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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
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    File URL: https://hal-ciheam.iamm.fr/hal-04297703v1/document
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    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. Snyder, Hannah, 2019. "Literature review as a research methodology: An overview and guidelines," Journal of Business Research, Elsevier, vol. 104(C), pages 333-339.
    4. 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.
    5. 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.
    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).
    7. 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.
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    1. Ruiheng Li & Xuaner Wang & Yuzhuo Cui & Yifei Xu & Yuhao Zhou & Xuechun Tang & Chenlu Jiang & Yihong Song & Hegan Dong & Shuo Yan, 2025. "A Semi-Supervised Diffusion-Based Framework for Weed Detection in Precision Agricultural Scenarios Using a Generative Attention Mechanism," Agriculture, MDPI, vol. 15(4), pages 1-25, February.

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