How animal agriculture is reaping the benefits of AI.

Imagine a second set of eyes on your pregnant cows and receiving an alert, with a high degree of precision and accuracy, when a cow is about to give birth. Now imagine those eyes trained on your pregnant cows 24 hours a day, seven days a week, taking neither breaks nor being called away to perform other tasks.

That is what Nikon is developing. The company, well known for its imaging technologies, is using its expertise to produce an advanced artificial intelligence (AI) system, using cameras to constantly monitor cows in individual calving pens. Employing advanced algorithms, the system can detect when a cow is about to give birth and immediately alert the farmer.

“The system constantly monitors mother cows with multiple surveillance cameras and analyzes the cow’s characteristic behaviors by processing the images with AI,” says Hirotaka Kono, section manager with Nikon.

“Data is sent to the data center via the internet, and notifications are sent to producers’ and owners’ smartphones by messages and audio. AI detects the signs through machine learning, visualization of behavior trajectories, and segmentation machine learning content.”

Training AI

Kono says the biggest challenge in development was improving the ability to detect specific changes in cows’ behavior. “In the initial stages of development, the accuracy of detecting the characteristic behaviors of calving was low, resulting in many false notifications,” he acknowledges. Performance has improved, he says, by collecting a lot of data and repeating AI learning, and by creating appropriate logic when generating alarms. He adds that the latest demonstrations show 95% detection accuracy for characteristic calving behavior.

AI training began in fall 2021, and the company continues to make improvements such as increasing the number of system-analyzed behavioral patterns.

“Monitoring of mother cows requires observation of their condition day and night, which poses major challenges such as the physical burden on the farmer and securing human resources for farms,” Kono says. “In the future we believe that it may be possible to expand this system to monitor not only cows but also other livestock. We would like to contributeto reducing the burden on livestock farmers through AI and image analysis technology.”

Development has focused on 100-cow operations, common in Japan. But Kono says it will be possible to increase the number of cameras and develop new systems to recognize and distinguish large numbers of cows. 

Closer to home, tools such as Ever. Ag’s Maternity Warden use AI to alert dairies to calving activity, helping farms ensure better birth outcomes for cows and calves. This device helps reduce incidence of dystocia and stillbirths, two areas of concern during this vulnerable period.

Near-Instant Recommendations

In agriculture it is becoming more common for computer programs to analyze mountains of data, increasing the number of more-informed management decisions. As more data is collected and analyzed, the result becomes even more useful.

But while “AI” may be a new phrase, the livestock industry has leveraged data management for decades.

Some form of livestock wearable tracking device is common. Those devices collect all kinds of data that producers can monitor to determine herd productivity and health. AI can collect that data, analyze it in real time, and provide near-instant recommendations.

“Dairy is the most advanced when it comes to using wearable collars and tags,” explains Paul Koffman, executive director of livestock technology solutions for Merck Animal Health. “Instead of someone doing heat detection two or three times a day on a dairy, we can track the cow’s activity and look at changes that would identify that she’s coming into heat.”

Koffman says technology isn’t necessarily replacing labor. “Instead we can use AI to identify specific cows to help do a better, more efficient job in the barn,” he says.

The power of AI for livestock is that it can find relations between inputs and outputs that may not be readily obvious, says Tara Baker, North American marketing manager for Nedap. “We can use AI to look at various combinations of data and components to find correlations,” she says.

That is where the value of AI really shows.

“Everyone thinks that AI is a new revolution; in reality, we have been using AI for many years,” Baker says. “However, much of the use cases have been reacting to the data collected. The transition to predictive AI now is that we are using the data to look forward and provide more futuristic information to help producers succeed.”

 Leveraging Multiple Sources

AI’s power also lies in its ability to compound vast amounts of information from several sources. 

“Nedap strongly believes in partnerships and integrations across the spectrum of technology suppliers to the farm that we are serving. We have sensors that are extremely robust and qualified in their own particular lanes,” Baker says. “But [we] also know there will be other technologies and other sectors that make sense for us to communicate with to extrapolate greater value for the farmer.”

Opportunities abound to create a value picture at the farm level, Baker says, by fine-tuning available sensors or tapping into those of other industries, and using AI software. “It’s the power of being able to have a broader set of inputs to create stronger algorithms for a more robust look at the insights and provide accurate outputs,” she says.

One of AI’s biggest learning experiences is it is only as strong as its development. “AI is like a young mind in school — [kids] can turn out a number of different ways, depending on their influences,” Baker says. “It is the same thing with AI. [It] is only going to be as strong as we teach it to be.”

Koffman of Merck calls AI part of the company’s overall animal health approach. “If we are able to monitor these animals, and can identify any potential issues early, we can intervene and provide the appropriate response to help those animals be healthier and more productive,” he says.

Although dairy herds have experienced the greatest use of data collection, the technology is working its way onto the feedlot and into swine barns. There’s an opportunity to help the entire animal production sector to augment collected data with AI to help in all aspects of operation. 

Power Beyond Production

Data management and AI can provide information to generate alternative income streams, or provide information that end users increasingly require.

“Producers are leveraging their data to create sustainability scores that are sought by processors and key players in the food sector and may one day be a commonplace requirement,”

Nedap’s Tara Baker says. “As firms look to sell more than just a commodity, they will want that information from the producer. And the producer can provide added value by being able to deliver. It opens a whole new use of data.”

What we’re seeing now, Baker says, is a shift to the captured data’s external values. Often those concern validation of protocolsor of animal welfare, or quantifying sustainability practices. “Ten or 15 years ago, those things may have been difficult to track,” she notes. “But the reality now is that we are seeing more processors require that information.”

Dairy has a great story to tell about being the ultimate caretakers of land and animals, Baker says, “but we have not done a great job of tracking the stewardship practices we have improved

over the years. More dairy purchasers are providing incentives to farmers that can verify [sustainable] practices. AI-powered tech can help the farmer create and optimize this value stream.”