YOLOv10 Computer Vision Performance Measurement for Agricultural Vacuum Seed Meters

A YOLOv10-based measurement software to analyze agricultural vacuum seed meters was developed to be widely utilized by growers to plant peanuts. The software can process videos recorded on a test stand or from a performing planter under field conditions. Initially, images under various light conditions and camera angles were captured and manually annotated to create a dataset. Advanced image augmentation techniques were applied to enhance the dataset, which was utilized to train a YOLOv10 object detection model, achieving an impressive mAP50 score of 99.4%. Paired with a counting algorithm, the model accurately identified singulated peanuts, empty cells (missing peanuts/spots), and multiple transported peanuts (doubles). Thus, the software automatically calculated and exported detection percentages for each category. The data enables the assessment of seed meter performance across different variables, offering valuable insights for optimizing seed plate and singulator design, vacuum pressure, and rotational speed. The software demonstrated a low error rate of 1.91% and ensured reliable performance measurement metrics.