East Malling Research, New Road, East Malling, ME19 6BJ Kent, UK.
School of Biological Sciences, University of Reading, Reading, RG6 6AJ UK.
Plant Methods. 2015 Dec 24;11:57. doi: 10.1186/s13007-015-0100-8. eCollection 2015.
Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance.
In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study.
Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.
丁香假单胞菌可引起包括樱桃、李、桃、七叶树和悬铃木在内的多种木本物种的茎坏死和溃疡。随着时间的推移，在木质组织中检测和量化病变进展是育种者选择抗性的关键特征。
本研究开发了一种使用图像识别和人工神经网络模型进行病变量化的通用、快速和可靠的方法。该方法用于筛选一系列丁香假单胞菌致病变种的毒力和一组樱桃和李属品种对细菌性溃疡病的抗性。所开发的方法比肉眼评分更客观，并可以检测出具有潜在抗性的植物材料，以供进一步研究。
自动化图像分析将有助于快速筛选对细菌性和其他植物病原体的抗性材料，从而更有效地选择和量化抗性反应。
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