Phenomics

 

Intro

Plant Phenomics and Controlled Environment Agriculture

An essential part of plant science is understanding how different plants respond to their environment, whether this is in the field or in controlled environments such as Greenhouses and more recently, Vertical Farms.  

The National Plant Phenomics Centre, funded by the BBSRC and Welsh Govt. is the first of its kind in a UK University and provides access to scientists across the UK and Globally to state-of-the-art phenotyping systems. The phenotyping systems are currently used in national and international research networks including PhenomUK, EPPN2020 and the China-UK Plant Phenomics Centre. The NPPC works closely with the Geography Dept and the Computer Science dept to develop novel solutions to data acquisition and analyses, including the development of deep learning solutions for agronomic issues.

Approach & Facilities

Approach and Facilities

Non-destructive non-contact sensors provide information on crop development and physiology, their environment, and the interaction between crop and environment. Such methods also allow repeated measurements across time, providing longitudinal trait information on genotype x environment x management interactions across the life cycle.

Emerging technologies include automated plant handling platforms, robotics, imaging and computer science (deep learning, semantic reasoning, etc), extending through to Remote Sensing and Earth Observation permit non-destructive collection of physical and physiological parameters across scales, from the sub cellular through to the field and landscape (Yang et al., 2020; 10.1016/j.molp.2020.01.008).

Whilst our commercial platforms are designed for traditional row (oats, wheat, rapeseed/canola, etc) and greenhouse crops, we have developed bespoke methodologies for roots, clover, forage grasses, grapevines, Miscanthus and peatland species. Biomedical technologies have been repurposed for plant science research.

Specialised glasshouses and controlled environment rooms (CERs) simulate climates from diverse latitudes.  To fill the gap between CERs and the field, large scale soil-plant-air units provide highly instrumented facilities for detailed physiological phenotyping.  Instrumented field plots along an altitudinal cline (sea level to 350m) and within a common photoperiod (10km on an approximately W-E transect) and mobile data acquisition platforms provide access to diverse outdoor growing environments all the way up to farm scale.

The Ecophysiology Lab provides expertise and state of the art instrumentation for deep physiological studies including  Scholander pressure chambers, infra red gas analyzers, and both single and continuous monitoring of chlorophyll fluorescence.

Complemented by metabolomics and next generation genomics, these approaches are being used to improve crop models that predict yield in the context of environmental variables, with a view to enhancing agricultural productivity and supply chains while reducing environmental impact.

To encourage the deployment of these technologies in agriculture generally and specifically in sustainable low-impact food production such as vertical farming, the new Aberystwyth Innovation Campus provides facilities and training for small businesses.

Projects

Projects

Links to some of our current and past projects can be found here.

Principal Investigators

Principal Investigators

Picture Name Email Telephone
Prof John Doonan jhd2@aber.ac.uk +44 (0) 1970 823080
Dr Matthew Hegarty ayh@aber.ac.uk +44 (0) 1970 622284
Dr Andrew Lloyd anl50@aber.ac.uk

Publications

Publications

Evershed, D, Durkan, EJ, Hasler, R, Corke, F, Doonan, JH & Howarth, CJ 2024, 'Critical Evaluation of the Cgrain Value™ as a Tool for Rapid Morphometric Phenotyping of Husked Oat (Avena sativa L.) Grains', Seeds, vol. 3, no. 3, pp. 436-455. 10.3390/seeds3030030, 10.3390/seeds3030030
Balouri, C, Poulios, S, Tsompani, D, Spyropoulou, Z, Ketikoglou, M-C, Kaldis, A, Doonan, JH & Vlachonasios, KE 2024, 'Gibberellin Signaling through RGA Suppresses GCN5 Effects on Arabidopsis Developmental Stages', International Journal of Molecular Sciences, vol. 25, no. 12, 6757. 10.3390/ijms25126757
van de Koot, WQM, Msonda, J, Olver, OP, Doonan, JH & Nibau, C 2024, 'Variation in Water-Holding Capacity in Sphagnum Species Depends on Both Plant and Colony Structure', Plants, vol. 13, no. 8, 1061. 10.3390/plants13081061
Yang, W, Doonan, JH, Guo, X, Yuan, X & Ling, F 2023, 'State-of-the-art technology and applications in crop phenomics, volume II', Frontiers in Plant Science, vol. 14, 1195377. 10.3389/fpls.2023.1195377
Romero-Vergel, AP 2023, 'TURION: A physiological crop model for yield prediction of asparagus using sentinel-1 data', European Journal of Agronomy, vol. 143, 126690. 10.1016/j.eja.2022.126690
Nibau, C, Van De Koot, W, Spiliotis, D, Williams, K, Kramaric, T, Beckmann, M, Mur, L, Hiwatashi, Y & Doonan, JH 2022, 'Molecular and physiological responses to desiccation indicate the abscisic acid pathway is conserved in the peat moss, Sphagnum', Journal of Experimental Botany, vol. 73, no. 13, pp. 4576-4591. 10.1093/jxb/erac133
Ghahremani, M, Williams, K, Corke, F, Tiddeman, B, Liu, Y, Wang, X & Doonan, JH 2021, 'Direct and accurate feature extraction from 3D point clouds of plants using RANSAC', Computers and Electronics in Agriculture, vol. 187, 106240. 10.1016/j.compag.2021.106240
Hamidinekoo, A, Garzon Martinez, GA, Ghahremani Boozandani, M, Corke, F, Zwiggelaar, R, Doonan, J & Lu, C 2020, 'DeepPod: A convolutional neural network based quantification of fruit number in Arabidopsis', GigaScience, vol. 9, no. 3, giaa012. 10.1093/gigascience/giaa012
Mendanha, T, Rosenqvist, E, Nordentoft Hyldgaard, B, Doonan, JH & Ottosen, CO 2020, 'Drought priming effects on alleviating the photosynthetic limitations of wheat cultivars (Triticum aestivum L.) with contrasting tolerance to abiotic stresses', Journal of Agronomy and Crop Science, vol. 206, no. 6, pp. 651-664. 10.1111/jac.12404

More publications on the Research Portal »