A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

作     者：K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa Tawfeeq Shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala 

作者机构：Department of Computer Science&EngineeringBapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Information TechnologyFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Science&EngineeringBapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Electrical EngineeringFaculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical EngineeringCollege of EngineeringNorthern Border UniversityArarSaudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第9期

页      面：3239-3260页

核心收录：

学科分类：07[理学] 0828[工学-农业工程] 0903[农学-农业资源与环境] 0901[农学-作物学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：This research work was funded by the Institutional Fund Projects under Grant No.(IFPIP:959-611-1443) The authors gratefully acknowledge the technical and financial support provided by the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia 

主　　题：Data mining crop prediction k-prototypes k-means cluster machine learning 

摘      要：Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making *** agriculture uses datamining to advance agricultural *** farmers aren’t getting the most out of their land because they don’t use precision *** harvest crops without a well-planned recommendation *** crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical *** existing research still needs to address data preprocessing and crop categorization/***,statistical analysis receives less attention,despite producing more accurate and valid *** study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH *** research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high *** presented algorithm converts discrete numerals to factors first,then reduces ***,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda *** presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and ***,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.