Bohnenkamp David, Behmann Jan, Paulus Stefan, Steiner Ulrike, Mahlein Anne-Katrin
Institute for Crop Science and Resource Conservation, Plant Diseases and Plant Protection, University of Bonn, 53115 Bonn, Germany.
Institute of Sugar Beet Research, 37079 Göttingen, Germany.
Phytopathology. 2021 Sep;111(9):1583-1593. doi: 10.1094/PHYTO-09-19-0335-R. Epub 2021 Oct 14.
This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.
这项工作建立了一个由不同真菌病原体诱导的小麦重要叶部病害的高光谱库，该库代表了从感染到症状出现的时间序列，用于检测光谱变化。数据是在叶片尺度的受控条件下生成的。评估了从健康叶片组织到患病叶片组织的转变，识别了光谱变化，并将其与组织学研究相结合，以确定每种病害发病机制中的发育阶段。将每种植物病害在发病机制中指示特定发育阶段（定义为转折点）的光谱特征组合成一个光谱库。应用并比较了机器学习分析方法，以测试该库在高光谱图像中检测和量化叶部病害的潜力。所有评估的分类器在检测和识别活体营养型和死体营养型真菌方面都具有很高的准确率（≤99%）。证明了将光谱分析方法与光谱库相结合用于检测和识别植物病害的潜力。对这些算法的进一步评估和开发应有助于建立一个针对不同发育阶段植物病害的强大检测和识别系统，并促进田间条件下植物病害的精准管理技术的推广和发展。
