17 research outputs found
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Precision farming robots, which target to reduce the amount of herbicides
that need to be brought out in the fields, must have the ability to identify
crops and weeds in real time to trigger weeding actions. In this paper, we
address the problem of CNN-based semantic segmentation of crop fields
separating sugar beet plants, weeds, and background solely based on RGB data.
We propose a CNN that exploits existing vegetation indexes and provides a
classification in real time. Furthermore, it can be effectively re-trained to
so far unseen fields with a comparably small amount of training data. We
implemented and thoroughly evaluated our system on a real agricultural robot
operating in different fields in Germany and Switzerland. The results show that
our system generalizes well, can operate at around 20Hz, and is suitable for
online operation in the fields.Comment: Accepted for publication at IEEE International Conference on Robotics
and Automation 2018 (ICRA 2018
Ocean Eddy Identification and Tracking using Neural Networks
Global climate change plays an essential role in our daily life. Mesoscale
ocean eddies have a significant impact on global warming, since they affect the
ocean dynamics, the energy as well as the mass transports of ocean circulation.
From satellite altimetry we can derive high-resolution, global maps containing
ocean signals with dominating coherent eddy structures. The aim of this study
is the development and evaluation of a deep-learning based approach for the
analysis of eddies. In detail, we develop an eddy identification and tracking
framework with two different approaches that are mainly based on feature
learning with convolutional neural networks. Furthermore, state-of-the-art
image processing tools and object tracking methods are used to support the eddy
tracking. In contrast to previous methods, our framework is able to learn a
representation of the data in which eddies can be detected and tracked in more
objective and robust way. We show the detection and tracking results on sea
level anomalies (SLA) data from the area of Australia and the East Australia
current, and compare our two eddy detection and tracking approaches to identify
the most robust and objective method.Comment: accepted for International Geoscience and Remote Sensing Symposium
201
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
The application of autonomous robots in agriculture is gaining increasing
popularity thanks to the high impact it may have on food security,
sustainability, resource use efficiency, reduction of chemical treatments, and
the optimization of human effort and yield. With this vision, the Flourish
research project aimed to develop an adaptable robotic solution for precision
farming that combines the aerial survey capabilities of small autonomous
unmanned aerial vehicles (UAVs) with targeted intervention performed by
multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview
of the scientific and technological advances and outcomes obtained in the
project. We introduce multi-spectral perception algorithms and aerial and
ground-based systems developed for monitoring crop density, weed pressure, crop
nitrogen nutrition status, and to accurately classify and locate weeds. We then
introduce the navigation and mapping systems tailored to our robots in the
agricultural environment, as well as the modules for collaborative mapping. We
finally present the ground intervention hardware, software solutions, and
interfaces we implemented and tested in different field conditions and with
different crops. We describe a real use case in which a UAV collaborates with a
UGV to monitor the field and to perform selective spraying without human
intervention.Comment: Published in IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp.
29-49, Sept. 202
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Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study
Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat