دورية أكاديمية
Application of a U-Net Neural Network to the Puccinia sorghi- Maize Pathosystem.
| العنوان: | Application of a U-Net Neural Network to the Puccinia sorghi- Maize Pathosystem. |
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| المؤلفون: | Holan KL; Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA 50014., White CH; Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523., Whitham SA; Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA 50014. |
| المصدر: | Phytopathology [Phytopathology] 2024 May; Vol. 114 (5), pp. 990-999. Date of Electronic Publication: 2024 Apr 22. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Phytopathological Society] Country of Publication: United States NLM ID: 9427222 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0031-949X (Print) Linking ISSN: 0031949X NLM ISO Abbreviation: Phytopathology Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: [St. Paul, Minn., etc., American Phytopathological Society] |
| مواضيع طبية MeSH: | Zea mays*/microbiology , Plant Diseases*/microbiology , Plant Diseases*/statistics & numerical data , Neural Networks, Computer* , Puccinia*/physiology , Machine Learning*, Plant Leaves/microbiology |
| مستخلص: | 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. Competing Interests: The author(s) declare no conflict of interest. |
| فهرسة مساهمة: | Keywords: Puccinia sorghi; Pucciniales; Zea mays; common rust of maize; fungal rust; machine learning; maize; plant disease phenotyping |
| تواريخ الأحداث: | Date Created: 20240128 Date Completed: 20240530 Latest Revision: 20240530 |
| رمز التحديث: | 20240531 |
| DOI: | 10.1094/PHYTO-09-23-0313-KC |
| PMID: | 38281155 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 0031-949X |
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| DOI: | 10.1094/PHYTO-09-23-0313-KC |