Route Planning of Agricultural Plant Protection Unmanned Helicopter Based on Interfered Fluid Dynamical System

2017 ◽  
Author(s):  
Siyi Fan ◽  
Ming Chen
Keyword(s):  
Dynamical System ◽  
Plant Protection ◽  
Route Planning ◽  
Unmanned Helicopter ◽  
Agricultural Plant

scholarly journals Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 737
Author(s):  
Fengjie Sun ◽  
Xianchang Wang ◽  
Rui Zhang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Feasible Solution ◽  
Learning Algorithm ◽  
Plant Protection ◽  
Agricultural Plant ◽  
Q Learning ◽  
Aerial Vehicle ◽  
Optimal Action

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.

scholarly journals Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 533 ◽  
Author(s):  
Zhang ◽  
Wang ◽  
Chen ◽  
Jiang ◽  
Lin
Keyword(s):  
Flight Control ◽  
Plant Protection ◽  
Route Planning ◽  
Visual Navigation ◽  
Recognition Method ◽  
Landmark Identification ◽  
Color Differences ◽  
Color Background ◽  
Dark Green ◽  
Landmark Location

GPS (Global Positioning System) navigation in agriculture is facing many challenges, such as weak signals in orchards and the high cost for small plots of farmland. With the reduction of camera cost and the emergence of excellent visual algorithms, visual navigation can solve the above problems. Visual navigation is a navigation technology that uses cameras to sense environmental information as the basis of an aircraft flight. It is mainly divided into five parts: Image acquisition, landmark recognition, route planning, flight control, and obstacle avoidance. Here, landmarks are plant canopy, buildings, mountains, and rivers, with unique geographical characteristics in a place. During visual navigation, landmark location and route tracking are key links. When there are significant color-differences (for example, the differences among red, green, and blue) between a landmark and the background, the landmark can be recognized based on classical visual algorithms. However, in the case of non-significant color-differences (for example, the differences between dark green and vivid green) between a landmark and the background, there are no robust and high-precision methods for landmark identification. In view of the above problem, visual navigation in a maize field is studied. First, the block recognition method based on fine-tuned Inception-V3 is developed; then, the maize canopy landmark is recognized based on the above method; finally, local navigation lines are extracted from the landmarks based on the maize canopy grayscale gradient law. The results show that the accuracy is 0.9501. When the block number is 256, the block recognition method achieves the best segmentation. The average segmentation quality is 0.87, and time is 0.251 s. This study suggests that stable visual semantic navigation can be achieved under the near color background. It will be an important reference for the navigation of plant protection UAV (Unmanned Aerial Vehicle).

scholarly journals The National Plant Diagnostic Network: Partnering to Protect Plant Systems

Plant Disease ◽  
2014 ◽  
Vol 98 (6) ◽  
pp. 708-715 ◽  
Author(s):  
James P. Stack ◽  
Richard M. Bostock ◽  
Raymond Hammerschmidt ◽  
Jeffrey B. Jones ◽  
Eileen Luke
Keyword(s):  
Plant Protection ◽  
Disease Outbreaks ◽  
The United States ◽  
Federal State ◽  
Data Repository ◽  
Plant Resources ◽  
Public And Private ◽  
Agricultural Plant ◽  
Local Plant ◽  
State And Local

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.

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