ICTACT Journal on Soft Computing

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Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network


Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, India
2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, Ethiopia
     

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Accurate and effective mapping of soil properties is regarded as a critical task in environmental and agricultural management. The evaluation of properties of soil is a daunting task while monitoring and sensing the environment. Existing sampling methods is a time-consuming and laborious job and they are limited based on the regions. However, the need of soil analysis and its properties is essential at landscape level. In this paper, we use Recurrent Convolution Neural Network (RCNN) to assess the soil properties via its classification task. The model in turn is compared with conventional geostatistical spatial interpolation methods. The utilization of Recurrent Neural Network (RNN) aims at studying the spatial and temporal variability of the properties of soil that adopts Kriging interpolation technique. The simulation is conducted to study the efficacy of the model under different soil conditions and the efficacy of RCNN is reported. The results of simulation shows that the proposed method achieves higher rate of classification accuracy than other models.

Keywords

Regional Convolutional Neural Network, Deep Learning, Soil Properties, Prediction.
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  • Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network

Abstract Views: 251  |  PDF Views: 1

Authors

S. Selvi
Department of Computer Science and Engineering, Government College of Engineering, Bargur, India
V. Saravanan
Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, Ethiopia

Abstract


Accurate and effective mapping of soil properties is regarded as a critical task in environmental and agricultural management. The evaluation of properties of soil is a daunting task while monitoring and sensing the environment. Existing sampling methods is a time-consuming and laborious job and they are limited based on the regions. However, the need of soil analysis and its properties is essential at landscape level. In this paper, we use Recurrent Convolution Neural Network (RCNN) to assess the soil properties via its classification task. The model in turn is compared with conventional geostatistical spatial interpolation methods. The utilization of Recurrent Neural Network (RNN) aims at studying the spatial and temporal variability of the properties of soil that adopts Kriging interpolation technique. The simulation is conducted to study the efficacy of the model under different soil conditions and the efficacy of RCNN is reported. The results of simulation shows that the proposed method achieves higher rate of classification accuracy than other models.

Keywords


Regional Convolutional Neural Network, Deep Learning, Soil Properties, Prediction.

References