Edge AI in agriculture involves deploying machine learning models directly on devices like drones, sensors, or farm equipment to process data locally. This approach reduces reliance on cloud connectivity, enables real-time decision-making, and addresses challenges like limited rural internet access. Here are three concrete examples of its use in farming.
One key application is crop health monitoring using drones or ground-based cameras. For instance, edge AI models like convolutional neural networks (CNNs) can analyze images of crops directly on drones to detect diseases, pests, or nutrient deficiencies. A raspberry Pi or Jetson Nano device mounted on the drone processes the images in real time, flagging areas needing attention. For example, a model trained to recognize blight in potato plants could trigger alerts for targeted pesticide application, reducing waste compared to blanket spraying. This avoids delays from uploading terabytes of imagery to the cloud and allows immediate action during short inspection windows.
Another use case is autonomous farm machinery. Tractors or harvesters equipped with edge AI can navigate fields using onboard cameras and lidar, avoiding obstacles like rocks or ditches. A YOLOv8 model running on an embedded GPU could identify weeds in real time, enabling precise herbicide application. For example, John Deere’s See & Spray system uses this approach to differentiate crops from weeds at high speeds. Edge processing is critical here because latency matters—waiting for cloud inference could cause a tractor to miss targets or collide with obstacles. Local model execution also ensures functionality in areas with poor cellular coverage.
A third example is livestock monitoring using wearable sensors. Edge devices on collars or ear tags can track an animal’s movement, temperature, and vocalizations to detect illness or estrus. For instance, accelerometer data processed by a TinyML model on a microcontroller could identify lameness in cows by analyzing gait patterns. Farmers receive alerts via LoRaWAN or Bluetooth without needing constant cloud connectivity. This is more efficient than manual checks, especially in large herds. Companies like Cainthus use edge-based facial recognition to monitor individual animals’ feeding behavior, optimizing feed schedules and reducing costs.
These examples highlight edge AI’s role in making agriculture more efficient and scalable. By running models directly on devices, developers can create systems that work reliably in remote environments, respond instantly to changing conditions, and minimize data transmission costs—key considerations for modern farming.