ORCID Profile
0000-0002-6780-2425
Current Organisations
CSIRO
,
Queensland University of Technology
,
Science and Engineering Faculty, Queensland University of Technology
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Publisher: IEEE
Date: 05-03-2022
Publisher: MDPI AG
Date: 08-11-2021
DOI: 10.3390/RS13214481
Abstract: Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner (2) offboard mode, which runs the POMDP-based planner across the flying area and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.
Publisher: Medknow
Date: 2016
Publisher: IEEE
Date: 05-03-2022
Publisher: IEEE
Date: 05-03-2022
Publisher: IEEE
Date: 08-2018
Publisher: MDPI AG
Date: 16-10-2020
DOI: 10.3390/RS12203386
Abstract: Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator.
Publisher: MDPI AG
Date: 03-12-2022
DOI: 10.3390/RS14236137
Abstract: White leaf disease (WLD) is an economically significant disease in the sugarcane industry. This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the pre-processing of the dataset, labelling, DL model tuning, and prediction. This study evaluated the performance of the existing DL models such as YOLOv5, YOLOR, DETR, and Faster R-CNN to recognize WLD in sugarcane crops. The experimental results indicate that the YOLOv5 network outperformed the other selected models, achieving a precision, recall, mean average precision@0.50 (mAP@0.50), and mean average precision@0.95 (mAP@0.95) metrics of 95%, 92%, 93%, and 79%, respectively. In contrast, DETR exhibited the weakest detection performance, achieving metrics values of 77%, 69%, 77%, and 41% for precision, recall, mAP@0.50, and mAP@0.95, respectively. YOLOv5 is selected as the recommended architecture to detect WLD using the UAV data not only because of its performance, but this was also determined because of its size (14 MB), which was the smallest one among the selected models. The proposed methodology provides technical guidelines to researchers and farmers for conduct the accurate detection and treatment of WLD in the sugarcane fields.
Publisher: Queensland University of Technology
Date: 2022
DOI: 10.5204/THESIS.EPRINTS.232513
Abstract: This study established a framework that increases cognitive levels in small UAVs (or drones), enabling autonomous navigation in partially observable environments. The UAV system was validated under search and rescue by locating victims last seen inside cluttered buildings and in bushlands. This framework improved the decision-making skills of the drone to collect more accurate statistics of detected victims. This study assists validation processes of detected objects in real-time when data is complex to interpret for UAV pilots and reduces human bias on scouting strategies.
Publisher: MDPI AG
Date: 24-09-2017
DOI: 10.3390/S17102196
Publisher: Universidad Nacional Abierta y a Distancia
Date: 19-03-2015
Abstract: En la actualidad, muchos estudios enfocados en el reconocimiento de patógenos biológicos, a través de los frutos de cultivos de fresa son efectivas, sin embargo la adquisición de la imagen se realiza mediante métodos destructivos que implican arrancar los frutos de la planta. En la presente investigación se ha propuesto el desarrollo de un algoritmo que permita analizar los frutos de un cultivo de fresa (Fragaria x ananassa), capaz de realizar una primera aproximación para distinguir Botrytis sp. y Sphaerotheca sp., usando un método no destructivo, es decir, recolectando las imágenes directamente del cultivo sin realizar intervención alguna por parte de los productores y/o investigadores. Las técnicas de procesamiento de imágenes implementadas incluyen suavizado, erosión, dilatación, detección de contornos, correspondencia de patrones, umbralización, entre otros. Los resultados obtenidos se visualizaron en una aplicación desarrollada en C# usando la librería Emgu CV, mostrando al usuario un diagnóstico de la planta de estudio. Se concluye que es posible ofrecer un servicio de monitoreo preliminar de incidencia de patógenos usando este algoritmo, ahorrando tiempo para productores e investigadores que requieran una primera aproximación del estado del cultivo, con la posibilidad de ejecutarse tanto en computadores de escritorio y portátiles como en robots aéreos (drones) que posibilitan automatizar esta tarea.
Publisher: MDPI AG
Date: 17-03-2023
DOI: 10.3390/RS15061633
Abstract: Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm ixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation.
Publisher: MDPI AG
Date: 02-12-2022
Abstract: Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration.
Publisher: FapUNIFESP (SciELO)
Date: 08-2016
DOI: 10.1590/1807-1929/AGRIAMBI.V20N8P716-721
Abstract: ABSTRACT In farming the measurement of leaf coverage is considered as an exhaustive task for the researchers due to most of the time they do not have access to the adequate tool for this purpose. A new algorithm, implemented in this investigation, allows to estimate by means of a non-destructive method, the leaf coverage value of strawberry plants (fragaria x ananassa) of the cultivar Albion in the Cajicá region, Colombia, by using digital image processing techniques ( DPI). The DPI based technique includes the smoothing, dilatation, contour detection, threshold and edges detection operations. The image acquisition system was conducted by means of photographic images in plants in study, directly from the beds of the crop and the captures were subsequently processed through the proposed algorithm. The obtained results show the measured values of the plants leaf coverage in cm2, with up to 90% of accuracy. This system gives an important contribution to the crop evolution analysis by computational tools, making easier the monitoring work.
