Deep Learning in Image-Based Plant Phenotyping

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

Plant phenotyping relevance

植物画像ベース表現型解析における深層学習の基礎、評価、応用、ベストプラクティスを扱う方法論レビューであり、表現型取得・抽出手法が中心である。

Code and data availability

This is a review article surveying deep learning in plant phenotyping. All datasets, tools, and repositories mentioned (IPPN/Zenodo images, TERRA-REF, iNaturalist, Kaggle challenges, PlantCV, SAM, etc.) are cited prior work or generic resources, not assets produced by this paper's own measurements or analysis. The only

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