Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interest...Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.展开更多
Unmanned aerial vehicles have been developed and applied to support agricultural production management.Compared with piloted aircraft,an Unmanned Aerial Vehicle(UAV)can focus on small crop fields at lower flight altit...Unmanned aerial vehicles have been developed and applied to support agricultural production management.Compared with piloted aircraft,an Unmanned Aerial Vehicle(UAV)can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision.They can also“fill in the gap”in locations where fixed winged or rotary winged aircraft are not readily available.In agriculture,UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials.Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management.Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well.This paper presents an overview of research involving the development of UAV technology for agricultural production management.Technologies,systems and methods are examined and studied.The limitations of current UAVs for agricultural production management are discussed,as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management.展开更多
Aerial spraying can support efficient defoliation without crop contact.With the recent introduction to unmanned aerial system(UAS)for aerial spraying in China,there is a need to determine the optimum application varia...Aerial spraying can support efficient defoliation without crop contact.With the recent introduction to unmanned aerial system(UAS)for aerial spraying in China,there is a need to determine the optimum application variables to achieve high efficacy and efficiency with low costs.The present research involved field studies across two annual cotton production seasons in North Xinjiang,China.Four factors,including volume rate(A),tank mix including spray adjuvants(B),flight altitude(C),flight speed(D)and three levels of L9(3^(4))orthogonal arrays were carried out to optimize the application parameters for three types of UASs.These included different numbers of rotors as follows:four-rotors,six-rotors and eight-rotors.Spray coverage,distribution uniformity(coefficient of variation(CV)of droplet coverage),rates of cotton defoliation and boll opening,application efficiency and cost were measured and assessed.Results showed that:(1)the rates of defoliation and boll opening by aerial cotton defoliant application could meet the requirement of cotton mechanized harvesting;(2)the optimal scenario for the three UASs was A_(3)B_(2)C_(1)D_(3),Volume rate(A3):48 L/hm^(2);Tank mix and concentration(B_(2)):(Tuotulong 225+Sujie 750+Ethephon 2250)mL/hm^(2),Flight altitude(C_(1)):1.5 m,and Flight speeds(D_(3))for unmanned helicopters with four-rotors,six-rotors and eight-rotors were 3.12 m/s,2.51 m/s and 3.76 m/s,respectively.These results can provide guidance for cotton defoliant aerial spraying in China using UAS.展开更多
This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors ha...This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.展开更多
Unmanned agricultural aircraft system(UAAS)hasbeen widely employed as a low-cost and reliable method to apply agrochemicals to small agricultural fields in China.The performance of battery-poweredmultirotor UAAS has a...Unmanned agricultural aircraft system(UAAS)hasbeen widely employed as a low-cost and reliable method to apply agrochemicals to small agricultural fields in China.The performance of battery-poweredmultirotor UAAS has attracted considerable attention from manufacturers and researchers.The objective of this research was to design a UAAS equippingwith a data acquisition system,to characterize its chemical application performance based on droplet deposition data and optimize the operating parameters.Each test was repeated three times to assess the reliability of the spraying system.Various flight parameters were also evaluated.The optimal spray pressure for the XR8001 and XR8002(TeeJet,Wheaton,IL,USA)nozzles was found to be 300 kPa,and the latter nozzle had a higher droplet deposition rate and spray volume.Spray volume was not significantly affected by the flight speed or droplet density and was negatively correlated with the nozzle pressure.The results of this study provide a basis for improving the efficiency of UAAS chemicalapplication systems in terms of large-scale application.展开更多
基金This research was supported by the Researchers Supporting Program(TUMAProject-2021-27)Almaarefa UniversityRiyadh,Saudi Arabia.Taif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia.
文摘Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.
