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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Owner
The Authors
Copyright Date
2018
Language
en
File Format
application/pdf
Recommended Citation
Singh, Asheesh Kumar; Ganapathysubramanian, Baskar; Sarkar, Soumik; and Singh, Arti, "Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives" (2018). Agronomy Publications. 550.
https://lib.dr.iastate.edu/agron_pubs/550
Included in
Agriculture Commons, Computer-Aided Engineering and Design Commons, Plant Sciences Commons
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.