This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A drone lifts off over a paddock at Boorowa Agricultural Research Station, the breeze from its rotors setting the tall, uncut grass below rippling. At the controls, Stuart Brown, a senior research technician at CSIRO, keeps a careful eye on the attached LiDAR (Light Detection and Ranging) sensor as it fires an invisible lattice of laser pulses at the grass, building a three-dimensional map of the paddock, one small cube of space at a time.
Once the flight is complete, Dr. Yusuke Kikuchi, a digital agronomist at CSIRO, leads the field team back to the same paddock—armed not with a drone but with shears and scales. They cut the pasture by hand, square meter by square meter, and weigh what they find.
It's precise, painstaking work—the kind producers have had to rely on for generations. "Graziers need to know the exact amount of living pasture biomass," Kikuchi said. "Get that figure right, and the benefits ripple outward: better-fed livestock, less risk of overgrazing and more efficient use of every paddock." That's not a marginal gain for an industry so reliant on strategic grazing decisions, Kikuchi explained—it is a decision that will affect everything downstream, from animal growth rates to environmental impact.
Everard Edwards, principal research scientist and team leader for Agrisensing Analytics at CSIRO, has watched producers try to solve this problem for years. "Pasture biomass tends to be estimated in a couple of ways," he said. At one end is freely available satellite data, feeding tools that use a measure called NDVI to estimate plant growth across a property.
NDVI stands for Normalized Difference Vegetation Index, a measure derived from satellite or sensor imagery that tracks how plants reflect and absorb different wavelengths of light. "It's considered a reliable indicator of how much live green vegetation is present," Edwards said, "but that accuracy drops away in dense canopies, where the index quickly saturates, an issue compounded by the lack of plant height information from satellites." At the other end of the scale are hand measurements such as the rising plate meter, a tool that estimates pasture height based on a calibration curve built from many hand-cut estimates. "Single readings are typically highly accurate, but the problem is that they're single-point and labor-intensive," Edwards said, adding, "We are currently missing a method in between these two ends of the scale." This was the challenge that Meat & Livestock Australia (MLA) identified, describing a demand from producers for something with the reach of satellite data and the precision of a plate meter.
The organization asked Edwards to explore how CSIRO's LiDAR expertise could be applied to pasture biomass. It helped that Edwards already knew where to start. CSIRO had significant expertise in using LiDAR, a sensor developed decades ago by the aviation industry that fires hundreds of laser pulses per second, using the time they take to bounce back to build a three-dimensional map.
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