{"title":"A grapevine trunks and intra-plant weeds segmentation method based on improved Deeplabv3 Plus","authors":"","doi":"10.1016/j.compag.2024.109568","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924009591","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.