Dr Ji Zhou

Head of Data Sciences
07786 363818

Dr Ji Zhou leads NIAB’s (Cambridge Crop Research) Data Sciences Department since his appointment in January 2020. He also holds a professorship at the China-UK Crop Phenomics Research Center, Nanjing Agricultural University (NAU, China) and is an honorary senior lecturer of Computer Vision at the University of East Anglia (UEA, UK).

Between 2015 and 2019, he led his laboratory at the Norwich Research Park (NRP) focusing on developing multi-scale crop phenotyping, phenotypic analysis, and AI-related algorithmic research and development for UK’s key crops such as wheat and lettuce. To address challenging food security questions in complex environments, Ji and his lab have developed a range of crop research tools and resources using computer vision, UAVs, Internet of Things, remote sensing, machine-learning and deep-learning techniques, and high-content screening systems (HCS).

Since his PhD in computer science at UEA in 2011, Ji has published over 23 research articles on top journals such as Nature, Plant Cell, Nature Plants, Horticulture Research and Traffic, 3 book chapters, and 3 IEEE/ACM conference proceedings, most of which Ji was a leading author. From 2015, his work has been cited over 950 times, with an i10-index over 14. Ji’s academic work has also led to successful patentable inventions, UKIPO patent (GB 2553631) granted in 2019 and was licensed in 2019. He is an associate editor for the Crop Journal, Plant Phenomics, and Horticulture Research. In 2019, he was selected as a Fellow of Royal Society of Biology (FRSB). 

In collaboration with leading research groups in the UK, Japan, and China (e.g. the John Innes Centre, the Kyoto University, and the Chinese Academy of Sciences), Ji’s key research areas include crop phenomics, AI-based solutions to assess crop performance associated with genetic gain, trait stability, and yield prediction. During his academic career, Ji has built a close relationship with leading industrial companies, including Syngenta, G’s Growers, and China Seeds. Prior to his career in academia, he worked in industry for nearly a decade, initially as a bilingual IT professional in Shanghai, China, then a systems analysis and a project consultant at Norwich Union (Aviva UK). He did his post-doctoral research at The Sainsbury Laboratory (TSL), during which time he was led the development of high-throughput bioimage informatics using HCS, confocal microscopes and hyper-spectral cameras.

Recent publications (* corresponding author)

Full publication list on Google Scholar

  • Bauer A, Bostrom A, Ball J, Applegate C, Laycock S, Moreno Rojas S, 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.
  • Ding G, Xu H, Wen M, Chen J, Wang X*, Zhou J* (2019). Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat. Journal of Agricultural Big Data 1(2): 19-31.
  • 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.
  • Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M, Zhou J*, Tardieu F* (2019). What is cost-efficient phenotyping – optimizing costs for different scenarios. Plant Science. 282(May): 14-22.
  • 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.

Additional information