ASHS 2025 Conference

Vidalia sweet onion (Allium cepa spp.), holds significant economic and cultural value in the Vidalia region, known for its sweet flavor and low pungency. The success of marketable bulb production is closely linked to the early crop stages, particularly seedling production and field-quality transplanting. Due to the limited availability of mechanical transplanters, transplanting is predominantly performed manually, resulting in variability in planting quality. A major issue is the presence of seedlings lying on the ground, which significantly reduces survival rates and leads to undersized bulbs. Assessing the number of such seedlings manually is labor-intensive and inefficient. Therefore, this study aimed to develop a rapid and accurate method to quantify the number of seedlings lying on the ground across the field using a unmanned aerial vehicle (UAV) imagery and the YOLOv8 deep learning model. On January 6, 2025, aerial RGB images were captured using a UAV over an experimental onion field located at the UGA Vidalia Onion and Vegetable Research Center in Lyons, GA. The seedlings were transplanted on December 15, 2024, under three treatment conditions: 100% properly transplanted plants, 100% lying on the ground, and 25% lying on the ground. The objective of this study was to detect and quantify the percentage of plants lying on the ground within each plot using computer vision techniques. The images were manually annotated and used to train a YOLOv8 object detection model. The dataset was split into 70% for training, 15% for validation, and 15% for testing. Model performance was evaluated using standard YOLOv8 accuracy metrics, including precision, recall, and F1-score. The YOLOv8 model demonstrated strong performance in detecting onion seedlings lying on the ground. It achieved moderate to high precision and recall, as well as a high F1-score on the test dataset. When applied to the annotated aerial images, the model accurately quantified the percentage of fallen seedlings within each treatment. In the 100% properly transplanted treatment, a small number of seedlings were falsely identified as lying on the ground. These results confirm the model's ability to reliably distinguish between transplant quality levels using UAV imagery. This approach offers a scalable solution for monitoring transplant quality and identifying problematic planting zones.