Date: Wednesday, June 19, 2 – 3:30pm GMT+1
Speaker: By Chungui Lu (Professor of Sustainable Agriculture in the School of Animal, Rural & Environmental Sciences at Nottingham Trent University)
About the Speaker
Chungui Lu is a Professor of Sustainable Agriculture in the School of Animal, Rural & Environmental Sciences at Nottingham Trent University (NTU). He gained his PhD in Plant Molecular Biology at the University of Nottingham. Prior to joining NTU, Chungui is Director of the Centre for Urban Agriculture at UoN. Recently, he has been awarded over £10.5m grants funded by Innovate UK, BBSRC, EU Horizon, and industrials. He leads Sustainable Agriculture and Food Security Research Group at NTU. He has published over 100 refereed papers in the plant nitrogen nutrition, horticulture, plant molecular biology and plant functional genomics including Nature – Systems Biology and Applications, Plant Cell, Plant Physiology and Plant Biotechnology Journal high impact journals.
About this seminar
World agriculture is undergoing a transition to new technological paradigms driven by innovations in sustainability and resource use efficiency. The development of this new paradigm is enabled by the ongoing “smart”, “green” and “growth” revolutions, and new demands of the markets. Sustainable agriculture and vertical farming technologies can enhance economic development through improving crop yields and quality, and offers the opportunity to grow a wider range of crops than possible in conventional systems.
Prof Lu will introduce vertical farming/sustainable agriculture technologies that can help deliver more and nutritious food for a time of disruption, improving global food security and provide local employment opportunities. For example, LED lights, nanoparticle and biostimulants regulate plant growth and food quality, increasing the nutrient-use efficiency and food productivity using SMART technologies (AI, IoT, and 3D-Multispectral Crop Phenotyping. In Lu’s group, agricultural big data obtained from Controlled Environment are used to predict plant growth changes using different machines/deep learning to obtain optimised light “recipes” and nutrient solution “recipes”.