Abstract:

Machine learning approaches are affecting all aspects of modern society, from autocorrect applications on cell phones to self‐driving cars to facial recognition, personalized medicine, and precision agriculture. Although machine learning has a long history, drastic improvements in these application areas recently have been driven by improvements to computational infrastructure; increased computing power; increased ability to collect, manage, and store very large amounts of data; and algorithmic advances. Multiple types of machine learning have been developed, each with its own techniques, strengths, and weaknesses, making certain approaches better matches for certain problems than others.

Supervised machine learning and the use of neural networks (e.g., deep learning; Table 1) underlie much of the recent accelerated application of machine learning to many biological problems, including those across 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 (Unger et al., 2016; Carranza‐Rojas et al., 2017), plant species distribution modeling (e.g., Zhang and Li, 2017; Botella et al., 2018), weed detection (Yu et al., 2019), and mercury damage to herbarium specimens (Schuettpelz et al., 2017). They are also being applied to questions of comparative genomics (e.g., Xu and Jackson, 2019) and gene expression (Mochida et al., 2018) and to conduct high‐throughput phenotyping (e.g., Singh et al., 2016; Ubbens and Stavness, 2017) for agricultural and ecological research. Moreover, novel approaches are poised to revolutionize studies of plant phenology (e.g., Pearson et al., 2020) and functional traits through application to more than 30 million images of herbarium specimens now available at iDigBio (http://www.idigbio.org) as well as other digital repositories.

Links:

Citation:

P. S. Soltis, G. Nelson, A. Zare, and E. K. Meineke, "Plants meet machines: Prospects in machine learning for plant biology," in Applications in Plant Sciences, vol. 8, num. 6, pp. e11371, 2020.

@Article {Soltis2020PlantsMeetMachines,
author = {P. S. Soltis and G. Nelson and A. Zare and E. K. Meineke},
title = {Plants meet machines: Prospects in machine learning for plant biology},  
journal = {Applications in Plant Sciences},  
volume = {8},  
number = {6},  
pages = {e11371},  
year = {2020},  
}