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

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized

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

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography

Background: The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. Results: In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with R2 and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with R2 and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the R2 and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. Conclusions: Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density

Magnetorheological Fluids Actuated Haptic-Based Teleoperated Catheter Operating System

During conventional catheter endovascular procedures, surgeons needs to adjust the catheter intervention moving direction and velocity according to the direct sensation. Moreover, in the conventional method, both the surgeon and the patient are inevitable exposed to a large amount of, and for a long period of time, X-ray radiation during the surgical procedure. The purpose of this paper is to ensure surgical safety and to protect the surgeon from X-ray radiation during the surgical procedure by adopting a novel haptic-based robot-assisted master-slave system mode. In this paper, a kind of magnetorheological fluids (MR fluids)-based haptic interface has been developed to generate a kind of controllable haptic sensation providing to the catheter operator, and the catheter intervention kinematics parameters measured the motion capture part to control the salve robotic catheter operating system following the master side kinematics. The slave catheter operating the mechanical system has also been designed and manufactured to manipulate the clinical catheter by mimicking the surgeon operating the catheter intervention surgical procedure, which has a 2-DOF (advance, retreat, and rotate) catheter motion characteristic; in addition, the interaction force between the catheter and inner wall of vasculature can be measured by its force sensing unit and the feedback to the master system. The catheter intervention synchronous evaluation experiments between the master and slave system are tested. Also, the advantages of integrating the controllable haptic sensation to the master-slave system experimental evaluations have been done in vitro. The experimental results demonstrated that the proposed haptic-based robot-assisted master-slave system mode can reduce the surgical time and protect the surgeon from X-ray radiation

Microbial transformation of ginsenosides extracted from Panax ginseng adventitious roots in an airlift bioreactor

Background: Ginsenoside is the most important secondary metabolite in ginseng. Natural sources of wild ginseng have been overexploited. Although root culture can reduce the length of the growth cycle of ginseng, the number of species of ginsenosides is reduced and their contents are lower in the adventitious roots of ginseng than in the roots of ginseng cultivated in the field. Results: In this study, 147 strains of β-glucosidase-producing microorganisms were isolated from soil. Of these, strain K35 showed excellent activity for converting major ginsenosides into rare ginsenosides, and a NCBI BLAST of its 16S rDNA gene sequence showed that it was most closely related to Penicillium sp. (HQ608083.1). Strain K35 was used to ferment the adventitious root extract, and the fermentation products were analyzed by high-performance liquid chromatography. The results showed that the content of the rare ginsenoside CK was 0.253 mg mL-1 under the optimal converting conditions of 9 d of fermentation at pH 7.0 in LL medium, which was significantly higher than that in the adventitious roots of ginseng. Conclusion: These findings may not only solve the problem of low productivity of metabolite in ginseng root culture but may also result in the development of a new valuable method of manufacturing ginsenoside CK

A Novel Master–Slave Interventional Surgery Robot with Force Feedback and Collaborative Operation

In recent years, master–slave vascular robots have been developed to address the problem of radiation exposure during vascular interventions for surgeons. However, the single visual feedback reduces surgeon immersion and transparency of the system. In this work, we have developed a haptic interface based on the magnetorheological fluid (MRF) on the master side. The haptic interface can provide passive feedback force with high force fidelity and low inertia. Additionally, the manipulation of the master device does not change the operating posture of traditional surgery, which allows the surgeon to better adapt to the robotic system. For the slave robot, the catheter and guidewire can be navigated simultaneously which allows the two degrees of action on the catheter and axial action of a guidewire. The resistance force of the catheter navigation is measured and reflected to the user through the master haptic interface. To verify the proposed master–slave robotic system, the evaluation experiments are carried out in vitro, and the effectiveness of the system was demonstrated experimentally

Design and Experiment of a Variable Spray System for Unmanned Aerial Vehicles Based on PID and PWM Control

Unmanned aerial vehicle (UAV) variable-rate spraying technology, as the development direction of aviation for plant protection in the future, has been developed rapidly in recent years. In the actual agricultural production, the severity of plant diseases and insect pests varies in different locations. In order to reduce the waste of pesticides, pesticides should be applied according to the severity of pests, insects and weeds. On the basis of explaining the plant diseases and insect pests map in the target area, a pulse width modulation variable spray system is designed. Moreover, the STMicroelectronics-32 (STM32) chip is invoked as the core of the control system. The system combines with sensor technology to get the prescription value through real-time interpretation of prescription diagram in operation. Then, a pulse square wave with variable duty cycles is generated to adjust the flow rate. A closed-loop Proportional-Integral-Derivative (PID) control algorithm is used to shorten the time of system reaching steady state. The results indicate that the deviation between volume and target traffic is stable, which is within 2.16%. When the duty cycle of the square wave is within the range of 40% to 100%, the flow range of the single nozzle varies from 0.16 L/min to 0.54 L/min. Variable spray operation under different spray requirements is achieved. The outdoor tests of variable spray system show that the variable spray system can adjust the flow rapidly according to the prescription value set in the prescription map. The proportion of actual droplet deposition and deposition density in the operation unit is consistent with the prescription value, which proves the effectiveness of the designed variable spray system