Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.

Improved Multi-party Quantum Key Agreement with Four-qubit Cluster States

Quantum key agreement is a promising key establishing protocol that can play a significant role in securing 5G/6G communication networks.Recently,Liu et al.(Quantum Information Processing 18(8):1-10,2019)proposed a multi-party quantum key agreement protocol based on four-qubit cluster states was proposed.The aim of their protocol is to agree on a shared secret key among multiple remote participants.Liu et al.employed four-qubit cluster states to be the quantum resources and the X operation to securely share a secret key.In addition,Liu et al.’s protocol guarantees that each participant makes an equal contribution to the final key.The authors also claimed that the proposed protocol is secure against participant attack and dishonest participants cannot generate the final shared key alone.However,we show here that Liu et al.protocol is insecure against a collusive attack,where dishonest participants can retrieve the private inputs of a trustworthy participant without being caught.Additionally,the corresponding modifications are presented to address these security flaws in Liu et al.’s protocol.

Securing Copyright Using 3D Objects Blind Watermarking Scheme

Recently,securing Copyright has become a hot research topic due to rapidly advancing information technology.As a host cover,watermarking methods are used to conceal or embed sensitive information messages in such a manner that it was undetectable to a human observer in contemporary times.Digital media covers may often take any form,including audio,video,photos,even DNA data sequences.In this work,we present a new methodology for watermarking to hide secret data into 3-D objects.The technique of blind extraction based on reversing the steps of the data embedding process is used.The implemented technique uses the features of the 3-D object vertex’discrete cosine transform to embed a grayscale image with high capacity.The coefficient of vertex and the encrypted picture pixels are used in the watermarking procedure.Additionally,the extraction approach is fully blind and is dependent on the backward steps of the encoding procedure to get the hidden data.Correlation distance,Euclidean distance,Manhattan distance,and the Cosine distance are used to evaluate and test the performance of the proposed approach.The visibility and imperceptibility of the proposed method are assessed to show the efficiency of our work compared to previous corresponding methods.