Campus Units

Agronomy, Mechanical Engineering

Document Type

Article

Publication Version

Published Version

Publication Date

10-2018

Journal or Book Title

Trends in Plant Science

Volume

23

Issue

10

First Page

883

Last Page

898

DOI

10.1016/j.tplants.2018.07.004

Abstract

Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image–based plant stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in plant science.

Comments

This article is published as Singh, Asheesh Kumar, Baskar Ganapathysubramanian, Soumik Sarkar, and Arti Singh. "Deep learning for plant stress phenotyping: trends and future perspectives." Trends in plant science 23 (2018): 883-898. doi: 10.1016/j.tplants.2018.07.004.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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