Convolutional Neural Network approach for the prediction of Soil texture properties

K Anandan

doi:10.17485/ijst/v14i3.2047

Abstract

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

Highlights

Agriculture is one of the most essential economic activity and plays significant role in social and environmental aspects of the countries that primarily depend on Agriculture
Soil texture is one of the main factors in agricultural production, and its precise prediction is important for the normal use and management of water resources
Dataset The dataset used for the evaluation of the proposed method is the LUCAS dataset, which consists of about 22,000 data points that include physical properties like the percentage of coarse fragments, the particle size distributions clay, sand and silt, the pH value, the organic carbon content, the carbonate content, the total nitrogen content, the extractable potassium content, the phosphorus content, the cation exchange capacity and metals

Summary

Introduction

Soil texture is one of the most important physical soil properties which allows water retention capacity, soil density, availability of nutrient and reaction of soil. The distribution of soil particles shows the feature of soil texture and it is characterized as clay, silt and sand. Silt and clay which have smaller particles have a larger surface area and allow soil to hold more water. Sand which has larger particles with a small surface area will hold only less water. Soil texture affects the crop selection and regulates the water transmission property and is shown by the authors in[9]. Soil images are processed through the different stages, preprocessing of soil images for image enhancement, extracting the region of interest for segmentation and the texture analysis for feature vector. Support Vector Machine classifier is used to classify the soil images using linear kernel

Methods

Results

Conclusion