Manually measuring the characteristics of plants is slow, laborious and expensive. Remote sensing technologies, coupled with advanced analytics, offer the promise of faster and more-accurate data collection to improve the speed at which plant breeders can bring better cultivars to the market, say scientists at Purdue University.

The scientists have developed a set of high-resolution red-green-blue cameras, hyperspectral imaging, light detection and ranging, and thermal-infrared sensors. Together they can be mounted to on-ground farm machinery or unmanned-aerial vehicles to detect plant height, canopy structure, plant architecture, biomass yield and more.

The U.S. Department of Energy may possibly use the sensing platform to advance sorghum as a feedstock for cellulosic biofuels. The technology could be used for many other feed, fiber and biofuel crops.

Mitch Tuinstra, a professor of plant breeding and genetics at Purdue University, said, “Next-generation phenotyping technologies enable plant scientists and plant breeders to collect data automatically by remote-sensing and processing it using computational algorithms.”

Tuinstra and colleagues founded GRYFN, a startup aimed at commercializing the sensing technology for plant-breeding programs. Ag Alumni Seed, Indiana’s foundation-seed company and a nonprofit Purdue affiliate, is working with GRYFN to see how the technology could reduce costs in its breeding program.

The researchers’ next steps will involve developing more-advanced models that incorporate weather information, tillage practices, nitrogen availability and data acquired by the sensors.

The project is referred to as the Transportation Energy Resources from Renewable Agriculture or TERRA program. The U.S. Department of Energy Advanced Research Projects Agency–Energy awarded in 2015 a $6.6 million grant for the project. The agency is funding a second phase of the project at $4.5 million. It's being matched with another $4.5 million by strategic partners from AgAlumni Seed, Corteva Agriscience, Beck’s Hybrids and Headwall Photonics.

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