| Sumario: | The Study investigates the use of Computer Vision Technology (CVT) combined with Convolutional Neural Networks (CNNs) to address challenges in the identification of dry Homeopathic herbs (fruits and seeds). A dataset of 50,000 high-resolution images, encompassing 50 different herb species used in Homeopathic remedies, was utilized to train the CNN model. The architecture comprised convolutional layers with filters and Dropout layers to ensure efficient feature extraction and prevent overfitting. The model achieved a peak training accuracy of 91.86%, with a validation accuracy ranging from 81% to 83%, and an inference time of 36 milliseconds per step, indicating its practical potential. Performance evaluations, including accuracy metrics and confusion matrices, revealed high prediction rates for distinct species. However, misclassifications among visually similar herbs highlighted the need for further dataset expansion and optimization. Recommendations include incorporating a more diverse database, additional species, and images captured from various angles and lighting conditions. Addressing class imbalances through data augmentation or resampling is also suggested. The study proposes advanced regularization techniques to enhance the model's generalization capabilities. This research bridges traditional Homeopathic herb identification methods with modern technological approaches, establishing a robust framework for leveraging AI and computer vision in Homeopathic medicine. The findings pave the way for the modernization and quality assurance of traditional remedies and emphasize the scalability of AI-driven solutions for large-scale applications, with potential future integration into cloud-based systems for broader use in Homeopathic practices.
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