Abstract: An online detection method of seeding distribution during wheat sowing based on image processing was proposed to address problems such as low manual calculation efficiency of seeding performance parameters and lack of online detection software. A criterion for adhesive seeds based on connected region area and contour perimeter was established, and an improved concave point segmentation adhesive seed method was created to count and coordinate the segmented seeds, achieving calculation and detection of seeding uniformity, accuracy, and dispersion. A seeding test bench was built and detection software was developed. The testing results showed that at different seeding rates and seeding travel speeds, the average accuracy of the improved concave point segmentation algorithm was above 95%, which was significantly higher than that of the concave point segmentation algorithm, indicating that the method had high recognition accuracy for the total number of seed particles; as the seeding rate was increased, the probability of seed adhesion was increased, and the chance of false concave points was increased, resulting in lower algorithm accuracy; as the travel speed was increased, the probability of seed deformation and distortion in the image was increased, leading to some adhered seeds being difficult or incorrectly segmented, and the algorithm accuracy also was decreased; seeding rate and seeding travel speed had no significant effect on seeding uniformity, accuracy and dispersion, which agreed with the manual calculation and measurement results, demonstrating the feasibility of this online detection method for seeding performance.