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Efficient feature selection using BoWs and SURF method for leaf disease identification

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Abstract

When plant diseases appear, they have an adverse effect on agricultural output. Food insecurity would worsen if plant diseases were not identified accurately in time. Without early identification of plant diseases, agricultural production management and decision-making would not be feasible. Now a days deep learning model is widely used for image classification to get more accurate result. But machine learning based classifier also produces good result if good feature selection technique is used. In this paper, we have used hybrid machine learning techniques for classifying leaf diseases present in species such as tomato, potato and pepper bell. We have used bag-of- feature for visually representing diseased leaf features. SURF technique is used to extract strongest number of features and for classification task SVM is used. The proposed method gives good result in terms of precision, accuracy, recall, F1-score, FPR, FNR, and MCC on all the three employed datasets. The classification accuracy obtained in our proposed model on dataset1, dataset2 and dataset3 are 97%, 97% and 93% respectively. We have also calculated percentage feature reduction for all three types of species which are approximately 45.21%, 40.65% and 34.73% for tomato, potato and pepper bell respectively. We have compared disease classification accuracy of several previous work done by various authors on tomato leaf dataset using various machine learning and deep learning technique with our proposed work and find that our method is performing better.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the GitHub repository, https://github.com/MonuBhagat11/Leafdisease_data.

Abbreviations

SURF:

Speeded Up Robust Features

TTB:

Tomato Late Blight

TSLS:

Tomato Septoria leaf Spot

TTS:

Tomato Target Spot

TMV:

Tomato Mosaic virus

PBBS:

Pepper Bell Bacterial Spot

PEB:

Potato Early Blight

PH:

Potato Healthy

FPR:

False Positive Rate

DoGs:

Difference of Gaussians

PVD:

PlantVillge Dataset

GAN:

Generative Adversarial Network

TSWV:

Tomato spotted wilt virus

CNN:

Convolutional Neural network

BoWs:

Bag-of-words

TLM:

Tomato Leaf Mold

TTSSM:

Tomato Two Spotted Spider Mite

TYLCV:

Tomato Yellow leaf Curl Virus

TH:

Tomato Healthy

PBH:

Pepper Bell Healthy

PLB:

Potato Late Blight

MCC:

Mathews Correlation Co-efficient

FNR:

False Negative Rate

SVM:

Support Vector Machine

RFD:

Real field Dataset

SIFT:

Scale Invariant Feature Transform

PCA:

Percentage Classification Accuracy

SRCNN:

Super-Resolution Convolutional Neural Network

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Acknowledgements

The authors would like to express their gratitude to the reviewers who provided valuable and insightful feedback.

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This study received no specific financing from governmental, private, or non-profit funding bodies.

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  1. Department of Computer Science & Engineering, National Institute of Technology Jamshedpur, 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. Efficient feature selection using BoWs and SURF method for leaf disease identification. Multimed Tools Appl 82, 28187–28211 (2023). https://doi.org/10.1007/s11042-023-14625-5

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