BioResource and Agricultural Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
Plant Sciences Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
Sensors (Basel). 2024 Mar 16;24(6):1916. doi: 10.3390/s24061916.
Downy mildew caused by is a severe disease in that significantly reduces crop yield and marketability. This study aims to evaluate different vegetation indices to assess different downy mildew infection levels in the variety Mildis using hyperspectral data. Artificial inoculation using sporangia suspension was conducted to induce different levels of downy mildew disease. Spectral measurements, spanning 350 nm to 1050 nm, were conducted on the leaves using an environmentally controlled setup, and the reflectance data were acquired and processed. The Successive Projections Algorithm (SPA) and signal sensitivity calculation were used to extract the most informative wavelengths that could be used to develop downy mildew indices (DMI). A total of 37 existing vegetation indices and three proposed DMIs were evaluated to indicate downy mildew (DM) infection levels. The results showed that the classification using a support vector machine achieved accuracies of 71.3%, 80.7%, and 85.3% for distinguishing healthy leaves from DM1 (early infection), DM2 (progressed infection), and DM3 (severe infection) leaves using the proposed downy mildew index. The proposed new downy mildew index potentially enables the development of an automated DM monitoring system and resistance profiling in breeding lines.
由 引起的霜霉病是 中一种严重的疾病，它会显著降低作物的产量和市场价值。本研究旨在评估不同的植被指数，以利用高光谱数据评估 品种 Mildis 中不同程度的霜霉病感染。使用 游动孢子悬浮液进行人工接种以诱导不同程度的霜霉病。在环境控制的设置下对叶片进行了 350nm 到 1050nm 的光谱测量，并采集和处理了反射率数据。使用连续投影算法（SPA）和信号灵敏度计算来提取最有用的波长，这些波长可用于开发霜霉病指数（DMI）。评估了总共 37 种现有的植被指数和三种提出的 DMI，以指示霜霉病（DM）感染水平。结果表明，使用支持向量机进行分类，使用提出的霜霉病指数区分健康叶片与 DM1（早期感染）、DM2（进展性感染）和 DM3（严重感染）叶片的准确率分别为 71.3%、80.7%和 85.3%。提出的新的霜霉病指数有可能开发出一种自动的 DM 监测系统和在 育种系中进行抗性分析。
