Agricultural Growth using Remote-sensing, IoT, Satellite and Autonomous Telecomm and Technology

Project Details

Description of project aims

The SAMS team contribute to the innovative, cross-disciplinary £5M project , lead by SuSeWi was supported in May 2020 by INNOVATE UK: ISCF Future food production systems. We are combining advanced bio-technology, marine microbial ecology, bio-geochemistry, ocean and atmosphere physics with numerical modelling and AI. Our efforts are concentrated on a dedicated study of the eastern periphery of Atlantic subtropical gyre and particularly the upwelling of cold, deep seawater to the surface under favourable winds. The primary task assigned to the SAMS team is to develop an automated prediction system which is capable of predicting the exact timing of arrival and duration of stay of nutrient-rich upwelled seawater near coastal intake pumps. These forecasts then is used to anticipate intake seawater quality and to approximate its nutrient content which then is integrated with other data streams (satellite and in-situ) within the proposed ‘Digital Twin’ (DT) of a large micro-algae production farm.

The short-term high-resolution environmental forecasting capability is the crucial and critical component for training (Machine Learning) of the AI-system to anticipate and perform necessary operations of the on-site in-pond IoT networked machinery to enhance overall sustainable algae productivity and resource efficiency.

Layman's description

AGRI-SATT amplifies SuSeWi's ultra-low carbon footprint micro-algal farms developed in coastal deserts with (a) advanced mobile network enabled remote-controlled seawater gates, paddlewheels and nutrient dosing pumps; (b) cutting-edge 'in-pond' Fast Repetition Rate fluorometers, which assess the physiological state and growth of our sustainable biomass, essential for natural protein-rich food and feed production in the UK and at global scale.

To achieve maximum algal growth and optimised AI-enabled system operations requires reliable short-term prediction of seawater quality at the entrance of intake pipes. Coastal upwelling of nutrient-rich deep seawater is sensitive to even small fluctuations in tropical trade winds in the presence of high mountains and tall islands. Existing regional basin-scale models have insufficient grid spacing (of kilometers) to resolve very-nearshore currents structure. SAMS contribution is to localize and couple the most advanced fine-scale atmospheric and ocean circulation models to drive sequentially nested domains of FVCOM with desired resolution at tens of meters. Such a modeling system will allow us to estimate the quality of seawater at specific times, places, and to contribute towards AI decision-making for enhanced productivity.

Key funding - quote all funding agency(s)

SAMS team is engaged in development of regional Ocean-Atmosphere coupled forecasting system for short-term prediction of coastal upwelling in the Tropical Atlantic
Short titleAGRI-SATT
Effective start/end date1/09/2031/03/23

Collaborative partners


  • Ocean-Atmosphere coupling, forecasting, coastal, upwelling


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