Tech

AI to improve cattle welfare, reduce carbon emissions by 30%, and combat labor shortages. –

In recent years, farming has come under fire because there is more to be done to harm the planet than to cure it.

The dairy sector, in particular, faces problems such as unsustainable dairy farming and inadequate fertilizer handling, which are exacerbating the problem of climate change and need to be addressed ASAP.

Technology comes to the rescue in the form of CattleEye, the first AI cattle monitoring company to partner with London-based AI V7 data engine to develop intelligent cattle monitoring solutions that can improve cattle welfare, reduce carbon footprint and help combating farm labor shortages. with automation.

The Tech Behind CattleEye

CattleEye works with trained veterinarians to build accurate computer vision models that can detect lameness in cows and other health issues as soon as possible.

To date, CattleEye systems are monitoring approximately 20,000 cows on farms across the UK and US, and the farms are benefiting from extended cow production cycles.

It works like this: CattleEye collects video data from multiple farms. The team then collaborates with experienced veterinarians who annotate data on V7, label cows while walking over the cameras, milking or eating, and highlight anomalies (eg, lame) or pregnancy.

The team also assigns mobility scores to better understand the health and performance of the cows. This then allows the system to immediately report any anomalies, such as poor mobility, whenever they occur when the technology is in use.

Annotated video data is then loaded v7where veterinarians would begin to distinguish between different cow behaviors and use it to train an AI model installed in the cameras mounted on the farms.

The cameras monitor the cattle, capture their movements, and alert farmers to any potential health issues or behavioral anomalies. The farmer may then take immediate action or keep a close eye on the cow.

After providing a few hundred examples, the teaching is accelerated as early AI models begin pre-completing training data to cover situations where their confidence is high.

Ranging from on-farm visits with veterinarians recording data manually to the full scoring of the videos in V7, we were able to greatly enrich the variety of training data from farms around the world. This saves the vets’ busy travel time as well as reducing the potential impact on the herd as visits to the farm do not have to interfere with a cow.

Ryan McMillan, Lead Data Engineer at CattleEye

The more data collected and annotated, the more accurate the model will be. AI CattleEye can see even the smallest movements of each cow, proving that AI can perform manual animal matching and on – site veterinarian visits, saving the farmer £ 350 a year on the farm. average.

Use AI to combat carbon footprinting with smart farming

Farms can save thousands of dollars thanks to technology developed by V7 and CattleEye, proving that AI can do the same job as farmers in detecting cow barriers – but better, faster and cheaper .

Not only that, but CattleEye CEO Terry Canning points that out

In 50 years, 90% of human labor on farms will be replaced by machines.

This is ideal for farmers who currently work 15-17 hours a day back, as well as farms struggling with labor shortages, and for farmers who simply see detecting cow lameness as a time – consuming support.

However, as awesome as all of this is, the company is driven by a bigger vision: Reducing the carbon footprint.

It’s an important vision, with total livestock emissions worldwide at present 14.5% of all anthropogenic GHG emissions.

And with cattle accounting for about 65% of these livestock emissions, it’s only natural for CattleEye to focus on this particular sector of the dairy industry.

Monitoring cattle health and behavior, before taking immediate proactive action to prevent any health issues, is at the heart of how CattleEye helps reduce our global carbon footprint.

According to CattleEye CEO Terry Canning:

We have indeed calculated that if you can reduce barrier levels by 10% on a farm, you will save half a tonne of carbon per cow per year.

The reason for this is to reduce the efficiency of cows by reducing latency levels.

In fact, CattleEye is on a mission to reduce its carbon footprint by 30% by 2050, with its machine learning system attracting key investors who share their vision.

Better AI training with people-in-the-loop

Artificial intelligence mimics human intelligence by learning from examples. For an AI model to learn how to detect a cow, identify its movements and assign it a mobility score, it must learn from a lot of human knowledge.

How does he do this?

This “knowledge” is called training data. The process by which people “teach” AI is given by the production of training data note. This involves dragging boxes or shapes around objects on software like v7 and then classify the “item,” as a cow, as a particular health condition, or give it a mobility score. The more training data there is, the better the algorithm.

Once sufficient training data has been collected, AI models can be trained to ingest this data and test themselves against invisible images. What set V7 technology apart is the ability for humans and AI models to work side by side on note – taking challenges.

Reliability is also a challenge.

While most engineering disciplines have predictable failures, it is not possible to “look” at AI models to analyze their contents na know. That is why understanding the details of AI training is crucial, because it serves as the complete knowledge repository of that AI.

When AI is “trained”, this information is compressed into the millions of values ​​of a neural network, which cannot be read by humans.

The future of AI in farming

With the United Nations proposing that our world population will reach 9,700,000,000 by 2050, the stark reality is that the agricultural industry as a whole will have to double its current production levels.

It’s a huge task, but ultimately technology like CattleEye can help farms become more profitable and efficient by keeping a close eye on livestock health, monitoring them daily for their food intake and activity level.

This is not the only piece of technology driven by machine learning and computer vision that is also available. Other systems have been created to help farmers inspect their plants, fertilize their crops – and much, much more.

In fact, it will not be so long into the future when a farmer will be able to do most of the work from the comfort of his kitchen using a smartphone device.

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For V7

V7 is a London-based AI startup founded in 2018. It develops an online training data platform to automate the AI ​​data annotation process, enabling AI-first companies to program labels to images or videos programmally. Its customers include GE, Boston Scientific, Fujifilm, and Miele. V7’s mission is to enable any business to create AI engines that automatically solve any task by mimicking human actions performed on their platform.

About CattleEye

Founded in 2019, CattleEye is on a mission to reform the dairy sector and reduce our global carbon footprint through technology that seeks to “release the potential of your cow,” which increases cow efficiencies. The technology is hands-free, and the company works closely with expert veterinarians to train algorithms that quickly and accurately detect cow anomalies that can be corrected immediately.

AI to improve cattle welfare, reduce carbon emissions by 30%, and combat labor shortages. –

Source link AI to improve cattle welfare, reduce carbon emissions by 30%, and combat labor shortages. –

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