Crop Disease Detection with Edge AI
Classifying plant leaf images to identify diseases, nutrient deficiencies, or pest damage. Deployed on battery-powered devices in agricultural fields where connectivity is limited. Models classify leaf images into healthy vs disease categories using quantized MobileNet or custom CNN architectures. Enables early intervention before diseases spread across crops. Training datasets like PlantVillage provide labeled images for common crops and disease types.
Hardware Requirements
| Minimum RAM | 128 KB |
| Minimum Flash | 512 KB |
| Sensor Inputs | camera |
| Typical Model Size | 150 KB (quantized int8) |
| Minimum Clock | 80 MHz |
Hardware Guides
No hardware guides for crop disease detection yet. Use the MCU Checker to find compatible hardware.
Industry Applications
Orchestrate Crop Disease Detection with ForestHub
Your devices run crop disease detection on-device. ForestHub on your Linux edge gateway ingests their results over MQTT/Modbus/OPC-UA, orchestrates the sense-reason-act loop as an auditable graph, and acts on the line — the LLM is one node among many.