Edge AI: The deployment of Artificial Intelligence applications in devices throughout the physical world ('the edge') rather than in a central cloud computing facility or private data center.
VarshaKrishi embeds Edge AI directly into the village gateway microcontrollers. This allows the system to make instantaneous irrigation decisions and disease predictions even when the internet is completely disconnected.
Edge AI runs machine-learning inference on local hardware — a gateway, microcontroller or single-board computer — instead of sending data to a cloud server. Models are compressed (quantised, pruned) to fit small processors, trading some capacity for the ability to answer in milliseconds with zero connectivity.
For agriculture, the case is structural: the majority of Indian farms sit behind unreliable or absent internet, and disease or irrigation decisions cannot wait for connectivity to return. Edge inference also keeps farm data on the farm — a growing concern for research institutions handling trial data.
VarshaKrishi's gateway runs disease-risk models (like TOMCAST-style disease severity values), FAO-56 evapotranspiration and anomaly detection on the sensor stream locally. Alerts fire on-site the moment thresholds are crossed, and the full record syncs whenever a connection happens to be available.
Cloud AI sends data to remote servers for inference — powerful, but dependent on connectivity and adding seconds-to-hours of latency. Edge AI runs the model on local hardware: instant answers, no internet dependence, and data stays on-site. Farms with unreliable coverage effectively require the edge approach.
Yes — disease-risk models are mostly driven by microclimate variables (leaf wetness duration, humidity, temperature) that local sensors already measure. The gateway evaluates those models continuously on-device; internet is only needed if you want remote dashboards.
See also: LoRaWAN, Microclimate — or browse the full smart farming glossary.
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