Background Medicinal plants have been utilized for centuries for their beneficial properties and significant impact on traditional medical systems worldwide. Indian medicinal plants and their therapeutic properties have recently gained increased attention from the global healthcare community. Accurate identification and classification of medicinal plants ensure proper therapeutic applications. However, botanists manually identify these plants, which is subjective, labor-intensive, and time-consuming. Objective The main objective of this study is to develop an accurate and automated classification of these medicinal plants for diverse applications such as conservation initiatives, quality assurance, and the formulation of novel herbal treatments. Methods A Deep Fused Explainable Neural Architecture termed MedPlantNet is proposed for robust recognition of medicinal plant leaves. The deep discriminative features are extracted from the medicinal plant images using a fine-tuned Deep Convolutional Neural Network (DCNN). A customized Support Vector Machine is developed to efficiently classify these extracted deep features. To enhance model interpretability, a meta-visualization framework incorporating Grad-CAM and Occlusion Sensitivity visualization is implemented to identify the regions of the image that contribute to the classification decisions. We utilized the Mendeley benchmark Medicinal plant leaf database with 30 classes to train and evaluate the model. Results To assess the effectiveness of the proposed model, accuracy, specificity, sensitivity, and AUC are calculated. The proposed system outperforms existing state-of-the-art (SOTA) algorithms for medicinal plant classification, achieving an optimal maximum validation accuracy of 99.87% and AUC of 1. To further validate the generalizability external validation was performed on Bangladeshi medicinal plant dataset and MedLeaves dataset where the proposed model MedPlantNet attained 99.27% and 99.80% validation accuracy respectively. Conclusion MedPlantNet provides an automatic framework for medicinal plant identification based on deep feature extraction, classification and explainable visualization techniques. The results obtained indicate its potential utility in supporting biodiversity conservation, quality assurance and herbal medicine research.

Deep fused explainable neural architecture for the automatic recognition of therapeutic medicinal plants

期刊论文
发表日期：

2026年7月11日

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Pooja Govindaraj,
N Sasikaladevi

摘要

Background

Medicinal plants have been utilized for centuries for their beneficial properties and significant impact on traditional medical systems worldwide. Indian medicinal plants and their therapeutic properties have recently gained increased attention from the global healthcare community. Accurate identification and classification of medicinal plants ensure proper therapeutic applications. However, botanists manually identify these plants, which is subjective, labor-intensive, and time-consuming.

Objective

The main objective of this study is to develop an accurate and automated classification of these medicinal plants for diverse applications such as conservation initiatives, quality assurance, and the formulation of novel herbal treatments.

Methods

A Deep Fused Explainable Neural Architecture termed MedPlantNet is proposed for robust recognition of medicinal plant leaves. The deep discriminative features are extracted from the medicinal plant images using a fine-tuned Deep Convolutional Neural Network (DCNN). A customized Support Vector Machine is developed to efficiently classify these extracted deep features. To enhance model interpretability, a meta-visualization framework incorporating Grad-CAM and Occlusion Sensitivity visualization is implemented to identify the regions of the image that contribute to the classification decisions. We utilized the Mendeley benchmark Medicinal plant leaf database with 30 classes to train and evaluate the model.

Results

To assess the effectiveness of the proposed model, accuracy, specificity, sensitivity, and AUC are calculated. The proposed system outperforms existing state-of-the-art (SOTA) algorithms for medicinal plant classification, achieving an optimal maximum validation accuracy of 99.87% and AUC of 1. To further validate the generalizability external validation was performed on Bangladeshi medicinal plant dataset and MedLeaves dataset where the proposed model MedPlantNet attained 99.27% and 99.80% validation accuracy respectively.

Conclusion

MedPlantNet provides an automatic framework for medicinal plant identification based on deep feature extraction, classification and explainable visualization techniques. The results obtained indicate its potential utility in supporting biodiversity conservation, quality assurance and herbal medicine research.