Sow Monitoring and Health Assessment with SIMKits

By Madonna Benjamin, Steven Yik, Michael Lavagnino, Daniel Morris

Precision livestock farming (PLF) promises individualized monitoring and care for livestock. Through individual animal management according to its needs, PLF has potential to both improve animal health and significantly raise farm productivity. However, achieving PLF for livestock will require a new generation of robust, in-farm sensing devices as well as big-data analyses and prediction that can infer individual animal health from sensor-based observations.

The SIMKit devices, developed at Michigan State University, provide individual sensing, monitoring and analyses to enable PLF breeding for sows.

SIMKits are inexpensive and yet robust to harsh conditions in farms such as vermin and splashing. Mounted overhead, they record 3D body shape of sows as they move along hallways. An artificial intelligence system on the device has been trained to detects sows and perform precise body joint estimation (see Figure). Over time the system builds up a personalized database that characterizes each sow. By mining these data, along with farm records, the productivity and health for each sow will be modeled and predicted based purely on SIMKit measurements along with historic health and production data. Adverse body conditions, such as being overweight, underweight, or lame, will be flagged. This will give quantitative guidance to farmers for individual sow management and corrective treatments.

SIMKit Features

Key features for the SIMKit system include:

  • Minimal labor – removes need for manual measurements of body condition
  • Accurate data
    more precise measurements without farm-hand variability
  • Identifies underfed or overfed sows – saves money in feed and in lost production
  • Can connect to “smart” feeding system for a fully automated farm

By saving feed, reducing labor-intensive activities, and by improving livestock outcomes, the SIMKit system promises a significant advance in PLF and farmer productivity. Further applications are being investigated including use for other livestock such as cattle, as well as analyses and prediction of additional health conditions.

Market Application

Once market share has been established in the U.S., the Company expects to evaluate potential partners that will extend the access to swine production data collected by several third party companies. Access to this data should further advance the potential applications of the SIMKit system.

Once established for swine, the Company will extend use of this technology to other farm animals including milk cows (anticipated first expansion), sheep, chickens, etc.

Investor Opportunities

Motion Grazer AI is seeking to raise a total of $2.1 MM (closing has already occurred for $50k). It is anticipated the remaining amount will be raised in financing rounds of $1.05 MM in 2022 and $1.0 MM in 2022. Investors being sought include seed and early-stage angel’s as well as VC’s and industry partners.

Email us to learn more about Investment Opportunities.