Deep learning in plant phenotyping: the first ten years

Abstract

As with many fields of science, plant science and agriculture have seen a rapid adoption of deep learning in recent years. The present moment is significant as it marks one decade since the first applications of deep learning began to appear in the literature on plant phenotyping. In this short time, a new research community was founded and new connections between computer vision and biology were established. In this letter, we reflect on this critical period of time from the inception of the field to where it stands today.

Plant phenotyping relevance

植物フェノタイピングにおける深層学習の発展を総括する方法論的レビューであり、対象分野の技術的進展が中心です。

Code and data availability

This is a short perspective/review article reflecting on ten years of deep learning in plant phenotyping. It cites prior datasets (PlantVillage, GWHD, CVPPP) and tools (RootNav 2, RootPainter, T-Rex2) but presents no original phenotype measurements, no author-collected data, and no analysis code with availability or de

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