scholarly journals A new rapid, low-cost and GPS-centric unmanned aerial vehicle incorporating in-situ multispectral oil palm trees health detection.

2018 ◽  
Vol 02 (04) ◽  
Author(s):  
Bryan D. See ◽  
Shaiful J. Hashim ◽  
Helmi Z. M. Shafri ◽  
Syaril Azrad ◽  
Mohd. Roshdi Hassan
2021 ◽  
Vol 173 ◽  
pp. 95-121
Author(s):  
Juepeng Zheng ◽  
Haohuan Fu ◽  
Weijia Li ◽  
Wenzhao Wu ◽  
Le Yu ◽  
...  

2020 ◽  
Vol 16 (2) ◽  
pp. 69-80
Author(s):  
Heri Santoso

Surveillance and Mapping of Basal Stem Rot Disease in Oil Palm Plantation Using Unmanned Aerial Vehicle (UAV) and Multispectral Camera Basal stem rot (BSR) disease caused by Ganoderma boninensis is still a major disease in oil palm plantations both in Indonesia and Malaysia. In some countries, remote sensing approach has been used for monitoring BSR in oil palm plantation. However, the utilization of satellite imagery in remote sensing especially in vegetation study on the tropical region was often limited by cloud cover. A drone or unmanned aerial vehicle (UAV) utilization is the best way to deal with cloud cover in the tropic region. Machine learning of random forest (RF) and satellite imagery used in the BSR study produced good accuracy. This research was aimed to identify and monitor the BSR infection on individual oil palm trees using an UAV and multispectral camera and RF classification. The results showed that the data acquired from UAV was affected by cloud shadows. The RF classification of healthy and infected oil palm trees by BSR disease and the spreading map of BSR infection was affected by cloud shadows. The highest accuracy of healthy and infected oil palm by BSR was 79.49%. Reflectance calibrator, digital to reflectance conversion, and model implications to build spreading map of BSR infection need to be conducted both on the clear area and the cloud shadow-covered area. Moreover, the UAV-based data should be considering the cloud view on the coverage area.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yunsheng Wang ◽  
Antero Kukko ◽  
Eric Hyyppä ◽  
Teemu Hakala ◽  
Jiri Pyörälä ◽  
...  

Abstract Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.


2018 ◽  
Vol 24 (6) ◽  
pp. 4276-4280
Author(s):  
Nasruddin Abu Sari ◽  
Abd Wahid Rasib ◽  
Nur Amalina Mohd Ropi ◽  
Asmala Ahmad ◽  
Mohd Yazid Abu Sari ◽  
...  

2018 ◽  
Vol 12 (11) ◽  
pp. 3535-3550 ◽  
Author(s):  
Richard Fernandes ◽  
Christian Prevost ◽  
Francis Canisius ◽  
Sylvain G. Leblanc ◽  
Matt Maloley ◽  
...  

Abstract. Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼2 and ∼15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.


2019 ◽  
Vol 12 (11) ◽  
pp. 6113-6124 ◽  
Author(s):  
Fan Zhou ◽  
Shengda Pan ◽  
Wei Chen ◽  
Xunpeng Ni ◽  
Bowen An

Abstract. Air pollution from ship exhaust gas can be reduced by the establishment of emission control areas (ECAs). Efficient supervision of ship emissions is currently a major concern of maritime authorities. In this study, a measurement system for exhaust gas from ships based on an unmanned aerial vehicle (UAV) was designed and developed. Sensors were mounted on the UAV to measure the concentrations of SO2 and CO2 in order to calculate the fuel sulfur content (FSC) of ships. The Waigaoqiao port in the Yangtze River Delta, an ECA in China, was selected for monitoring compliance with FSC regulations. Unlike in situ or airborne measurements, the proposed measurement system could be used to determine the smoke plume at about 5 m from the funnel mouth of ships, thus providing a means for estimating the FSC of ships. In order to verify the accuracy of these measurements, fuel samples were collected at the same time and sent to the laboratory for chemical examination, and these two types of measurements were compared. After 23 comparative experiments, the results showed that, in general, the deviation of the estimated value for FSC was less than 0.03 % (m/m) at an FSC level ranging from 0.035 % (m/m) to 0.24 % (m/m). Hence, UAV measurements can be used for monitoring of ECAs for compliance with FSC regulations.


Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4705 ◽  
Author(s):  
Adil Shah ◽  
Joseph Pitt ◽  
Khristopher Kabbabe ◽  
Grant Allen

Point-source methane emission flux quantification is required to help constrain the global methane budget. Facility-scale fluxes can be derived using in situ methane mole fraction sampling, near-to-source, which may be acquired from an unmanned aerial vehicle (UAV) platform. We test a new non-dispersive infrared methane sensor by mounting it onto a small UAV, which flew downwind of a controlled methane release. Nine UAV flight surveys were conducted on a downwind vertical sampling plane, perpendicular to mean wind direction. The sensor was first packaged in an enclosure prior to sampling which contained a pump and a recording computer, with a total mass of 1.0 kg. The packaged sensor was then characterised to derive a gain factor of 0.92 ± 0.07, independent of water mole fraction, and an Allan deviation precision (at 1 Hz) of ±1.16 ppm. This poor instrumental precision and possible short-term drifts made it non-trivial to define a background mole fraction during UAV surveys, which may be important where any measured signal is small compared to sources of instrumental uncertainty and drift. This rendered the sensor incapable of deriving a meaningful flux from UAV sampling for emissions of the order of 1 g s−1. Nevertheless, the sensor may indeed be useful when sampling mole fraction enhancements of the order of at least 10 ppm (an order of magnitude above the 1 Hz Allan deviation), either from stationary ground-based sampling (in baseline studies) or from mobile sampling downwind of sources with greater source flux than those observed in this study. While many methods utilising low-cost sensors to determine methane flux are being developed, this study highlights the importance of adequately characterising and testing all new sensors before they are used in scientific research.


10.14311/754 ◽  
2005 ◽  
Vol 45 (4) ◽  
Author(s):  
P. Kaňovský ◽  
L. Smrcek ◽  
C. Goodchild

The study described in this paper deals with the issue of a design tool for the autopilot of an Unmanned Aerial Vehicle (UAV) and the selection of the airdata and inertial system sensors. This project was processed in cooperation with VTUL a PVO o.z. [1]. The feature that distinguishes the autopilot requirements of a UAV (Figs. 1, 7, 8) from the flight systems of conventional manned aircraft is the paradox of controlling a high bandwidth dynamical system using sensors that are in harmony with the low cost low weight objectives that UAV designs are often expected to achieve. The principal function of the autopilot is flight stability, which establishes the UAV as a stable airborne platform that can operate at a precisely defined height. The main sensor for providing this height information is a barometric altimeter. The solution to the UAV autopilot design was realised with simulations using the facilities of Matlab® and in particular Simulink®[2]. 


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