Hacking The Second Green Revolution​

PI - Paul Scholefield (NERC Centre For Ecology And Hydrology)
Food Side Co-Investigators - Toby Waine (Cranfield University)
STFC Side Co-Investigators - Yonghuai Liu (Aberystwyth University), Rene Breton (University Of Manchester), Joseph Fennell (University Of Manchester), Hugh Mortimer (STFC RAL Space)

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Understanding crop yield prediction and crop productivity requires a scaling of measurement and monitoring for a global approach. While remote sensing techniques provide high temporal resolution and broad coverage, the hyperspectral imaging provides data to distinguish the growth conditions and species of plants. Here the key issue is how to combine these two kinds of data for such purposes of plant growth monitoring and yield prediction at high spatial resolution such as in small scale crop production. While each has its own characteristics,this project intends to bring STFC data scientists together to analyse both hyperspectral and LIDAR data to calibrate plant canopy reflectance. In return, the reflection properties can be used to predict the plant growth conditions and ultimately yield. Such projects involve several stages of data fusion and analysis such as data capture, noise removal/data normalization, data modelling, model testing and improvement. Various tools and methods will be implemented and developed for noise removal, data normalization, 3D modelling, visualization, skeletonization, trait measurement, association of light reflection and plant growth conditions and yield, and for the statistical analysis of the effects of different features within the models. New data will be collected in due course for testing during model development. The research findings will be presented in international conferences and workshops and published in the international journals.

​2018 AWARDED SCOPING PROJECT - More scoping projects