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Vision01:24

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Herbify: an ensemble deep learning framework integrating convolutional neural networks and vision transformers for

Farhan Sheth1, Ishika Chatter1, Manvendra Jasra1

  • 1Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.

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|July 27, 2025
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Summary

This study developed an AI-powered computer vision system for accurate herb identification, achieving over 99% accuracy. The system, utilizing deep learning models, offers a reliable tool for botanical applications and herbal medicine.

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Convolutional neural networksDeep LearningEnsembleHerb classificationMedicinal plantsTransfer learningVision transformers

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Botany

Background:

  • Herbal medicine offers a cost-effective, holistic alternative to synthetic drugs, but accurate identification remains a challenge.
  • Synthetic drugs often have adverse side effects and growing affordability concerns.
  • Renewed interest in traditional herbal remedies necessitates reliable identification methods.

Purpose of the Study:

  • To develop a computer vision framework for accurate and automated herb identification.
  • To create a novel, high-quality dataset for herb recognition tasks.
  • To evaluate the performance of deep learning models for herb classification.

Main Methods:

  • Compiled and refined a novel dataset (Herbify) of 6104 herb images across 91 species.
  • Standardized data using the Preprocessing Algorithm for Herb Detection (PAHD).
  • Employed transfer learning with Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in an ensemble framework.

Main Results:

  • EfficientNet v2-Large achieved an F₁-score of 99.13%.
  • An ensemble model (EfficientL-ViTL) combining EfficientNet v2-Large and ViT-Large/16 reached an F₁-score of 99.56%.
  • Developed an AI application, 'Herbify', for practical herb identification.

Conclusions:

  • The proposed AI system provides a highly accurate and operationally viable solution for herb identification.
  • The study demonstrates the significant potential of AI in botanical applications.
  • The developed framework addresses key challenges in automated herb recognition.