Projects per year
Abstract / Description of output
Results: We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement.
Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1768 images in total). A full protocol is provided, including all software components and an additional test data set.
Conclusions: PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap.
Keywords / Materials (for Non-textual outputs)
- arabidopsis thaliana
- leaf angle
- machine learning
- near-infrared (NIR) LEDs
FingerprintDive into the research topics of 'A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth'. Together they form a unique fingerprint.
- 2 Finished
14 ERA-CAPS PHYTOCAL: Phytochrome Control of Resource Allocation and Growth in Arabidopsis and in Brassicaceae crops
Halliday, K., Krahmer, J. & Romanowski, A.
1/10/15 → 30/03/19
1/09/15 → 31/03/19