Assessment of Corn (Zea mays) Emergence using High-Resolution Spatial UAV-Based Remote Sensing

Timing and uniformity of seedling emergence can affect corn growth and development through interplant competition. Assessing uniformity of emergence can be time consuming when done visually by researchers. Research was conducted to compare accuracy of a UAV platform used to estimate corn stand within the first five days of emergence compared with hand counting emerged seedlings. Analysis of UAV-derived estimates of emergence fit a linear regression model for seedlings emerging one (R = 0.45) and two (R = 0.75) days after the first seedlings were observed in a trial with multiple corn hybrids across multiple locations. By days 3 and 4 after initial seedling emergence, virtually all seedlings had emerged and no further improvements in the relationships between UAV vs hand counted measurements were found. These data suggest that UAV-derived data can be used to determine the uniformity of emergence of corn in a manner similar to collecting data using the traditional method of hand counting but with less time and therefore increases the likelihood that this important measurement for hybrid comparisons will be used.