Improvement Design of Agricultural Plant Protection UAV

Author(s):  
Xi Wang ◽  
Yongyi Gu ◽  
Chuanyuan Ye
Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 737
Author(s):  
Fengjie Sun ◽  
Xianchang Wang ◽  
Rui Zhang

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.


Plant Disease ◽  
2014 ◽  
Vol 98 (6) ◽  
pp. 708-715 ◽  
Author(s):  
James P. Stack ◽  
Richard M. Bostock ◽  
Raymond Hammerschmidt ◽  
Jeffrey B. Jones ◽  
Eileen Luke

The National Plant Diagnostic Network (NPDN) has developed into a critical component of the plant biosecurity infrastructure of the United States. The vision set forth in 2002 for a distributed but coordinated system of plant diagnostic laboratories at land grant universities and state departments of agriculture has been realized. NPDN, in concept and in practice, has become a model for cooperation among the public and private entities necessary to protect our natural and agricultural plant resources. Aggregated into five regional networks, NPDN laboratories upload diagnostic data records into a National Data Repository at Purdue University. By facilitating early detection and providing triage and surge support during plant disease outbreaks and arthropod pest infestations, NPDN has become an important partner among federal, state, and local plant protection agencies and with the industries that support plant protection.


2021 ◽  
Vol 11 (16) ◽  
pp. 7258
Author(s):  
Qi Liu ◽  
Shengde Chen ◽  
Guobin Wang ◽  
Yubin Lan

Background: Unmanned Aerial Vehicles (UAVs) applied to agricultural plant protection is widely used, and the field of operation is expanding due to their high efficiency and pesticide application reduction. However, the work on pesticide drift lags behind the development of the UAV spraying device. Methods: We compared the spray drift potential at four liquid pressures of 2, 3, 4, and 5 bar ejected from the hydraulic nozzles mounted on a UAV test platform exposed to different wind speeds of 2, 4, and 6 m/s produced by a wind tunnel. The combination of the wind tunnel and the UAV test platform was used to obtain strict test conditions. The droplet size distribution under spray drift pressures was measured by a laser diffraction instrument. Results: Increasing the pressure leads to smaller droplet volume diameters and produced fine droplets of less than 100 µm. The deposition in the drift area was elevated at most of the sampling locations by setting higher pressure and faster wind speed. The deposition ratios were all higher than the flow ratios under three wind speeds after the adjustment of pressures. For most samples within a short drift distance (2–8 m), the drift with the rotor motor off was more than an order of magnitude higher than that with the rotor motor on at a pressure of 3 bar. Conclusions: In this study, the wind speed and liquid pressure all had a significant effect on the UAV spray drift, and the rotor wind significantly inhibited a large number of droplets from drifting further.


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