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 Acute Application of Imidacloprid Alters the Sensitivity of Direction Selective Motion Detecting Neurons in an Insect Pollinator

2021 ◽  
Vol 12 ◽  
Author(s):  
Elisa Rigosi ◽  
David C. O’Carroll
Keyword(s):  
Flight Control ◽  
Visual Processing ◽  
Visual Cues ◽  
Visual Motion ◽  
Plant Protection ◽  
Control Treatment ◽  
Wide Field ◽  
General Effect ◽  
The Brain

Cholinergic pesticides, such as the neonicotinoid imidacloprid, are the most important insecticides used for plant protection worldwide. In recent decades, concerns have been raised about side effects on non-target insect species, including altered foraging behavior and navigation. Although pollinators rely on visual cues to forage and navigate their environment, the effects of neonicotinoids on visual processing have been largely overlooked. To test the effect of acute treatment with imidacloprid at known concentrations in the brain, we developed a modified electrophysiological setup that allows recordings of visually evoked responses while perfusing the brain in vivo. We obtained long-lasting recordings from direction selective wide-field, motion sensitive neurons of the hoverfly pollinator, Eristalis tenax. Neurons were treated with imidacloprid (3.9 μM, 0.39 μM or a sham control treatment using the solvent (dimethylsulfoxide) only. Exposure to a high, yet sub-lethal concentration of imidacloprid significantly alters their physiological response to motion stimuli. We observed a general effect of imidacloprid (3.9 μM) increasing spontaneous activity, reducing contrast sensitivity and giving weaker directional tuning to wide-field moving stimuli, with likely implications for errors in flight control, hovering and routing. Our electrophysiological approach reveals the robustness of the fly visual pathway against cholinergic perturbance (i.e., at 0.39 μM) but also potential threatening effects of cholinergic pesticides (i.e., evident at 3.9 μM) for the visual motion detecting system of an important pollinator.

scholarly journals Research on Low-Resolution Pattern Coding Recognition Method Based on Hu-DBN

2020 ◽  
Vol 38 (6) ◽  
pp. 1218-1224
Author(s):  
Tianfan Zhang ◽  
Zhe Li ◽  
Xiao Jing ◽  
Bin Hu ◽  
Yahui Zhu
Keyword(s):  
Recognition Rate ◽  
Visual Navigation ◽  
Motion Blur ◽  
The Body ◽  
Low Resolution ◽  
Recognition Method ◽  
Image Code ◽  
Characteristic Image ◽  
Motion Speed ◽  
Characteristic Signal

The feature image code represented by the two-dimensional code is the key reference for global positioning in the visual navigation of mobile robots. Although reducing the acquired low-resolution image helps to reduce the real-time performance of the algorithm, the acquired feature image is more susceptible to motion blur-based interference and affects the accuracy of recognition, which causes the positioning failure of the whole multi-intelligence, in which the body control system is invalid. In this paper, an optimized low-resolution feature image code recognition method is proposed. In the preprocessing part, the characteristic image is converted into the characteristic signal matrix of Hu invariant moments, and then the characteristic image is added to the characteristic signal matrix as a characteristic component, and then the Hu-DBN neural network signal classifier is used to construct the signal matrix so as to achieve accurate recognition of low-resolution custom image signature images under high motion tolerance conditions. It not only avoids the problem of classical pattern recognition relying on model experience and poor adaptability of the scene, but also avoids the problem of high computational complexity and recognition efficiency of directly deep learning methods such as YOLO. The deployment of the mobile robot instance deployment test shows that the average recognition rate is of 96.3% at a resolution of 640×480@Pixs and motion speed of 0.5 m/s, which proves the effectiveness of the present method.

scholarly journals The cholinergic pesticide imidacloprid impairs contrast and direction sensitivity in motion detecting neurons of an insect pollinator

2018 ◽  
Author(s):  
Elisa Rigosi ◽  
David C. O’Carroll
Keyword(s):  
Flight Control ◽  
Visual Processing ◽  
Visual Cues ◽  
Visual Motion ◽  
Plant Protection ◽  
Wide Field ◽  
Insect Pollinator ◽  
Direction Sensitivity ◽  
Spontaneous Firing Rate

AbstractCholinergic pesticides such as the neonicotinoid imidacloprid are the most important insecticides used for plant protection worldwide. In recent decades concerns have been raised about side effects on non-target insect species, including altered foraging behaviour and navigation. Although pollinators rely on visual cues to forage and navigate their environment, the effect of neonicotinoids on visual processing have been largely overlooked. Here we describe a modified electrophysiological setup that allowed recordings of visually evoked responses while perfusing the brain in vivo. Long-lasting recordings from wide-field motion sensitive neurons of the hoverfly pollinator, Eristalis tenax, revealed that sub-lethal exposure to imidacloprid alters their physiological response to motion stimuli. We observed substantially increased spontaneous firing rate, reduced contrast sensitivity and weaker directional tuning to wide-field moving stimuli. This approach reveals sub-lethal effects of imidacloprid in the visual motion detecting system of an important pollinator with likely implications for flight control, hovering and routing.

