scholarly journals WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5473 ◽  
Author(s):  
Jie Hu ◽  
Tuan Wang ◽  
Jiacheng Yang ◽  
Yubin Lan ◽  
Shilei Lv ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Direction ◽  
Raspberry Pi ◽  
Target Area ◽  
Droplet Deposition ◽  
Communication Latency ◽  
Pesticide Drift ◽  
Adjustment System ◽  
Drift Control

Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV.

scholarly journals Effect of Droplet Size Parameters on Droplet Deposition and Drift of Aerial Spraying by Using Plant Protection UAV

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 195 ◽  
Author(s):  
Shengde Chen ◽  
Yubin Lan ◽  
Zhiyan Zhou ◽  
Fan Ouyang ◽  
Guobin Wang ◽  
...  
Keyword(s):  
Droplet Size ◽  
Drift Rate ◽  
Plant Protection ◽  
Buffer Zone ◽  
Target Area ◽  
Droplet Deposition ◽  
Factors Affecting ◽  
Median Diameter ◽  
Pesticide Drift ◽  
Aerial Spraying

In the field of pesticide spraying, droplet size is one of the most important factors affecting droplet deposition and drift. In order to study the effect of different droplet size parameters on droplet deposition distribution and drift of aerial spraying by using plant protection UAV, an aerial spraying test with the same spraying rate and different size droplets in rice canopy was carried out by using multi-rotor unmanned aerial vehicles (UAV) and four TEEJET nozzles with different orifice sizes (these droplets with a volume median diameter (VMD) of 95.21, 121.43, 147.28, and 185.09 μm, respectively), and the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were compared and analyzed. The results showed that the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were influenced by the droplet size. The droplet deposition rate in the upper and lower rice canopies were increased in the target area with the increase of droplet size. The penetration results of droplets also increased with the increase of droplet size, and that of droplets with a VMD of 185.09 μm was the best, reaching 38.13%. The average values of the cumulative drift rate of droplets in the rice canopy in the four tests were 73.87%, 50.26%, 35.91%, and 23.06%, respectively, and the cumulative drift rate and the drift distance of droplets decreased with the increase of droplet size, which indicated that the increase of droplet size can effectively reduce droplet drift. It demonstrated that the droplet size is one of the most important factors affecting droplet deposition and drift for pesticide spraying by plant protection UAV, and for the application of plant protection UAV with extra-low volume spraying, the use of droplets with VMD less than 160 μm should be avoided and a more than 10 m buffer zone should be considered downwind of the spraying field to avoid drug damage caused by pesticide drift. The results have fully revealed the effect of droplet size parameters on droplet deposition and drift of aerial spraying. Moreover, the influence of the wind field below the rotors on the distribution of droplet deposition was surmised and analyzed from the perspective of plant protection UAV. It is important for optimizing the droplet parameters of aerial spraying, increasing the spraying efficiency, and realizing precision agricultural aviation spray.

Short-Term Wind Speed Combination Prediction Model of Neural Network and Time Series

2012 ◽  
Vol 608-609 ◽  
pp. 764-769
Author(s):  
Hao Zheng ◽  
Jian Yan Tian ◽  
Fang Wang ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Prediction Model ◽  
Wind Direction ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Input Variables

This paper uses neural network combined with time series to establish rolling neural network model to predict short-term wind speed in the wind farm. In order to improve wind speed prediction accuracy, this paper analyzes effects of wind direction on wind speed by gray correlation analysis and obtains the correlation coefficient between wind speed at next moment and current wind direction is the largest by calculating. Then wind direction at current moment, historical wind speed and residuals which determined by time series are used as input variables to establish wind prediction model with rolling BP neural network. The simulation results show that neural network combined with time series which considers wind direction could improve the prediction accuracy when wind speed fluctuation is large.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

scholarly journals A hybrid deep interval prediction model for wind speed forecasting

