Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

作     者：Ashit Kumar Dutta Yasser Albagory Abdul Rahaman Wahab Sait Ismail Mohamed Keshta 

作者机构：Department of Computer Science and Information SystemsCollege of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer EngineeringCollege of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and CommunicationKing Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：899-915页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

基　　金：This research was supported by the Researchers Supporting Program(TUMAProject-2021-27)Almaarefa University Riyadh,Saudi Arabia.Taif University Researchers Supporting Project number(TURSP-2020/161),Taif University,Taif,Saudi Arabia 

主　　题：Autonomous systems object detection precision agriculture unmanned aerial vehicles deep learning parameter tuning 

摘      要：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,*** the interesting applications of autonomous systems,their applicability in agricultural sector becomes *** unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop *** 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 *** recently developed deep learning(DL)models have exhibited effective performance in several data classification *** this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)*** proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target ***,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing ***,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of *** the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is *** 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%.