Aerial CV That Sees What the Human Eye Can’t
Advancing sustainable farming with aerial crop analysis and smart detection
The Challenge
Our Approach
We delivered a Deep Learning-Powered CV Pipeline to process aerial images:
- Semantic segmentation of crops, soil, water bodies, and anomalies
- NDVI (Normalized Difference Vegetation Index) modeling to detect stress
- Alert generation for pest signs, fungal patches, or irrigation leaks
- Scalable via batch uploads from drone partners or direct UAV integration
Built with a combination of PyTorch, OpenCV, and cloud inference APIs.
Stats
💰 Saved ₹12 lakhs/season in agro inputs
The Outcome
- Detected early-stage disease patches with 91% accuracy
- Saved up to ₹12 lakhs/season in reduced pesticide and fertilizer usage
- Improved yield by 14–18% across pilot farms
- Enabled custom farm reports for over 50+ clients
Case Study
- All Posts
- AI Agents
- AI Chat Bot
- AI Kickstater
- Computer Vision
- Data Engineering
- Gen AI
- Machine Learning
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