Augmented intelligent platforms (AIPs) to improve the environmental and financial sustainability of small and medium (SME’s) in agriculture.
De Montfort University Leicester (DMU) is working with partner university EAFIT in Colombia to apply risk and machine learning concepts to improve crops’ environmental and financial sustainability, preventing vast plant crops being lost to disease.
The Royal Academy of Engineering’s Distinguished International Associates (DIA) programme is supporting the project, bringing together EAFIT University in Colombia and the Institute of Artificial Intelligence, through the Research in Societal Enhancement group at DMU with industry partners Avocado Crop Praga, UNIBAN, and UNIPALMA.
Our research will help small farmers get better access to finance and insurance and make small-scale agriculture more sustainable. It will as well contribute significantly to global food security by enabling better distributed and socially resilient farming practices.
Professor Mario Gongora
Our main goals are to:
To create financial and sustainability metrics to assess the performance of machine and deep learning models in characterising threats to the health of three different crops (banana, avocado, oil palm)
To launch a spin-out to improve the management of threats to crop health in small and medium agricultural enterprises
To promote the development of precision agriculture from engineering supported by undergraduate programmes in agricultural engineering, computer science engineering, and a masters in risk management and in agribusiness
To achieve visibility of the project through the Royal Academy of Engineering to achieve the continuity of research processes in this field and the funding of the spin-out activities
Parametric insurance is set to revolutionise precision agriculture in the next decade through risk management. According to a World Bank study, parametric insurance could help 50 million smallholder farmers in developing countries to protect their crops against climate risks, which could increase agricultural production by 10 per cent and reduce rural poverty by 20 per cent.
