Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due ...Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases.展开更多
This study addresses the formation of anisotropic compact star models in the background of f(T,T)gravity(where T and T represent the torsion and trace of the energy momentum tensor,respectively).f(T,T)gravity is an ex...This study addresses the formation of anisotropic compact star models in the background of f(T,T)gravity(where T and T represent the torsion and trace of the energy momentum tensor,respectively).f(T,T)gravity is an extension of the f(T)theory,and it allows a general non-minimal coupling between T and T.In this setup,we apply Krori and Barua's solution to the static spacetime with the components ξ=Br^(2)+c and ψ=Ar^(2).To develop viable solutions,we select a well-known model f(T,T)=αT^(m)+βT+Ф(where α and β are coupling parameters,and Ф indicates the cosmological constant).We adopt the conventional matching of interior and exterior space time to evaluate the unknowns,which are employed in the stellar configuration.We present a comprehensive discussion on the stellar properties to elaborate the anisotropic nature of compact stars corresponding to well-known models:PSRJ1416-2230,4U1608-52,CenX-3,EXO1785-248,and SMCX-1.Via physical analysis,it is observed that the solution of compact spheres satisfy the acceptability criteria,and its models behave optimally and depict stability and consistency,in accordance with f(T,T)gravity.展开更多
文摘Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases.
文摘This study addresses the formation of anisotropic compact star models in the background of f(T,T)gravity(where T and T represent the torsion and trace of the energy momentum tensor,respectively).f(T,T)gravity is an extension of the f(T)theory,and it allows a general non-minimal coupling between T and T.In this setup,we apply Krori and Barua's solution to the static spacetime with the components ξ=Br^(2)+c and ψ=Ar^(2).To develop viable solutions,we select a well-known model f(T,T)=αT^(m)+βT+Ф(where α and β are coupling parameters,and Ф indicates the cosmological constant).We adopt the conventional matching of interior and exterior space time to evaluate the unknowns,which are employed in the stellar configuration.We present a comprehensive discussion on the stellar properties to elaborate the anisotropic nature of compact stars corresponding to well-known models:PSRJ1416-2230,4U1608-52,CenX-3,EXO1785-248,and SMCX-1.Via physical analysis,it is observed that the solution of compact spheres satisfy the acceptability criteria,and its models behave optimally and depict stability and consistency,in accordance with f(T,T)gravity.
