Katherine M Murphy, Ella Ludwig, Jorge Gutierrez, Malia A Gehan
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A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.
期刊介绍:
The Annual Review of Plant Biology is a peer-reviewed scientific journal published by Annual Reviews. It has been in publication since 1950 and covers significant developments in the field of plant biology, including biochemistry and biosynthesis, genetics, genomics and molecular biology, cell differentiation, tissue, organ and whole plant events, acclimation and adaptation, and methods and model organisms. The current volume of this journal has been converted from gated to open access through Annual Reviews' Subscribe to Open program, with all articles published under a CC BY license.