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Bio-optimized complex valued spatiotemporal GNN for herbal species classification.

Prashant Vats1, Shailender Vats2, Avani Sharma3

  • 1Department of Computer Science and Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan, 303007, India.

Scientific Reports
|April 1, 2026
PubMed
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This summary is machine-generated.

Automated herb classification using a novel complex-valued spatiotemporal graph convolutional neural network (AHC-CVSTGCN) achieves high accuracy. This computer vision system aids in identifying medicinal plants, benefiting healthcare practitioners.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Botany

Background:

  • Herbal medicine relies on accurate plant identification, which is currently labor-intensive.
  • Thousands of herb species exist, making manual classification challenging and time-consuming.
  • Automated systems are needed to streamline the process of herbal species identification.

Purpose of the Study:

  • To develop an automated computer vision system for accurate herb classification.
  • To create a robust and optimized classification model using graph neural networks.
  • To leverage morphological features of herbal leaves for improved identification.

Main Methods:

  • Proposed a novel automated herb classification via complex-valued spatiotemporal graph convolutional neural network (AHC-CVSTGCN).
Keywords:
Botanical featuresGraph convolutional neural networkGraph neural networkHerb classificationHierarchical manta ray foraging optimizationLeaf morphologySustainable agriculture

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  • Utilized Multiple Local Particle Filter (MLPF) for image preprocessing and Revised Tunable Q-Factor Wavelet Transform (RQFWT) for feature extraction (shape, color, texture).
  • Employed hierarchical manta ray foraging optimization (HMRFO) to enhance the classification accuracy of the CVSTGCN model.

    • Main Results:

    • The AHC-CVSTGCN model achieved a high accuracy of 99.40%.
    • The system demonstrated excellent performance with 99.11% precision and 99.12% recall.
    • Experimental results significantly outperformed existing methods in automated herbal species classification.

    Conclusions:

    • The developed AHC-CVSTGCN system offers a reliable and effective solution for automated herbal species classification.
    • This automated approach can significantly reduce the time and labor involved in identifying medicinal plants.
    • The study presents a paradigm shift, empowering medicinal plant science and healthcare practitioners with informed decision-making tools.