Plants meet machines: Prospects in machine learning for plant biology
Published 2020 View Full Article
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Title
Plants meet machines: Prospects in machine learning for plant biology
Authors
Keywords
-
Journal
Applications in Plant Sciences
Volume 8, Issue 6, Pages -
Publisher
Wiley
Online
2020-07-01
DOI
10.1002/aps3.11371
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- (2019) Jens Kattge et al. GLOBAL CHANGE BIOLOGY
- The Sequenced Angiosperm Genomes and Genome Databases
- (2018) Fei Chen et al. Frontiers in Plant Science
- Herbarium specimens reveal increasing herbivory over the past century
- (2018) Emily K. Meineke et al. JOURNAL OF ECOLOGY
- Machine learning for image based species identification
- (2018) Jana Wäldchen et al. Methods in Ecology and Evolution
- Museum specimens provide novel insights into changing plant–herbivore interactions
- (2018) Emily K. Meineke et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
- Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets
- (2018) Keiichi Mochida et al. Frontiers in Plant Science
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- (2017) Daniel Pflugfelder et al. Plant Methods
- Old Plants, New Tricks: Phenological Research Using Herbarium Specimens
- (2017) Charles G. Willis et al. TRENDS IN ECOLOGY & EVOLUTION
- Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
- (2017) Jordan R. Ubbens et al. Frontiers in Plant Science
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- (2016) Jakob Unger et al. BMC EVOLUTIONARY BIOLOGY
- X-Ray Computed Tomography Reveals the Response of Root System Architecture to Soil Texture
- (2016) Eric D. Rogers et al. PLANT PHYSIOLOGY
- Machine Learning for High-Throughput Stress Phenotyping in Plants
- (2016) Arti Singh et al. TRENDS IN PLANT SCIENCE
- Herbarium records are reliable sources of phenological change driven by climate and provide novel insights into species' phenological cueing mechanisms
- (2015) Charles C. Davis et al. AMERICAN JOURNAL OF BOTANY
- The global distribution of diet breadth in insect herbivores
- (2014) Matthew L. Forister et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Data access for the 1,000 Plants (1KP) project
- (2014) Naim Matasci et al. GigaScience
- Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing
- (2013) Alina Zare et al. IEEE SIGNAL PROCESSING MAGAZINE
- TRY - a global database of plant traits
- (2011) J. KATTGE et al. GLOBAL CHANGE BIOLOGY
- Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field
- (2010) Samuel Trachsel et al. PLANT AND SOIL
- How many species of flowering plants are there?
- (2010) L. N. Joppa et al. PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
- Mountain pine beetle and forest carbon feedback to climate change
- (2008) W. A. Kurz et al. NATURE
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