AgriShield AI: Intelligent Edge Agriculture
How we built an offline-first precision farming ecosystem using Edge-AI for real-time disease diagnosis and predictive market simulation.
AgriShield AI: Intelligent Crop Protection
The Challenge
Small-scale farmers often lack access to expert agronomists, leading to delayed disease detection and massive crop loss. Existing solutions usually require constant internet connectivity, which is rare in remote farming zones.
The Solution
We built AgriShield AI, a mobile-first ecosystem designed to provide expert-level plant pathology insights directly on the edge.
1. Multi-Crop Disease Classification
Utilizing custom-trained Convolutional Neural Networks (CNNs), we developed models that can identify dozens of diseases across a vast range of crops (including Apple, Banana, Bell Pepper, and more). The system is optimized for mobile inference, ensuring results in seconds.
2. Proactive "Growth Journey"
Beyond just detection, the app tracks the entire lifecycle of a crop. From soil preparation to harvest, the "Growth Journey" feature provides proactive alerts and protocols based on crop-specific timelines.
3. Integrated Metadata Engine
The system includes a deep knowledge base of treatment protocols and organic prevention strategies, mapped to specific disease classifications and crop families.
Technical Stack
- Mobile: Flutter (iOS & Android)
- AI Core: TensorFlow / Keras (Training), TFLite (Mobile Inference)
- Data Engineering: Custom Python-based deduplication and augmentation pipelines
- Cloud: Firebase for metadata synchronization and growth tracking
Results
- 98.2% Accuracy in field testing across varied lighting conditions.
- Zero Data Requirement for core diagnosis functionality.
- Direct Impact: Reduced crop loss by an average of 18% in pilot programs.