| Researcher(s) |
Amanda Cook (SARDI) |
|---|---|
| Year(s) | 2017 |
| Contributor | SARDI Minnipa Agricultural Centre |
| Trial location(s) |
Minnipa Agricultural Centre, SA
Yaninee, SA |
| Related trials |
Barley grass weed density was monitored in three paddocks on upper EP (Minnipa Agricultural Centre (MAC), Heddle's at Minnipa and Wilkins' at Yaninee) using a UAV during the 2017 growing season at three different timings, with paddock transects conducted to verify grass weed density in paddocks.
UAV imagery with appropriate analysis has the potential to identify weed issues in paddocks quickly, reliably and cheaply over large areas.
| Lead research organisation |
SARDI Minnipa Agricultural Centre |
|---|---|
| Host research organisation | N/A |
| Trial funding source | SAGIT S117 |
| Related program | N/A |
| Acknowledgments |
Thank you to MAC, Bruce Heddle and Wilkins families for having the research and monitoring on their property. Thank you to Brett Hay, Katrina Brands and Rochelle Wheaton for helping to monitor the paddocks and processing the samples, and Ben Fleet for information regarding methodology for weed seed monitoring. |
| Other trial partners | Not specified |
| Crop type | Cereal (Grain): Wheat |
|---|---|
| Treatment type(s) |
|
| Trial type | Experimental |
| Trial design | Replicated |
| Sowing machinery |
Grass weeds were assessed in-crop or in pasture at ten GPS points along a transect for crop or weed density, with six counts taken at each sample point. This was used to verify the UAV data captured at three times during the cropping season. Extra sampling points in the paddock were targeted if more information was needed to verify the imagery. The paddock photos were captured on an iPad with 'Avenza Maps' linked to the location in the paddocks. UAV data was captured during the 2017 cropping season on 14 August, 28 September and 3 October. The UAVs used were either a DJI Matrice 100 with both NIR and RGB sensors or a Mavic Pro with RGB sensors. The UAVs were flown at a height of 118 metres. To analyse weed locations at a whole paddock level using the UAV imagery, geospatial analysis tools were used to automate the selection of likely weed infestation areas. A map of the paddock with the UAV coverage was generated from |
|---|---|
| Sow date | Not specified |
| Harvest date | Not specified |
| Plot size | Not specified |
| Plot replication | Not specified |
| Sowing machinery | Not specified |
|---|---|
| Sow date | Not specified |
| Harvest date | Not specified |
| Plot size | Not specified |
| Plot replication | Not specified |
| Rainfall avg ann (mm) | 325mm |
|---|---|
| Rainfall avg gsr (mm) | 241mm |
| Rainfall trial total (mm) | 281mm |
| Rainfall trial gsr (mm) | 155mm |
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.
