Mahlein Anne-Katrin
Institute for Crop Science and Resource Conservation (INRES) - Phytomedicine, University of Bonn, Meckenheimer Allee 166a, 53115 Bonn, Germany.
Plant Dis. 2016 Feb;100(2):241-251. doi: 10.1094/PDIS-03-15-0340-FE. Epub 2016 Jan 18.
Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.
植物病害的早期准确检测与诊断是植物生产以及减少作物产量质量和数量损失的关键因素。光学技术，如RGB成像、多光谱和高光谱传感器、热成像或叶绿素荧光，已在自动化、客观且可重复的检测系统中展现出其潜力，可在病害流行的早期阶段对植物病害进行识别和定量。最近，三维扫描也作为一种光学分析手段被纳入其中，它能提供有关作物活力的额外信息。从近距离到遥感的不同平台可用于对单个作物器官或整个田地进行多尺度监测。高度复杂和创新的数据分析方法有助于准确可靠地检测病害，这些方法能从传感器数据中获得有关复杂植物 - 病原体系统的新见解。基于传感器的无损方法支持并扩展了用于植物病害评估的视觉和/或分子方法。基于传感器分析的最相关应用领域是精准农业和植物表型分析。
