• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

作物检测技术、机械除草执行部件与智能机械除草的工作性能:综述

Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review.

作者信息

Xiang Meiqi, Qu Minghao, Wang Gang, Ma Zhongyang, Chen Xuegeng, Zhou Zihao, Qi Jiangtao, Gao Xiaomei, Li Hailan, Jia Honglei

机构信息

College of Biological and Agricultural Engineering, Jilin University, Changchun, China.

出版信息

Front Plant Sci. 2024 Mar 14;15:1361002. doi: 10.3389/fpls.2024.1361002. eCollection 2024.

DOI:10.3389/fpls.2024.1361002
PMID:38550283
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10973117/
Abstract

Weeding is a key link in agricultural production. Intelligent mechanical weeding is recognized as environmentally friendly, and it profoundly alleviates labor intensity compared with manual hand weeding. While intelligent mechanical weeding can be implemented only when a large number of disciplines are intersected and integrated. This article reviewed two important aspects of intelligent mechanical weeding. The first one was detection technology for crops and weeds. The contact sensors, non-contact sensors and machine vision play pivotal roles in supporting crop detection, which are used for guiding the movements of mechanical weeding executive parts. The second one was mechanical weeding executive part, which include hoes, spring teeth, fingers, brushes, swing and rotational executive parts, these parts were created to adapt to different soil conditions and crop agronomy. It is a fact that intelligent mechanical weeding is not widely applied yet, this review also analyzed the related reasons. We found that compared with the biochemical sprayer, intelligent mechanical weeding has two inevitable limitations: The higher technology cost and lower working efficiency. And some conclusions were commented objectively in the end.

摘要

除草是农业生产中的关键环节。智能机械除草被认为是环保的,与人工除草相比,它极大地减轻了劳动强度。然而,智能机械除草只有在多个学科交叉融合时才能实现。本文综述了智能机械除草的两个重要方面。第一个方面是作物和杂草的检测技术。接触式传感器、非接触式传感器和机器视觉在支持作物检测方面发挥着关键作用,它们用于引导机械除草执行部件的运动。第二个方面是机械除草执行部件,包括锄头、弹齿、指状部件、刷子、摆动和旋转执行部件,这些部件是为适应不同的土壤条件和作物农艺而设计的。事实上,智能机械除草尚未得到广泛应用,本综述还分析了相关原因。我们发现,与生化喷雾器相比,智能机械除草有两个不可避免的局限性:技术成本较高和工作效率较低。最后对一些结论进行了客观的评论。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/f6f70c09ac8d/fpls-15-1361002-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/bc0baed0eb50/fpls-15-1361002-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/400f0832aff8/fpls-15-1361002-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/e9fbb1c2b7cf/fpls-15-1361002-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/9c8aaba9d42a/fpls-15-1361002-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/2d3c8a7f590c/fpls-15-1361002-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/f6f70c09ac8d/fpls-15-1361002-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/bc0baed0eb50/fpls-15-1361002-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/400f0832aff8/fpls-15-1361002-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/e9fbb1c2b7cf/fpls-15-1361002-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/9c8aaba9d42a/fpls-15-1361002-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/2d3c8a7f590c/fpls-15-1361002-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd92/10973117/f6f70c09ac8d/fpls-15-1361002-g006.jpg

