Advancements and developments in the detection and control of invasive weeds: a global review of the current challenges and future opportunities

被引:5
作者
Roberts, Jason [1 ]
Florentine, Singarayer [1 ,2 ]
机构
[1] Federat Univ Australia, Inst Innovat Sci & Sustainabil, Future Reg Res Ctr, Ballarat, Vic, Australia
[2] RMIT Univ, Sch Sci, Melbourne, Vic, Australia
关键词
Agricultural development; artificial intelligence; climate change; herbicide resistance; integrated systems; invasive species; weed control; CLIMATE-CHANGE; MANAGEMENT; ROBOT; TECHNOLOGIES; SYSTEMS; LEAVES; BEET;
D O I
10.1017/wsc.2024.13
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Weed invasion has become increasingly recognized as a major threat to the practice of sustainable agriculture and the maintenance of natural ecosystems around the world. Without effective and ongoing management strategies, many weed species have the aggressive capacity to alter ecosystem functions and reduce the economic potential of the land into which they have been introduced. Although traditional weed management strategies can be useful in eliminating certain weeds, these approaches can be costly, economically damaging, and laborious and can result in variable long-term success. To further add to these challenges, several weed species have now developed resistance to a range of herbicide modes of action, which, to date, have been the major mechanism of weed control. As a result, it is anticipated that the use of emerging technology will help to provide a solution for the economical and environmentally sustainable management of various weeds. Of particular interest, emerging technology in the areas of weed detection and control (chemical, mechanical, electrical, laser, and thermal) has shown promising signs of improving long-term weed management strategies. These methods can also be assisted by, or integrated alongside, other technology, such as artificial intelligence or computer vision techniques for improved efficiency. To provide an overview of this topic, this review evaluates a range of emerging technology used for the detection and control of various weeds and explores the challenges and opportunities of their application within the field.
引用
收藏
页码:205 / 215
页数:11
相关论文
共 120 条
[1]   Weed and crop discrimination using image analysis and artificial intelligence methods [J].
Aitkenhead, MJ ;
Dalgetty, IA ;
Mullins, CE ;
McDonald, AJS ;
Strachan, NJC .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2003, 39 (03) :157-171
[2]   Precision Chemical Weed Management Strategies: A Review and a Design of a New CNN-Based Modular Spot Sprayer [J].
Allmendinger, Alicia ;
Spaeth, Michael ;
Saile, Marcus ;
Peteinatos, Gerassimos G. ;
Gerhards, Roland .
AGRONOMY-BASEL, 2022, 12 (07)
[3]   Weed Management of the Future [J].
Amend, Sandra ;
Brandt, David ;
Di Marco, Daniel ;
Dipper, Tobias ;
Gaessler, Gabriel ;
Hoeferlin, Markus ;
Gohlke, Maurice ;
Kesenheimer, Katharina ;
Lindner, Peter ;
Leidenfrost, Roland ;
Michaels, Andreas ;
Mugele, Tobias ;
Mueller, Arthur ;
Riffel, Tanja ;
Sampangi, Yeshwanth ;
Winkler, Jan .
KUNSTLICHE INTELLIGENZ, 2019, 33 (04) :411-415
[4]   On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat [J].
Anderegg, Jonas ;
Tschurr, Flavian ;
Kirchgessner, Norbert ;
Treier, Simon ;
Schmucki, Manuel ;
Streit, Bernhard ;
Walter, Achim .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2023, 204
[5]   Weed discrimination using ultrasonic sensors [J].
Andujar, D. ;
Escola, A. ;
Dorado, J. ;
Fernandez-Quintanilla, C. .
WEED RESEARCH, 2011, 51 (06) :543-547
[6]   Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry [J].
Andujar, Dionisio ;
Calle, Mikel ;
Fernandez-Quintanilla, Cesar ;
Ribeiro, Angela ;
Dorado, Jose .
SENSORS, 2018, 18 (04)
[7]   Hot foam: Evaluation of a new, non-chemical weed control option in perennial crops [J].
Antonopoulos, Nikolaos ;
Kanatas, Panagiotis ;
Gazoulis, Ioannis ;
Tataridas, Alexandros ;
Ntovakos, Dimitris ;
Ntaoulis, Vasilis-Nektarios ;
Zavra, Spyridoula-Marina ;
Travlos, Ilias .
SMART AGRICULTURAL TECHNOLOGY, 2023, 3
[8]  
Aygun I., 2017, INT J ADV SCI ENG TE, V5, P33
[9]   Systematic design of an autonomous platform for robotic weeding [J].
Bakker, Tijmen ;
van Asselt, Kees ;
Bontsema, Jan ;
Muller, Joachim ;
van Straten, Gerrit .
JOURNAL OF TERRAMECHANICS, 2010, 47 (02) :63-73
[10]   Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics [J].
Balafoutis, Athanasios ;
Beck, Bert ;
Fountas, Spyros ;
Vangeyte, Jurgen ;
van der Wal, Tamme ;
Soto, Iria ;
Gomez-Barbero, Manuel ;
Barnes, Andrew ;
Eory, Vera .
SUSTAINABILITY, 2017, 9 (08)