Journal
TRENDS IN PLANT SCIENCE
Volume 23, Issue 10, Pages 883-898Publisher
CELL PRESS
DOI: 10.1016/j.tplants.2018.07.004
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Funding
- Iowa State University (Presidential Initiative for Interdisciplinary Research)
- Plant Science Institute
- Iowa Soybean Association
- Monsanto Chair in Soybean Breeding
- R F Baker Center for Plant Breeding
- United States Department of Agriculture-NIFA project
- USDA-CRIS project [IOW04314]
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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.
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