Videometer种子表型组学：种子质量研究新工具

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).

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