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Accepted manuscript

Drone-Based Herbicide Application: Opportunities and Challenges

Published online by Cambridge University Press:  26 February 2026

Olumide S. Daramola
Affiliation:
Postdoctoral Fellow, Kansas State University, Agricultural Research Center, Hays, KS, USA
Thomas R. Butts
Affiliation:
Clinical Assistant Professor, Purdue University, Department of Botany and Plant Pathology, West Lafayette, IN, USA
Simerjeet Virk
Affiliation:
Associate Professor, Auburn University, Biosystems Engineering, Auburn, AL, USA
Bholuram Gurjar
Affiliation:
Postdoctoral Research Associate, Texas A&M University, Department of Soil and Crop Sciences, College Station, TX, USA
Ubaldo Torres
Affiliation:
Graduate Student, Texas A&M University, Department of Soil and Crop Sciences, College Station, TX, USA;
Muthukumar Bagavathiannan
Affiliation:
Professor, Texas A&M University, Department of Soil and Crop Sciences, College Station, TX, USA
J. Anita Dille
Affiliation:
Professor, Kansas State University, Department of Agronomy, Manhattan, KS, USA
Augustine K. Obour
Affiliation:
Professor, Kansas State University, Agricultural Research Center, Hays, KS, USA
Koffi Badou-Jeremie Kouame*
Affiliation:
Assistant Professor, Kansas State University, Agricultural Research Center, Hays, KS, USA
*
Author for correspondence: Koffi Badou-Jeremie Kouame, Assistant Professor, Kansas State University, Agricultural Research Center, 1232 240th Ave, Hays, KS 67601.Email: jkouame@ksu.edu
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Abstract

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Advancements in precision agriculture have driven the development of spray drones for herbicide application, offering the potential to address challenges associated with current application methods and improve weed management. This review synthesizes current research on spray drones to develop broad-use recommendations and identify challenges and knowledge gaps. Although spray drones use lower carrier volumes than ground-based sprayers (high-volume backpack or tractor-mounted sprayers), studies report comparable or superior weed control as well as herbicide cost savings. However, spray drone performance is highly sensitive to operational parameters, as spray distribution and coverage/deposition are strongly affected by flight height and speed, carrier volume, nozzle design, crop growth stage, weed, and weather conditions. The bell-shaped curve of a single-pass spray pattern, which results in most spray deposition occurring directly under the unmanned aerial vehicle (UAV), coupled with advances in imaging, remote sensing, and machine learning, demonstrate the strong potential of spray drones for site-specific weed management. Vegetation indices, multispectral imagery, canopy height models, and Light Detection And Ranging (LiDAR) technology have enabled crop-weed discrimination, though accuracy varies with species, growth stage, and image resolution. Deep-learning models such as ‘You Only Look Once’ (YOLO), Residual Neural Network (ResNet) and Mask Region-based Convolutional Neural Network (Mask R-CNN) achieve high performance for weed detection and/or segmentation but remain limited by training data quality and reduced accuracy with small, overlapping, or dense weed populations. Spray drone-based offline mapping has enabled substantial herbicide savings by delineating weed patches, whereas real-time weed detection is constrained by onboard processing limits, battery life, and lower spatial resolution at operational flight heights. Ground-based smart sprayers offer higher real-time detection precision but lack the field accessibility advantages of spray drones. Despite their potential, spray drones face challenges, including limited payload, off-target movement of pesticides, short battery life, regulatory challenges, and extensive license and complex software and calibration requirements. The downwind spray drift potential of spray drones is greater than ground applications but smaller than manned aerial applications. An upwind swath offset is an ideal best management practice to reduce off-target pesticide movement to susceptible areas from both manned and spray drone equipment. Future research should evaluate spray drones within integrated weed management systems, focusing on preemergence and foliar-applied contact herbicides, adjuvant use, environmental and operational interactions to develop spray drone-specific guidelines and optimize spray performance.

Information

Type
Review
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of Weed Science Society of America