Optimised hybrid classification approach for rice leaf disease prediction with proposed texture features  

作　　者：Sakhamuri Sridevi K.Kiran Kumar 

机构地区：[1]Department of Computer Science and Engineering,Koneru Lakshmaiah Education Foundation,Vaddeswaram,India

出　　处：《Journal of Control and Decision》2024年第1期84-97,共14页控制与决策学报（英文）

摘　　要：This paper aims to frame a new rice disease prediction model that included three major phases.Initially,median filtering(MF)is deployed during pre-processing and then‘proposed Fuzzy Means Clustering(FCM)based segmentation’is done.Following that,‘Discrete Wavelet Transform(DWT),Scale-Invariant Feature Transform(SIFT)and low-level features(colour and shape),Proposed local Binary Pattern(LBP)based features’are extracted that are classified via‘MultiLayer Perceptron(MLP)and Long Short Term Memory(LSTM)’and predicted outcomes are obtained.For exact prediction,this work intends to optimise the weights of LSTM using Inertia Weighted Salp Swarm Optimisation(IW-SSO)model.Eventually,the development of IW-SSO method is established on varied metrics.

关 键 词：Rice disease improved fuzzy hybrid classifiers optimised LSTM IW-SSO algorithm 

分 类 号：S435.11[农业科学—农业昆虫与害虫防治]