Prof Ji Zhou (周济) is Head of the Data Sciences Department at NIAB. He is an expert in multi-scale plant and crop phenotyping, computer vision (CV) based trait analysis, IoT sensing, and artificial intelligence (AI) modelling. By combining phenotyping, genotyping and environmental data, Ji and his group are developing open-source hardware and software to address yield, quality, and disease related challenges for UK’s key agricultural and horticultural crops such as wheat, Brassica, strawberry, and orchard fruits. Since his appointment at NIAB in early 2020, he contributes to the global plant and crop research community, including AI and CV powered analytic solutions such as SeedGerm, CropQuant-3D, AirMeasurer, AirSurf, CropSight, and YieldQuant-Mobile, ranging from cell and tissue to plant and population.
His group collaborates closely with leading research groups around the world, including UK (e.g. the University of Cambridge and the John Innes Centre), France (e.g. INRAe and the University of Angers), Japan (the Tokyo University), and China (the Chinese Academy of Sciences, the Chinese Academy of Agricultural Sciences, and the Zhejiang University). Through joint research efforts, challenges in crop and plant research such as the quantification of genetic gain, trait stability, yield and quality prediction, genotype-to-phenotype linkage, and the identification of molecular markers to sustain plant performance under complex field conditions are being addressed together. Moreover, Ji works closely with many breeding and growing companies such as Bayer Crop Science, Limagrain, Syngenta and the POME consortium, for both commercial and academic research and development.
Ji is a Fellow of the Royal Society of Biology (FRSB), a core member of PhenomUK, a deputy committee leader for the China-Britain Self-Award scholarship invited by the Chinese Embassy, an associate editor for reputable journals such as Horticulture Research, Plant Phenomics, the Crop Journal, and a guest editor for Plant Biotechnology Journal. Since 2013, he has published 30+ research articles (both English and Chinese) on journals such as Nature, Nature Plants, Plant Cell, New Phytologist and Plant Physiology. Some of his work has led to patentable inventions and commercial licensing with companies in Europe and Far East Asia. Presently, he holds a Professorship (chair) at the Nanjing Agricultural University (a leading agricultural and crop research university in China), course lecturer and project supervisor for MPhil in Crop Sciences and computer science undergraduates at the University of Cambridge.
Prior to joining NIAB, he was a project leader at Earlham, a joint research fellow between JIC and TGAC, and a post-doctoral fellow at The Sainsbury Laboratory (TSL), all of which were based at Norwich Research Park (2011-2019). He 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. He received his PhD in computer science at the University of East Anglia in 2011 with sponsorship from Norwich Union and UEA International Scholarship.
Current projects
POME: Precision Orchard Management (Co-PI)
Duration: November 2023–October 2028
Partners: POME consortium
Funders: Innovate UK
RootMeasurer: developing a cost-effective and in-field corn root phenotyping device to unravel the hidden half of the crop (PI)
Duration: May 2024–July 2028
Partners: University of Cambridge, UK
Funders: Bayer Crop Science (US)
Developing AI to bridge lab and field plant research (PI)
Duration: Feb 2024–September 2025
Partners: University of Angers, France
Funders: BBSRC
Combining cloud- and smartphone-based AI solutions for varietal identification of wheat and potato (PI)
Duration: November 2022-December 2025
Partners: International Potato Center (CIP)
Funders: SeedEqual, One CGIAR
ENSA - GM barley field phenotyping
Duration: Feb 2023–Oct 2025
Partners: Crop Science Centre, University of Cambridge
Funders: ENSA Phase 2
Developing Climate Resilient Crops for Africa: novel AI approaches to detect nematodes (Co-PI)
Duration: Jan 2023–December 2027
Partners: Crop Science Centre, University of Cambridge
Funders: Gray Foundation, The Royal Society
Recent publications (* corresponding author)
- Ding G, Shen L, et al., Zhou J*. (2023) The dissection of Nitrogen response traits using drone phenotyping and dynamic phenotypic analysis to explore N responsiveness and associated genetic loci in wheat. Plant Phenomics, 5:128.
- Sun G, Lu H, et al., Han B*, Zhou J* (2022). AirMeasurer: open-source software to quantify static and dynamic traits derived from multi-seas7on aerial phenotyping to empower genetic mapping studies in rice. New Phytologist, 236: 1584-1604.
- Zhu Y, Sun G, et al., Ober E, Zhou J* (2021). Large-scale field phenotyping using LiDAR and CropQuant-3D to measure structural responses in wheat. Plant Physiology, 187 (2): 716-738.
- Colmer J, 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.
- Bauer A, Bostrom A, et al., Zhou J* (2019). Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: a case study of lettuce. Horticulture Research, 6(1):1-12.
- Reynolds D, et al., Zhou J*. (2019). CropSight: a scalable open and distributed data management system for crop phenotyping and IoT based crop management. GigaScience, 8(3):1-11.