Perspective | Open Access

Volume 2026 |Article ID 100062 | https://doi.org/10.1016/j.plaphe.2025.100062

Deep learning in plant phenotyping: the first ten years

Jordan Ubbens ,¹ Ian Stavness,² Michael P. Pound,³ Wei Guo^4,5

¹Aquatic and Crop Resource Development, National Research Council Canada, Canada
²Department of Computer Science, University of Saskatchewan, Canada
³Computer Vision Laboratory, School of Computer Science, University of Nottingham, UK
⁴Graduate School of Agricultural and Life Sciences, University of Tokyo, Japan
⁵Given his role as Senior Editor, Wei Guo had no involvement in the peer review of this article and had no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to another journal editor.

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.