传统的种子真菌检测方法费时费力,需要专门的分析员对种子上的致病真菌进行鉴定。将多光谱成像(MSI)与机器视觉相结合,作为检测黑燕麦种子中德雷克斯孢菌替代方法。种子接种德雷克斯孢菌,然后孵育24、72和120小时。在365至970 nm的光谱范围内,在19个波长下获得了未感染和感染种子的多光谱图像。利用种子图像的反射率、颜色和纹理特征,建立了基于线性判别分析(LDA)的分类模型。所开发的模型显示MSI在检测黑燕麦种子中的德雷克斯孢菌方面表现出很高的性能,尤其是使用孵化120小时的种子的颜色和纹理特征,独立验证的准确度为0.86。分类器的高精度表明,使用在紫外A区域(365 nm)捕获的图像的方法可以很容易地根据黑燕麦种子的健康状况对其进行分类,并且与传统方法相比,可以更快、更有效地获得结果。
Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging
Abstract
Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.
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