All Outputs (5)

Supporting data for "High-fidelity Wheat Plant Reconstruction using 3D Gaussian Splatting and Neural Radiance Fields" (2025)
Data
(2025). Supporting data for "High-fidelity Wheat Plant Reconstruction using 3D Gaussian Splatting and Neural Radiance Fields". [Data]. https://doi.org/10.5524/102661

The reconstruction of 3D plant models can offer advantages over traditional 2D approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction techniques often produce sparse or noi... Read More about Supporting data for "High-fidelity Wheat Plant Reconstruction using 3D Gaussian Splatting and Neural Radiance Fields".

Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles (2022)
Data
Brocklehurst, C., & Radenkovic, M. (2022). Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles. [Data]. https://doi.org/10.3390/jsan11030035

The increased interest in autonomous vehicles has led to the development of novel networking protocols in VANETs In such a widespread safety-critical application, security is paramount to the implementation of the networks. We view new autonomous veh... Read More about Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles.

Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot (2022)
Data
(2022). Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot. [Data]. https://doi.org/10.5281/zenodo.5504299

Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot

X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil - [https://doi.org/10.1002/ppj2.20036]... Read More about Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot.

Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures" (2019)
Data
French, A., Wells, D. M., Atkinson, J., Pound, M., Yasrab, R., & Pridmore, T. (2019). Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures". [Data]. https://doi.org/10.5524/100651

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously... Read More about Supporting data for "RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures".

Supporting data for "Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping" (2016)
Data
(2016). Supporting data for "Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping". [Data]. https://doi.org/10.5524/100343

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection; hence the motivation... Read More about Supporting data for "Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping".