Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

作     者：Jigna Patel Anand Ruparelia Sudeep Tanwar Fayez Alqahtani Amr Tolba Ravi Sharma Maria Simona Raboaca Bogdan Constantin Neagu 

作者机构：Department of Computer Science and EngineeringInstitute of TechnologyNirma UniversityAhmedabad382481India Software Engineering DepartmentCollege of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science DepartmentCommunity CollegeKing Saud UniversityRiyadh11437Saudi Arabia Centre for Inter-Disciplinary Research and InnovationUniversity of Petroleum and Energy StudiesDehradun248001India Doctoral SchoolUniversity Politehnica of BucharestBucharest060042Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI RmValceaRamnicu Valcea240050Romania Power Engineering DepartmentGheorghe Asachi Technical University of IasiIasi700050Romania 

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

年 卷 期：2023年第77卷第10期

页      面：1281-1301页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the Researchers Supporting Project Number(RSP2023R 509) King Saud University Riyadh Saudi Arabia 

主　　题：Precision Agriculture Deep Learning brinjal weed detection ResNet-18 YOLOv3 CenterNet Faster RCNN 

摘      要：The overgrowth of weeds growing along with the primary crop in the fields reduces crop *** solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used *** application of herbicide is effective but causes environmental and health ***,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary *** by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this *** key objective of this study is to detect plant and non-plant(weed)parts from crop *** the help of object detection,the precise location of weeds from images can be *** dataset is collected manually from a private farm in Gandhinagar,Gujarat,*** combined approach of classification and object detection is applied in the proposed *** Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object *** have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed *** outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.