13November 2026, Gatton, AU: Researchers at the University of Queensland have built a sensor-equipped robot to measure something breeders have long struggled to see directly: how different wheat varieties draw water from the soil as a crop grows, and at what depth and growth stage they do it.
The university’s Queensland Alliance for Agriculture and Food Innovation (QAAFI) published details of the work on November 3, 2026, crediting researcher Dr Raul Gimenez with developing the measurement system. Field trials ran at UQ’s Gatton campus, southwest of Brisbane, across the 2024 and 2025 growing seasons, assessing 36 wheat varieties sown at different planting densities.
The core tool is an autonomous, high-clearance machine the team calls the RootBot, fitted with electromagnetic induction sensors that can read soil moisture at different depths without disturbing the root zone. Gimenez’s team paired the ground-based readings with drone-based sensing to track how water use changed across a field and over the course of a season. Together, the system lets researchers build a picture of not just how much water a wheat variety uses, but when in its growth cycle it draws on reserves and how deep in the soil profile it reaches to find them, data that is difficult to collect at this scale using manual soil sampling.
“We found cultivars differed in their total water use, when they used water, and the depth they took it from,” Gimenez said in the university’s release. The trials also pointed to a trade-off tied to planting density: sowing wheat more densely tended to increase early-season water use, radiation capture and canopy growth, but the release cautions that pattern can leave less water available later in the season, when demand during grain fill matters most for final yield. The university’s release did not publish the underlying water-use figures or name the specific varieties tested, describing the findings only in qualitative terms.
Breeders have traditionally selected for drought tolerance using above-ground indicators such as leaf rolling, canopy temperature or final yield under water stress, measures that reveal how a plant copes with limited water but not the root-level mechanics behind it. A tool that can routinely characterize water-use timing and depth across large numbers of breeding lines could let programs select varieties suited to specific rainfall patterns. For example, a region with reliable early-season rain but a dry finish might favor a variety that conserves water for grain fill, while a region with the opposite pattern might favor the reverse strategy.
Beyond wheat, into other crops and nitrogen use
Gimenez said the same sensing approach, built around the RootBot and drone platform, has already been deployed in sorghum, chickpea and barley trials, with canola and maize planned next, suggesting the university sees the measurement system itself, not just the wheat results, as the more broadly useful output of the project. The team is also working to adapt the tool to measure nitrogen use by matching where a crop’s active roots are drawing water with where nitrogen is available in the soil, a step toward breeding for efficient use of both resources together.
The work is funded by Australia’s Grains Research and Development Corporation alongside the University of Queensland, with QAAFI itself established and supported by the state’s primary industries department. Gimenez’s team is running parallel trials with Agriculture Victoria, Victoria’s environment department and Western Australia’s Department of Primary Industries and Regional Development, testing whether the water-use patterns identified at Gatton hold up across different soils and climates. Gimenez said he expects variety rankings to shift somewhat from site to site given those differences, which is itself useful information for matching varieties to specific growing regions.
The findings were presented at the Agronomy Australia Conference in Darwin and at ACB Breeders Week on the Gold Coast. The university’s release did not cite a peer-reviewed publication, describing the work instead as a research platform that Gimenez’s team is now focused on simplifying for use by commercial breeders and agronomists.
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