Smart Precision Weeding in Agriculture Using 5IR Technologies

Chaw Thiri San; Vijay Kakani

doi:10.3390/electronics14132517

journal article Open Access Jun 20, 2025

Smart Precision Weeding in Agriculture Using 5IR Technologies

Chaw Thiri San

Vijay Kakani

Electronics Vol. 14 No. 13 pp. 2517 · MDPI AG

View at Publisher Save 10.3390/electronics14132517

Abstract

The rise of smart precision weeding driven by Fifth Industrial Revolution (5IR) technologies symbolizes a quantum leap in sustainable agriculture. The modern weeding systems are becoming promisingly efficient, intelligently autonomous, and environmentally responsible by introducing artificial intelligence (AI), robotics, Internet of Things (IoT), 5G connectivity, and edge computing technologies. This review discusses a comprehensive analysis of the traditional and contemporary weeding techniques, thereby focusing on the technological innovations paving way for the smart systems. Primarily, this work investigates the application of 5IR technologies in weed detection and decision-making with particular emphasis on the role of the aspects such as AI-driven models, drone-robot integration, GPS-guided practices, and intelligent sensor networks. Additionally, the work outlines key commercial solutions, sustainability metrics, data-driven decision support systems, and blockchain traceable practices. The prominent challenges in the context of global agricultural equity pertaining to cost, scalability, policy alignment, and adoption barriers in accordance to the low-resource environments are discussed in this study. The paper concludes with strategic recommendations and future research directions, highlighting the potential of 5IR technologies on the smart precision weeding.

Topics

No keywords indexed for this article. Browse by subject →

References

176

[1]

The Future of Food

Charis M. Galanakis

Foods 10.3390/foods13040506

[2]

FAO (2025, February 12). FAOSTAT—Food Security Indicators Visualization. Available online: https://www.fao.org/faostat/en/#data/FS/visualize.

[3]

Fipke "Application of non-selective herbicides in the pre-harvest of wheat damages seed quality" Am. J. Plant Sci. (2018) 10.4236/ajps.2018.91010

[4]

Otekunrin "A Critical Assessment of the Interplay of Conflict, Hunger, Poverty, and Food Insecurity in Africa" Food Humanit. (2025) 10.1016/j.foohum.2025.100544

[5]

Vasileiou "Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning" Crop. Prot. (2024) 10.1016/j.cropro.2023.106522

[6]

Ekwealor "Economic importance of weeds: A review" Asian J. Plant. Sci. (2019)

[7]

Crop Diversification for Improved Weed Management: A Review

Gourav Sharma, Swati Shrestha, Sudip Kunwar et al.

Agriculture 10.3390/agriculture11050461

[8]

Tshewang "Weed management challenges in rice (Oryza sativa L.) for food security in Bhutan: A review" Crop. Prot. (2016) 10.1016/j.cropro.2016.08.031

[9]

Belz "Allelopathy in crop/weed interactions—An update" Pest Manag. Sci. Former. Pestic. Sci. (2007) 10.1002/ps.1320

[10]

"Phenolic allelochemicals: Achievements, limitations, and prospective approaches in weed management" Weed Biol. Manag. (2021) 10.1111/wbm.12230

[11]

Arora "Allelochemicals as biocontrol agents: Promising aspects, challenges and opportunities" S. Afr. J. Bot. (2024) 10.1016/j.sajb.2024.01.029

[12]

Elstone, L., How, K.Y., Brodie, S., Ghazali, M.Z., Heath, W.P., and Grieve, B. (2020). High speed crop and weed identification in lettuce fields for precision weeding. Sensors, 20. 10.3390/s20020455

[13]

Giua "Smart farming technologies adoption: Which factors play a role in the digital transition?" Technol. Soc. (2022) 10.1016/j.techsoc.2022.101869

[14]

Kovari "Industry 5.0: Generalized definition key applications opportunities and threats" Acta Polytech. Hung. (2024) 10.12700/aph.21.3.2024.3.17

