Application of a U-Net Neural Network to the Puccinia sorghi– Maize Pathosystem

Abstract

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. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Plant phenotyping relevance

U-Net画像解析を用いてトウモロコシ葉の病害症状の発生量を定量化し、学習データ量と性能を評価する方法開発・検証研究であるため。

Code and data availability

The paper's Data availability section states that the full training and testing datasets (raw TIF leaf images, CSV annotation files, and all 140 model analysis CSV files) are publicly available at the Iowa State University Figshare repository (DOI 10.25380/iastate.23669328), which is an allowed URL. This directly hosts