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Plant leaf species identification using LBHPG feature extraction and machine learning classifier technique

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Abstract

This paper presents the identification and classification of Indian agricultural crop species using a novel combined local binary histogram pattern of gradient (LBHPG) image feature extraction technique. Initially, a partition of the leaf image background is done through the newly developed fast adaptive fuzzy C-mean clustering (FAFCM) technique. After that, leaf objects within the image are identified using the LBHPG method. For the classification, KNN, PNN, and SVM shallow machine learning classifiers are used for crop species identification. The performance evaluation is done using LBP and HOG individually along with the new proposed LBHPG technique for classification using KNN, PNN, and SVM Classifiers. The performance evaluation is based on six metrics parameters of the confusion matrix, viz., accuracy, sensitivity, specificity, precision, recall, and F-measure. The experimental results show that the proposed novel LBHP feature extraction technique with PNN Classifier gives the highest accuracy of 94.58%.

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Data availability

The data sets analyzed during the current study are available in the [Plant Village Image Data Set: Kaggle] repository, https://www.kaggle.com/datasets/abdallahalidev/plantvillagedataset.

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Acknowledgements

We are thankful to Professor A.S. Patil, Department of Plant Pathology, Agriculture Research Centre, Kolhapur, Maharashtra, India for finalizing the Plant Image Database and Guidance to attain the objective of this research work.

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The authors declare that there has no funding has been received for this research.

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Authors and Affiliations

  1. School of Computer and Systems Sciences (SC&SS), Jawaharlal Nehru University (JNU), New Delhi, India

    Sachin B. Jadhav

  2. Shri Chhatrapati Shivajiraje College of Engineering, Pune, Maharashtra, India

    Sanjay B. Patil

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  1. Sachin B. Jadhav
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  2. Sanjay B. Patil
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Contributions

The corresponding author contributed to the study conception and design. Material preparation, data collection and analysis were performed by [SBJ]. The first draft of the manuscript was written by [full name] and the co-author has contributed to the study by supervision: [SBP]. All authors read and approved the final manuscript. Conceptualization: [SBJ]; methodology: [SBJ], formal analysis and investigation: [SBJ]; writing—original draft preparation: [SBJ]; writing—review and editing: [SBJ]; supervision: [SBP].

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Correspondence to Sachin B. Jadhav.

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Jadhav, S.B., Patil, S.B. Plant leaf species identification using LBHPG feature extraction and machine learning classifier technique. Soft Comput 28, 5609–5623 (2024). https://doi.org/10.1007/s00500-023-09358-4

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