plant_recognition_rf_model

Model Card for Model ID

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This model card describes the machine learning model used in Healthify – Virtual Herbal Garden, a web-based AI platform for plant identification and herbal treatment recommendations.

Model Details

Model Description

Healthify Plant Classification Model is a traditional machine learning image classification model designed to identify plant species from uploaded images. It uses Histogram of Oriented Gradients (HOG) feature extraction combined with a Random Forest classifier.

The model is integrated into a Flask backend and supports a React (Vite + TypeScript) frontend interface.

• Developed by: Shauryam Singh
• Shared by [optional]: Shauryam Singh
• Model type: Traditional ML Image Classification (HOG + Random Forest)
• Finetuned from model [optional]: Not applicable (trained from scratch using Scikit-learn)

Uses

Direct Use

Plant species classification from uploaded images. Educational botany learning. Academic ML demonstrations (feature extraction + ensemble models). Integration into lightweight web applications without GPU dependency.

Out-of-Scope Use

Medical diagnosis or prescription. Commercial agricultural automation. Identification of toxic plants for safety-critical systems. High-risk healthcare or environmental decision systems.

Bias, Risks, and Limitations

Limited to plant classes present in the training dataset. Performance sensitive to lighting, blur, and background noise. May misclassify visually similar species. Dataset sourced from Kaggle — may not represent full biodiversity diversity.

Recommendations

Always display a medical disclaimer for herbal treatment suggestions. Expand dataset diversity (regional plants, varied lighting conditions). Upgrade to CNN-based deep learning model for higher robustness. Add confidence score display to reduce over-trust.