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Medicinal plants recognition using heterogeneous leaf features: an intelligent approach

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Abstract

In this research, an intelligent approach for the identification of Indian medicinal plant leaves is presented. Herein, heterogeneous features extracted from leaves of Indian medicinal herbs serve as input attributes for classifier models. A bilateral approach is employed to bring out heterogeneous features. In the first approach, a framework for extracting handcrafted feature descriptors characterized by edge histograms, oriented gradients and binary patterns is proposed. In the second approach, deep features are extracted by Convolutional Neural Networks (CNN) using a transfer learning approach. Z score normalization is applied for the normalization of extracted features. Normalized features are fused using intermediate level fusion techniques to bring out heterogeneous features. ‘Uniform Manifold Approximation and Projection (UMAP)’ is employed to tackle the challenge offered by the high dimensional heterogeneous feature vector dataset. Finally, the reduced heterogeneous feature vectors are fed into different classifiers for the identification and classification of medicinal herbs. We compare the models’ performances in terms of accuracy, precision, sensitivity, Probability of False Alarm (PFA), Matthews Correlation Coefficient Score (MCC), Jaccard Score and Receiver Operating Characteristics Curve (ROC). The results reveal that the Random Forest classifier with hybrid feature vector attains accuracy, precision and sensitivity of more than 99% and attains a very low PFA of 0.02%. Hence the proposed models with heterogeneous feature framework not only improve the identification and classification performance but at the same time reduce the probability of false alarm to substantial amounts. Our model presents remarkable results and marks up to 03% performance improvement in contrast to other available models and it demonstrates significant improvement in the identification and classification accuracy of Indian medicinal plant leaves.

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Data availability

The dataset is publicly available at https://data.mendeley.com/datasets/nnytj2v3n5 (accessed on 29 June. 2022).

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Authors and Affiliations

  1. Department of ECE, Giani Zail Singh Campus College of Engineering & Technology, MRSPTU, Bathinda, India

    Manoj Sharma & Sukhjinder Singh

  2. Department of ECE, UIET, Panjab University, Chandigarh, India

    Naresh Kumar

  3. School of Computer Science and Engineering Technology, Bennett University, Greater Noida, U.P, India

    Shallu Sharma

  4. SEEE, Centre for Space Research, Division of R&D, Lovely Professional University, Phagwara, Punjab, India

    Sumit Kumar

  5. Department of Biotechnology, UIET, Panjab University, Chandigarh, India

    Seema Mehandia

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  1. Manoj Sharma
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Correspondence to Manoj Sharma, Naresh Kumar or Sumit Kumar.

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Sharma, M., Kumar, N., Sharma, S. et al. Medicinal plants recognition using heterogeneous leaf features: an intelligent approach. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17639-1

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