ArticleHeliyon2024
Assessing deep convolutional neural network models and their comparative performance for automated medicinal plant identification from leaf images.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep learning for early detection of guava fruit anthracnose and fruit fly infestation.BMC plant biology · 2026Article
- A lightweight hybrid CNN and transformer model for medicinal leaf disease classification with explainable AI.Scientific reports · 2026Article
- FloraMediX: AI-enabled recognition and therapeutic profiling analysis of indian medicinal flora.Frontiers in bioinformatics · 2026Article
- CISCS: Classification of inter-class similarity based medicinal plant species groups with machine learning.MethodsX · 2025Article
- Medicinal plant leaf disease classification using optimal weighted features with dilated adaptive DenseNet and attention mechanism.Scientific reports · 2025Article
- ADHD diagnostics and severity assessment using topological manifold learning of resting-state functional magnetic resonance imaging (rs-fMRI).Neuroimage. Reports · 2025Article
- Chemotaxonomy, an Efficient Tool for Medicinal Plant Identification: Current Trends and Limitations.Plants (Basel, Switzerland) · 2025Review
- Identification of medicinal plant parts using depth-wise separable convolutional neural network.PloS one · 2025Article
- IndoHerb: Indonesia medicinal plants recognition using transfer learning and deep learning.Heliyon · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. Against this backdrop, we attempted to assess the performance of seven advanced deep learning algorithms in the automated identification of the plants from their leaf images and to suggest the best model from a comparative study of the models. We meticulously trained VGG16, VGG19, DenseNet201, ResNet50V2, Xception, InceptionResNetV2, and InceptionV3 deep neural network models. This training utilized a dataset comprising 5878 images encompassing 30 medicinal species distributed among 20 families. Our approach involved two avenues: the utilization of public data (P
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