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Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka

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Abstract

Soil salinization is one of the most frequent environmental concerns that contribute to the degradation of agricultural land, particularly in arid and semi-arid regions. The correct methods must be developed by farm owners and decision-makers in order to reduce soil erosion and increase crop output. For this, accurate spatial forecasting and soil salinity modeling in agricultural areas are needed. The accurate consideration of environmental elements under the scale effects, which have received less attention in prior research, is essential for digital soil mapping. The goal of this research is to create a special technique for predicting soil salinity. Preprocessing is done on the sentinel image input first. The next step is to determine the spectral channels, salinity index, and vegetation index. The development of transformation-based features also takes advantage of enhanced PCA. The suggested hybrid classifier uses "Deep Belief Network (DBN) and Bidirectional Long Short Term Memory (Bi-LSTM)" to predict salinity while accounting for these variables. The final forecast result is determined by the increased score level fusion. To improve the precision and accuracy of the prediction, Self Upgraded BSO (SU-BSO) calibrates the weights of the Bi-LSTM and DBN. The MSE values of the suggested technique are lower than those of other conventional methods like CNN, DBN, SVM, BI-LSTM, MLP-FFA, and MLSR metrics, achieving lower values of 0.13, 0.07, 0.03, 0.05, 0.09, and 0.094%, respectively. Finally, numerous measurements are employed to demonstrate the value of the selected approach.

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Data Availability

The data underlying this article are available in https://rrc.cvc.uab.es/?ch=3&com=downloads.

Abbreviations

ANN:

Artificial Neural Networks

BSO:

Beetle Swarm Optimization

BWO:

Black Widow Optimization

Bi-LSTM:

Bidirectional Long Short-Term Memory

CM:

Cow Manure

CNN:

Convolutional Neural Network

DWT:

Discrete Wavelet Transform

DNN:

Deep Neural Network

DSM:

Digital Soil Mapping

DNDC:

Denitrification Decomposition

DA:

Dragonfly Algorithm

DBN:

Deep Belief Network

ELM :

Extreme Learning Machine

EC:

Electrical Conductivity

FF:

Firefly

GRC:

Gravitational Reservoir Computing

GSA:

Gravitational Search Algorithm

GF:

Gaussian Filtering

HBA:

Honey Badger Algorithm

HC:

Hybrid Classifiers

IWT:

Inverse Wavelet Transform

LP:

Learning Percentage

LST:

Land Surface Temperature

MLSR:

Multiple Linear Stepwise Regression

MAE:

Mean Absolute Error

ML:

Machine Learning

MLP-FFA:

Multilayer Perceptron—Firefly Algorithm

MLP-NN:

Multilayer Perceptron Neural Networks

NDVI:

Normalized Difference Vegetation Index

ENS:

Nash-Sutcliffe Coefficient

NDSI:

Normalized Difference Salinity Index

RMSE:

Root Mean Square Error

RF:

Random Forest

PCA:

Principal Component Analysis

PLS-SVM:

Partial Least-Squares-SVM

RCELM:

Reservoir Computing ELM

RVI:

Ratio Vegetation Index

SVM:

Support Vector Machine

SOM:

Soil Organic Matter

SU-BSO:

Self Upgraded Bso

SAVI:

Soil-Adjusted Vegetation Index

TWI:

Topographic Wetness Index

WFPS:

Water-Filled Pore Space

WT:

Wavelet Transform

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Funding

This research did not receive any specific funding.

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, JSS Academy of Technical Education, Visvesvaraya Technological University, Belagavi, 590018, India

    V. Vijayalakshmi

  2. Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Visvesvaraya Technological University, Belagavi, 590018, India

    D. Mahesh Kumar

  3. Department of Electronics and Instrumentation Engineering, RV College Of Engineering, Visvesvaraya Technological University, Belagavi, 590018, India

    S. C. Prasanna Kumar

  4. GIS Specialist, GIS Cell, Ecology and Conservation Biology Wing, Periyar Tiger Conservation Foundation, Periyar Tiger Reserve, Kerala Forests and Wildlife Department, Thekkady, Kerala, 685503, India

    S. Veeramani

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  1. V. Vijayalakshmi
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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to V. Vijayalakshmi.

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Vijayalakshmi, V., Kumar, D.M., Kumar, S.C.P. et al. Soil salinity prediction based on hybrid classifier: study on Bellary and Chamarajanagar district in Karnataka. Multimed Tools Appl 83, 47225–47246 (2024). https://doi.org/10.1007/s11042-023-16652-8

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