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Yuhong Li ◽  
Xinbiao Chen ◽  
Chaoshun Li ◽  
Geng Tang ◽  
Zhenhao Gan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Speed Forecasting ◽  
Interval Prediction

scholarly journals 2.5D Flexible Wind Sensor Using Differential Plate Capacitors

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3101
Author(s):  
Yu Wan ◽  
Zhenxiang Yi
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Parallel Plate ◽  
Electrode Array ◽  
Copper Electrode ◽  
Windward Side ◽  
Leeward Side ◽  
Electrode Layer ◽  
Young’S Moduli ◽  
Speed Up

In this paper, a novel 2.5-dimensional (2.5D) flexible wind sensor is proposed based on four differential plate capacitors. This design consists of a windward pillar, two electrode layers, and a support layer, which are all made of polydimethylsiloxane (PDMS) with different Young’s moduli. A 2 mm × 2 mm copper electrode array is located on each electrode layer, forming four parallel plate capacitors as the sensitive elements. The wind in the xy-plane tilts the windward pillar, decreasing two capacitances on the windward side and increasing two capacitances on the leeward side. The wind in the z-axis depresses the windward pillar, resulting in an increase of all four capacitances. Experiments demonstrate that this sensor can measure the wind speed up to 23.9 m/s and the wind direction over the full 360° range of the xy-plane. The sensitivities of wind speed are close to 4 fF·m−1·s and 3 fF·m−1·s in the xy-plane and z-axis, respectively.

scholarly journals A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 215892-215903
Author(s):  
Ji Jin ◽  
Bin Wang ◽  
Min Yu ◽  
Jiang Liu ◽  
Wenbo Wang
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Atmospheric Motion ◽  
Self Adaptive

Movement of Large Convective Rainstorms in Relation to Winds Aloft

1958 ◽  
Vol 39 (3) ◽  
pp. 129-136 ◽  
Author(s):  
C. W. Newton ◽  
Sey Katz
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Rainfall Data ◽  
Squall Lines ◽  
Hourly Rainfall ◽  
The Difference ◽  
Strong Winds

By means of hourly rainfall data from the Hydroclimatic Network, the motions of large rainstorms, of the kind associated with squall lines, are examined in relation to the winds aloft. Very little correlation is found between the speed of movement of the rainstorms and the wind speed at any level, although the fastest moving storms were associated with strong winds aloft. Significant correlation is found between direction of motion of rainstorms, and wind direction at 700 mb or higher levels. On the average, the rainstorms move with an appreciable component toward right of the wind direction. The difference between these results, and those from other studies based on small precipitation areas, is ascribed to propagation. The mechanism involved is discussed briefly.

scholarly journals Surface gas pollutants in Lhasa, a highland city of Tibet – current levels and pollution implications

2014 ◽  
Vol 14 (19) ◽  
pp. 10721-10730 ◽  
Author(s):  
L. Ran ◽  
W. L. Lin ◽  
Y. Z. Deji ◽  
B. La ◽  
P. M. Tsering ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Fossil Fuels ◽  
Trace Gases ◽  
Early Morning ◽  
Current Status ◽  
Urban Site ◽  
Large Variability ◽  
Mixing Ratios ◽  
Low Wind Speed

Abstract. Through several years of development, the city of Lhasa has become one of the most populated and urbanized areas on the highest plateau in the world. In the process of urbanization, current and potential air quality issues have been gradually concerned. To investigate the current status of air pollution in Lhasa, various gas pollutants including NOx, CO, SO2, and O3, were continuously measured from June 2012 to May 2013 at an urban site (29.40° N, 91.08° E, 3650 m a.s.l.). The seasonal variations of primary gas pollutants exhibited a peak from November to January with a large variability. High mixing ratios of primary trace gases almost exclusively occurred under low wind speed and showed no distinct dependence on wind direction, implying local urban emissions to be predominant. A comparison of NO2, CO, and SO2 mixing ratios in summer between 1998 and 2012 indicated a significant increase in emissions of these gas pollutants and a change in their intercorrelations, as a result of a substantial growth in the demand of energy consumption using fossil fuels instead of previously widely used biomass. The pronounced diurnal double peaks of primary trace gases in all seasons suggested automobile exhaust to be a major emission source in Lhasa. The secondary gas pollutant O3 displayed an average diurnal cycle of a shallow flat peak for about 4–5 h in the afternoon and a minimum in the early morning. Nighttime O3 was sometimes completely consumed by the high level of NOx. Seasonally, the variations of O3 mixing ratios displayed a low valley in winter and a peak in spring. In autumn and winter, transport largely contributed to the observed O3 mixing ratios, given its dependence on wind speed and wind direction, while in spring and summer photochemistry played an important role. A more efficient buildup of O3 mixing ratios in the morning and a higher peak in the afternoon was found in summer 2012 than in 1998. An enhancement in O3 mixing ratios would be expected in the future and more attention should be given to O3 photochemistry in response to increasing precursor emissions in this area.