相似文献

1
Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review.作物检测技术、机械除草执行部件与智能机械除草的工作性能:综述
Front Plant Sci. 2024 Mar 14;15:1361002. doi: 10.3389/fpls.2024.1361002. eCollection 2024.
2
A peanut and weed detection model used in fields based on BEM-YOLOv7-tiny.一种基于BEM-YOLOv7-tiny的用于田间花生和杂草检测模型。
Math Biosci Eng. 2023 Oct 17;20(11):19341-19359. doi: 10.3934/mbe.2023855.
3
Attention-aided lightweight networks friendly to smart weeding robot hardware resources for crops and weeds semantic segmentation.面向作物与杂草语义分割的、对智能除草机器人硬件资源友好的注意力辅助轻量级网络。
Front Plant Sci. 2023 Dec 21;14:1320448. doi: 10.3389/fpls.2023.1320448. eCollection 2023.
4
Effects of mechanical weeding on soil fertility and microbial community structure in star anise (Illicium verum Hook.f.) plantations.机械除草对八角(Illicium verum Hook.f.)人工林土壤肥力和微生物群落结构的影响。
PLoS One. 2022 Apr 12;17(4):e0266949. doi: 10.1371/journal.pone.0266949. eCollection 2022.
5
Improving U-net network for semantic segmentation of corns and weeds during corn seedling stage in field.改进U-net网络用于田间玉米苗期玉米和杂草的语义分割。
Front Plant Sci. 2024 Feb 9;15:1344958. doi: 10.3389/fpls.2024.1344958. eCollection 2024.
6
Weed Management and Crop Establishment Methods in Rice ( L.) Influence the Soil Microbial and Enzymatic Activity in Sub-Tropical Environment.水稻(L.)的杂草管理与作物种植方法对亚热带环境中土壤微生物和酶活性的影响
Plants (Basel). 2022 Apr 14;11(8):1071. doi: 10.3390/plants11081071.
7
Effects of reduced chemical application by mechanical-chemical synergistic weeding on maize growth and yield in East China.华东地区机械化学协同除草减少化学药剂施用量对玉米生长和产量的影响
Front Plant Sci. 2022 Sep 26;13:1024249. doi: 10.3389/fpls.2022.1024249. eCollection 2022.
8
Non-chemical weed management: Harnessing flame weeding for effective weed control.非化学除草管理:利用火焰除草实现有效的杂草控制。
Heliyon. 2024 Jun 11;10(12):e32776. doi: 10.1016/j.heliyon.2024.e32776. eCollection 2024 Jun 30.
9
Learning Semantic Graphics Using Convolutional Encoder-Decoder Network for Autonomous Weeding in Paddy.使用卷积编码器-解码器网络学习语义图形以实现稻田自动除草
Front Plant Sci. 2019 Oct 31;10:1404. doi: 10.3389/fpls.2019.01404. eCollection 2019.
10
[Effects of weeding methods on weed community and its diversity in a citrus orchard in southwest Zhejiang].[除草方法对浙西南柑橘园杂草群落及其多样性的影响]
Ying Yong Sheng Tai Xue Bao. 2010 Jan;21(1):23-8.

本文引用的文献

1
Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm.基于YOLOV5和卡尔曼滤波跟踪算法的复杂背景下玉米幼苗计数方法
Front Plant Sci. 2022 Nov 7;13:1030962. doi: 10.3389/fpls.2022.1030962. eCollection 2022.
2
Effects of reduced chemical application by mechanical-chemical synergistic weeding on maize growth and yield in East China.华东地区机械化学协同除草减少化学药剂施用量对玉米生长和产量的影响
Front Plant Sci. 2022 Sep 26;13:1024249. doi: 10.3389/fpls.2022.1024249. eCollection 2022.
3
A novel deep learning-based method for detection of weeds in vegetables.
一种基于深度学习的新型蔬菜杂草检测方法。
Pest Manag Sci. 2022 May;78(5):1861-1869. doi: 10.1002/ps.6804. Epub 2022 Feb 2.
4
Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields.基于全卷积网络的稻田秧苗期水稻苗和杂草图像分割。
PLoS One. 2019 Apr 18;14(4):e0215676. doi: 10.1371/journal.pone.0215676. eCollection 2019.
5
Detection of broadleaf weeds growing in turfgrass with convolutional neural networks.利用卷积神经网络检测草坪中的阔叶杂草。
Pest Manag Sci. 2019 Aug;75(8):2211-2218. doi: 10.1002/ps.5349. Epub 2019 Mar 8.
6
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.基于深度卷积神经网络的杂草生长阶段估算器。
Sensors (Basel). 2018 May 16;18(5):1580. doi: 10.3390/s18051580.
7
Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops.用于玉米作物中选择性除草剂施用的双子叶杂草定量算法
Sensors (Basel). 2016 Nov 4;16(11):1848. doi: 10.3390/s16111848.
8
Fully Convolutional Networks for Semantic Segmentation.全卷积网络用于语义分割。
IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651. doi: 10.1109/TPAMI.2016.2572683. Epub 2016 May 24.
9
Robust crop and weed segmentation under uncontrolled outdoor illumination.在不受控的户外光照条件下进行健壮的作物和杂草分割。
Sensors (Basel). 2011;11(6):6270-83. doi: 10.3390/s110606270. Epub 2011 Jun 10.
10
A logical calculus of the ideas immanent in nervous activity. 1943.神经活动中内在思想的逻辑演算。1943年。
Bull Math Biol. 1990;52(1-2):99-115; discussion 73-97.