of microplastics stress in rice seedling: an interpretable deep learning approach using excitation emission matrix fluorescence spectra of root exudates
Summary
Researchers developed a non-destructive, interpretable deep learning framework using excitation-emission matrix fluorescence spectra of root exudates to detect microplastic stress in rice seedlings at early stages. The approach enables early-warning detection of plastic contamination effects on crop health without damaging the plants.
In summary, this study establishes a novel, non-destructive, and interpretable framework for the early detection of MPs stress in rice seedlings based on EEMF spectra of root exudates combined with deep learning.