AI Algorithms Detect Early Signs of Cow Diseases Three Times Faster than Standard Methods
Early disease detection became a critical goal for farmers seeking to reduce antibiotic use, prevent illness from spreading, and safeguard milk production. Despite having access to loads of data, farmers struggled to turn them into actionable health insights to cut down on antibiotics use.
This project shows how AI and IoT can transform animal welfare. With scalable architecture and unified data pipelines, any dairy operation can benefit from predictive health monitoring.
This project shows how AI and IoT can transform animal welfare. With scalable architecture and unified data pipelines, any dairy operation can benefit from predictive health monitoring.
AI offers farmers a way to spot health issues long before visible symptoms appear
Farmers already collect huge amounts of data, but without the means to interpret it, early intervention remains a challenge. AI provides the missing capability by detecting trends and anomalies too subtle for humans to identify manually. When integrated into existing farm systems, AI models can forecast health risks and notify farmers before problems escalate. This allows healthier herds, lower treatment costs, and more sustainable antibiotic practices.
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DAC.digital joined as a technical partner to build a predictive AI system for caughting early disease symptoms
Farmers collected valuable but disconnected data from milking robots and cow collars, leaving early warning signs of illness buried across systems.
Each farm uses different devices, which can make analyzing inconsistent data challenging.
To help, DAC.digital partnered with research institutions and pilot farms in Latvia and the Czech Republic to gather milk parameters (notably fat/protein ratio) from robotic milkers and biometric readings (pH and temperature via fistula probes) from collars. The mixed formats, sampling rates, and device types made reliable analysis difficult, so a farm-agnostic approach to normalize, aggregate, and validate those heterogeneous data streams was required before predictive models could provide timely, actionable alerts.
As a result, our new system architecture enables the integration of any sensors. As experts in Big Data, we have designed a scalable solution that can handle any number of data sources, making it suitable for small and large dairy farms.
An AI-based health monitoring system that works across data sources
DAC.digital’s AI expert, Marek Tatara, PhD, developed two AI algorithms for cows’ health monitoring:
- An algorithm that utilized data on pH and temperature from cow collars to predict potential malnutrition issues (and therefore potential disease or poor feeding) with almost 100% accuracy,
- An algorithm that analyzed milk composition, specifically the fat/protein ratio, to detect early signs of acidosis and ketosis.
Until now, information about the possibility of developing the disease was determined based on persistent alarming symptoms obtained by 12 readings taken every 15 minutes. Created by Marek Tatara, PhD, an artificial intelligence algorithm based on the data from cows’ collars, a precisely recurrent neural network, could predict whether a cow was sick based on temperature and pH. Thanks to our predictive algorithm, we have shortened detection time by three times.Â
Our AI expert investigated the possibility of detecting early symptoms of acidosis and ketosis by analyzing milk composition and, more specifically, by testing the fat/protein ratio thanks to data obtained from milking robots. His algorithm enabled the disease to be forecasted at a very early stage.
IoT experts designed a steam processing engine to gather and process all incoming sensor data in one place
Our Solutions Architect developed Steam Processing Engine, a robust infrastructure for aggregating sensor data from multiple farms. This system was designed to be agnostic to farm specifics, data types, and communication protocols, facilitating seamless data collection.
We have combined our experience in Big Data and AI to develop a comprehensive solution that detects anomalies at a very early stage and processes huge amounts of data collected from many sensors into useful information in real-time. Our system enables farmers to take actions that will prevent the development of diseases.
3x faster identification of early signs of health issues in cows
The project achieved significant outcomes:
An algorithm for detecting health issues in cows with almost 100% accuracy significantly reduces the time needed to identify sick animals.
An algorithm for cows’ disease forecasting based on milk quality, enabling predictive maintenance of livestock.
The development of a scalable solution that can be adapted to various farm sizes and types, potentially transforming livestock management practices.
An improvement in animal welfare through early disease detection and intervention, reducing the overall impact of diseases on livestock productivity.
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Technology stack
- Kafka for efficient data streaming.
- MQTT protocol for lightweight messaging across networked devices.
- Python and TensorFlow for the development and training of AI models.
- Java/Spring boot for microservices development.
- Kubernetes and Helm for infrastructure management and monitoring.
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