The Weed Manager’s Guide To Remote Detection
The Weed Manager’s Guide To Remote Detection
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Georeferencing data sets is a crucial step in enhancing the accuracy and reliability of weed detection imagery. This process becomes particularly significant for subsequent analyses and the validation of detections. In optimising the accuracy and usability of weed detection imagery, particularly when working with multispectral (MS) and hyperspectral (HS) data, georeferencing emerges as a critical component.
An effective strategy in this regard is the integration of high-resolution Red Green Blue (RGB) imagery as the base map for georeferencing MS and HS datasets. This approach proves especially advantageous when pinpointing specific targets (weed species) for model training, as the high-resolution RGB imagery streamlines labeling and verification processes. Georeferencing remains crucial even after model predictions, as it enables a seamless transition from digital analyses to real-world verification in the field. Once the model generates predictions, having georeferenced information allows for precise localisation of identified targets on the ground. This spatial context is invaluable during field visits, as it provides clear coordinates that can guide researchers or professionals to the exact locations flagged by the model. For a great description of the importance of georeferencing datasets, and the differences between the four approaches used for georeferencing (i.e. Standard Drone GPS, Ground Control Points, Real-Time Kinematic Positioning (RTK) and Post-Processing Kinematic Positioning (PPK)) head to the Geo Nadir website
For more information and support about how to use state RTK GNSS networks for RTK and PPK positioning in your area, contact your state government department of land and surveying for more information on how to get started - examples are provided here:
Australia-wide geographic positioning information can also be found at: