An unmanned aerial vehicle (UAV), or drone, that acquires aerial imagery in the visible and near infrared spectrum, allowing it to better identify stressed crops, is being piloted over the summer by scientists at Fera.
Many UAVs have the ability to acquire imagery in the visible spectrum during a single flight, but this only provides an aerial view of the crops and some types of plant stress may be visually discernible only when it is fairly well advanced, when it may be too late for farmers and growers to act.
When both visible imagery and imagery in the near infrared spectrum are acquired simultaneously, however, it is possible to formulate vegetation indices maps showing healthy crops as bright green and unhealthy ones as red – even those which, to the naked eye, don’t appear to be affected. Imagery taken in this way can be linked to precise geospatial coordinates resulting in the production of highly accurate mapping.
Paul Brown, GI Remote Sensing Scientist, at Fera explains: “The UAV, a new investment by Fera, will initially be used to study crop stress in Yorkshire and beyond. So, over the summer, we’ll be working with farmers and growers to help develop methods of analysing crop stress and plant identification. We’re keen to hear from farmers with other ideas about how the UAV might make things easier, for example, do you need it to map the advance of black grass, to point out areas of water stress, for the precision application of pesticides and fertilisers or will highly accurate mapping benefit the farm? Working with farmers will enable us to develop a service that really reflects farmers’ needs.”
Though imagery will be a key part of the UAV’s methods of sensing problems in crops, it will also be able, in the future, to carry other scientific equipment, such as the spore and pathogen traps currently being developed by Fera. In the longer term it can also be used in other ways; to assist water companies in spotting leaks, quickly and easily identifying issues affecting gas and electricity supply or finding missing livestock.