Publisher: IEEE
Date: 21-06-2022
Publisher: IEEE
Date: 21-06-2022
Publisher: IEEE
Date: 05-03-2022
Publisher: Oriental Scientific Publishing Company
Date: 25-12-2017
DOI: 10.13005/BBRA/2572
Abstract: ABSTRACT: Improper application of pesticides in agricultural crops and indirect effects caused by exposure to them through consumption of contaminated crops, nowadays represent a serious risk to public health harmony. It is vital then, to know the degree of toxicity of each of these chemicals in order to properly regulate its application and sensitize the population at risk. Therefore, this paper shows the results of an algorithm with the ability to predict the effects on the reproductive system in Sprague Dawley rats, caused by the intake of food exposed with Fenthion. The original data were processed using the Naïve Bayes classifier, then optimized using genetic algorithms. It is concluded that the prediction algorithm does the job properly, processing qualitative information with relatively low computational cost, which allows its easy portability to different development platforms.
Publisher: IEEE
Date: 04-03-2023
Publisher: MDPI AG
Date: 16-02-2018
DOI: 10.3390/S18020605
Abstract: The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be limited due to the ground s ling resolution or cloud cover. Straightforward and accurate surveillance methods are needed to quantify rates of grass invasion, offer appropriate vegetation tracking reports, and apply optimal control methods. This paper presents a pipeline process to detect and generate a pixel-wise segmentation of invasive grasses, using buffel grass (Cenchrus ciliaris) and spinifex (Triodia sp.) as ex les. The process integrates unmanned aerial vehicles (UAVs) also commonly known as drones, high-resolution red, green, blue colour model (RGB) cameras, and a data processing approach based on machine learning algorithms. The methods are illustrated with data acquired in Cape Range National Park, Western Australia (WA), Australia, orthorectified in Agisoft Photoscan Pro, and processed in Python programming language, scikit-learn, and eXtreme Gradient Boosting (XGBoost) libraries. In total, 342,626 s les were extracted from the obtained data set and labelled into six classes. Segmentation results provided an in idual detection rate of 97% for buffel grass and 96% for spinifex, with a global multiclass pixel-wise detection rate of 97%. Obtained results were robust against illumination changes, object rotation, occlusion, background cluttering, and floral density variation.
Publisher: IEEE
Date: 03-2020
Publisher: Universidad del Cauca
Date: 2016
Publisher: MDPI AG
Date: 02-11-2022
DOI: 10.3390/AGRICULTURE12111838
Abstract: Parthenium weed (Parthenium hysterophorus L. (Asteraceae)), native to the Americas, is in the top 100 most invasive plant species in the world. In Australia, it is an annual weed (herb/shrub) of national significance, especially in the state of Queensland where it has infested both agricultural and conservation lands, including riparian corridors. Effective control strategies for this weed (pasture management, biological control, and herbicide usage) require populations to be detected and mapped. However, the mapping is made difficult due to varying nature of the infested landscapes (e.g., uneven terrain). This paper proposes a novel method to detect and map parthenium populations in simulated pastoral environments using Red-Green-Blue (RGB) and/or hyperspectral imagery aided by artificial intelligence. Two datasets were collected in a control environment using a series of parthenium and naturally co-occurring, non-parthenium (monocot) plants. RGB images were processed with a YOLOv4 Convolutional Neural Network (CNN) implementation, achieving an overall accuracy of 95% for detection, and 86% for classification of flowering and non-flowering stages of the weed. An XGBoost classifier was used for the pixel classification of the hyperspectral dataset—achieving a classification accuracy of 99% for each parthenium weed growth stage class all materials received a discernible colour mask. When parthenium and non-parthenium plants were artificially combined in various permutations, the pixel classification accuracy was 99% for each parthenium and non-parthenium class, again with all materials receiving an accurate and discernible colour mask. Performance metrics indicate that our proposed processing pipeline can be used in the preliminary design of parthenium weed detection strategies, and can be extended for automated processing of collected RGB and hyperspectral airborne unmanned aerial vehicle (UAV) data. The findings also demonstrate the potential for images collected in a controlled, glasshouse environment to be used in the preliminary design of invasive weed detection strategies in the field.
Publisher: MDPI AG
Date: 22-03-2018
DOI: 10.3390/S18040944
Location: Australia
Location: Australia
Location: Australia
Location: Australia
No related grants have been discovered for Juan Sandino.