IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i10p1004-d656079.html
   My bibliography  Save this article

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

Author

Listed:
  • Nur Adibah Mohidem

    (Department of Agriculture Technology, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Malaysia)

  • Nik Norasma Che’Ya

    (Department of Agriculture Technology, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Malaysia)

  • Abdul Shukor Juraimi

    (Department of Crop Science, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Malaysia)

  • Wan Fazilah Fazlil Ilahi

    (Department of Agriculture Technology, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Malaysia)

  • Muhammad Huzaifah Mohd Roslim

    (Department of Crop Science, Faculty of Agricultural Science and Forestry, University Putra Malaysia Bintulu Campus, Bintulu 97000, Malaysia)

  • Nursyazyla Sulaiman

    (Department of Agriculture Technology, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Malaysia)

  • Mohammadmehdi Saberioon

    (Section 1.4 Remote Sensing and Geoinformatics, German Research Centre for Geosciences (GFZ), Telegrafenberg, 14473 Potsdam, Germany)

  • Nisfariza Mohd Noor

    (Department of Geography, Faculty of Arts and Social Sciences, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

Suggested Citation

  • Nur Adibah Mohidem & Nik Norasma Che’Ya & Abdul Shukor Juraimi & Wan Fazilah Fazlil Ilahi & Muhammad Huzaifah Mohd Roslim & Nursyazyla Sulaiman & Mohammadmehdi Saberioon & Nisfariza Mohd Noor, 2021. "How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?," Agriculture, MDPI, vol. 11(10), pages 1-27, October.
  • Handle: RePEc:gam:jagris:v:11:y:2021:i:10:p:1004-:d:656079
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/10/1004/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/11/10/1004/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    2. Francisco Manuel Jiménez-Brenes & Francisca López-Granados & Jorge Torres-Sánchez & José Manuel Peña & Pilar Ramírez & Isabel Luisa Castillejo-González & Ana Isabel de Castro, 2019. "Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-21, June.
    3. Huasheng Huang & Jizhong Deng & Yubin Lan & Aqing Yang & Xiaoling Deng & Lei Zhang, 2018. "A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    4. Prabakaran, G. & Vaithiyanathan, D. & Ganesan, Madhavi, 2021. "FPGA based effective agriculture productivity prediction system using fuzzy support vector machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 1-16.
    5. Olatunji, Obafemi O. & Akinlabi, Stephen & Madushele, Nkosinathi & Adedeji, Paul A., 2020. "Property-based biomass feedstock grading using k-Nearest Neighbour technique," Energy, Elsevier, vol. 190(C).
    6. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haotian Pei & Youqiang Sun & He Huang & Wei Zhang & Jiajia Sheng & Zhiying Zhang, 2022. "Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4," Agriculture, MDPI, vol. 12(7), pages 1-18, July.

