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Abstract

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.

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/content/journals/10.1146/annurev-arplant-070523-042828
2024-02-21
2024-04-19
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  • Article Type: Review Article
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