Deep learning in plant phenotyping: the first ten years

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DOI	Resolve DOI: https://doi.org/10.1016/j.plaphe.2025.100062
Author	Search for: Ubbens, Jordan¹ORCID identifier: https://orcid.org/0000-0002-3162-5252; Search for: Stavness, Ian²; Search for: Pound, Michael P.³; Search for: Guo, Wei⁴
Affiliation	National Research Council Canada. Aquatic and Crop Resource Development University of Saskatchewan University of Nottingham University of Tokyo
Format	Text, Article
Subject	machine learning; deep learning; image analysis
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.
Date published	2025-08-20
Publisher	Elsevier B.V. Nanjing Agricultural University
Copyright statement	Crown Copyright © 2025 Published by Elsevier B.V. on behalf of Nanjing Agricultural University.
Licence	Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
In	Plant Phenomics 7, no. 4, 100062 (20 August 2025): 1–6.
Language	English
Peer reviewed	Yes
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Record identifier	ec8a214b-fdfb-4804-8ad6-0f238ec17249
Record created	2026-04-28
Record modified	2026-06-10

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Date modified:: 2026-07-14