To provide growers with the tools needed to adopt site-specific weed management (SSWM) strategies as a result of a commercially viable weed ID and mapping system being demonstrated.
Key messages
Commercial viability has been difficult to demonstrate.
Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.
Lead research organisation
Society of Precision Agriculture Australia
Host research organisation
N/A
Trial funding source
GRDC SPAA114
Trial funding source
SAGIT
Related program
N/A
Acknowledgments
Personnel who participated in the project: Sam Trengove, Stuart Sherriff, Hermann Leithold (Agricon), Steffen Müller (Agricon), Adelaide University Weed Science Research Group, Co-operators - James Venning, Bill Trengove, Kenton Angel, Scott Weckert, Rod Sherriff, Neville Adams and Matt Dare, Hart Field Site Group.
Trial source data and summary not available Check the trial
report PDF for trial results.
Climate
Derived climate information
No observed climate data available for this trial. Derived climate data is
determined from trial site location and national weather sources.
Mildura VIC
SILO weather estimates sourced from https://www.longpaddock.qld.gov.au/silo/
Jeffrey, S.J., Carter, J.O., Moodie, K.B. and Beswick, A.R. (2001). Using spatial interpolation to
construct a comprehensive archive of Australian climate data , Environmental Modelling and Software, Vol
16/4, pp 309-330. DOI: 10.1016/S1364-8152(01)00008-1.
