Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 3
May-June 2026

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A Robust Model for Classification of Plants Based On Leaf

Author(s) Ms. Shweta Yadav, Ms. Simran Vasisth, Mr. Vansh Singhal, Mr. Aman Yadav, Dr. Satendra Kumar
Country India
Abstract This study develops an image-based identification framework that employs transfer learning on a VGG16 backbone to construct species-specific convolutional models for 5 crops , including key medicinal and fruit plants such as aloe, jamun as well as staple and horticultural species like pepper, rose and money plant. Publicly available Kaggle datasets are curated into per-species multiclass problems that distinguish healthy leaves from a diverse set of bacterial, fungal, viral, and nutrient-related disorders, while explicitly encoding multiple disease stages where available.
Across the trained models, validation accuracy consistently exceeds 95% for money plant, pepper indicating reliable generalization for these species , whereas Jamun and Rose display lower performance and higher validation loss, reflecting greater inter-class similarity and dataset imbalance in complex medicinal data . The architecture is exposed through a lightweight graphical interface that accepts leaf from end users, returns the top-ranked disease class and inferred severity stage, and links each prediction to rule-based treatment and management guidance, enabling near real-time decision support in field condition.
The results demonstrate that decomposing the problem into species-specialized VGG16 models, combined with curated multiclass medicinal plant datasets and an accessible interface, forms a practical foundation for decision-support tools aimed at early , species-aware disease management in resource-constrained agricultural settings.
Keywords VGG16, transfer learning, plant disease detection, convolutional neural network, deep learning .
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-20
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5847
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