期刊
JOURNAL OF EXPERIMENTAL BOTANY
卷 72, 期 8, 页码 2807-2810出版社
OXFORD UNIV PRESS
DOI: 10.1093/jxb/erab108
关键词
Digital agriculture; drones; genome-wide association studies; machine learning; phenotype-genotype; remote sensing; unmanned aerial vehicles
The convergence of autonomous platforms, advances in machine learning, and rapid sequencing for genome description offer new insights and discoveries in plant sciences. Researchers leverage these tools to bridge the gap between field-based phenotyping and genetic loci identification in the lab, highlighting the need for further collaboration across multidisciplinary fields in digital agriculture.
The convergence of autonomous platforms for field-based phenotyping with advances in machine learning for big data analytics and rapid sequencing for genome description herald the promise of new insights and discoveries in the plant sciences. Han et al. (2021) leverage these emerging tools to navigate the challenging path from field-based mapping of phenotypic features to identifying specific genetic loci in the laboratory: in this case, loci responsible for regulating daily flowering time in lettuce. While their contribution neatly illustrates these exciting technological developments, it also highlights the work that remains to bridge these multidisciplinary fields to more fully deliver upon the promise of digital agriculture.
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