Machine vision or ‘automated digital image analysis’ is an untested approach to assessing soil properties on gravel soils. Digital analysis provides rapid output and has the potential for use in soil science for measuring specific attributes of soil, particularly gravel content which is often only visually assessed. Knowledge on gravel size distribution and association with mineralogy of the soil could provide information on preferential flow of water (and soluble nutrients), susceptibility to compaction and nutrient availability. High gravel content is also linked to a high phosphorus (P) buffering capacity in Western Australia and therefore a higher P requirement.
A low cost approach to sampling inverted soils is required where we have limited knowledge of how crop roots are responding to changes in spatial and temporal nutrient supply as a result of soil disturbance and redistribution of organic matter and other materials from the soil surface.
Funding is provided by Royalties for Regions, with in-kind support from the Department of Agriculture and Food WA and the University of Western Australia.