According to the Food and Agriculture Organization of the United Nations up to 40 per cent of global crop production is lost to pests annually. Each year, plant diseases cost the global economy over $220bn, and invasive insects at least $70bn.
The Midlands-based research will be using strawberries to test the new technology. According to Aston University, the fruit is worth £350m to the UK economy but is vulnerable to potato aphid which has the potential to wipe out an annual harvest.
Crops can be treated with pesticides, but pressure is increasing to find alternatives due to their environmental impact.
One method is to use integrated pest management (IPM) to create an early warning system. It monitors plants for build-up of insects and diseases rather than spraying plants with chemicals, but so far it is said to have proven unreliable and expensive.
The new project uses recent developments in photonics technology that can analyse low levels of volatile organic compounds (VOCs) emitted by plants, which indicate their health, coupled with machine learning hardware.
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In a statement, Professor David Webb of Aston Institute of Photonic Technologies (AIPT) said: “Better invertebrate pest and plant disease monitoring technologies will significantly help cut crop losses.
“However most electronic noses use electrochemical sensors, which suffer from sensitivity issues, sensor drift/aging effects and lack specificity.
“We intend to address this by building on the fast-moving technology of photonics - the science of light - whilst collaborating with scientists in other disciplines.”
The 12-month project is to receive £200,000 from the Biotechnology and Biological Sciences Research Council (BBSRC) and the Natural Environment Research Council.
Dr Joe Roberts from Harper Adams University said: “With the projected increase in the global population there is increasing pressure on the agricultural sector to achieve higher crop yields.
“Reducing crop losses within existing production systems will improve food security without increasing resource use.”
Dr Roberts continued: “We intend to establish an interdisciplinary community of agricultural science, optical sensing and machine learning experts to develop novel plant health monitoring platforms that enhance agricultural production through localised pest and disease monitoring to detect hotspots.”
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