Numerical Investigation of Thermal Flow Field of Air-Cooled Condensers for Yongshou Plant

2012 ◽  
Vol 248 ◽  
pp. 391-394
Author(s):  
Wen Zhou Yan ◽  
Wan Li Zhao ◽  
Qiu Yan Li
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Speed ◽  
Power Plant ◽  
Flow Field ◽  
Wind Direction ◽  
Rotational Speed ◽  
Power Plants ◽  
Thermal Flow ◽  
Air Cooled Condensers

By using the computational fluid dynamics code, FLUENT, Numerically simulation is investigated for Youngshou power plant. Under the constant ambient temperature, the effects of different wind speed and wind direction on the thermal flow field are qualitatively considered. It was found that when considering about the existing and normally operating power plants, the thermal flow field is more sensitive to wind direction and wind speed. Based on the above results, three improved measures such as: increasing the wind-wall height and accelerating the rotational speed of the fans near the edge of the ACC platform and lengthen or widen the platform are developed to effectively improving the thermal flow field, and enhanced the heat dispersal of ACC.

What can idealized storm surge simulations tell us about worst case scenarios?

2021 ◽  
Author(s):  
Elin Andrée ◽  
Jian Su ◽  
Martin Drews ◽  
Morten Andreas Dahl Larsen ◽  
Asger Bendix Hansen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Level ◽  
North Sea ◽  
Wind Direction ◽  
Water Levels ◽  
Sea Levels ◽  
The North ◽  
The North Sea ◽  
Sea Coast ◽  
North Sea Coast

<p>The potential impacts of extreme sea level events are becoming more apparent to the public and policy makers alike. As the magnitude of these events are expected to increase due to climate change, and increased coastal urbanization results in ever increasing stakes in the coastal zones, the need for risk assessments is growing too.</p><p>The physical conditions that generate extreme sea levels are highly dependent on site specific conditions, such as bathymetry, tidal regime, wind fetch and the shape of the coastline. For a low-lying country like Denmark, which consists of a peninsula and islands that partition off the semi-enclosed Baltic Sea from the North Sea, a better understanding of how the local sea level responds to wind forcing is urgently called for.</p><p>We here present a map for Denmark that shows the most efficient wind directions for generating extreme sea levels, for a total of 70 locations distributed all over the country’s coastlines. The maps are produced by conducting simulations with a high resolution, 3D-ocean model, which is used for operational storm surge modelling at the Danish Meteorological Institute. We force the model with idealized wind fields that maintain a fixed wind speed and wind direction over the entire model domain. Simulations are conducted for one wind speed and one wind direction at a time, generating ensembles of a set of wind directions for a fixed wind speed, as well as a set of wind speeds for a fixed wind direction, respectively.</p><p>For each wind direction, we find that the maximum water level at a given location increases linearly with the wind speed, and the slope values show clear spatial patterns, for example distinguishing the Danish southern North Sea coast from the central or northern North Sea Coast. The slope values are highest along the southwestern North Sea coast, where the passage of North Atlantic low pressure systems over the shallow North Sea, as well as the large tidal range, result in a much larger range of variability than in the more sheltered Inner Danish Waters. However, in our simulations the large fetch of the Baltic Sea, in combination with the funneling effect of the Danish Straits, result in almost as high water levels as along the North Sea coast.</p><p>Although the wind forcing is completely synthetic with no spatial and temporal structure of a real storm, this idealized approach allows us to systematically investigate the sea level response at the boundaries of what is physically plausible. We evaluate the results from these simulations by comparison to peak water levels from a 58 year long, high resolution ocean hindcast, with promising agreement.</p>

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