investigating the potential of the innovative yolov8s model for detecting bloomed damask roses in open fields

Manually picking the flowers of the damask rose is significantly challenging due to the numerous thorns on its stems. consequently, the accurate detection of bloomed damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. considering the high speed and precise capabilities of deep convolutional neural networks (dcnn), the objective of this study is to investigate the effectiveness of the optimized yolov8s model in detecting bloomed damask roses. to assess the impact of the yolo model size on network performance, the precision and detection speed of other yolo network versions, including v5s and v6s, were also examined. images of damask roses were taken under two lighting conditions: normal light conditions (from civil twilight to sunrise) and intense light conditions (from sunrise to 10 am). the outcomes demonstrated that yolov8s exhibited the highest performance, with a mean average precision (map50) of 98% and a detection speed of 243.9 fps. this outperformed the map50 and detection speed of yolov5s and yolov6s networks by margins of 0.3%, 6.1%, 169.3 fps and 198.6 fps, respectively. experimental results show that yolov8s performs better on images taken in normal lighting than on those taken in intense lighting. a decline of 5.2% in map50 and 2.4% in detection speed signifies the adverse influence of intense ambient light on the model’s effectiveness. this research indicates that the real-time detector yolov8s provides a feasible solution for the identification of damask rose and provides guidance for the detection of other similar plants.

investigating the potential of the innovative yolov8s model for detecting bloomed damask roses in open fields

manually picking the flowers of the damask rose is significantly challenging due to the numerous thorns on its stems. consequently, the accurate detection of bloomed damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. considering the high speed and precise capabilities of deep convolutional neural networks (dcnn), the objective of this study is to investigate the effectiveness of the optimized yolov8s model in detecting bloomed damask roses. to assess the impact of the yolo model size on network performance, the precision and detection speed of other yolo network versions, including v5s and v6s, were also examined. images of damask roses were taken under two lighting conditions: normal light conditions (from civil twilight to sunrise) and intense light conditions (from sunrise to 10 am). the outcomes demonstrated that yolov8s exhibited the highest performance, with a mean average precision (map50) of 98% and a detection speed of 243.9 fps. this outperformed the map50 and detection speed of yolov5s and yolov6s networks by margins of 0.3%, 6.1%, 169.3 fps and 198.6 fps, respectively. experimental results show that yolov8s performs better on images taken in normal lighting than on those taken in intense lighting. a decline of 5.2% in map50 and 2.4% in detection speed signifies the adverse influence of intense ambient light on the model’s effectiveness. this research indicates that the real-time detector yolov8s provides a feasible solution for the identification of damask rose and provides guidance for the detection of other similar plants.