Optimizing unmanned aerial vehicle–based stand counts in soybean: Effects of flight timing, altitude, and analytical method

Abstract The accurate estimation of soybean ( Glycine max ) stand establishment is essential for evaluating crop emergence and informing early‐season management practices. Recent advances in unmanned aerial vehicle (UAV) imagery and computer vision offer opportunities to automate plant population assessments; however, limited information exists on their accuracy in soybeans. This study evaluated two commercial UAV‐based plant counting platforms, a point‐based (convolutional neural network–derived) and a line‐based (Hough transform–derived) approach across two growing seasons, three flight altitudes (15.2, 45.7, and 91.4 m), and seven plant removal treatments, including a control (no removal). UAV imagery was collected at 7‐ to 10‐day intervals from 10 to 36 days after planting (DAP), and predictions were compared to manual on‐ground counts. The point‐based method provided the highest accuracy (within ±12% of ground‐truth; R 2 = 0.81) when imagery was collected between 14 and 20 DAP at 15.2‐m altitude. Accuracy declined beyond 27 DAP as canopy overlap increased. The line‐based method remained more stable across altitudes and later growth stages but consistently overestimated plant counts, particularly in dense and narrow row canopies. Incorporating on‐ground calibration areas improved accuracy by an average of 28% and up to 48% for the line‐based approach in narrow rows. Row spacing and plant removal patterns had minimal effects on prediction error, although short repeating gaps were poorly detected by the line‐based method. Overall, UAV‐based plant counts in soybean are feasible and dependable when flights are timed during early vegetative growth and supported by calibration, providing a practical tool for in‐season management and field‐based crop monitoring.