A grapevine trunks and intra-plant weeds segmentation method based on improved Deeplabv3 Plus

Abstract

Accurate identification of grapevine trunks and interplant weeds is crucial for the intelligent development of weeding sessions in vineyards. Challenges arise due to the nonuniform planting of wine grapes, obscuration of grapevine trunks by interplant weeds, and variations in trunk characteristics across different growth stages, complicating the accurate segmentation of grapevine trunks and intraplant weeds. This study presents a new identification model that employs an improved Deeplabv3 Plus framework with lightweight Mobilenetv2 as its central network, supplemented by a coordinate attention block to boost feature extraction capabilities. The model was deployed using the robot operating system (ROS) on a crawler robot for field operations. We developed datasets for grapevine trunks and intraplant weeds, and upon training and testing, the model achieved a mean intersection over union (MIoU) of 84.4 % and a pixel accuracy of 92.03 %. Field trials integrating the ROS system demonstrated a grapevine trunk miss detection rate of 3.6 %, a false detection rate of 2.4 %, and a detection speed of 22 frames per second (FPS). The results show that our method effectively balances recognition accuracy and speed, offering valuable technical support for developing intelligent field weeders for wine grape cultivation.