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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #374380

Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Estimating corn emergence date using UAV-based imagery

item VONG, CHIN NEE - University Of Missouri
item STEWART, STIRLING - University Of Missouri
item ZHOU, JIANFENG - University Of Missouri
item Kitchen, Newell
item Sudduth, Kenneth - Ken

Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: 5/5/2020
Publication Date: 7/15/2020
Citation: Vong, C., Stewart, S.A., Zhou, J., Kitchen, N.R., Sudduth, K.A. 2020. Estimating corn emergence date using UAV-based imagery. In: Proceedings of the 2020 American Society of Agricultural and Biological Engineers (ASABE) International Annual International Virtual Meeting.

Interpretive Summary: Corn stand may not be uniform when during the emergence period soils become cold and wet causing stress to the germinating seed. A characteristic of uniformity is plants emerging at about the same time (i.e., within a few days of each other). When they don’t emerge uniformly, grain yield can be reduced. When emergence is spread over many days, farmers are faced with the decision of whether they should replant portions or all of a field. This research was conducted to evaluate if corn emergence could be accurately determined using unmanned aerial vehicle (UAV) images. The research showed corn plant features were distinguishable within the first week of emergence. Plant features captured that were most helpful in predicting emergence date were plant diameter, area, and the major axis length divided by the area. Using these features one could predict the day the plant emerged correctly about 50% of the time during the first week. However, for the second week after emergence, accuracy of predicting day of emergence was much less (20 to 35%). When relaxing the estimation window to be within three days of actual day of emergence, predicted emergence day from UAV images was 55 to 90% correct. Findings demonstrated that UAV imagery can detect newly-emerged corn plants and estimate their emergence date. If this process could be more completely automated, farmers could utilize UAV scouting to help evaluate fields or portions of fields that need replanting.

Technical Abstract: Assessing corn (Zea Mays L.) emergence uniformity soon after planting is important for relating to grain production and for 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 detecting emergence date. The objective of this study was to estimate days after corn emergence (DAE) using UAV imagery. 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 5m above ground level resulted in a ground sampling distance 1.5 mm/pixel. Seedling size and shape features derived from UAV imagery were used for DAE classification based on the Random Forest machine learning model. Results showed image features were distinguishable for different DAE (single day) 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 three-day window (± 1 DAE), overall 3-day classification accuracies ranged from 0.54 to 0.88. Diameter, area, and major axis length/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 establishing emergence uniformity. Additional studies are needed for fine-tuning image collection procedures and image feature identification in order to improve accuracy.