Automated Inventory Management and Quality Assessment of Ornamental Nursery Crops Using End-to-End Deep Learning

Abstract Purpose Ornamental crop production is a rapidly developing sector of US agriculture. Accurate inventory management and quality assessment are critical tasks for monitoring sales, yield, space requirements, production timing, and quality improvements of ornamental nurseries. Current inventory practices rely on manual counting, which is labor-intensive and often inaccurate. This study develops an automated system for plant counting and quality assessment of container nursery stock. Methods An unmanned teleoperated ground vehicle, “NurseryBot,” was developed for plant counting and quality assessment of container nursery stock. The NurseryBot utilized RGB cameras to collect video data from a downward-facing perspective. A deep learning framework based on YOLOv11 model variants was trained and tested on a panoramic dataset to detect and segment plants efficiently. Cross-species knowledge adaptation approaches were explored to improve segmentation generalization. Morphological parameters, including area, perimeter, eccentricity, and circularity, were extracted to systematically evaluate plant quality using image-based parameters. Results The YOLOv11-m instance segmentation model showed the highest precision, recall, and counting accuracy among the evaluated variants for plant detection and segmentation. For bounding boxes, the model attained precision, recall, and mAP50 values of 0.971, 0.970, and 0.941 for azalea and 0.968, 0.966, and 0.915 for sunshine, respectively. For mask segmentation, the model achieved precision, recall, and mAP50 values of 0.969, 0.968, and 0.901 for azalea and 0.967, 0.966, and 0.907 for sunshine, respectively. The proposed system achieved counting accuracies of 95.59% for azalea plants and 95.37% for sunshine plants. Conclusions This study demonstrates an end-to-end robotic and deep learning-based system for automated inventory counting and plant quality assessment in container nurseries. The proposed approach provides an accurate alternative to manual inventory practices under the nursery conditions evaluated in this study. This technology provides growers and researchers with an efficient way to accurately maintain inventory counts and assess plant quality.