.png){kind=icon}
.png){kind=icon}
Return
Efficient wheat variety identification using Raman hyperspectral imaging in combination with deep learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.saa.2025.126722.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Developed the SAM-OneHSE method for efficient and automatic segmentation of grain regions across all HSI modalities. • Assessed the modeling capability of RHSI for wheat variety classification. • Selected Raman characteristic peaks derived from wheat-related chemical prior knowledge. • Accurately identified wheat varieties using the selected peaks. • Developed the RSAN model to further enhance classification accuracy with the selected peaks.
Keywords:
SAM-OneHSE
RHSI
Raman characteristic peaks
wheat variety classification
RSAN model
Journal
S
IF:
4.6
Papers:
1.5K
Citations:
15