This study was conducted to compare the effectiveness of different image processing algorithms in the automatic counting of early-stage corn (Zea mays L.) seedlings using high-resolution images obtained from unmanned aerial vehicles (UAVs). Within the scope of the research, images with a resolution of 2016x3024 pixels obtained from a DJI Mini 3 Pro UAV at an altitude of 10 meters were examined. Otsu Thresholding based on the Excess Green (ExG) index, HSV Color Space Filtering, and Canny Edge Detection methods were applied for seedling detection and isolation; the performance of the algorithms was statistically validated against manual counts (ground truth) performed by an expert agricultural engineer. The pilot study results showed that the ExG+Otsu method exhibited the closest performance to manual data, with an accuracy rate of 83.70% and an R² of 0.58. In contrast, the HSV filtering method remained at an accuracy rate of 72.05% due to its sensitivity to variable light conditions, while the Canny edge detection algorithm remained at an accuracy rate of 62.60% due to structural noise in the soil texture. Visual and numerical analyses proved that the Otsu method offers a more resilient structure than other techniques in preserving corn seedling morphology and ensuring object integrity. In conclusion, it has been determined that low-cost UAV data and basic image processing workflows can detect plant populations with acceptable accuracy without the need for complex deep learning models; future studies aim to integrate advanced morphological filters into the system for the separation of overlapping seedlings.