IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04297703.html
   My bibliography  Save this paper

Transforming weed management in sustainable agriculture with artificial intelligence: a systematic literature review towards weed identification and deep learning

Author

Listed:
  • Marios Vasileiou

    (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos])

  • Leonidas Sotirios Kyrgiakos

    (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos])

  • Christina Kleisiari

    (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos])

  • Georgios Kleftodimos

    (CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes, UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • George Vlontzos

    (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos])

  • Hatem Belhouchette

    (CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes, UMR ABSys - Agrosystèmes Biodiversifiés - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • Panos M. Pardalos

    (UF - University of Florida [Gainesville])

Abstract

Highlights: • AI in weed management potentials for transforming agricultural ecosystems. • AI influence in economic, social, technological, and environmental dimensions. • AI's role in enhancing food safety by reducing pesticides residues. • Digital literacy as a crucial enabler empowering stakeholders to use AI effectively. Abstract: In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture.

Suggested Citation

  • Marios Vasileiou & Leonidas Sotirios Kyrgiakos & Christina Kleisiari & Georgios Kleftodimos & George Vlontzos & Hatem Belhouchette & Panos M. Pardalos, 2024. "Transforming weed management in sustainable agriculture with artificial intelligence: a systematic literature review towards weed identification and deep learning," Post-Print hal-04297703, HAL.
    • Handle: RePEc:hal:journl:hal-04297703
    DOI: 10.1016/j.cropro.2023.106522
    Note: View the original document on HAL open archive server: https://hal-ciheam.iamm.fr/hal-04297703v1
    as

    Download full text from publisher

    File URL: https://hal-ciheam.iamm.fr/hal-04297703v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.cropro.2023.106522?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. R. Karina Gallardo & Johannes Sauer, 2018. "Adoption of Labor-Saving Technologies in Agriculture," Annual Review of Resource Economics, Annual Reviews, vol. 10(1), pages 185-206, October.
    2. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    3. Benjamin Costello & Olusegun O. Osunkoya & Juan Sandino & William Marinic & Peter Trotter & Boyang Shi & Felipe Gonzalez & Kunjithapatham Dhileepan, 2022. "Detection of Parthenium Weed ( Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence," Agriculture, MDPI, vol. 12(11), pages 1-23, November.
    4. Leonidas Sotirios Kyrgiakos & Georgios Kleftodimos & George Vlontzos & Panos M. Pardalos, 2023. "A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability," Operational Research, Springer, vol. 23(1), pages 1-38, March.
    5. Hui Zhang & Zhi Wang & Yufeng Guo & Ye Ma & Wenkai Cao & Dexin Chen & Shangbin Yang & Rui Gao, 2022. "Weed Detection in Peanut Fields Based on Machine Vision," Agriculture, MDPI, vol. 12(10), pages 1-15, September.
    6. Movedi, Ermes & Valiante, Daniele & Colosio, Alessandro & Corengia, Luca & Cossa, Stefano & Confalonieri, Roberto, 2022. "A new approach for modeling crop-weed interaction targeting management support in operational contexts: A case study on the rice weeds barnyardgrass and red rice," Ecological Modelling, Elsevier, vol. 463(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. McFadden, Jonathan & Njuki, Eric & Griffin, Terry, 2023. "Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms," USDA Miscellaneous 333550, United States Department of Agriculture.
    2. Genz, Sabrina & Schnabel, Claus, 2023. "Digitalization is not gender-neutral," Economics Letters, Elsevier, vol. 230(C).
    3. Barth, Erling & Davis, James C. & Freeman, Richard B. & McElheran, Kristina, 2023. "Twisting the demand curve: Digitalization and the older workforce," Journal of Econometrics, Elsevier, vol. 233(2), pages 443-467.
    4. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    5. Bárány, Zsófia L. & Siegel, Christian, 2020. "Biased technological change and employment reallocation," Labour Economics, Elsevier, vol. 67(C).
    6. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    7. Dilip Mookherjee & Debraj Ray, 2022. "Growth, Automation and the Long-Run Share of Labor," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 46, pages 1-26, October.
    8. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    9. Fedorets Alexandra & Kirchner Stefan & Adriaans Jule & Giering Oliver, 2022. "Data on Digital Transformation in the German Socio-Economic Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 242(5-6), pages 691-705, December.
    10. Milan Miric & Nan Jia & Kenneth G. Huang, 2023. "Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents," Strategic Management Journal, Wiley Blackwell, vol. 44(2), pages 491-519, February.
    11. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    12. Davide Antonioli & Alberto Marzucchi & Francesco Rentocchini & Simone Vannuccini, 2022. "Robot Adoption and Innovation Activities (last revised: December 2023)," Munich Papers in Political Economy 21, Munich School of Politics and Public Policy and the School of Management at the Technical University of Munich.
    13. Lionel Fontagné & Ariell Reshef & Gianluca Santoni & Giulio Vannelli, 2024. "Automation, global value chains and functional specialization," Review of International Economics, Wiley Blackwell, vol. 32(2), pages 662-691, May.
    14. Melih Erdem & Hasan Burak Ağır, 2024. "Enhancing Dairy Farm Welfare: A Holistic Examination of Technology Adoption and Economic Performance in Kahramanmaraş Province, Turkey," Sustainability, MDPI, vol. 16(7), pages 1-22, April.
    15. Nikolaos Terzidis & Raquel Ortega‐Argilés, 2021. "Employment polarization in regional labor markets: Evidence from the Netherlands," Journal of Regional Science, Wiley Blackwell, vol. 61(5), pages 971-1001, November.
    16. Gabriel Loumeau, 2022. "Land Consolidation Reforms: A Natural Experiment on the Economic and Political Effects of Agricultural Mechanization," CER-ETH Economics working paper series 22/376, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    17. Caleb Peppiatt, 2024. "The Future of Work: Inequality, Artificial Intelligence, and What Can Be Done About It. A Literature Review," Papers 2408.13300, arXiv.org.
    18. Cauvel, Michael & Pacitti, Aaron, 2022. "Bargaining power, structural change, and the falling U.S. labor share," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 512-530.
    19. Hernnäs, Sofia, 2023. "Automation when skills are bundled," Working Paper Series 2023:2, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    20. Huanan Liu & Yan Wang & Zhoufu Yan, 2024. "Artificial Intelligence and Food Processing Firms Productivity: Evidence from China," Sustainability, MDPI, vol. 16(14), pages 1-18, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04297703. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.