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, assessment of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.

Expected final online publication date for the , Volume 75 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.


Article metrics loading...

Loading full text...

Full text loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error