欧亚国际

最近，科学家利用Videometer多光谱成像系统，在期刊Front. Plant Sci.发表了题为“Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality”的文章，这是今年利用此设备在该期刊发表的第3篇文章。

种子的健康质量对农业至关重要。这是因为病原真菌损害了种子的生理质量，阻止了田间植物的形成，从而给农民造成损失。多光谱图像技术结合机器学习算法可以优化健康花生种子的识别，大大提高健康质量。本研究目的是验证多光谱图像技术和人工智能工具是否能有效鉴别热带花生种子中的致病真菌。为此，使用被真菌（黄曲霉、黑曲霉、青霉菌和根霉）感染的干花生种子获取不同波长（365至970nm）的图像。发现了花生种子健康质量的多光谱标记。216小时的孵化期是通过多光谱图像区分健康种子和含真菌种子的最重要的时期。质地（百分比）、颜色（CIELab L*）和反射率（490nm）在区分花生种子的健康质量方面非常有效。机器学习算法（LDA、MLP、RF和SVM）在种子健康状态的自主检测中显示出高精度（90%至100%）。因此，结合机器学习算法的多光谱图像对于筛选具有优良健康质量的花生种子是有效的。

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接近400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。

Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality

The sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi (A. flavus, A. niger, Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of peanut seed health quality were found. The incubation period of 216 h was the one that most contributed to discriminating healthy seeds from those containing fungi through multispectral images. Texture (Percent Run), color (CIELab L*) and reflectance (490 nm) were highly effective in discriminating the sanitary quality of peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status (90 to 100%). Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.