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.

Uncertainties of landslide susceptibility prediction:Influences of different spatial resolutions,machine learning models and proportions of training and testing dataset

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.