Tomato pollination flower recognition method based on improved YOLOv5s
In the facility agriculture planting environment,tomato flowers have phenomenons such as leaf obstruction,flower stacking,and complex background environment,which lead to low accuracy of tomato flower detection models and limit the intelligent development of pollination robots.To address this issue,this paper proposed a tomato flower object detection method based on an improved YOLOv5s model.The original YOLOv5s model structure was improved by replacing the C3 module with the C2f_ScConv module,introducing the LSKA attention mechanism,and adding the ADown downsampling structure module.Finally,the models were trained and tested before and after improvement.The improved YOLOv5s tomato flower recognition model could effectively recognize tomato flowers with overlapping occlusion and small targets.Compared with the initial model,its mAP value had increased by 6.3 percentage points,recall rate had increased by 5.6 percentage points,accuracy had increased by 6.7 percentage points,and it has stronger feature extraction ability and robustness.This model could meet the recognition accuracy requirements of pollination robots for tomato flowers in facility environments.
万方数据
维普
