Indian Journal of Science and Technology
Year: 2021, Volume: 14, Issue: 3, Pages: 190-196
K Anandan1*, R Shankar2, S Duraisamy2
1PhD Research Scholar, Department of Computer Science, Chikkanna Government Arts College, Tiruppur, Tamilnadu, India
2Assistant Professor, Department of Computer Science, Chikkanna Government Arts College, Tiruppur, Tamilnadu, India
Email: [email protected]
Received Date:13 November 2020, Accepted Date:01 December 2021, Published Date:25 January 2021
Background/Objectives: The main objective is to achieve improved performance of soil properties prediction for hyperspectral data. In this work, convolutional neural network is trained to understand the pattern of hyperspectral data by spatial interpolation. Methods/Statistical analysis: The proposed methodology is used to predict six soil properties- Organic Carbon content (OC), Cation Exchange Capacity (CEC), Nitrogen Content (N), pH level in water, Clay particle and Sand Particle. Soil texture which defines the relative content of soil particles is determined by the percentage of clay, sand and silt in the soil. The input to the Convolutional Neural Network (CNN) is the Hyperspectral data in the form of multiple arrays. The statistical evaluation of model performance is evaluated using root-mean-square error and r square. Findings: In this research, deep learning approach is used to capture the pattern hidden in the soil. Deep learning is a kind of neural network which can model complex relationship for representing non-linearity for a scalable data. The main challenge is predicting a soil type, as it involves complex structural characteristics and soil features. Novelty/Improvements: The performance of soil texture prediction is improved by automatic feature learning capability in the proposed CNN model. The average rmse value obtained in proposed method for all the six soil texture properties is 5.68%.
Keywords: Soil texture; convolutional neural network; hyperspectral data; deep learning
© 2021 Anandan et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)
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