Using R‐Based Image Analysis to Quantify Rusts on Perennial Ryegrass-Reference-Cited by-同舟云学术

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Using R‐Based Image Analysis to Quantify Rusts on Perennial Ryegrass

Published:2019-01 Issue:1 Volume:2 Page:1-10
ISSN:2578-2703
Container-title:The Plant Phenome Journal
language:en
Short-container-title:Plant phenome j.

Author:

Heineck Garett C.¹,McNish Ian G.²,Jungers Jacob M.²,Gilbert Erin³,Watkins Eric¹

Affiliation:

1. Dep. of Horticultural ScienceUniv. of Minnesota1970 Folwell Ave.St. PaulMN55108

2. Dep. of Agronomy and Plant GeneticsUniv. of Minnesota1991 Upper Buford CircleSt. PaulMN55108

3. Dep. of Plant PathologyUniv. of Minnesota1991 Upper Buford CircleSt. PaulMN55108

Funder

Minnesota Agricultural Experiment Station

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.2135/tppj2018.12.0010

Reference28 articles.

1. Field-Based Scoring of Soybean Iron Deficiency Chlorosis Using RGB Imaging and Statistical Learning

2. Effects of pathogens on perennial pasture grasses

3. Recurrent Selection for Partial Resistance to Crown Rust in Oat

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1. Investigating tradeoffs in perennial ryegrass turfgrass performance and seed yield capacity in the US Midwest;Agrosystems, Geosciences & Environment;2024-07-25

2. Application of a U-Net Neural Network to the Puccinia sorghi–Maize Pathosystem;Phytopathology®;2024-05

3. StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust;Plant Phenomics;2024-01

4. Development of 3‐D point cloud imaging and 2‐D image processing methods for high‐throughput phenotyping of perennial ryegrass spikes;The Plant Phenome Journal;2023-08-27

5. HopBox: An image analysis pipeline to characterize hop cone morphology;The Plant Phenome Journal;2023-08-22

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