Drone-Based Herbicide Application: Opportunities and Challenges

Abstract 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.