Supports low power IoT cellular and LPWAN networks for rural remote connectivity used in precision farming and herd health applications

Farming Precision Equipment and Predictive Maintenance

Monitoring for suboptimal functionality and forthcoming equipment faults/failure allows maintenance to be scheduled intelligently. Even equipment degradation can be caught early and scheduled for maintenance before failures occur in the field. Early warning depends on the analysis of mechanical anomalies detectable by sensitive IoT edge sensors.

Continuously monitoring of equipment allows for planned maintenance cycles. While significantly reducing the costs and downtime associated with unplanned and inconvenient maintenance, predictive maintenance also reduces unnecessary manual check-ups and the premature replacement of equipment before the end of normal life cycles.


Maintaining the optimal working condition of farm machinery is key for maximum yield and ROI. Without IoT equipment monitoring, operators may not know if a piece of machinery is malfunctioning, or is about to malfunction, until it’s too late. This leads to delays in operational productivity and expensive repairs.

Where SensiML Can Help

SensiML Analytics Toolkit can locally analyze complex sensor data from multiple sensors (vibration, audio, pressure, current, voltage, load) in real-time providing insight even in remote rural locations with limited connectivity where cloud-based AI is impractical or impossible. With self-learning algorithms, SensiML-enabled IoT sensors can identify system anomalies linked to reduced operating efficiency and impending breakdowns. Benefits include:

  • Reduce maintenance costs through IoT equipment monitoring
  • Improved yields through increased reliability and process control
  • Better planning and scheduling with forecast maintenance needs

Herd Health and Wearables

With over 300 suppliers now offering some form of animal sensing technology, from simple RFID tags to wearable sensors and ingestibles, the market for animal wearables has come of age and is already becoming extremely competitive. Given most devices are utilizing a common set of low-cost sensor inputs, differentiation in animal wearables comes down to demonstrating ROI, ensuring accuracy of results, and delivering responsive insights. Such insights include animal vitals, motion and audio that are indicative of sick or calving animals, as well as location.


Farmers are moving towards technology that allows them to monitor their herd from anywhere. Challenges include device power efficiency and battery life in small form factor sensors, degree of meaningful insight achievable from low-power endpoints and integration with existing livestock operations.

Where SensiML Can Help

With efficient algorithms capable of running on battery-powered animal wearables with long battery life, SensiML enables true insight from motion, audio, temperature, and biosensor feeds. Since the algorithms run locally on the sensor itself, communicated data can be limited to sparse event notifications that are compatible with long-range, low-power networks like NB-IoT and LoRA. SensiML has demonstrated a variety of animal-based wearable algorithms including:

  • Eating and drinking event detection
  • Lameness and activity monitoring
  • Audio-based poultry health

Use Cases

Keeping grain silos, greenhouses, livestock barns in healthy air quality condition require continual fan state monitoring and PDM.

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