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Performance evaluation of PCA based reduced features of leaf images extracted by DWT using random Forest and XGBoost classifier

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Abstract

The leaf disease classification is a method for putting diseases into groups based on their properties, like texture, shape, and color. Even though DL features are very good at classifying leaf diseases, some authors focused on handcrafted features for leaf disease classification and got quite good results with similar accuracy. In this paper, we have also focused on handcrafted features and ML based shallow classifier to get comparable accuracy of DL models. Handcrafted features and shallow ML based classifier are used for leaf disease detection and classification mainly for three species such as tomato, bell pepper and potato. Here we have used 3- level decomposition based 2D-DWT for image feature extraction and PCA for dimensionality reduction of features. We have used stratified K-Fold validation because the dataset is small and there is a need to maintain the class ratio for classification. For classification Random Forest and XGBoost are used. The proposed method is made up of 4 steps: image pre-processing, feature extraction, feature reduction, and classification. We evaluate the proposed model’s classification accuracy against the classification accuracy of several scholarly works. When applied to Datasets 1, 2, 3, and 4, RF classifiers achieve accuracies of 98.45%, 100%, 98.33%, and 98.55%, respectively, while XGBoost achieves accuracies of 99.11%, 98.72%, 98.23%, and 97.73%.

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Data availability

Access the datasets used in the current study on the GitHub repository, https://github.com/MonuBhagat11/Leafdisease_data.

Abbreviations

CNN:

Convolutional Neural network

SVM:

Support Vector Machine

KNN:

K-Nearest Neighbor

ROC:

Receiver Operating Characteristic

DL:

Deep Learning

HIS:

Hyperspectral Imaging

MCC:

Moving Center Classifier

LDC:

Linear Discriminant Classifier

FNN:

Fuzzy Neural Network

RF:

Random Forest

EM:

Expectation-maximization

CCD:

Centroid Contour Distance Curve

ML:

Machine Learning

CA:

Classification Accuracy

HSV:

Hue Saturation Value

RGB:

Red Green Blue

LBPs:

Local Binary Patterns

GLCM:

Grey-Level Co-occurrence Matrix

SGDM:

Spatial Gray Level Dependence Matrix

SIFT:

Scale Invariant and Feature Transformation

MSF:

Multiscale fusion

GWO:

Grey Wolf optimizer

GSA:

Gravitational Search Algorithm

RGA:

Real coded Genetic Algorithm

CA:

Classification Accuracy

CHT:

Circular Hough Transform

DRNN:

Deep Residual Neural Network

ABC:

Ant Bee Colony Optimization

IGA:

Improved Genetic Algorithm

PCA:

Principal Component Analysis

ANN:

Artificial Neural Networks

GA:

Genetic Algorithm

PNN:

Probabilistic Neural Network

SURF:

Speeded Up Robust Features

PCA:

Principal Component Analysis

RGA:

Real coded Genetic Algorithm

ACH:

Angle Code Histogram

BoWs:

Bag-of-words

CSM:

Chaotic spider monkey

DWT:

Discrete Wavelet Transform

PSO:

Particle Swarm Optimization

YOP:

Year of Publication

FRT:

Feature Reduction Technique

DiffN:

Difference of Normal Orientations

BRBFNN:

Bacteria Foraging Algorithm Radial Basis Function Neural Network

CFO:

Central force optimization

MDM:

Multiscale Distance Matrix

DNM:

Decomposed Newton’s Method

MMC:

Maximum Margin Criterion

MTS:

Mahalanobis Taguchi System

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Acknowledgements

The authors would like to express their gratitude to the esteemed reviewers for their insightful and useful suggestions.

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No public, private, or charitable organizations provided any direct funding for this investigation.

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  1. Department of Computer Science & Engineering, National Institute of Technology Jamshedpur, Jharkhand, India

    Monu Bhagat & Dilip Kumar

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  1. Monu Bhagat
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Correspondence to Monu Bhagat.

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Bhagat, M., Kumar, D. Performance evaluation of PCA based reduced features of leaf images extracted by DWT using random Forest and XGBoost classifier. Multimed Tools Appl 82, 26225–26254 (2023). https://doi.org/10.1007/s11042-023-14370-9

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