Rapid online plant leaf area change detection with high-throughput plant image data-Reference-Cited by-同舟云学术

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Rapid online plant leaf area change detection with high-throughput plant image data

Published:2022-12-05 Issue: Volume: Page:1-15
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Zhan Yinglun¹,Zhang Ruizhi¹,Zhou Yuzhen¹,Stoerger Vincent²,Hiller Jeremy³,Awada Tala³,Ge Yufeng⁴

Affiliation:

1. Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA

2. Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, USA

3. School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA

4. Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA

Funder

USDA

NIFA

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2022.2150753

Reference42 articles.

1. Plant segmentation by supervised machine learning methods

2. Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform

3. Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis

4. Detection of early plant stress responses in hyperspectral images

5. Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution

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1. Editorial to the special issue: modern streaming data analytics;Journal of Applied Statistics;2023-10-05

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