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The future of farming: IoT wearables and data in animal care

Farmers and pet owners alike could benefit from the use of IoT in animal healthcare. With monitoring and machine learning models, veterinarians can treat animals more effectively.

IoT can create a whole world of magic for the healthcare sector, including medical and consumer wearables, data aggregation and analytics, and predictive systems that help entire nations avoid lifestyle-related illness and other diseases.

What's been a slightly overlooked topic is how IoT devices, married with data and AI, can help our furry friends.

In 2020, the veterinary pet market in the U.K. was worth 2.1 billion pounds, according to Statista. The value shouldn't come as a surprise because 59% of households own an animal now, versus 41% in the previous year before the pandemic pushed more remote work.

General veterinary care covers more than just pets. Animal husbandry also plays a huge role in the picture. Just for illustration, the sheep and lamb numbers in the U.K. totaled 32.7 million animals in 2020. IoT services can make the lives of our beloved pets better, but it will also affect the future of IoT in agriculture.

IoT in veterinary care

We could take all the knowledge and technologies known in human healthcare and apply them to veterinary care. However, at the moment, animal care lags behind the curve.

Veterinary care uses clever machines, such as CT and MRI scanners, but there is an untapped opportunity in making medical devices intelligent by applying IoT connectivity. Veterinary facilities could use the data gathered from medical hardware for analytics. By applying machine learning (ML) and AI, veterinary services can create insights that help professionals provide more accurate diagnostics and predictions and make veterinary care more efficient and available.

Of course, there are several differences to human healthcare that go beyond the obvious biological factors. Veterinary care has one significant advantage compared to human healthcare: a lower regulation bar that allows for more advanced data collection. There is no GDPR. Without strict regulations, veterinary care facilities can test new ideas and analytics more easily because animals don't need to "opt-in."

However, medical coding doesn't have standardized protocols. Applying all we know about IoT in healthcare to veterinary medicine would require more effort than human care, especially for complex AI and natural language processing.

Lastly, remember that animals can't speak, so the impact of technology on helping them can be profound.

Increasing wellbeing and productivity on animal farms

Have you ever thought of measuring a cow's daily steps or electrocardiogram? And why would you?

The welfare of animals on livestock farms has been a trending topic in the last few years for environmental and humanitarian reasons. Farmers must follow a whole set of regulations.

Small-scale farming has also taken off in recent years. Purchasing meat from local farms and butchers has become increasingly popular among consumers to combat environmental issues, support the economy and as a result of the pandemic.

However, anyone who has ever watched the documentary series Clarkson's Farm will know that any animal farming is not a smooth ride. The costs of both machinery and veterinary care are high. Over a fifth of U.K. farms failed to make positive Farm Business Income in 2019 and 2020.

So how can IoT help?

Let's say farmers want to monitor and control the quality of milk. They can use hardware wearables and connected devices physically attached to animals to measure various relevant parameters, such as their movement, temperature, medical parameters and food intake. The farmers can then collect the data for processing and create AI systems to improve the wellbeing of any livestock, increase product quality and productivity. By monitoring the conditions and analyzing data, we can understand better which factors cause a change. We can learn, for example, that a certain food component is affecting the animal's temperature, its wellbeing and eventually, the milk quality.

While not widely used, IoT in agriculture is not entirely new. Scientists have already used ML to predict sleep stages and even cattle fertility as part of precision agriculture.

IoT in animal healthcare framework

In practical terms, the IoT in an animal healthcare framework should consist of a wearable device, a data aggregation device and a data center. It's important to remember that connecting each sensor directly to unreliable broadband internet in an open space wouldn't be a clever idea. Connecting numerous sensor devices to the cloud is neither power nor cost efficient. Instead, users can look to build a local wireless network, integrating Bluetooth Low-Energy or LoRa for maximum efficiency and low cost-infrastructure.

The data transfer unit communicates the sensed data to the data center via a gateway of wireless communication medium. In the data center unit, data received from the gateway is used to create analysis and visualizations that allow users to view real-time conditions of animal health. The data is stored in the cloud for future use and analytics.

It's worth mentioning that today, IoT hardware is much more accessible than 10 years ago. There are more off-the-shelf products available and custom-build is also easier. Using IoT in animal healthcare is no longer a sci-fi story.

Remote diagnostics

Another use case for IoT technology in agriculture and animal care could lie with remote care and diagnostics for hard-to-access locations and urgent incidents. By allowing a medical professional to access data and diagnostics remotely from wearable hardware, animals can receive immediate help and improvement of their condition. Farmers save time and resources that they would have otherwise spent on veterinary visits.

IoT-based remote diagnostics could also improve pets' medical care. Telemedicine is already a booming market, with startups such as offering remote services and diagnostic. Enabling professionals' access to real-time medical data would take the quality of care up to the next level. IoT combined with teleservices also offers the potential to bring costs down and make services not only more accurate but also more widely available.

The IoT-fueled future of smart farming

Animal healthcare can only unlock the full power of IoT if data analytics and AI are applied. Suppose we collect data from wearables across farms, analyze this data and use ML and AI. In that case, we can create data-driven insights and build models that work at collective levels and allow for both prediction and prevention.

The sky is the limit. Echocardiograms, temperature and motion data could be captured and fed into an AI algorithm to detect diseases and urgent cases. Computer vision would extract specific movement patterns that suggest disease and illness to prevent serious cases, thanks to early diagnostics. Incorporating a variety of technologies and using them for stream analytics can make insights even more powerful.

Today, we can create digital twins for farms, using real-world data to build simulations and, most importantly, predictions.

Technology can help farmers -- no matter the scale of their business -- predict challenges, improve animal productivity and well-being and increase profitability, which will benefit both consumers and the wider economy.

About the author

Henry Brown is the data and analytics director of Ciklum. Brown has experience in leading data transformation, machine learning adoption and cloud-native projects across organizations and sectors such as the public sector, retail, commodities, financial services and manufacturing. Brown has a wealth of knowledge and experience in data and delivering value through data, including working on data predictions for both the U.K.'s 2016 Brexit vote and the U.S. election. At Ciklum, Brown works with Fortune 500 companies and startups, helping them grow and leverage business opportunities through strategic consultancy and custom-built data, analytics and AI solutions. He also has a Ph.D. in particle physics from the University of Liverpool, where he worked on the Large Hadron Collider beauty experiment at CERN, the Conseil Européen pour la Recherche Nucléaire, which translates to English as European Council for Nuclear Research.

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