利用Videometer多光谱技术以及磁共振成像进行种子病害分析

近期，科学家利用Videometer多光谱技术以及磁共振成像对麻风树种子病害进行了分析，发表了题为“A novel approach for Jatropha curcas seed health analysis based on multispectral and resonance imaging techniques”的文章，文章发表在Industrial Crops and Products Volume 161, March 2021, 113186上。

摘要：

基于强大的光谱空间传感器，已经在现代种业领域开发出了革新性的先进的技术。本研究中，我们提出了一种基于多光谱成像和机器视觉算法结合来鉴定麻风树种子健康的一种新方法。另外，我们首次介绍了一种基于MRI（核磁共振成像）的新方法来鉴别感染了不同致病真菌的麻风树种子的解剖学变化。首先，首先将种子人工接种龙眼焦腐病菌（Lasiodiplodia theobromae）, Colletotrichum siamense（炭疽病菌）以及Colletotrichum truncatum（大豆炭疽病，在接种 24, 48, 72, 96, 120, 144和168 h后拍摄多光谱照片。研究使用了MRI方法，种子接种了168h。研究结果表明多光谱成像技术结合统计模型可在接种48h后，区分麻风树种子感染的不同真菌，精度高 (>80 %)。

推荐的MRI方法可鉴别出感染了L. theobromae, C. siamense 和 C. truncatum 的内胚组织的不同损伤模式。由此推断，多光谱成像和MRI技术是快速、精确检测麻风树种子不同真菌类型的有用工具。

A novel approach for Jatropha curcas seed health analysis based on multispectral and resonance imaging techniques

Innovative methods have been developed in the state-of-the-art technologies based on robust spectral-spatial sensors for modern seed industry.

In this study we proposed a novel approach based on multispectral imaging combined with machine learning algorithm to classify Jatropha curcas seed health. Furthermore, we present for the first time a methodology based on magnetic resonance imaging (MRI) to identify anatomical changes in J. curcas seeds infected with different pathogenic fungi. First, seeds were artificially inoculated with Lasiodiplodia theobromae, Colletotrichum siamense and Colletotrichum truncatum, and multispectral images were acquired after 24, 48, 72, 96, 120, 144 and 168 h of incubation. The MRI method was applied using incubated seeds for 168 h. Our results showed that the multispectral imaging technique combined with statistical models has the potential to distinguish different fungal species in J. curcas seeds after 48 h of incubation, with high accuracy (>80 %). The proposed MRI methodology allowed the identification of different damage patterns in the endosperm tissues infected with L. theobromae, C. siamense and C. truncatum. Therefore, multispectral imaging and MRI can be useful tools for rapid and accurate detection of different fungal species in J. curcas seeds.

Keywords

Optical sensors，Machine learning，Seed health，Lasiodiplodia ，heobromae，Colletotrichum siamense，Colletotrichum truncatum