Efficacy of Mechanical Weeding Tools: A Study Into Alternative Weed Management Strategies Enabled by Robotics

被引:49
作者
McCool, Chris [1 ]
Beattie, James [1 ]
Firn, Jennifer [1 ]
Lehnert, Chris [1 ]
Kulk, Jason [1 ]
Bawden, Owen [1 ]
Russell, Raymond [1 ]
Perez, Tristan [1 ]
机构
[1] Queensland Univ Technol, Sci & Engn Fac, Brisbane, Qld 4000, Australia
来源
IEEE ROBOTICS AND AUTOMATION LETTERS | 2018年 / 3卷 / 02期
关键词
Robotics in agriculture and forestry; agricultural automation; CROPS; VISION;
D O I
10.1109/LRA.2018.2794619
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
The rise of herbicide resistant weed species has reinvigorated research in nonchemical methods for weed management. Robots, such as AgBot II, that can detect and classify weeds as they traverse a field are a key enabling factor for individualised treatment of weed species. Integral to the invidualized treatment of weed species are the the nonherbicide methods through which the weeds are managed. This letter explores mechanical methods as an alternative to weed management. Three implements are considered: below-surface tilling (arrow hoe), above-surface tilling (tines), and a cutting mechanism. These mechanisms were evaluated in a controlled field with varying rates of application to herbicide-resistant weeds of interest for Queensland, Australia. Statistical analysis demonstrated the efficacy of these implements and highlighted the importance of early intervention. It was found that a tine, deployed automatically on AgBot II, was effective for all of the weeds considered in this study, leading to an overall survival probability of 0.28 +/- 0.15. Further analysis demonstrated the significance of treatment time with late intervention commencing at week 6 resulting in a survival probability of 0.54 +/- 0.08 vs 0.24 +/- 0.18 for earlier intervention at week 4.
引用
收藏
页码:1184 / 1190
页数:7
相关论文
共 13 条
  • [1] [Anonymous], 2009, POPUL DEV REV, V35, P837
  • [2] Robot for weed species plant-specific management
    Bawden, Owen
    Kulk, Jason
    Russell, Ray
    McCool, Chris
    English, Andrew
    Dayoub, Feras
    Lehnert, Chris
    Perez, Tristan
    [J]. JOURNAL OF FIELD ROBOTICS, 2017, 34 (06) : 1179 - 1199
  • [3] Robotic weed control using machine vision
    Blasco, J
    Aleixos, N
    Roger, JM
    Rabatel, G
    Moltó, E
    [J]. BIOSYSTEMS ENGINEERING, 2002, 83 (02) : 149 - 157
  • [4] Charles G., 2011, COTTON PEST MANAGEME, P88
  • [5] Deng Wei, 2010, International Journal of Agricultural and Biological Engineering, V3, P52, DOI 10.3965/j.issn.1934-6344.2010.04.052-060
  • [6] NONPARAMETRIC-ESTIMATION FROM INCOMPLETE OBSERVATIONS
    KAPLAN, EL
    MEIER, P
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1958, 53 (282) : 457 - 481
  • [7] Langsenkamp F., 2014, P WORLD C CIGR
  • [8] Automatic GPS-based intra-row weed knife control system for transplanted row crops
    Perez-Ruiz, M.
    Slaughter, D. C.
    Gliever, C. J.
    Upadhyaya, S. K.
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2012, 80 : 41 - 49
  • [9] An evaluation of the performance of mechanical weeding mechanisms for use in high speed inter-row weeding of arable crops
    Pullen, DWM
    Cowell, PA
    [J]. JOURNAL OF AGRICULTURAL ENGINEERING RESEARCH, 1997, 67 (01): : 27 - 34
  • [10] Sellmann F, 2014, PROC INT C MACH CONT, P19