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

Nur Adibah Mohidem, Nik Norasma Che’Ya, Abdul Shukor Juraimi, Wan Fazilah Fazlil Ilahi, Muhammad Huzaifah Mohd Roslim, Nursyazyla Sulaiman, Mohammadmehdi Saberioon and Nisfariza Mohd Noor
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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

Agriculture, 2021, vol. 11, issue 10, 1-27

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.

Keywords: precision agriculture; unmanned aerial vehicle; weed (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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