High elevation spruce forests of the European Alps are frequently infected by the needle rust Chrysomyxa rhododendri, 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 n = 62) and by the use of a semiprofessional quadcopter (n = 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 (R 2 = 0.87) and with black background (R 2 = 0.95), juvenile plants (R 2 = 0.94), and top views and side views of entire tree crowns of adult trees (R 2 = 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 C. rhododendri and provides several advantages compared to conventional assessment by manual counting or visual estimations.

中文翻译：

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利用图像分析对挪威云杉针叶锈病进行定量评估


欧洲阿尔卑斯山的高海拔云杉林经常受到杜鹃针锈病的感染，这种病原体会导致严重的落叶、树木生长减少和再生有限。不同空间尺度上疾病严重程度的精确量化对于监测、管理和抗性育种活动至关重要。根据被攻击的针头明显的黄色变色，研究了数码照片的图像分析是否可以用于量化疾病的严重程度，并与传统评估相比在时间、精力和应用范围方面改善表型。所开发的数字 RGB 图像预处理和分析协议能够在地面调查（分析图像总数 n = 62）和使用半专业四轴飞行器（n = 13）获得的图像上识别疾病症状和健康针区域。获得的疾病严重程度与所有方法中通过手动计数健康和患病针叶获得的结果呈线性相关，包括具有自然背景 (R 2 = 0.87) 和黑色背景 (R 2 = 0.95) 的单个枝条的图像、幼年植物 (R 2 = 0.94），以及成年树整个树冠的俯视图和侧视图（R 2 分别 = 0.98 和 0.88）。结果强调，可以从标准数码相机和无人机记录的图像中提取与挪威云杉针囊锈病症状相关的明确信号。所提出的协议能够对杜鹃花引起的疾病症状进行精确且省时的量化，并且与手动计数或目视估计的传统评估相比具有多种优势。