High resolution imaging sharpens selection decisions in plant breeding

"Crop consistently respond to their environment... but many of those responses are not visible by the human eyes. By measuring chlorophyll content, canopy temperature and spectral reflectance, researchers can identify subtle physiological changes before visual symptoms appear in the field."

"Each dataset is geo-referenced using RTK GPS, allowing researchers to pinpoint exact field locations and generate thousands of measurements in a single pass, creating what Singh describes as 'a digital twin or detailed signature of your field trial.'"

"Digital phenotyping allows breeders to screen more genotypes, evaluate multiple traits simultaneously and capture repeated measurements across environments, strengthening selection decisions and accelerating the path from genetic discovery to commercial release."

Remote sensing technology equipped with visible, multispectral, hyperspectral, thermal and LIDAR sensors captures high-resolution crop data processed through artificial intelligence and machine learning. These systems detect physiological changes invisible to the human eye by measuring chlorophyll content, canopy temperature and spectral reflectance. UAV-based imaging has identified nitrogen deficiencies in canola and wheat before visible yellowing appears, and differentiated herbicide-resistant kochia biotypes before injury symptoms developed. Geo-referenced datasets create detailed field signatures enabling thousands of measurements per pass. Digital phenotyping accelerates plant breeding by allowing simultaneous evaluation of multiple traits across environments, strengthening selection decisions and expediting commercial release timelines.

Read at Realagriculture