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Researchers are helping farmers to predict and prevent problems with their crops using augmented AI technology.

Â鶹ӰԺ Leicester (Â鶹ӰԺ) 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.

Work is led by Professor Juan Alejandro Peña Palacio, a visiting researcher at Â鶹ӰԺ’s Institute of Artificial Intelligence, in collaboration with Professor Mario Gongora, Professor in Applied Intelligent Systems at Â鶹ӰԺ.

Professor Peña was one of 11 people to be awarded up to £10,000 funding from the , to build the engineering talent base around the world.




He will be funded until March 2025 to work on the project and share findings through visits, workshops, conferences and public engagement activities.  



The DIA-supported project brings together EAFIT University in Colombia and the Institute of Artificial Intelligence at Â鶹ӰԺ with industry partners Avocado Crop Praga, UNIBAN, and UNIPALMA.

Its main goals are 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)
• launch a spin-out to improve the management of threats to crop health in small and medium agricultural enterprises
• 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
• 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

So far, the project has been implemented with Unibán for the risk assessment in banana crops, also with small avocado farmers, as well as with the National Chocolates, in the early prediction of monilesis in cocoa crops, a type of fungus which can cause devatasting losses; and Pajonales, an organization based in Tolima, to detect weeds in rice cultivation, and coffee farmers.

Professor Gongora said: “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.”

By using technology such as drones to take images of the crops and machine learning algorithms to process the images and give information about diseases or plant requirements, farmers are able to intervene on a more localised level, reducing pesticide and fertiliser use.

The predictive power of AI will also help to develop cheaper insurance for farmers through parametric insurance, which covers the probability of an event taking place.  

Professor Peña said: “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.”