AI-Based Medicinal Plant Leaf Analysis System
A full-stack AI application that can identify medicinal plants from leaf images, detect healthy vs. diseased leaves, and provide structured outputs like scientific name, medicinal properties, and care recommendations.
Why it matters
This AI-powered system can help make medicinal plant identification and disease detection more accessible, reducing the dependence on domain experts.
Key Points
- 1Addresses challenges like lack of accessible plant identification tools, difficulty in disease detection, and dependence on domain experts
- 2Combines computer vision, backend APIs, and a modern frontend into a single deployable system
- 3Trained on labeled medicinal plant datasets using transfer learning and data augmentation techniques
- 4Provides features like confidence-based predictions, knowledge integration, and unknown class handling for non-medicinal inputs
Details
This project aims to make traditional medicinal plant knowledge systems more accessible through AI-powered automation. The system uses computer vision to classify plant species and detect leaf health status from uploaded images. The backend API handles file uploads, runs the ML model for inference, and returns structured JSON responses containing the scientific name, medicinal properties, and care recommendations. The frontend provides a user-friendly interface for image upload and displaying the prediction results. The ML model was trained on labeled medicinal plant datasets using transfer learning and data augmentation techniques to improve performance. Key features include confidence-based predictions, integration with a structured knowledge base, and handling of unknown/non-medicinal inputs. Overall, this project demonstrates how AI can be leveraged to digitize and make traditional knowledge systems more accessible to a wider audience.
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