Article
?
AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button.
Tier 2
?
Original research — experimental, observational, or case-control study. Direct primary evidence.
Detection Methods
Sign in to save
Detection of microplastics stress on rice seedling by visible/near-infrared hyperspectral imaging and synchrotron radiation Fourier transform infrared microspectroscopy
Frontiers in Plant Science2025
Score: 38
?
0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Hongxin Xie,
Hongxin Xie,
Chaojie Wei,
Hongxin Xie,
Chaojie Wei,
Wei Wang,
Hongxin Xie,
Hongxin Xie,
Chaojie Wei,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Hongxin Xie,
Hongxin Xie,
Hongxin Xie,
Yufeng Li
Yufeng Li
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Xiaorong Wang,
Wei Wang,
Wei Wang,
Xiaorong Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Ziwei Song,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
F. Z. Chen,
F. Z. Chen,
Wei Wang,
F. Z. Chen,
Wei Wang,
F. Z. Chen,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Yufeng Li
Yufeng Li
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Wei Wang,
Yufeng Li
Wei Wang,
Wei Wang,
Yufeng Li
Yufeng Li
Summary
Researchers used visible/near-infrared hyperspectral imaging combined with synchrotron radiation analysis and deep learning to detect physiological and biochemical stress responses in rice seedlings exposed to microplastics, developing a rapid and interpretable early-detection method for microplastic stress in crops.
In conclusion, the combination of spectral technology and deep learning to capture the physiological and biochemical reactions of leaves could provide a rapid and interpretable method for detecting rice seedlings under MPs stress. This method could provide a solution for the early detection of external stress on other crops.