Download PDF
---nasdaq--amzn_14.jpg "Amazon Web Services Logo")
From Cows to the Cloud: How TINE is Revolutionizing the Norwegian Dairy Industry Using Machine Learning on AWS
Technology Category
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Agriculture
- Food & Beverage
Applicable Functions
- Discrete Manufacturing
- Quality Assurance
Use Cases
- Predictive Maintenance
- Machine Condition Monitoring
- Real-Time Location System (RTLS)
Services
- Data Science Services
- Cloud Planning, Design & Implementation Services
The Challenge
TINE SA, a Norwegian cooperative owned by farmers, has been collaborating with Norway’s farmers for over 160 years to understand their challenges and help them drive efficiency, productivity, and high-quality dairy product development. As market demands increase, so does each farmer’s need to bring products to market more efficiently. TINE has a long history of developing decision-making tools for farmers and collecting data from different dairy products at the farm level. However, as TINE considered the future of dairy production at both the farm and national level, its data science team realized there would be changes to the breadth and depth of its data sources, types of data, and data analysis capabilities available to develop decision-making tools for farmers to use in production. TINE knew it would have to change its approach to data and technology by becoming more data driven as an organization to drive better predictability of milk production and other key data points related to a cow’s health and the quality of milk produced.
About The Customer
TINE SA is Norway's largest producer, distributor, and exporter of dairy products with 11,400 members (owners) and 9,000 cooperative farms. TINE’s mission is to provide consumers with food that provides a healthier and more positive food experience. TINE has a long history of developing decision-making tools for farmers and collecting data from different dairy products at the farm level. The Norwegian farmers who work with TINE are technologically savvy and have understood the value of collecting data on their animals and dairy production for decades. TINE has conducted structured research to examine the ‘optimal cow’ for dairy production using collected data. They found that focusing on the individuality of a cow enables farmers to discern when each cow is happy, stressed, anxious, and healthy. When their animals are healthier and happier, the quality of their milk is improved, which allows TINE to make even better dairy products while improving the welfare of the animal.
The Solution
To identify the technologies and platforms that would help improve TINE’s insights, predictions, and analyses, TINE brought in the experts at Crayon, an AWS Partner Network (APN) Advanced Consulting Partner and AWS Machine Learning (ML) Competency Partner. Inmeta, a subsidiary of Crayon, worked directly with the customer, providing ideas for data-driven innovation. Crayon approached the innovation project with TINE in four distinct phases. First, the team focused on data readiness, availability, quality, and relevance. During this phase, Crayon concluded that TINE had useful and relevant data but lacked historical timeseries. Next, the team moved on to Methodology and model selection. Crayon chose a simple model initially to demonstrate the use of a convolutional neural network to predict milk production based on the condition on the farm. During this phase, the teams concluded that the model and data were viable for milk production predictions. Then Crayon moved on to revising and refining the modelling process. Based on the finding from the previous phase, the team chose a decision tree model and then expanded to predict the conditions on the farm in the future, which would support better and more accurate forecasting. The model, which uses an ML solution that runs on AWS, predicts cow births and the total number of cows in the herd in addition to milk production. Finally, Crayon established a data lake optimized for analysis and ML models for prediction. This enables further improvements in the ML models while supporting ML initiatives for other applications within TINE.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Intelligent Farming with ThingWorx Analytics
Z Farms was facing three challenges: costly irrigation systems with water as a limited resource, narrow optimal ranges of soil moisture for growth with difficult maintenance and farm operators could not simply turn on irrigation systems like a faucet.
Case Study
The Kellogg Company
Kellogg keeps a close eye on its trade spend, analyzing large volumes of data and running complex simulations to predict which promotional activities will be the most effective. Kellogg needed to decrease the trade spend but its traditional relational database on premises could not keep up with the pace of demand.
Case Study
HEINEKEN Uses the Cloud to Reach 10.5 Million Consumers
For 2012 campaign, the Bond promotion, it planned to launch the campaign at the same time everywhere on the planet. That created unprecedented challenges for HEINEKEN—nowhere more so than in its technology operation. The primary digital content for the campaign was a 100-megabyte movie that had to play flawlessly for millions of viewers worldwide. After all, Bond never fails. No one was going to tolerate a technology failure that might bruise his brand.Previously, HEINEKEN had supported digital media at its outsourced datacenter. But that datacenter lacked the computing resources HEINEKEN needed, and building them—especially to support peak traffic that would total millions of simultaneous hits—would have been both time-consuming and expensive. Nor would it have provided the geographic reach that HEINEKEN needed to minimize latency worldwide.
Case Study
Greenhouse Intelligent Monitoring and Control Solution
Farming Orchids is the most successful form of precision farming in Taiwan, and also the most exported flower. Orchids need a specific temperature and humidity conditions to grow and bloom, and its flowering time may not be in line with market demands, so the price collapses when there is overproduction. Therefore, some farmers began to import automated greenhouse control systems for breeding and forcing, which not only improves quality, but also effectively controls the production period and yield to ensure revenue. In 2012, an orchid farmer built a Forcing Greenhouse of about 200 pings (approximately 661 Square Meters) in Tainan, Taiwan. The system integrator adopted Advantech’s APAX-5000 series programmable automation controllers to build the control platform, coupled with Advantech WebAccess HMI/SCADA software, to achieve cloud monitoring. The staff of the orchid field can monitor important data anytime via smart phone, iPad, and other handheld devices, and control the growth and flowering conditions. System requirements: In the past, most environmental control systems of orchid greenhouses in Taiwan used PLCs (Programmable Logic Controller) with poorscalability and control, and could not be connected to the Internet formonitoring from the cloud. For advanced database analysis and networking capability, the PC platform must be adopted. Therefore, PAC Systems (Programmable Automation Controller) with both PLC programming capabilities andPC functions is a better choice.The environmental control of the Orchid greenhouse switches on and off devices like fan, shade net, cooling/heat pump, liquid flow control, water-cooling wall etc. It is controlled by a control panel of electric controllers, and is driven by a motor, to adjust the greenhouse temperature, humidity, and other environmental conditions to the set parameters.
