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An IoT and Machine Learning Based Intelligent System for the Classification of Therapeutic Plants

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Abstract

The work aim to develop an automatic recognition model using IoT and machine learning (ML) techniques for the classification of therapeutic plants to enrich the traditional medicinal system. Ayurveda, the oldest Indian system of medicine is still practiced today as it widely promotes herbs as medicines for treating different health conditions and presents many advantages, such as low cost, availability in abundance, and minimal side effects. While many countries have accepted conventional medicine as the best alternative to synthetic drugs, the lack of knowledge and unsupported evidence has raised concerns and reduced its usage. Herein, an intelligent system is proposed using Raspberry Pi 3 Model B + (RPi) and the RPi camera to identify real-time images of Indian medicinal herbs and reveal their respective medicinal properties. Four ML models are developed in the work, of which one model is proposed to identify the details of a captured medicinal leaf on the RPi user interface. The proposed model predicts an accuracy (top-1) of 98.98% on a custom leaf dataset of 25 different medicinal species, containing 1500 leaf images, by combining two feature extraction techniques, namely scale invariant feature transform (SIFT) and histogram of oriented gradients. Bag of Visual Words is obtained by applying k-means clustering on SIFT descriptors as a feature selection and assessed using a support vector machine classifier. The suggested model integrated into RPi shows a real-time top-3 accuracy of 99%. The designed system has the advantages of being built solely for medicinal herbs, with reduced camera cost, and even works efficiently in remote areas.

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Acknowledgements

The authors are highly thankful to the Biotechnology Centre, Department of Horticulture, India for helping in the collection of medicinal leaves to build the dataset and identify the respective medicinal uses.

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Authors and Affiliations

  1. Department of Computer Science and Engineering (AI&ML) and (Cybersecurity), Faculty of Engineering and Technology, JAIN (Deemed-to-Be University), Bengaluru, India

    Roopashree Shailendra

  2. VTU-RRC, Visvesvaraya Technological University, Belagavi, India

    Roopashree Shailendra

  3. Department of Computer Science and Engineering, RV Institute of Technology and Management, Bengaluru, India

    Anitha Jayapalan

  4. Department of CSE, Sona College of Technology, Salem, Tamil Nadu, India

    Sathiyamoorthi Velayutham

  5. Department of ECE, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India

    Arunadevi Baladhandapani

  6. Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Ashutosh Srivastava

  7. School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, India

    Sachin Kumar Gupta

  8. School of Computer Science, University of Petroleum and Energy Studies (UPES), Bidholi, Dehradun, India

    Manoj Kumar

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  1. Roopashree Shailendra
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  2. Anitha Jayapalan
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Correspondence to Manoj Kumar.

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Appendix 1

Appendix 1

The medicinal leaf dataset associated with the article can be found online at https://doi.org/10.17632/nnytj2v3n5.1

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Shailendra, R., Jayapalan, A., Velayutham, S. et al. An IoT and Machine Learning Based Intelligent System for the Classification of Therapeutic Plants. Neural Process Lett 54, 4465–4493 (2022). https://doi.org/10.1007/s11063-022-10818-5

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