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

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2024-10-31 DOI:10.1016/j.compag.2024.109568

Shuming Yang , Zheng Cui , Maoqiang Li , Jinhai Li , Dehua Gao , Fulong Ma , Yutan Wang

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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.

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基于改进型 Deeplabv3 Plus 的葡萄树干和植株内杂草分割方法

准确识别葡萄树干和株间杂草对葡萄园智能除草工作的开展至关重要。由于酿酒葡萄种植不均匀、植株间杂草对葡萄树干的遮挡以及不同生长阶段葡萄树干特征的差异，导致葡萄树干和植株间杂草的准确分割变得复杂。本研究提出了一种新的识别模型，该模型采用了改进的 Deeplabv3 Plus 框架，以轻量级 Mobilenetv2 作为中心网络，并辅以坐标注意力块来提高特征提取能力。该模型通过机器人操作系统（ROS）部署在爬行机器人上，用于田间作业。我们开发了葡萄树干和植株内杂草的数据集，经过训练和测试，该模型的平均交集比结合率（MIoU）达到 84.4%，像素准确率达到 92.03%。集成 ROS 系统的实地试验表明，葡萄树干漏检率为 3.6%，误检率为 2.4%，检测速度为每秒 22 帧 (FPS)。结果表明，我们的方法有效地平衡了识别精度和速度，为开发酿酒葡萄种植智能田间除草机提供了宝贵的技术支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

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.