AI-Driven Plant Disease Detection
AI solution for plant disease detection using a trained CNN model deployed in a Flask app with a web UI for farmers and agronomists.
Project Overview
Built an AI-driven pipeline that trains a Convolutional Neural Network (CNN) on curated plant image datasets, optimizes for high classification accuracy, and deploys the model behind a Flask API. A React-based UI enables image upload and displays predictions and confidence scores to help with rapid field diagnostics.
Key Technologies
- AI: Python, TensorFlow
- Backend: Flask
- Frontend: React
Core Features
- Image Upload & Classification
- Trained CNN Model
- Web-based Diagnostics UI
- High Accuracy Predictions
Challenges & Solutions
Collecting and labeling a representative dataset Optimizing model size for inference latency Providing interpretable predictions for users
**Solution:** Applied data augmentation and transfer learning to improve accuracy, quantized the model for faster inference, and surfaced top-class activation maps to aid interpretation.
Results & Impact
- Achieved high accuracy on test dataset (validated in experiments)
- Reduced inference latency to enable near-real-time predictions
- Delivered a usable web UI for quick diagnostics
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