Abstract Detail

The Potential of Machine Learning for Plant Biology

Soltis, Pamela [1], Nelson, Gil [1].

The Potential of Machine Learning for Plant Biology.

Machine learning approaches are highly promising technologies to help address a range of scientific questions in plant science. For example, deep learning technologies have recently achieved impressive performance on a variety of predictive tasks, such as species identification, plant trait recognition, plant species distribution modelling, weed detection, and mercury damage to herbarium specimens. They are also being applied to questions of comparative genomics and gene expression and to conduct high-throughput phenotyping for agricultural and ecological research. Moreover, novel approaches are poised to revolutionize studies of plant phenology and functional traits through application to nearly 30 million images of herbarium specimens now available at iDigBio (www.idigbio.org) as well as other digital repositories. For example, such approaches could also be used to identify plant phenophase (important for assessing the effects of climate change on plant growth and reproduction and for comparing plant responses with those of pollinators, migratory birds, and other species that rely on plants for food and/or nesting sites) or to extract other evolutionary or ecological traits, such as leaf shape and size, leaf margins, and flower color, to name a few. However, despite the promise of applying deep learning to herbarium specimen images to address a range of questions, this emerging field also raises challenging methodological questions about how to avoid any bias and misleading conclusions when analyzing the produced data. In this symposium, we will provide an introduction to the field of machine learning and its potential for plant biology. Speakers will then describe progress, challenges, and solutions for use of machine learning techniques for species identification (from photographs of plants in nature and images of herbarium specimens), analyses of phenology and morphological/functional traits, assessment of damage to herbarium specimens, and stress phenotyping. A special issue of Applications in Plant Sciences will publish papers resulting from this symposium.

1 - University Of Florida, Florida Museum Of Natural History, Po Box 117800, Gainesville, FL, 32611, United States

Keywords:
machine learning
deep learning
convolutional neural networks
herbarium specimen images
phenology
phenotyping
cyberinfrastructure.

Presentation Type: Symposium Presentation
Session: SYM1, The Potential of Machine Learning for Plant Biology
Location: Tucson F/Starr Pass
Date: Monday, July 29th, 2019
Time: 8:15 AM
Number: SYM1SUM
Abstract ID:34
Candidate for Awards:None