文摘Unmanned aerial vehicles have been developed and applied to support agricultural production management.Compared with piloted aircraft,an Unmanned Aerial Vehicle(UAV)can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision.They can also“fill in the gap”in locations where fixed winged or rotary winged aircraft are not readily available.In agriculture,UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials.Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management.Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well.This paper presents an overview of research involving the development of UAV technology for agricultural production management.Technologies,systems and methods are examined and studied.The limitations of current UAVs for agricultural production management are discussed,as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management.
基金The authors acknowledge that this work was financially supported by the Science and Technology Plan of Guangdong Province of China(Project No.2017B090907031,2017B090903007,2015B020206003)Innovative Research Team of Guangdong Province Agriculture Research System(2017LM2153).
文摘Aerial spraying can support efficient defoliation without crop contact.With the recent introduction to unmanned aerial system(UAS)for aerial spraying in China,there is a need to determine the optimum application variables to achieve high efficacy and efficiency with low costs.The present research involved field studies across two annual cotton production seasons in North Xinjiang,China.Four factors,including volume rate(A),tank mix including spray adjuvants(B),flight altitude(C),flight speed(D)and three levels of L9(3^(4))orthogonal arrays were carried out to optimize the application parameters for three types of UASs.These included different numbers of rotors as follows:four-rotors,six-rotors and eight-rotors.Spray coverage,distribution uniformity(coefficient of variation(CV)of droplet coverage),rates of cotton defoliation and boll opening,application efficiency and cost were measured and assessed.Results showed that:(1)the rates of defoliation and boll opening by aerial cotton defoliant application could meet the requirement of cotton mechanized harvesting;(2)the optimal scenario for the three UASs was A_(3)B_(2)C_(1)D_(3),Volume rate(A3):48 L/hm^(2);Tank mix and concentration(B_(2)):(Tuotulong 225+Sujie 750+Ethephon 2250)mL/hm^(2),Flight altitude(C_(1)):1.5 m,and Flight speeds(D_(3))for unmanned helicopters with four-rotors,six-rotors and eight-rotors were 3.12 m/s,2.51 m/s and 3.76 m/s,respectively.These results can provide guidance for cotton defoliant aerial spraying in China using UAS.
基金funding from the Australian Government,via Grant No.AUSMURIB000001 associated with ONR MURI Grant No.N00014-19-1-2571。
文摘This paper considers a time-constrained data collection problem from a network of ground sensors located on uneven terrain by an Unmanned Aerial Vehicle(UAV),a typical Unmanned Aerial System(UAS).The ground sensors harvest renewable energy and are equipped with batteries and data buffers.The ground sensor model takes into account sensor data buffer and battery limitations.An asymptotically globally optimal method of joint UAV 3D trajectory optimization and data transmission schedule is developed.The developed method maximizes the amount of data transmitted to the UAV without losses and too long delays and minimizes the propulsion energy of the UAV.The developed algorithm of optimal trajectory optimization and transmission scheduling is based on dynamic programming.Computer simulations demonstrate the effectiveness of the proposed algorithm.
基金This work was partially financially supported by the National Key Research and Development Program of China(Grant No.2016YFD0200701).
文摘Unmanned agricultural aircraft system(UAAS)hasbeen widely employed as a low-cost and reliable method to apply agrochemicals to small agricultural fields in China.The performance of battery-poweredmultirotor UAAS has attracted considerable attention from manufacturers and researchers.The objective of this research was to design a UAAS equippingwith a data acquisition system,to characterize its chemical application performance based on droplet deposition data and optimize the operating parameters.Each test was repeated three times to assess the reliability of the spraying system.Various flight parameters were also evaluated.The optimal spray pressure for the XR8001 and XR8002(TeeJet,Wheaton,IL,USA)nozzles was found to be 300 kPa,and the latter nozzle had a higher droplet deposition rate and spray volume.Spray volume was not significantly affected by the flight speed or droplet density and was negatively correlated with the nozzle pressure.The results of this study provide a basis for improving the efficiency of UAAS chemicalapplication systems in terms of large-scale application.
