利用多光谱成像和X光图像进行麻风树种子品质鉴别：种子表型研究
最近，来自Aarhus大学以及丹麦理工大学的科学家利用多光谱成像设备以及X光设备发表了题为Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality的文章，结论显示MSI多光谱成像技术以及X光图像在麻风树的种子生理性能研究上有强相关性。 此类技术可作为未来替代方法用于快速、有效、可持续、无损鉴别麻风树种子品质，克服传统种子品质分析内在主观性。
研究中使用了Videometer开发的多光谱成像系统，该系统是种子品质以及种子表型组学研究，目前为止，利用该设备已经发表了多达250多篇文章。除了与X光方法结合，也可以与计算机断层扫描法CT结合，实现种子三维结构的研究。
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 analysis.
麻风树作为一种新兴的资源植物，因其生物柴油用途和综合利用潜力，受到世界各国及科学家的高度关注。本文综述了近年来国内外麻风树植物学研究的概况，系统地介绍了麻风树系统分类与资源分布，形态结构，生殖生物学特性，组织培养，种子生理、胁迫生理及植物生态学等方面的研究现状。在分类及分布方面，介绍了麻风树目前的分类地位研究现状，及其在世界范围的地理分布情况；在形态结构研究方面，开展了麻风树植物学基本特征、染色体核型、根、茎、叶、花、果实、种子的结构研究；蛋白质和基因结构研究集中在功能蛋白和关键基因的发现和克隆；生殖生物学的研究分别开展了繁育系统、传粉过程、开花生物学等研究；在植物生理学方面，重点开展了麻风树的组织培养、种子生理和胁迫生理的研究；在植物生态学方面，光合生理生态是目前的重点。分析研究进展的不足，提出了麻风树植物学研究的主要方向。
研究中使用了Videometer开发的多光谱成像系统，该系统是种子品质以及种子表型组学研究，目前为止，利用该设备已经发表了多达250多篇文章。除了与X光方法结合，也可以与计算机断层扫描法CT结合，实现种子三维结构的研究。
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 analysis.
麻风树作为一种新兴的资源植物，因其生物柴油用途和综合利用潜力，受到世界各国及科学家的高度关注。本文综述了近年来国内外麻风树植物学研究的概况，系统地介绍了麻风树系统分类与资源分布，形态结构，生殖生物学特性，组织培养，种子生理、胁迫生理及植物生态学等方面的研究现状。在分类及分布方面，介绍了麻风树目前的分类地位研究现状，及其在世界范围的地理分布情况；在形态结构研究方面，开展了麻风树植物学基本特征、染色体核型、根、茎、叶、花、果实、种子的结构研究；蛋白质和基因结构研究集中在功能蛋白和关键基因的发现和克隆；生殖生物学的研究分别开展了繁育系统、传粉过程、开花生物学等研究；在植物生理学方面，重点开展了麻风树的组织培养、种子生理和胁迫生理的研究；在植物生态学方面，光合生理生态是目前的重点。分析研究进展的不足，提出了麻风树植物学研究的主要方向。
