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A comprehensive survey on leaf disease identification & classification

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Abstract

This paper presents survey on various techniques used to classify plants and its disease. Classification is concerned with classifying each sample into different classes. Classification is a method of separating a healthy and diseased leaf on its morphological features such as texture, color, shape, pattern and so on. Due to resemblance in the visual properties among plants, sorting and classification are complicated to carry out especially in large area. There are various methods based on image processing techniques and computer vision. Choosing the suitable classification technique is quite difficult as the result varies on different input data. Classification of leaf diseases in plants has wide applications in different fields such as agriculture and biological research. This paper provides a general idea of few existing methods, its pros and cons, state of art of different techniques used by several authors in leaf disease identification and classification such as preprocessing techniques, feature extraction and selection techniques, datasets used, classifiers and performance metrics. Apart from these some challenges and research gaps are identified and their probable solutions are pointed out.

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Abbreviations

CNN:

Convolutional Neural network

CHT:

Circular Hough Transform

SVM:

Support Vector Machine

DRNN:

Deep Residual Neural Network

KNN:

K-Nearest Neighbor

ABC:

Ant Bee Colony Optimization

ROC:

Receiver Operating Characteristic

IGA:

Improved Genetic Algorithm

DL:

Deep Learning

PCA:

Principal Component Analysis.

HIS:

Hyperspectral Imaging

ANN:

Artificial Neural Networks

MCC:

Moving Center Classifier

GA:

Genetic Algorithm

DI:

Disease index

PNN:

Probabilistic Neural Network

LDC:

Linear Discriminant Classifier

CNN:

Convolutional Neural network

FNN:

Fuzzy Neural Network

SURF:

Speeded Up Robust Features

RF:

Random Forest

PCA:

Principal Component Analysis

FCM:

Fuzzy C-means Clustering

PLS:

Partial Least Square

IoU:

Intersection of Union

CCR:

Correct Classification Rate

HC:

Hierarchical Clustering

UAVs:

Unmanned Aerial Vehicles

ML:

Machine Learning

LAI:

Leaf Area Index

FE:

Feature Extraction

BoWs:

Bag-of-words

CA:

Classification Accuracy

SAR:

Synthetic Aperture Radar

GANs:

Generative Adversarial Networks

PMI:

Powdery mildew index

NN:

Neural Network

CSM:

Chaotic spider monkey

DNN:

Deep Neural Network

YRI:

Yellow rust-index

HSV:

Hue Saturation Value

QNN:

Quantum Neural Network

RGB:

Red Green Blue

PSO:

Particle Swarm Optimization

LBPs:

Local Binary Patterns

FET:

Feature Extraction Technique

GLCM:

Grey-Level Co-occurrence Matrix

FRT:

Feature Reduction Technique

SGDM:

Spatial Gray Level Dependence Matrix.

DiffN:

Difference of Normal Orientations

SIFT:

Scale Invariant and Feature Transformation

DCNN:

Deep Convolutional Neural Network

CLAHE:

Contrast Limited Adaptive Histogram Equalization

SRCNN:

Super-Resolution Convolutional Neural Network

SMOTE:

Synthetic Minority Over-Sampling Technique

ANFIS:

Adaptive neuro-fuzzy inference System

BRBFNN:

Bacteria Foraging Algorithm Radial Basis Function Neural Network

YOP:

Year of Publication

HPCCDD:

Homogeneous Pixel Counting Technique for Cotton Diseases Detection

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Acknowledgements

The authors would like to thank respected reviewers for their valuable and helpful comments. Further we would like to thank the Ministry of Human Resource Development, India and the National Institute of Technology Jamshedpur, India for financial assistance.

Funding

This research was supported by the National Institute of Technology Jamshedpur, India under the MHRD doctoral research fellowship.

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  1. Department of Computer Science and Engineering, NIT Jamshedpur, Jamshedpur, Jharkhand, India

    Monu Bhagat & Dilip Kumar

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  1. Monu Bhagat
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  2. Dilip Kumar
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Correspondence to Monu Bhagat.

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Bhagat, M., Kumar, D. A comprehensive survey on leaf disease identification & classification. Multimed Tools Appl 81, 33897–33925 (2022). https://doi.org/10.1007/s11042-022-12984-z

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