[15]

Alves, J., Lima, T.M., and Gaspar, P.D. (2023). Is industry 5.0 a human-centred approach? A systematic review. Processes, 11. 10.3390/pr11010193

[16]

Tiwari "Hand weeding tools in vegetable production systems: An agronomic, ergonomic and economic evaluation" Int. J. Agric. Sustain. (2022) 10.1080/14735903.2021.1964789

[17]

Olson "Sheep: A method for controlling rangeland weeds" Sheep Res. J. Spec. Issue (1994)

[18]

Kaur "Understanding crop-weed-fertilizer-water interactions and their implications for weed management in agricultural systems" Crop. Prot. (2018) 10.1016/j.cropro.2017.09.011

[19]

Osipitan "Impact of cover crop management on level of weed suppression: A meta-analysis" Crop. Sci. (2019) 10.2135/cropsci2018.09.0589

[20]

Aslam "Allelopathy in agro-ecosystems: A critical review of wheat allelopathy-concepts and implications" Chemoecology (2017) 10.1007/s00049-016-0225-x

[21]

Koteish "AGRO: A smart sensing and decision-making mechanism for real-time agriculture monitoring" J. King Saud-Univ.-Comput. Inf. Sci. (2022) 10.1016/j.jksuci.2022.06.017

[22]

Idoje "Survey for smart farming technologies: Challenges and issues" Comput. Electr. Eng. (2021) 10.1016/j.compeleceng.2021.107104

[23]

Johnston "Differential GPS positioning" Electron. Commun. Eng. J. (1995) 10.1049/ecej:19950104

[24]

Bhuiyan "Enrichment, sources and ecological risk mapping of heavy metals in agricultural soils of dhaka district employing SOM, PMF and GIS methods" Chemosphere (2021) 10.1016/j.chemosphere.2020.128339

[25]

Agency, E.S. (2025, February 27). Sentinel-2. Available online: https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2.

[26]

Radočaj, D., Jurišić, M., and Gašparović, M. (2022). The role of remote sensing data and methods in a modern approach to fertilization in precision agriculture. Remote Sens., 14. 10.3390/rs14030778

[27]

Dangi, R., Lalwani, P., Choudhary, G., You, I., and Pau, G. (2021). Study and investigation on 5G technology: A systematic review. Sensors, 22. 10.3390/s22010026

[28]

Bikov, T., Mihaylov, G., Iliev, T., and Stoyanov, I. (July, January 30). Drone surveillance in the modern agriculture. Proceedings of the 2022 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE), Ruse, Bulgaria. 10.1109/eeae53789.2022.9831375

[29]

Westwood "Weed management in 2050: Perspectives on the future of weed science" Weed Sci. (2018) 10.1017/wsc.2017.78

[30]

Monteiro, A., and Santos, S. (2022). Sustainable approach to weed management: The role of precision weed management. Agronomy, 12. 10.3390/agronomy12010118

[31]

Moond "Strategies and Technologies in Weed Management: A Comprehensive" Curr. J. Appl. Sci. Technol. (2023) 10.9734/cjast/2023/v42i294203

[32]

Liu "Benefits of mechanical weeding for weed control, rice growth characteristics and yield in paddy fields" Field Crop. Res. (2023) 10.1016/j.fcr.2023.108852

[33]

Stockman "Poisoning of cattle with British ragwort" J. Comp. Pathol. Ther. (1917) 10.1016/s0368-1742(17)80011-4

[34]

Ascard, J., Hatcher, P., Melander, B., Upadhyaya, M., and Blackshaw, R. (2007). 10 Thermal weed control. Non-Chemical Weed Management: Principles, Concepts and Technology, CABI. 10.1079/9781845932909.0155

[35]

