1st Call for Funding: Dr Bo Li – NIAB Investigation of microwave imaging for internal fruit quality and below-ground phenotyping Total Fund Requested – £24,783

1st Call for Funding: Prof Bruce Grieve – Electronic Engineering, University of Manchester Implementation of Active 3D Multispectral Imaging and Photometric Stereo systems as Early Stage Phenotyping Tool for Morphological Features and Biotic Stress Quantification within Complementary Demonstrator Phenotyping Centres (IBERS & P3) Total Fund Requested: £18,480

1st Call for Funding: Dr Ji Zhou – NIAB Developing 2D-3D fusion to enable high-throughput phenotypic analysis of key yield-related traits of bread wheat using cost-effective UAV imagery. Total Fund Requested: £24,940

1st Call for Funding: Dr Wenhao Zhang – Centre for Machine Vision, Bristol Robotics Laboratory, UWE, Bristol. An intelligent, low-cost adaptive 3D multi-scale imaging system for advanced plant phenotyping (PS-Plant+) Total Fund Requested – £24,881

2nd Call for Funding: John Ferguson – University of Nottingham. Project Title Leveraging optical topography for rapid reconstructions of 3D leaf surfaces to address key questions in plant biology. Total Fund Requested – £25,000.00

2nd Call for Funding: Dr Matt Jones – Institute of Molecular, Cell, and Systems Biology University of Glasgow Project Title: Measuring the consequences of abiotic stress in vivo using fluorescent and bioluminescent probes Total Fund Requested – £23 687

2nd Call for Funding: Eric Ober – NIAB Project Title In-field 3D imaging for high resolution morphometric phenotyping in wheat Total Fund Requested –£24,772

2nd Call for Funding: Dr. Michael Pound – Computer Vision Laboratory, School of Computer Science, University of Nottingham Project Title Predicting plant root growth from time-series data using deep learning Total Fund Requested – £24,677.34

3rd Funding Call: Yonghuai Liu, Edge Hill University Project Title DeepEarNet: Accurate Segmentation and Measurement of Cereal Grain Spikes Directly in Point Clouds Using Latest Deep Learning and Domain Knowledge. Total Fund Requested – £24,639

Project Title: Seeing the unseeable –Scaling Electrical Impedance Tomography (EIT) to enable a whole plant, high-throughput, phenotyping demonstrator asset for the Phenom-UK community. Total fund Requested £20,000