多光譜成像和X光圖像在鑒別麻風樹種子品質與種子表型研究的應用
最近，來自Aarhus大學以及丹麥理工大學的科學家利用多光譜成像設備以及X光設備發表了題為Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality的文章，結論顯示MSI多光譜成像技術以及X光圖像在麻風樹的種子生理性能研究上有強相關性。 此類技術可作為未來替代方法用于快速、有效、可持續、無損鑒別麻風樹種子品質，克服傳統種子品質分析內在主觀性。
研究中使用了Videometer開發的多光譜成像系統，該系統是種子品質以及種子表型組學研究的利器，目前為止，利用該設備已經發表了多達250多篇文章。
Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality
seed viability, non-destructive analysis, machine vision, artificial intelligence
Jean M Carstensen
Danmarks Tekniske Universitet
Birte Boelt
Aarhus Universitet
Background: Jatropha curcas
is an oilseed plant with great potential for biodiesel
production. In agricultural industry, the seed quality
is still estimated by manual inspection, using
destructive, time-consuming and subjective tests
that depend on the seed analyst experience. Recent advances in machine vision
combined with
artificial intelligence algorithms can provide spatial and spectral information for characterization
of biological images, reducing subjectivity and optimizing the analysis process.
Results: We present a new method for automatic characterization of jatropha seed quality, based
on multispectral imaging (MSI) combined with X-ray
imaging. We propose an approach along with X-ray images in order
to investigate
internal problems such as damages in the embryonic axis and endosperm, considering the fact
that seed surface profiles can be negatively affected, but without reaching important internal
regions of the seeds. Our studies included the application of a normalized canonical discriminant analyses (nCDA)
algorithm as a supervised transformation building method to classify spatial and spectral patters according to
the classes of seed quality. Spectral reflectance signatures in a range
of 780 to 970 nm and the X-ray images can efficiently
predict quality traits such as normal seedlings,
abnormal seedlings and dead seeds.
Conclusions: MSI and X-ray images have a strong relationship with physiological performance of Jatropha curcas L. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of jatropha seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality