Crop Science Centre

Driven by impact, fuelled by excellence

Ji Zhou

Ji Zhou
2018-present, Pool of experts and invited committee member for BBSRC Committee B; interview panellist for UK Research and Innovation (UKRI) projects; 2019-presnt, Fellow of the Royal Society of Biology (FRSB), UK; 2018-presnt, board member for the PhenomU

Head of Data Sciences at NIAB


 Dr Ji Zhou leads NIAB’s Data Sciences Department since early 2020. His department focuses on developing multi-scale indoor and in-field plant phenotyping through satellite, Agri-Drones, LiDAR, self-developed low-cost remote sensing, Videometer and Opera HCS system. Utilising the large-scale and multi-dimensional phenotyping datasets acquired from different plant organs, ranging from cell and seed to plant and population, Ji and his China-UK lab develop open-source phenotypic analytic solutions to address yield, quality and disease related challenges using Artificial Intelligence (AI, machine learning and deep learning techniques), computer vision, Internet of Things (IoT) and big data analytics for UK’s key agricultural and horticultural crops such as wheat, Brassica, strawberry, lettuce and orchard fruits (e.g. apple). Some impactful work from his lab includes high-throughput 3D crop mapping using LiDAR (CropQuant-3D), low-cost distributed phenotyping platforms (CropQuant, CropSight and Leaf-GP), large-scale aerial crop analytic software (AirMeasurer and AirSurf), automated AI seed screening system (SeedGerm), and cellular trait analysis packages (PDQuant, CalloseMeasurer and StomataMeasurer). Ji’s department collaborates closely with leading research groups in the UK, Japan and China (e.g. the University of Cambridge, the John Innes Centre, the Kyoto University and the Chinese Academy of Sciences), through which joint research efforts are made to assess plant performance in the context of global climate change that quantifies genetic gain, trait stability, yield prediction and genotyping-to-phenotyping linkage from larger populations and across different sites. Ji also built a close relationship with leading industrial companies such as Syngenta and China Seeds.

Prior to his career in Cambridge, he was a project leader at Norwich Research Park, a post-doctoral research fellow at The Sainsbury Laboratory (TSL), and worked in industry for nearly a decade, initially as a bilingual IT professional in Shanghai, then a systems analyst and a project consultant at the Aviva group, Norwich UK. Since his PhD in computer science at the University of East Anglia (UEA) in 2011, Ji has published over 25 research articles on top journals such as Nature, Nature Plants, Plant Cell, New Phytologist and Plant Physiology, 3 book chapters, and 3 IEEE/ACM conference proceedings, many of which Ji was a corresponding or leading author. From 2016 onwards, his research has been cited over 1,550 times, with an i10-index over 21. Ji’s academic work has also led to successful patentable inventions, e.g. UKIPO patent (GB 2553631) was granted and licensed in 2019. He holds a full professorship at the China-UK Crop Phenomics Research Center, Nanjing Agricultural University (NAU, a leading agricultural and crop research university in China), an honorary senior lecturer of Computer Vision at UEA, and an associate editor for reputable journals such as the Crop Journal, Plant Phenomics and Horticulture Research.



Publications (* Corresponding or co-corresponding author):


    1. Zhu Y, Sun G, Ding G, et al., Ober E, Zhou J* (2021). Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural responses in wheat. Plant Physiology, July: kiab324.
    2. Colmer J, O’Neill C, Wells R, et al., Penfield S*, Zhou J* (2020). SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination. New Phytologist, 228(2): 778-793.
    3. Bauer A, Bostrom A, et al., Kirwan J, Zhou J* (2019). Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: a case study of lettuce production. Horticulture Research, 6(1):1-12.
    4. Alkhudaydi T*, Reynolds D, Griffiths S, Zhou J*, De La Iglesia B (2019). An exploration of deep learning based phenotypic analysis to detect spike regions in field conditions for UK bread wheat. Plant Phenomics, (736876): 1-17.
    5. Reynolds D, Baret F, Welcker C, et al., Zhou J*, Tardieu F* (2019). What is cost-efficient phenotyping – optimizing costs for different scenarios. Plant Science, 282(May): 14-22.
    6. Reynolds D, Ball J, Bauer A, Griffiths S, Zhou J*. (2019). CropSight: a scalable open data and distributed data management system for crop phenotyping and IoT based crop management. GigaScience, 8(3):1-11.
    7. *, Tardieu F, Pridmore T, . 植物表型组学: 发展、现状与挑战[J]. 南京农业大学学报, 2018, 41(4): 580-588.
    8. Watson A, Ghosh S, Williams M, Cuddy WS, Simmonds J, Rey M-D, Hatta MAM, Hinchliffe A, Steed A, Reynolds D, et al. (2018). Speed breeding: a powerful tool to accelerate crop research and breeding. Nature Plants, 4(1): 23-29.
    9. Zhou J*, Applegate C, et al. (2017). Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat. Plant Methods, 13:117.
    10. Bevan MW, Uauy C, Wulff BH, Zhou J, Krasileva K, Clark MD (2017). Genomic innovation for crop improvement. Nature, 543:346–354.   
    11. Faulkner C#, Zhou J#, et al., Eckes P, Robatzek S. (2017). An automated quantitative image analysis approach for identifying microtubule patterns. Traffic, 11(2): 683-93.  
    12. Meteignier LV, Zhou J, Cohen M, et al., Moffett P (2016). NB-LRR signalling induces translational repression of viral transcripts and the formation of RNA processing bodies through mechanisms differing from those activated by UV stress and RNAi. Journal of Experimental Botany. 67(8): 2353-66.
    13. Zhou J, Spallek, T., Faulkner, C., Robatzek, S. (2013). CalloseMeasurer: a novel software solution to measure callose deposition and callose patterns. Plant methods, 8: 49.
    14. Beck, M., Zhou J, et al., Robatzek, S. (2012). Spatio-temporal cellular dynamics of Arabidopsis flagellin receptor reveal activation status-dependent endosomal sorting. Plant Cell, 24: 4205–19.