Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Wen, Sheng; Zhang, Quanyong; Yin, Xuanchun; Lan, Yubin; Zhang, Jiantao; Ge, Yufeng

doi:10.3390/s19051112

Cited by 35 publications

(16 citation statements)

References 41 publications

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“…According to Sheng et al.’s method [ 49 ], 0.1 g/L Rhodamine-B solution (analytically pure) was used to spray rather than pesticide. As shown in Figure 3 b, polyethylene terephthalate (PET) sheets (85.60 mm × 53.98 mm) were placed at 154 (11 × 14) sample points 0.3 m above the ground, and the spacing between them was 0.5 m. The UAV is equipped with four nozzles (XR11001VS, Tejet Spray Technology Co. Ltd., Tianjin, China), which are located on two sides of the UAV ( X = ±265, Z = ±711 mm).…”

Section: Application and Verification Of Cfd Simulationmentioning

confidence: 99%

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Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Wang

Liu

et al. 2021

Sensors

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Multi-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China’s agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to solve this problem. The distribution characteristics of droplet deposition under different spray states (flight state, environment state, nozzle state) are obtained through computational fluid dynamics simulation. In the verification experiment, the wind velocity error of most sample points is less than 1 m/s, and the deposition ratio error is less than 10%, indicating that the simulation is reliable. A simulation data set is used to train support vector regression and back propagation neural network with multiple parameters. An optimal regression model with the root mean square error of 6.5% is selected. The UAV offset and nozzle flow of the variable spray system can be obtained in accordance with the current spray state by multi-sensor fusion and the predicted deposition distribution characteristics. The farmland experiment shows that the deposition volume error between the prediction and experiment is within 30%, thereby proving the effectiveness of the system. This article provides a reference for the improvement of UAV intelligent spray system.

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Section: Application and Verification Of Cfd Simulationmentioning

confidence: 99%

“…Wen et al. designed a variable spray system based on wind tunnel test data and neural network model [ 49 ]. The outdoor experimental results show that this system can spray in accordance with the demand in complex states.…”

Section: Introductionmentioning

confidence: 99%

Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Wang

Liu

et al. 2021

Sensors

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“…Naturally, these surface ripples are likely to propagate into the water and affect the vertical distribution of water column characteristics (e.g., physical, chemical, and biological properties) before water sampling can be carried out. Thanks to the development of pesticide spraying UAS, much research has been carried out to understand the effects of downwash from UAS rotors on land (Wen et al 2019; Wu et al 2019). However, downwash effects on water masses have not yet been evaluated.…”

Section: Figurementioning

confidence: 99%

Effects of downwash during unmanned aircraft system‐assisted water sampling on water mass structure directly below the UAS

Miura

Kohzu

2020

Limnology & Ocean Methods

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In recent years, unmanned aircraft system (UAS) have played an increasing role in fields such as topographic mapping using optical cameras and thermal mapping using infrared cameras in aquatic environmental research. UAS-assisted water sampling has important advantages, such as reducing labor, increasing sampling speed, and enabling sample collection from as-yet unexplored lakes and marshes. However, downwash effects on water masses have not yet been evaluated. We conducted two experiments to evaluate the impact of UAS downwash on water mass characteristics. In the first experiment, we used temperature sensors and thermal imaging cameras to show that downwash generates bottom-water upwelling. The results of this experiment are consistent with our hypothesized mechanism for water mass perturbation. In the second experiment, we directly measured downwash speeds and used the data to recommend a maximum downwash speed of 4.0 m s −1 for UAS-assisted water sampling. However, we caution that even slower downwash speeds may be required for certain field conditions and research questions. Our results showed that the ongoing development of UAS-assisted water sampling must include efforts to measure and minimize downwash.

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“…Thanks to artificial intelligence algorithms, complex agricultural ecosystems can be better described and understood. Wen et al developed an unmanned aerial vehicles variable spray system based on an error back propagation artificial neural network (ANN) [25]. The ANN was trained to predict droplet deposition based on environment temperature, humidity, flight speed and altitude, wind speed, prescription value, nozzle pitch, and propeller pitch.…”

Section: Introductionmentioning

confidence: 99%

Average Degree of Coverage and Coverage Unevenness Coefficient as Parameters for Spraying Quality Assessment

Cieniawska

Pentoś

2021

Agriculture

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The purpose of the research was to determine the influence of selected factors on the average degree of coverage and uniformity of liquid spray coverage using selected single and dual flat fan nozzles. The impact of nozzle type, spray pressure, driving speed, and spray angle on the average degree of coverage and coverage unevenness coefficient were studied. The research was conducted with special spray track machinery designed and constructed to control and change the boom height, spray angle, driving speed, and spray pressure. Based on the research results, it was found that the highest average coverage was obtained for single standard flat fan nozzles and dual anti-drift flat fan nozzles. At the same time, the highest values of unevenness were observed for these nozzles. Inverse relationships were obtained for air-induction nozzles. Maximization of coverage with simultaneous minimization of unevenness can be achieved by using a medium droplet size for single flat fan nozzles (volume median diameter (VMD) = 300 μm) and coarse droplet size for dual flat fan nozzles (VMD = 352 μm), with low driving speed (respectively 1.1 m∙s−1 and 1.6 m∙s−1) and angling of the nozzle by 20° in the opposite direction to the direction of travel.

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Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Cited by 35 publications

References 41 publications

Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis

Effects of downwash during unmanned aircraft system‐assisted water sampling on water mass structure directly below the UAS

Average Degree of Coverage and Coverage Unevenness Coefficient as Parameters for Spraying Quality Assessment

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