    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. İlkay Unay-Gailhard & Mark A. Brennen, 2022. "How digital communications contribute to shaping the career paths of youth: a review study focused on farming as a career option," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 39(4), pages 1491-1508, December.
    2. Mahin Ghafari & Vali Baigi & Zahra Cheraghi & Amin Doosti-Irani, 2016. "The Prevalence of Asymptomatic Bacteriuria in Iranian Pregnant Women: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-10, June.
    3. Santos Urbina & Sofía Villatoro & Jesús Salinas, 2021. "Self-Regulated Learning and Technology-Enhanced Learning Environments in Higher Education: A Scoping Review," Sustainability, MDPI, vol. 13(13), pages 1-12, June.
    4. Nadine Desrochers & Adèle Paul‐Hus & Jen Pecoskie, 2017. "Five decades of gratitude: A meta‐synthesis of acknowledgments research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2821-2833, December.
    5. Maryono, Maryono & Killoes, Aditya Marendra & Adhikari, Rajendra & Abdul Aziz, Ammar, 2024. "Agriculture development through multi-stakeholder partnerships in developing countries: A systematic literature review," Agricultural Systems, Elsevier, vol. 213(C).
    6. Alene Sze Jing Yong & Yi Heng Lim & Mark Wing Loong Cheong & Ednin Hamzah & Siew Li Teoh, 2022. "Willingness-to-pay for cancer treatment and outcome: a systematic review," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 1037-1057, August.
    7. Agnieszka A. Tubis & Katarzyna Grzybowska, 2022. "In Search of Industry 4.0 and Logistics 4.0 in Small-Medium Enterprises—A State of the Art Review," Energies, MDPI, vol. 15(22), pages 1-26, November.
    8. García-Poole, Chloe & Byrne, Sonia & Rodrigo, María José, 2019. "How do communities intervene with adolescents at psychosocial risk? A systematic review of positive development programs," Children and Youth Services Review, Elsevier, vol. 99(C), pages 194-209.
    9. Qing Ye & Bao-Xin Qian & Wei-Li Yin & Feng-Mei Wang & Tao Han, 2016. "Association between the HFE C282Y, H63D Polymorphisms and the Risks of Non-Alcoholic Fatty Liver Disease, Liver Cirrhosis and Hepatocellular Carcinoma: An Updated Systematic Review and Meta-Analysis o," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-17, September.
    10. Bishal Mohindru & David Turner & Tracey Sach & Diana Bilton & Siobhan Carr & Olga Archangelidi & Arjun Bhadhuri & Jennifer A. Whitty, 2020. "Health State Utility Data in Cystic Fibrosis: A Systematic Review," PharmacoEconomics - Open, Springer, vol. 4(1), pages 13-25, March.
    11. Neal R. Haddaway & Matthew J. Page & Chris C. Pritchard & Luke A. McGuinness, 2022. "PRISMA2020: An R package and Shiny app for producing PRISMA 2020‐compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis," Campbell Systematic Reviews, John Wiley & Sons, vol. 18(2), June.
    12. Ding Zhu & Mindan Wu & Yuan Cao & Shihua Lin & Nanxia Xuan & Chen Zhu & Wen Li & Huahao Shen, 2018. "Heated humidification did not improve compliance of positive airway pressure and subjective daytime sleepiness in obstructive sleep apnea syndrome: A meta-analysis," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-16, December.
    13. Pelai, Ricardo & Hagerman, Shannon M. & Kozak, Robert, 2020. "Biotechnologies in agriculture and forestry: Governance insights from a comparative systematic review of barriers and recommendations," Forest Policy and Economics, Elsevier, vol. 117(C).
    14. Wesam Salah Alaloul & Muhammad Altaf & Muhammad Ali Musarat & Muhammad Faisal Javed & Amir Mosavi, 2021. "Systematic Review of Life Cycle Assessment and Life Cycle Cost Analysis for Pavement and a Case Study," Sustainability, MDPI, vol. 13(8), pages 1-38, April.
    15. Claudia Peters & Agnessa Kozak & Albert Nienhaus & Anja Schablon, 2020. "Risk of Occupational Latent Tuberculosis Infection among Health Personnel Measured by Interferon-Gamma Release Assays in Low Incidence Countries—A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 17(2), pages 1-16, January.
    16. Sehee Kim & Mihyeon Park & Sukhee Ahn, 2022. "The Impact of Antepartum Depression and Postpartum Depression on Exclusive Breastfeeding: A Systematic Review and Meta-Analysis," Clinical Nursing Research, , vol. 31(5), pages 866-880, June.
    17. Habarurema Jean Baptiste & Yan Guang Cai & A. Y. M. Atiquil Islam & Nzabalirwa Wenceslas, 2022. "A Systematic Review of University Social Responsibility in Post-Conflict Societies: The Case of the Great Lakes Region of East Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(1), pages 439-475, November.
    18. Yafei Shen & Weide Shao, 2022. "Influence of Hybrid Pedagogical Models on Learning Outcomes in Physical Education: A Systematic Literature Review," IJERPH, MDPI, vol. 19(15), pages 1-16, August.
    19. Nicola Andreij Rieg & Birgitta Gatersleben & Ian Christie, 2021. "Organizational Change Management for Sustainability in Higher Education Institutions: A Systematic Quantitative Literature Review," Sustainability, MDPI, vol. 13(13), pages 1-18, June.
    20. Alessandro Concari & Gerjo Kok & Pim Martens, 2020. "A Systematic Literature Review of Concepts and Factors Related to Pro-Environmental Consumer Behaviour in Relation to Waste Management Through an Interdisciplinary Approach," Sustainability, MDPI, vol. 12(11), pages 1-50, May.

    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:gam:jagris:v:11:y:2021:i:10:p:1004-:d:656079. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.