Automated Medicinal Plant Identification Using Deep Learning for Improved Healthcare

Author(s)	Mr. KANCHAN N, DHANANJAYAN V, JAIWIN FRANCIS C A, XAVIER MARY A
Country	India
Abstract	The identification of medicinal plants is crucial for healthcare, drug manufacturing, and environmental management. Traditional manual identification methods are often timeconsuming and prone to errors, potentially leading to adverse health effects. To address this challenge, this project proposes an automated system for medicinal plant classification using deep learning. Leveraging convolutional neural networks (CNNs) with Xception-based feature extraction, the system ensures high accuracy and classification of medicinal plants. Additionally, it incorporates a symptom-based recommendation module, enabling users to input symptoms and receive suggestions for the appropriate medicinal plant. By automating plant identification and providing targeted recommendations, this system enhances efficiency, reduces errors, and facilitates safer and more effective use of medicinal plants in healthcare.
Keywords	Medicinal Plant Classification, Deep Learning, Symptom-Based Recommendation, Feature Extraction, Convolutional Neural Networks, Machine Learning.
Field	Medical / Pharmacy
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-15
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.39132
Short DOI	https://doi.org/g89v93

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research