Deep learning-based saliency maps for the quantification of grape powdery mildew at the microscopic level

Abstract

<b><sc>Abstract.</sc></b> <b>Powdery mildew (PM) is one of the most widespread plant diseases and can damage a wide range of crops, causing significant economic losses annually. This urges the breeding of PM resistant crop cultivars and the development of management practices. A major bottleneck is the accuracy and efficiency of image analysis at the microscopic level, which is essential to understand PM infection and accelerate crop breeding and management practice development. The overall goal of this study was to develop a deep learning-based saliency map approach that can quantify PM infection in images of high spatial resolution. A subset of a total of 2690 images of 1-cm leaf disks was randomly selected to extract a total of 21,162 image patches of 224x224 pixels. A custom thresholding method was used to mask out irrelevant background information from a leaf disk image. The remaining image part was cropped into image patches of 224x224 pixels to be classified by pretrained CNN classifiers. For the patches predicted as infected, patch saliency maps were generated using several saliency methods. All patch saliency maps were re-assembled to construct a leaf-level infection map for the quantification of PM infection in leaf disk images. Experimental results showed that with a well-trained CNN classifier (validation accuracy of 95.66%), our approach achieved remarkable accuracy of the localization and quantification of PM hyphae by using only patch-level class annotations, suggesting a great potential of reducing annotation cost for deep learning-based quantification. Compared with the manual assessment, our approach also improved the processing speed by 20 to 60 times. Therefore, the developed approach can be an effective and efficient analysis tool for PM disease research in the future.</b>