Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning (ISQP-DL). The work considers the chemical, physical and biological factors of soil in particular area to estimate the soil quality. Firstly, pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression (DNNR) model. Secondly, soil nutrient data has been given as second input to the DNNR model. By utilizing this data set, the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated. For training and testing, the model uses Deep Neural Network Regression (DNNR), by utilizing the dataset. The results show that the proposed model is effective for SQP (Soil Quality Prediction Model) with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy. The results show that the proposed model achieves 96.7% of accuracy rate compared with existing models.     

A novel method of compressive sensing MRI reconstruction based on sandpiper optimization algorithm (SPO) and mask region based convolution neural network (mask RCNN)

Multimedia Tools and Applications - A compressive sensing method is a current structure for signal sampling and reclamation. It allows signal acquisition with fewer sampling than the...     

An efficient multilayer deep detection perceptron (MLDDP) methodology for detecting testicular anomalies with or without congenital heart disease (TACHD)

The Journal of Supercomputing - In the real world, the detection of heart diseases is a challenging process. For detecting testicular anomalies with or without congenital heart disease, 2D...     

Optimization of dry sliding wear parameters of AA336 aluminum alloy-boron carbide and fly ash reinforced hybrid composites by stir casting process

Boron carbide (10 wt%) and fly ash (5 wt%) particles are reinforced in AA336 aluminium alloy by stir casting process. Microstructure of samples are investigated and dry sliding wear factors viz., load (10 N–50 N), sliding distance (500 m–2500 m) and sliding velocity (1 ms⁻¹–5 ms⁻¹) are considered. Response surface methodology is used to design the experiments and wear weight loss of samples is measured. Regression equation is developed to predict the weight loss. Analysis of variance, significance test and confirmation test are used to find the significant wear parameters which affects the weight loss and the wear factors are optimized for obtaining lowest weight loss. Microstructure of samples showed uniform dispersion of particles in AA336 aluminium alloy. Wear test results showed that weight loss increased with increasing load and sliding distance. However, weight loss of samples decreased with increasing sliding velocity. Optimum dry sliding wear factors are found to be a load of 18.1 N, sliding distance of 905.4 m with a sliding velocity of 4.18 ms⁻¹.     

Chaos based encryption of quantum images

Multimedia Tools and Applications - Content, be it any form text, image or video, which needs to be sent from one place to another is vulnerable to third party attacks if it is sent without proper...     

Unsupervised linear contact distributions segmentation algorithm for land cover high resolution panchromatic images

Multimedia Tools and Applications - Automatic segmentation of land use and land cover from high resolution remote sensing imagery has been an essential research area in image processing for the...     

An innovative Azadirachta indica gum-mediated synthesis of cocoon-shaped nano-AgHAp from Lamellidens marginalis shells

In this work, the evaluations of noble nanoparticles for the structural and morphological characteristics are focused. The control of desired particles size and morphology for hydroxyapatite (HAp) and Ag-substituted hydroxyapatite (AgHAp) derived from Lamellidens marginalis shells using Azadirachta Indica (AI) gum as a potential surfactant for the synthesis of stable nanoparticles are reported. The morphological change with respect to the concentration of AI gum is analyzed. The functional group (FTIR) and crystallographic (XRD) characterization of the HAp and AgHAp nanoparticles confirm the presence of HAp with desired apatite functional peaks. The morphological evaluation (FE-SEM) exhibited the formation of cocoon-shaped nanoparticles for the AI gum-medicated synthesis. Higher AI gum concentration reduces the particle size along with the formation of unique surface morphology. The average diameter of the synthesized AgHAp nanoparticles was found to be ≤30 nm which is revealed from HR-TEM. The bacterial investigation against bacterial strains substantiates the higher resistance of bacterial growth for Staphylococcus aureus was observed than Escherichia coli for the AgHAp particles. Hence, embedding silver nanoparticles in the HAp is an efficient approach to enhance the long-term antibacterial effect of the orthopedic and dental applications.