RoseTracker: A system for automated rose growth monitoring

Abstract

In cut-flower cultivation, production planning is an important task because demand fluctuates throughout the year. For precise cultivation planning, understanding the cultivation status is necessary by the growing stage. However, manually counting all the roses in the greenhouse to determine the cultivation status is difficult without incurring considerable time and labor. Some studies have engaged in detecting the number of flowers, but these studies used close-up images and could not count flowers without omissions or overlapping in an entire farm. In addition, limited datasets for object detection based on cut-flower blooming stages are available. In this study, we propose the RoseBlooming dataset and an efficient rose-monitoring system called RoseTracker to bridge the gap between computer vision techniques and the horticulture cultivation industry. The RoseBlooming dataset is the innovative dataset of labeled images for cut flowers at the growing stage. RoseTracker can detect small roses from various angles while moving the camera, reduces detection omissions, and achieves an F1 score of 0.950, thereby outperforming conventional models. For application, we used overhead images captured under actual growing conditions. RoseTracker and the RoseBlooming dataset contribute to constructing the rose-growth monitoring system in high demand worldwide.