种子表型组学：一种利用新兴光学技术和人工智能的方法作为新标记评估花生种子质量

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种子表型组学：一种利用新兴光学技术和人工智能的方法作为新标记评估花生种子质量

发表时间：2022-04-20 10:02:54点击：942

来源：北京博普特科技有限公司

Videometer Lab4多光谱种子表型成像系统是丹麦理工大学与丹麦Videometer公司开发，是用于种子研究先进的多光谱表型成像设备，典型客户为ISTA国际种子检验协会、ESTA欧洲种子检验协会、John Innes Centre、LGC化学家集团、奥胡斯大学等等，利用该系统发表的文章已经超过300篇。

Videometer种子表型表型成像系统可测量种子如尺寸、颜色、形状等，间接测定种子参数如种子纯度、发芽百分比、发芽率、种子健康度、种子成熟度、中寿命等。种子活力综合种子活力是种子发芽和出苗率、幼苗生长的潜势、植株抗逆能力和生产潜力的总和（发芽和出苗期间的活性水平与行为），是种子品质的重要指标，具体包括吸涨后旺盛的代谢强度、出苗能力、抗逆性、发芽速度及同步性、幼苗发育与产量潜力。种子活力是植物的重要表型特征，传统检测方法包括低温测试、高温加速衰老测试、幼苗生长测定等。

该系统也可以对细菌、虫卵、真菌等进行高通量成像测量，进行病理学、毒理学或其它研究。对于拟南芥等冠层平展的植物，可以进行自动的叶片计数等。

一种利用新兴光学技术和人工智能的方法作为新标记评估花生种子质量

高生理质量的种子取决于其优越的发芽能力和均匀的幼苗形成。本文研究了多光谱图像与机器学习模型相结合能否有效地对花生种子的品质进行分类。传统上，通过多光谱图像（面积、长度、宽度、亮度、叶绿素荧光、花青素和反射率：365至970 nm）评估七批种子的质量（种子重量、含水量、发芽率和活力）。评估每批种子的幼苗的光合能力（荧光和叶绿素指数、F0、Fm和Fv/Fm）和胁迫指数（花青素和NDVI）。人工智能特征（QDA方法）应用于从高质量和低质量的种子图像中提取的数据。低质量种子的幼苗叶片中花青素含量较高。因此，这一信息是有价值的，因为幼苗的初始行为反映了种子的质量。证实了有效筛选花生种子品质的新标记的存在。物理特性（面积、长度、宽度和外壳亮度）、色素（叶绿素荧光和花青素）和光反射率（660、690和780 nm）的组合可以高效地识别品质优良的花生种子地块（准确率98%）。

An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality

Seeds of high physiological quality are defined by their superior germination capacity and uniform seedling establishment. Here, it was investigated whether multispectral images combined with machine learning models can efficiently categorize the quality of peanut seedlots. The seed quality from seven lots was assessed traditionally (seed weight, water content, germination, and vigor) and by multispectral images (area, length, width, brightness, chlorophyll fluorescence, anthocyanin, and reflectance: 365 to 970 nm). Seedlings from the seeds of each lot were evaluated for their photosynthetic capacity (fluorescence and chlorophyll index, F0, Fm, and Fv/Fm) and stress indices (anthocyanin and NDVI). Artificial intelligence features (QDA method) applied to the data extracted from the seed images categorized lots with high and low quality. Higher levels of anthocyanin were found in the leaves of seedlings from low quality seeds. Therefore, this information is promising since the initial behavior of the seedlings reflected the quality of the seeds. The existence of new markers that effectively screen peanut seed quality was confirmed. The combination of physical properties (area, length, width, and coat brightness), pigments (chlorophyll fluorescence and anthocyanin), and light reflectance (660, 690, and 780 nm), is highly efficient to identify peanut seedlots with superior quality (98% accuracy).