A highly accurate, fully-automatic, ground-based weed mapping system enabling growers to target key aspects of their black-grass management with great precision should be available for commercial use within the next two years, according to the latest report from the eyeWeed project co-funded by Innovate UK.
What is more, it is set to form the basis of a season-long platform for monitoring and mapping a growing range of valuable precision farming parameters. The project consortium of commercial and academic partners led by Agrii and including the University of Reading, Concurrent Solutions, Knight Farm Machinery, Syngenta, and Patchwork Technology has been successfully turning the University’s original proof of concept into a practical system for reliable farm black-grass mapping use over the past five years.
The advanced eyeWeed prototype being put through its paces this season comprises six spray boom-mounted cameras linked to sophisticated computer software that can accurately map black-grass patches within wheat crops in mid-June at much higher resolution than is possible with current aerial imagery.
Importantly too, it can produce black-grass infestation maps in real-time without any extra operations and with none of weather-related limitations of drone-based or satellite detection.
“We have developed the system in detailed work on a number of fields across several farms over three full seasons to map black-grass patches with great accuracy in wheat during T3 spraying,” explained Agrii decision support services manager, John Lord who leads the project.
“Unlike other approaches, no field walking is required to ‘ground truth’ the weeds identified and all the work can be done as part of normal field operations, whenever the weather is suitable for spraying.
“The maps we are producing can be automatically employed in management zoning as part of precision farming systems for variable seed rates as well as targeted pre-, peri- or post-em patch spraying.
“In all but the worst cases, black-grass is concentrated into well-defined field patches. So it makes sense to concentrate higher seed rates and the most robust autumn herbicide stacks and sequences on these wherever possible, rather than employing them across the whole area. Not least because a patch-spraying programme can increase your gross margin by £50/ha or more as well as delivering valuable environmental benefits at a time when both are becoming increasingly important to most growers.
“The maps can also be used for automatic summer patch-spraying with glyphosate, if necessary, and for greater precision in autumn cultivations and stale seedbed management where feasible.” John Lord added. “At the same time, they provide the best possible field-by-field records for planning future black-grass management and tracking the success of control strategies, not to mention the development of resistance.”
John Lord explains that the eyeWeed data will be made available to growers and their agronomists through the Agrii Precision Services (APS) portal with the company’s SoilQuest soil mapping, MetQuest weather station data and other precision agronomy and decision support services. While black-grass management has been its initial driver, he stresses that the system emerging from the project offers huge potential for use in a range of other mapping and crop management applications linked to sprayer work throughout the year.
Currently being explored by the team are the summer use of the camera system to map black-grass in barley and development of alternative algorithms to identify other weeds such as ryegrass, wild oats and brome in wheat.
“We’ve also designed the unit to take another sensor at each camera housing, so it could be employed to provide NVDI maps for variable nitrogen application or any of a number of other sensing technologies under development too,” he pointed out.
“Essentially, we’re producing a ground-based multiple-sensing platform that can be added to modern sprayers to enable the most accurate crop monitoring and mapping throughout the season as part of their normal operations.