Improving throughput and accuracy of plant phenotyping is core to continued advances in breeding to ensure geneticgain to meet global food demand. Current manual phenotyping requires enormous investments in time, cost, and laboras quantitative values are required for thousands of genetic varieties across different environments. In soybean, agenotype’s maturity governs the geography for which it is adapted and has an impact on yield, which must becontrolled for in breeding to realize genetic gain. In this work, we developed and validated a method for highthroughputphenotyping of soybean maturity using high resolution, visual, RGB, imagery collected using an unmannedaerial vehicle (UAV). We illustrate a method to automatically derive maturity date by modeling change through timeof a quantitative assessment of canopy greenness on a per plot basis. The efficacy of the analytical framework iscompared to the manual scoring system by evaluating phenotypic and genetic correlations and genetic repeatabilitymeasures. Analysis of replicated experiments from multiple locations yielded high phenotypic correlations (R~2 = 0.85- 0.96) between manual and UAV derived maturity scores. Heritability of the maturity estimates from the proposedremote sensing method is comparable to that of manual scoring. Implementation of this system has allowed forimproved scale, cost efficiencies and data quality for soy maturity data collected via UAV remote sensing.
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