Maloney "Confirmation of glyphosate-resistant Palmer amaranth (Amaranthus palmeri) populations in New York and responses to alternative chemistries" Weed Sci. (2024) 10.1017/wsc.2024.48

[36]

Ellstrand "Crops gone wild: Evolution of weeds and invasives from domesticated ancestors" Evol. Appl. (2010) 10.1111/j.1752-4571.2010.00140.x

[37]

Upadhyay "Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review" Comput. Electron. Agric. (2024) 10.1016/j.compag.2024.109363

[38]

Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012, January 17). Fog computing and its role in the internet of things. Proceedings of the SIGCOMM ’12: ACM SIGCOMM 2012 Conference, Helsinki, Finland. 10.1145/2342509.2342513

[39]

Kalyani, Y., and Collier, R. (2021). A systematic survey on the role of cloud, fog, and edge computing combination in smart agriculture. Sensors, 21. 10.3390/s21175922

[40]

Pedersen "The UN sustainable development goals (SDGs) are a great gift to business!" Procedia Cirp. (2018) 10.1016/j.procir.2018.01.003

[41]

Liu, J., Abbas, I., and Noor, R.S. (2021). Development of deep learning-based variable rate agrochemical spraying system for targeted weeds control in strawberry crop. Agronomy, 11. 10.3390/agronomy11081480

[42]

Von Bargen, K., Meyer, G.E., Mortensen, D.A., Merritt, S.J., and Woebbecke, D.M. (1992, January 16). Red/near-infrared reflectance sensor system for detecting plants. Proceedings of the Optics in Agriculture and Forestry, Boston, MA, USA. 10.1117/12.144032

[43]

Giles, D., Delwiche, M., and Dodd, R. (1989). Sprayer Control by Sensing Orchard Crop Characteristics: Orchard Architecture and Spray Liquid Savings, FAO. 10.1016/s0021-8634(89)80024-1

[44]

Tresanchez "Real-time tree-foliage surface estimation using a ground laser scanner" IEEE Trans. Instrum. Meas. (2007) 10.1109/tim.2007.900126

[45]

Ghimire "SSRT: A Sequential Skeleton RGB Transformer to Recognize Fine-grained Human-Object Interactions and Action Recognition" IEEE Access (2023) 10.1109/access.2023.3278974

[46]

Lamm "Precision weed control system for cotton" Trans. ASAE (2002)

[47]

Prabakaran "A Bidirectional LSTM approach for written script auto evaluation using keywords-based pattern matching" Nat. Lang. Process. J. (2023) 10.1016/j.nlp.2023.100033

[48]

Holzinger "From industry 5.0 to forestry 5.0: Bridging the gap with human-centered artificial intelligence" Curr. For. Rep. (2024) 10.1007/s40725-024-00231-7

[49]

Robotic arms in precision agriculture: A comprehensive review of the technologies, applications, challenges, and future prospects

Tantan Jin, Xiongzhe Han

Computers and Electronics in Agriculture 2024 10.1016/j.compag.2024.108938

[50]

Lund "Application accuracy of a machine vision-controlled robotic micro-dosing system" Biosyst. Eng. (2007) 10.1016/j.biosystemseng.2006.11.009

Showing 50 of 176 references

Metrics

8

Citations

176

References

Details

Published: Jun 20, 2025
Vol/Issue: 14(13)
Pages: 2517
License: View

Authors

C

Chaw Thiri San

Department of Integrated System Engineering, Inha University, Incheon 22212, Republic of Korea

V

Vijay Kakani

Department of Integrated System Engineering, Inha University, Incheon 22212, Republic of Korea

Funding

Inha University Award: This work is supported by INHA UNIVERSITY Research Grant

Cite This Article

Chaw Thiri San, Vijay Kakani (2025). Smart Precision Weeding in Agriculture Using 5IR Technologies. Electronics, 14(13), 2517. https://doi.org/10.3390/electronics14132517

Smart Precision Weeding in Agriculture Using 5IR Technologies

You May Also Like