Numerical modelling of dry-out in pulsating heat pipe using VOF model

Journal of Mechanical Science and Technology - Operational limits are critical in the continuous functioning of a pulsating heat pipe (PHP). A computational fluid dynamics study was conducted to...     

Influence of Industrial and Agricultural by-Products as Cementitious Blends in Self-Compacting Concrete – A Review

Silicon - Globally, the annual generation of industrial and agricultural by-products is exceeding a thousand million tons and forecasted to tremendously inflate in the future. The by-products are...     

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.