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AgroHealth
A comprehensive AI-driven platform for real-time crop disease detection and agricultural analysis, integrating custom IoT hardware with deep learning models.
The Problem
Early detection of crop diseases is critical for food security, yet traditional manual inspection is labor-intensive, slow, and prone to human error. Farmers lack accessible, real-time tools to identify pathogens before they spread.
The Solution
AgroHealth bridges the gap between advanced computer vision and on-field application. It combines a high-accuracy deep learning model with a custom hardware device to provide instant disease diagnosis.
Technical Implementation
Deep Learning Pipeline
- Model: Custom CNN architecture built with TensorFlow/Keras.
- Dataset: Trained on a hybrid dataset combining public archives with 500+ self-collected annotated images to ensure local variety performance.
- Performance: Achieved 94% accuracy across 20+ common agricultural pathogens.
IoT Hardware
- Device: Custom-engineered ESP32-based monitoring unit.
- Functionality: Captures real-time images and environmental data (temperature, humidity).
- Connectivity: Uploads data via Google Drive API for cloud processing.
Impact
- Speed: Reduced detection time from hours of manual inspection to seconds.
- Accessibility: Low-cost hardware design makes precision agriculture accessible to small-scale farmers.
- Precision: High-confidence detection enables targeted treatment, reducing unnecessary pesticide usage.