Tri-Category Medicinal Plant Classification Using Fine-Grained Lightweight Transfer Learning Model

Abstract

The identification of medicinal plants plays a vital role in healthcare, agriculture, and traditional medicine systems such as Ayurveda. Traditionally, plant identification has relied on manual observation by experts, which is time-consuming and requires extensive domain knowledge. With advancements in Artificial Intelligence (AI) and Computer Vision (CV), automated approaches have emerged to improve accuracy and efficiency. However, conventional systems often depend on basic image processing and limited feature extraction techniques, leading to lower accuracy and poor generalization across diverse plant datasets. These challenges necessitate a robust and intelligent classification system. This study proposes an advanced image-based framework for medicinal plant identification. The system employs InceptionResNetV2 for deep feature extraction, capturing essential visual characteristics such as leaf shape, texture, and structural patterns. Extracted features are used to train multiple Machine Learning (ML) models, including Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Restricted Boltzmann Machine (RBM) with Logistic Regression (LR), and a proposed hybrid model, Support Vector Extra Trees Network (SVETNet). SVETNet integrates Support Vector Classification (SVC) with Extra Trees (ET) to enhance classification performance. Experimental results demonstrate that SVETNet achieves superior accuracy, with 0.9947 for main class classification and 0.9594 for sub-class classification, outperforming baseline models. The system also includes a Graphical User Interface (GUI) and a Flask-based client–server architecture for remote predictions. Additionally, an explainable analysis module verifies whether the input image contains a medicinal plant before classification, ensuring reliability and scalability for real-world applications.