Computer vision, edge inference and local-language access in one field workflow.
The technical stack is organized so visual diagnosis, confidence handling, guidance retrieval and speech can be optimized independently.
A structured pipeline is easier to validate than one unconstrained image answer.
The system can separate crop recognition, plant-part detection, problem classification, confidence estimation and guidance retrieval.
Models are trained centrally and optimized for local deployment.
The development pathway can use NVIDIA GPUs for training and fine-tuning, then optimize suitable vision models for low-latency inference on edge hardware. Advanced device generations can use NVIDIA Jetson-class hardware and TensorRT-optimized models.
- Pretrained vision backbones and fine-tuning
- Dataset balancing and field-image augmentation
- Quantization and model optimization
- TensorRT inference on compatible NVIDIA edge hardware
- Local storage for model and guidance updates
The system can say when the image is not reliable enough.
A low-confidence result can trigger another scan rather than presenting an overly certain diagnosis. Guided prompts can request a closer image, another leaf surface or a different plant part.
Check framing and visibility first.
Blur, darkness and poor framing can be detected before diagnosis.
Rank likely conditions.
The interface can show confidence and alternative possibilities.
Send difficult cases later.
When a connection is available, uncertain cases can be synchronized for expert review.