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Somadetect - SCC Diagnostic Systems
Monitoring Every Cow at Every Milking, Automated, in-line sensors passively capture critical health, quality, and reproduction data with no lost milk and no added chores.
Better Herd Health
Effectively manage herd SCC and enhance animal welfare, increase daily yield, and reduce involuntary culling.
Improved Reproduction
Remove the guesswork by knowing the reproductive status of every cow and reduce DIM average.
Reduce Expenses
With our system, you can reduce the total cost of your operating expenses.
Improved Milk Quality
Over time, our sensors will increase your yearly revenue and improve your milk quality and production. Soma Sensors will increase revenue by allowing you to refine and improve KPI’s such as SCC herd levels, days open, and calving interval.
Constantly Delivering Value
Once the sensor is installed on the farm, it immediately starts collecting data. As more data is collected, our algorithms become smarter and deliver data that is more useful to your farm. Over time, the accuracy and quality of your results increase, allowing you to improve your whole herd and farm.
In-line optical sensor technology is at the core of our innovative platform. We use light scattering for rapid, on-site determinations of compounds in raw milk. Our monitoring equipment fits into existing milking lines, making it accessible and cost-effective for dairy producers. Our system does not require the use of any chemicals, consumables, or reagents meaning that the milk used to take measurements is unaltered and can be returned to the milking line after measurement. This reduces operational costs and the use of chemicals inside the dairy barn.
Our product relies on the principle of light scattering: a beam of directed light passes through a medium, in our case raw milk, and hits small particles that cause the beam to change direction and scatter. During this process, a portion of the light may be absorbed or reflected, altering the intensity of the scattered beam. Particles of different sizes, in various concentrations, will have unique scattering patterns that can be observed.
Until our technology, the application of forward-angle light scattering in fluids has been limited to specific applications and too costly for non-laboratory uses. With our sensor, it is now possible to detect several compounds across a wide range of particulate sizes, using a cost-effective sensor system.
Artificial neural networks use algorithms, big data, and the computational power of graphic processing units (GPUs) to find patterns and connections between data sets. This branch of machine learning enables computers to achieve complex calculations and learn at speed, accuracy, and scale that has not been possible before.
We use computer vision and deep neural networks to build robust algorithms to predict the presence and concentrations of herd health indicators in raw milk. We have collected thousands of milk samples to train a predictive model. This data set has yielded algorithms that act as the baseline of prediction, while additional data sets will allow us to improve the predictiveness, accuracy, and consistency across a wide range of use cases.
This approach has enabled us to develop a series of models to detect somatic cell count and reproduction status, with future implications for fat and protein content. This makes it possible to diagnose dairy diseases, including sub-clinical and clinical mastitis, and improve reproductive performance.
