Scarboro Clifton G, Ruzsa Stephanie M, Doherty Colleen J, Kudenov Michael W
Department of Electrical and Computer Engineering Optical Sensing Laboratory North Carolina State University Raleigh NC USA.
Department of Molecular and Structural Biochemistry North Carolina State University Raleigh NC USA.
Plant Direct. 2021 Mar 24;5(3):e00317. doi: 10.1002/pld3.317. eCollection 2021 Mar.
Gray mold disease caused by the fungus damages many crop hosts worldwide and is responsible for heavy economic losses. Early diagnosis and detection of the disease would allow for more effective crop management practices to prevent outbreaks in field or greenhouse settings. Furthermore, having a simple, non-invasive way to quantify the extent of gray mold disease is important for plant pathologists interested in measuring infection rates. In this paper, we design and build a bispectral imaging system for discriminating between leaf regions infected with gray mold and those that remain unharmed on a lettuce ( spp.) host. First, we describe a method to select two optimal (high contrast) spectral bands from continuous hyperspectral imagery (450-800 nm). We then explain the process of building a system based on these two spectral bands, located at 540 and 670 nm. The resultant system uses two cameras, with a narrow band-pass spectral filter mounted on each, to measure the bispectral reflectance of a lettuce leaf. The two resulting images are combined using a normalized difference calculation that produces a single image with high contrast between the leaves' infected and healthy regions. A classifier was then created based on the thresholding of single pixel values. We demonstrate that this simple classification produces a true-positive rate of 95.25% with a false-positive rate of 9.316% in laboratory conditions.
由这种真菌引起的灰霉病会损害全球许多农作物宿主，并造成巨大的经济损失。对该病进行早期诊断和检测，将有助于采取更有效的作物管理措施，以预防田间或温室环境中的病害爆发。此外，对于有兴趣测量感染率的植物病理学家来说，拥有一种简单、非侵入性的方法来量化灰霉病的严重程度非常重要。在本文中，我们设计并构建了一个双光谱成像系统，用于区分生菜（ 属）宿主上感染灰霉病的叶片区域和未受损害的叶片区域。首先，我们描述了一种从连续高光谱图像（450 - 800纳米）中选择两个最佳（高对比度）光谱带的方法。然后，我们解释了基于位于540和670纳米的这两个光谱带构建系统的过程。最终的系统使用两个相机，每个相机上都安装了一个窄带通光谱滤光片，以测量生菜叶片的双光谱反射率。通过归一化差异计算将这两张图像合并，生成一张在叶片感染区域和健康区域之间具有高对比度的单一图像。然后基于单个像素值的阈值创建了一个分类器。我们证明，在实验室条件下，这种简单的分类方法产生的真阳性率为95.25%，假阳性率为9.316%。
