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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model

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Abstract

Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%.

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Data Availability

The data that support the findings of this study are openly available in the public domain [kaggle Tomato leaf disease detection] https://www.kaggle.com/kaustubhb999/Tomatoleaf [8]. We have provided the link to the dataset and the code to access it. We have given Google Drive link to access code https://drive.google.com/drive/folders/155H1Q9-9D77fu-pEWb2ncStDeUPIPTPg?usp=share_link. We will provide the code access publicly through our GitHub repository once the article is published.

Abbreviations

AOA:

Arithmetic Optimization Algorithm

AROA:

Archimedes Optimization Algorithm

BES:

Bald Eagle Search

BI-GRU:

Bidirectional Gated Recurrent Unit

CNN:

Convolutional Neural Network

DNN:

Deep Neural Network

DT:

Decision Tree

DL:

Deep Learning

DBN:

Deep Belief Network

ICRMBO:

Improved Crossover-Based Monarch Butterfly Optimization

FPR:

False Positive Rate

FNR:

False Negative Rate

GAN:

Generative Adversarial Network

HC:

Hybrid classifiers

LGP:

Local Gabor Pattern

LBP:

Local Binary Pattern

LP:

Learning Percentage

LDF:

Local Distribution Feature

MCC:

Matthews Correlation Coefficient

MF:

Median Filtering

ML:

Machine Learning

MLP:

Multilayer Perceptron Neural Network

NPV:

Negative Predictive Value

RF:

Random Forest

PCA:

Principal Component Analysis

PRI:

Photochemical Reflectance Index

ResNet101:

Residual Network101

WOA:

Whale Optimization Approach

SVM:

Support Vector Machine

SGD:

Stochastic Gradient Descent

SSA:

Salp Swarm Algorithm

SISS-OB:

Self Improved Shark Smell With Opposition Behavior

STDA:

Stepwise Discriminant Analysis

SSO Shark:

Smell Optimization

TL-DCNN:

Transfer Learning-Based Deep CNN

TL:

Transfer Learning

VIs:

Vegetation Indices

VAEs:

Variational Auto Encoders

WT:

Watershed Transform

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Acknowledgements

The Department of CSE, School of Engineering and Technology, Christ(Deemed to be University), Kengeri Campus, Bengaluru, India, and the Department of CSE, G. Narayanamma Institute of Technology and Science, Shaikpet, Hyderabad, Telangana, India, supports this work includes non-financial support.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Bengaluru, Karnataka, 560074, India

    Manoj A. Patil & M. Manohar

  2. Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Hyderabad, Telangana, 500104, India

    Manoj A. Patil

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Patil, M.A., Manohar, M. Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model. SN COMPUT. SCI. 4, 710 (2023). https://doi.org/10.1007/s42979-023-02245-7

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