Abstract: This review presents an overview of artificial intelligence–driven approaches for medicinal plant identification using leaf image analysis. It focuses on advanced techniques such as hybrid convolutional neural network (CNN) models, transfer learning, feature extraction, and image preprocessing. The paper also summarizes key datasets, performance metrics, and real-time frameworks including Flask and YOLO. Despite notable progress, challenges persist in dataset diversity, integration of phytochemical information, and practical deployment. Overall, AI-based models show strong potential to improve the accuracy, efficiency, and accessibility of medicinal plant recognition, contributing to sustainable healthcare and biodiversity preservation.
Keywords: Medicinal plant identification, Artificial Intelligence, Deep Learning, CNN, Image recognition, Transfer Learning, Feature Extraction, Flask, YOLO.
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DOI:
10.17148/IJARCCE.2025.141047
[1] Madhura Wankhade, Samruddhi Gholap, Pranali Ghugarkar, Ravindra Ahire, Ms. Sneha Bankar, "AI-Based Medicinal Plant Detection via Leaf Image Recognition," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141047
