Drone technology & research

Image Surveys, working with partners in a consortium, is now at the testing stage of an AI based system designed to help UK farmers meet the growing demands placed on them.

Farmers must perform for at least two masters at once. On one side are the major players in the food chain — the large UK supermarkets and processors — with their increasingly strict requirements for quality, efficacy and sustainability. On the other is the regulatory and financial reporting framework of DEFRA in England (and the devolved equivalents, including the Welsh Government's farming schemes), which farmers must satisfy to qualify for subsidies. That burden is already heavy and likely to grow as the drive to cut carbon, raise sustainability and deliver Biodiversity Net Gain (BNG) takes hold — all against a changing climate, more frequent adverse weather, and with little headroom to raise prices.

The programme brings three things together:

• Survey-grade baseline mapping. We capture a precise 2D and 3D baseline of the farm, giving an accurate, measurable record of the land to build everything else on.
• Live data collection. That baseline is continually enriched with automatically gathered data — wildlife and habitat records, land use, local weather, soil information and satellite imagery — alongside relevant market information.
• AI interpretation. AI turns this into clear reports and a simple dashboard, so farmers can see what matters, weigh up options and plan ahead using up-to-date information — without adding to the working day, because the data is collected and processed by the system rather than by hand.

Underlying the brief is that the system must be easy to use and intuitive to the busy farmer or estate manager using the system and be flexible as new rules and requirements come in to play.

As the programme develops, the system is designed to adapt and learn — in time generating "heat maps" that flag areas needing urgent attention, and crop maps that help apply fertilisers and other inputs more precisely, using less where less is needed to cut both cost and environmental impact.

This work is ongoing, and we will share more as testing progresses.