This study explores the efficacy of drone-acquired RGB images and the YOLO model in detecting the invasive species Siam weed (Chromolaena odorata) in natural environments. Siam weed is a perennial scrambling shrub from tropical and sub-tropical America that is invasive outside its native range, causing substantial environmental and economic impacts across Asia, Africa, and Oceania. First detected in Australia in northern Queensland in 1994 and later in the Northern Territory in 2019, there is an urgent need to determine the extent of its incursion across vast, rugged areas of both jurisdictions and a need for distribution mapping at a catchment scale. This study tests drone-based RGB imaging to train a deep learning model that contributes to the goal of surveying non-native vegetation at a catchment scale. We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. Drone-based RGB images were acquired from four sites in the Townsville region of Queensland to train and test a deep learning model (YOLOv5). Validation was performed through expert visual interpretation of the detection results in image tiles. The YOLOv5 model demonstrated over 0.85 in its F1-Score, which improved to over 0.95 with improved exposure to the images. A reliable detection model was found to be sufficiently trained with approximately 1000 image tiles, with additional images offering marginal improvement. Increased model complexity did not notably enhance model performance, indicating that a smaller model was adequate. False positives often originated from foliage and bark under high solar illumination, and low exposure images reduced these errors considerably. The study demonstrates the feasibility of using YOLO models to detect invasive species in natural landscapes, providing a safe alternative to the current method involving human spotters in helicopters. Future research will focus on developing tools to merge duplicates, gather georeference data, and report detections from large image datasets more efficiently, providing valuable insights for practical applications in environmental management at the catchment scale.

Remote sensing data are valuable for detecting, mapping, and managing invasive weed species. This article introduces an innovative algorithm for mapping Siam weed infestations using a bounding box approach from a deep learning object detection model. Supplemented with georeferencing of individual aerial images obtained from an uncrewed aerial vehicle (UAV), the study demonstrates the potential for large-scale mapping. High–resolution RGB images were used to develop a Siam weed detection model with YOLOv5, achieving 0.95 precision, 0.82 recall, and 0.88 F1–Score, indicating suitability for detecting flowering Siam weed. The validated model was applied to an independent single–flight dataset, enabling consecutive detections on high–resolution images. Georeferencing the detections was accomplished using a customized raycasting algorithm. To address fragmented and duplicated detections from overlapping images, the authors proposed buffering, dissolving, and filtering the georeferenced detection boxes. Therefore, revealing an extensive Siam weed infestation boundaries and pattern for a whole flight area. The developed algorithm successfully maps Siam weed infestations using an object detection bounding boxes approach on individual aerial images which is potentially advantageous for speed and scalability. It presents an opportunity to upscale weed detection and mapping with reduced overlapping percentage and higher payload aircraft, such as helicopters, allowing for larger–scale surveys and enhancing weed management teams' capacity to monitor extensive landscapes.