Impact of Melting Heat Transfer and Variable Characteristics on an MHD Non-Newtonian Shear-Thinning Fluid Flow with Gyrotactic Microorganisms over a Nonlinear Stretched Surface

The objective of this work is to examine how temperature-dependent thermal conductivity and concentration-dependent molecular diffusion affect Reiner-Philippoff nanofluid flow past a nonlinear stretching sheet. At the interface of the elongated surface zero-mass flux and melting heat condition are incorporated. The formulated mathematical problem is simplified by implementing suitable similarity transformations. For the numerical solution bvp4c is utilized. The parameters emerging in the model are discussed versus allied profiles through graphical illustrations. It is perceived that the velocity of the fluid decays on incrementing the Bingham number. The gyrotactic microorganism profile declines on amplifying the Peclet number. The validation of the proposed model is also added to this study. .

Recognition of weeds at asparagus fields using multi-feature fusion and backpropagation neural network

In order to solve the problem of low recognition rates of weeds by a single feature,a method was proposed in this study to identify weeds in Asparagus(Asparagus officinalis L.)field using multi-feature fusion and backpropagation neural network(BPNN).A total of 382 images of weeds competing with asparagus growth were collected,including 135 of Cirsium arvense(L.)Scop.,138 of Conyza sumatrensis(Retz.)E.Walker,and 109 of Calystegia hederacea Wall.The grayscale images were extracted from the RGB images of weeds using the 2G-R-B factor.Threshold segmentation of the grayscale image of weeds was applied using Otsu method.Then the internal holes of the leaves were filled through the expansion and corrosion morphological operations,and other interference targets were removed to obtain the binary image.The foreground image was obtained by masking the binary image and the RGB image.Then,the color moment algorithm was used to extract weeds color feature,the gray level co-occurrence matrix and the Local Binary Pattern(LBP)algorithm was used to extract weeds texture features,and seven Hu invariant moment features and the roundness and slenderness ratio of weeds were extracted as their shape features.According to the shape,color,texture,and fusion features of the test samples,a weed identification model was built.The test results showed that the recognition rate of Cirsium arvense(L.)Scop.,Calystegia hederacea Wall.and Conyza sumatrensis(Retz.)E.Walker were 82.72%(color feature),72.41%(shape feature),86.73%(texture feature)and 93.51%(fusion feature),respectively.Therefore,this method can provide a reference for the study of weeds identification in the asparagus field.