SVETNet: A Novel Explainable Deep Learning Approach for Medicinal Plant Species Recognition

Abstract

Medicinal plant identification is an important task in areas such as healthcare, agriculture, and traditional medicine systems like Ayurveda. Traditionally, this process has been carried out by experts through manual observation, which is not only time-consuming but also requires deep domain knowledge. With the rise of artificial intelligence and computer vision, automated methods have been introduced to make plant identification faster and more efficient. However, many existing approaches still rely on basic image processing and limited feature extraction, which often results in lower accuracy, especially when dealing with complex and diverse plant images. To overcome these challenges, this study proposes an intelligent image-based system for identifying medicinal plants. The system uses the InceptionResNetV2 model to extract important visual features such as leaf shape, texture, and structural patterns. These features are then used to train several machine learning models, including Gaussian Naïve Bayes (GNB), K-Nearest Neighbors (KNN), and a combination of Restricted Boltzmann Machine (RBM) with Logistic Regression (LR). In addition, a hybrid model called SVETNet is introduced, which combines Support Vector Classification (SVC) and Extra Trees (ET) to improve classification performance. The experimental results show that Support Vector Extra Trees Network (SVETNet) performs better than the other models, achieving an accuracy of 0.9947 for main class prediction and 0.9594 for sub-class prediction. The system also includes a user-friendly interface for easy interaction and a Flask-based client–server setup that allows remote predictions. An explainable analysis module is added to first check whether the input image contains a medicinal plant before performing classification. This work presents a reliable and scalable solution for medicinal plant identification, which can be useful in real-world applications such as healthcare, agriculture, and environmental monitoring