U.S. Department of Agriculture: Agricultural Research Service, Lincoln, Nebraska


Estimation of corn emergence date using UAV imagery

Chin Nee Vong, University of Missouri
Stirling A. Stewart, University of Missouri
Jianfeng Zhou, University of Missouri
Newell R. Kitchen, USDA Agricultural Research Service
Kenneth A. Sudduth, USDA Agricultural Research Service

Document Type Article


Assessing corn (Zea mays L.) emergence uniformity soon after planting is important for relating to grain production and making replanting decisions. Unmanned aerial vehicle (UAV) imagery has been used for determining corn densities at vegetative growth stage 2 (V2) and later, but not as a tool for quantifying emergence date. The objective of this study was to estimate days after corn emergence (DAE) using UAV imagery and a machine learning method. A field experiment was designed with four planting depths to obtain a range of corn emergence dates. UAV imagery was collected during the first, second, and third weeks after emergence. Acquisition height was approximately 5 m above ground level, which resulted in a ground sampling distance of 1.5 mm pixel-1. Seedling size and shape features derived from UAV imagery were used for DAE classification based on a random forest machine learning model. Results showed that 1-day DAE could be distinguished based on image features within the first week after initial corn emergence with a moderate overall classification accuracy of 0.49. However, for the second week and beyond, the overall classification accuracy diminished (0.20 to 0.35). When estimating DAE within a 3-day window (-1 to +1 day), the overall 3-day classification accuracies ranged from 0.54 to 0.88. Diameter, area, and the ratio of major axis length to area were important image features to predict corn DAE. Findings demonstrated that UAV imagery can detect newly-emerged corn plants and estimate their emergence date to assist in assessing emergence uniformity. Additional studies are needed for fine-tuning the image collection procedures and image feature identification to improve accuracy.