外文摘要：Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. Copyright
外文关键词：maize;machine learning;Zea mays;common rust of maize;fungal rust;plant disease phenotyping;Puccinia sorghi;Pucciniales
作者：Holan, Katerina L;White, Charles H;Whitham, Steven A
作者单位：Iowa State Univ;Colorado State Univ
期刊名称：PHYTOPATHOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：114（5）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台