Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality.

Vitor de Jesus Martins BianchiniGabriel Moura MascarinLúcia Cristina Aparecida Santos SilvaValter ArthurJens Michael CarstensenBirte BoeltClíssia Barboza da Silva

Published in: Plant methods (2021)

Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

Keyphrases

high resolution
fluorescence imaging
magnetic resonance imaging
deep learning
machine learning
computed tomography
electronic health record
sensitive detection