DeepPlate Analyse

DeepPlate is a deep learning pipeline for analysing plate images of Arabidopsis and Brachypodium, and this page presents its main segmentation performance, example outputs, and key limitations to guide future improvements

You can access the project (code, figure, explanation …) through this link, the code for the algorithm at this link and you can find more informations at this link.

DeepPlate is a deep learning pipeline designed to analyse plate images of Arabidopsis thaliana and Brachypodium distachyon, including roots, seeds and aerial tissues. Starting from raw time-series images, it performs preprocessing, segmentation and trait extraction to generate quantitative descriptors of plant growth and architecture. On this page, I summarise the main performance metrics and example outputs of DeepPlate. I show how well the models segment different organs, how robust the pipeline is across experiments, and how the extracted traits relate to manual measurements. The goal is to document what works, where the current limitations are, and how these results can guide future improvements of the tool.