Imaging-based high throughput phenotyping (HTP) systems have demonstrated promising solutions to enhance genetic understanding of grapevine powdery mildew (PM) resistance and have accelerated PM-resistant cultivar breeding. The accuracy and throughput of extracting phenotypic traits from images are still the bottleneck of modern HTP systems, especially at the microscopic level. The goal of this study was to develop a saliency-based processing pipeline for the quantification of PM infection in microscopic images and comprehensively evaluate its performance for genetic analyses. An input image was segregated into subimages that were classified as infected or healthy by a pretrained CNN classifier. Saliency maps from the classification were generated post-hoc and used for the quantification of PM infection in the input image at the pixel level without the use of mask annotations. A total of seven phenotypic traits were extracted from images collected for a biparental population. Experimental results showed that optimal combinations of convolutional neural network and saliency methods achieved strong measurement correlations (r = 0.74 to 0.75) with human assessments at the image patch level, and the traits calculated by the saliency-based processing pipeline were highly correlated (r = 0.87 to 0.88) with reference PM infection ratings at the leaf image level. The high quantification accuracy of the saliency-based pipeline led to the increased explanation of phenotypic variance and reliable identification of quantitative trait loci. Therefore, the saliency-based processing pipeline can be used as an effective and efficient analysis tool for PM disease research and breeding programs in the future, especially agricultural and life science studies requiring microscopic image analysis.

中文翻译：

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基于显着性的高通量葡萄白粉病微观量化抗病育种

基于成像的高通量表型 (HTP) 系统已展示了有前景的解决方案，可增强对葡萄白粉病 (PM) 抗性的遗传理解，并加速了抗 PM 品种的育种。从图像中提取表型特征的准确性和吞吐量仍然是现代 HTP 系统的瓶颈，尤其是在微观层面。本研究的目的是开发一种基于显着性的处理流程，用于量化显微图像中的 PM 感染，并全面评估其在遗传分析中的性能。输入图像被分成子图像，这些子图像被预训练的 CNN 分类器分类为感染或健康。来自分类的显着性图是事后生成的，用于在像素级别对输入图像中的 PM 感染进行量化，而无需使用掩码注释。从为双亲群体收集的图像中提取了总共七个表型特征。实验结果表明，卷积神经网络和显着性方法的最佳组合与图像块级别的人类评估具有很强的测量相关性（r = 0.74 到 0.75），并且基于显着性的处理管道计算的特征高度相关（r = 0.87 到 0.88）在叶子图像级别具有参考 PM 感染等级。基于显着性的管道的高量化准确性导致增加了对表型变异的解释和数量性状基因座的可靠识别。所以，