Ganthaler A, Losso A, Mayr S
Department of Botany University Innsbruck Sternwartestrasse 15 Innsbruck A-6020 Austria.
Plant Pathol. 2018 Jun;67(5):1122-1130. doi: 10.1111/ppa.12842. Epub 2018 Mar 1.
High elevation spruce forests of the European Alps are frequently infected by the needle rust , a pathogen causing remarkable defoliation, reduced tree growth and limited rejuvenation. Exact quantification of the disease severity on different spatial scales is crucial for monitoring, management and resistance breeding activities. Based on the distinct yellow discolouration of attacked needles, it was investigated whether image analysis of digital photographs can be used to quantify disease severity and to improve phenotyping compared to conventional assessment in terms of time, effort and application range. The developed protocol for preprocessing and analysis of digital RGB images enabled identification of disease symptoms and healthy needle areas on images obtained in ground surveys (total number of analysed images =62) and by the use of a semiprofessional quadcopter (=13). Obtained disease severities correlated linearly with results obtained by manual counting of healthy and diseased needles for all approaches, including images of individual branches with natural background ( = 0.87) and with black background ( = 0.95), juvenile plants ( = 0.94), and top views and side views of entire tree crowns of adult trees ( = 0.98 and 0.88, respectively). Results underline that a well-defined signal related to needle bladder rust symptoms of Norway spruce can be extracted from images recorded by standard digital cameras and using drones. The presented protocol enables precise and time-efficient quantification of disease symptoms caused by and provides several advantages compared to conventional assessment by manual counting or visual estimations.
欧洲阿尔卑斯山高海拔云杉林经常受到针叶锈病的感染，这种病原体导致显著的落叶、树木生长减缓以及再生受限。在不同空间尺度上准确量化疾病严重程度对于监测、管理和抗性育种活动至关重要。基于受攻击针叶明显的黄色变色，研究了数字照片的图像分析是否可用于量化疾病严重程度，并在时间、精力和应用范围方面与传统评估相比改进表型分析。所开发的用于数字RGB图像预处理和分析的协议能够在地面调查（分析图像总数=62）以及使用半专业四轴飞行器（=13）获得的图像上识别疾病症状和健康针叶区域。对于所有方法，包括具有自然背景（=0.87）和黑色背景（=0.95）的单个树枝图像、幼树（=0.94）以及成年树整个树冠的顶视图和侧视图，获得的疾病严重程度与通过手动计数健康和患病针叶得到的结果呈线性相关（分别为=0.98和0.88）。结果强调，可以从标准数码相机和使用无人机记录的图像中提取与挪威云杉针叶膀胱锈病症状相关的明确信号。所提出的协议能够精确且高效地量化由[病原体名称未给出]引起的疾病症状，并且与通过手动计数或视觉估计的传统评估相比具有多个优势。
