Medicinal Herbs Identification Using Deep Learning
DOI:
https://doi.org/10.48001/978-81-966500-7-0-3Keywords:
CNN, MCN, VNPlant, XceptionAbstract
In recent years, because of the potential health advantages, the identification and classification of plants for medicinal purposes has generated a lot of attention. This project introduces an innovative AI-based approach for medicinal plant identification using deep learning, specifically employing the Xception architecture. Developed in Python, our model achieves remarkable training accuracy of 93.34% and validation accuracy of 96.79%. Utilizing the VNPlant-200 dataset, which includes 17,973 images of medicinal plants across 200 categories, our model leverages diverse visual characteristics to enable robust identification. Through meticulous training, the Xception-based model learns intricate patterns within the images, effectively distinguishing between different species. Hyperparameter tuning and fine-tuning of the Xception architecture further optimized the model’s performance. The high accuracies obtained validate the model’s capability to reliably recognize and categorize medicinal plants, significantly enhancing the accuracy and efficiency of identification processes. Our AI-based approach contributes to automated identification systems in herbal medicine, aiding researchers, botanists, and healthcare professionals in rapidly identifying medicinal plants. This project showcases the potential of AI and deep learning, particularly the Xception architecture, in advancing medicinal plant identification. The successful application on the VNPlant-200 dataset opens avenues for further research and development, promoting advancements in herbal medicine and botanical studies. Overall, this project demonstrates the efficacy of using deep learning techniques for medicinal plant identification, fostering innovation in the field of herbal medicine.
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References
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