Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

作     者：Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood 

作者机构：Department of Computer ScienceCollege of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer ScienceFaculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software EngineeringCollege of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI LaboratoryLa Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information SystemsCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and TechnologyFaculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt 

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11期

页      面：2545-2564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：The authors extend their appreciation to the Deputyship for Research&Innovation Ministry of Education in Saudi Arabia for funding this research work through the Project Number RI-44-0450 

主　　题：Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation 

摘      要：The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.