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AI-Based Crop Disease Detection System
PythonFlaskReactYOLOMachine LearningData Analysis
The Problem
Traditional crop disease identification is manual, time-consuming, and prone to human error.
The Solution
Implemented a YOLO-based object detection model to classify 10+ crop diseases, replacing an underperforming CNN approach.
Architecture
Deep learning model deployed via Flask API and integrated with a responsive React frontend.
Role
Computer Vision Engineer
Impact
Achieved 83%+ classification accuracy and significantly improved detection reliability compared to baseline CNN models.
Lessons I Learned
Choosing the right architecture (object detection vs. basic CNN) is critical for contextual visual understanding.