A Real-Time Route Planning Method Based on DeepLabV3+ for Plant Protection UAVs

2021 ◽  
Author(s):  
Junjie Wan ◽  
Lijun Qi ◽  
Hao Zhang ◽  
Zhong�ao Lu ◽  
Jiarui Zhou
Keyword(s):  
Real Time ◽  
Plant Protection ◽  
Route Planning ◽  
Planning Method

scholarly journals A method for using unmanned aerial vehicles for emergency investigation of single geo-hazards and sample applications of this method

2017 ◽  
Vol 17 (11) ◽  
pp. 1961-1979 ◽  
Author(s):  
Haifeng Huang ◽  
Jingjing Long ◽  
Wu Yi ◽  
Qinglin Yi ◽  
Guodong Zhang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Route Planning ◽  
Implementation Process ◽  
Site Investigation ◽  
Three Gorges Reservoir Area ◽  
Ground Control ◽  
Aerial Vehicles ◽  
Fast Processing ◽  
Key Techniques

Abstract. In recent years, unmanned aerial vehicles (UAVs) have become widely used in emergency investigations of major natural hazards over large areas; however, UAVs are less commonly employed to investigate single geo-hazards. Based on a number of successful investigations in the Three Gorges Reservoir area, China, a complete UAV-based method for performing emergency investigations of single geo-hazards is described. First, a customized UAV system that consists of a multi-rotor UAV subsystem, an aerial photography subsystem, a ground control subsystem and a ground surveillance subsystem is described in detail. The implementation process, which includes four steps, i.e., indoor preparation, site investigation, on-site fast processing and application, and indoor comprehensive processing and application, is then elaborated, and two investigation schemes, automatic and manual, that are used in the site investigation step are put forward. Moreover, some key techniques and methods – e.g., the layout and measurement of ground control points (GCPs), route planning, flight control and image collection, and the Structure from Motion (SfM) photogrammetry processing – are explained. Finally, three applications are given. Experience has shown that using UAVs for emergency investigation of single geo-hazards greatly reduces the time, intensity and risks associated with on-site work and provides valuable, high-accuracy, high-resolution information that supports emergency responses.

scholarly journals Route-Planning Method for Plant Protection Rotor Drones in Convex Polygon Regions

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2221
Author(s):  
Shaoxing Hu ◽  
Tianliang Xu ◽  
Bingke Wang
Keyword(s):  
Coordinate System ◽  
Convex Polygon ◽  
Plant Protection ◽  
Route Planning ◽  
Grid Method ◽  
Operating Efficiency ◽  
Optimal Route ◽  
Heading Angle ◽  
Waste Rate ◽  
Local Plane

Aiming at the problem of low operating efficiency due to the poor endurance of plant protection rotor drones and the small volume of pesticide carried, this paper proposes a route-planning algorithm for convex polygon regions based on the initial heading angle. First, a series of coordinate conversion methods ranging from the Earth coordinate system to the local plane coordinate system are studied. Second, in the local plane coordinate system, a route generation method based on subregion is proposed; therefore, multiple routes can be generated with different initial heading angles. Lastly, the optimal route and the best initial heading angle can be obtained after the comparison according to the three evaluation criteria: number of turns, route distance, and pesticide waste rate. The simulation results show that, compared with the common grid method, the route generation method based on subregion reduces the route distance and pesticide waste rate by 2.27% and 13.75%, respectively. Furthermore, it also shows that, compared with the route generated by the initial heading angle of 0°, the optimal route reduces the number of turns, route distance, and pesticide waste rate by 60%, 17.65%, and 38.18%, respectively. The route was optimized in three aspects and reached the best overall result using this method, which in turn proved its feasibility.

SENSORIMOTOR CONVERGENCE IN VISUAL NAVIGATION AND FLIGHT CONTROL SYSTEMS

2005 ◽  
Vol 38 (1) ◽  
pp. 253-258 ◽  
Author(s):  
J. Sean Humbert† ◽  
Richard M. Murray ◽  
Michael H. Dickinson
Keyword(s):  
Control Systems ◽  
Flight Control ◽  
Visual Navigation
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