The Digital Cow: What Is Proven, and What Awaits a Trial?

Twenty Holstein cows in Romania were separated from their herd for four hours after milking, and researchers recorded the calls they made: 1,144 in all. A model was then trained to listen, and it distinguished a close-range call made with the mouth closed from a long-range call made with the mouth open, with an accuracy of about 89.4% (Gavojdian and colleagues, 2024). The episode is an honest way into what is happening on cattle farms: real results, but narrower than the headlines suggest.

What did the computer hear?

Low-frequency calls are used for close contact, while high-frequency calls are linked to higher stress and long-distance contact. The deep-learning model separated them with an accuracy of 89.4%, but recognising each individual cow by its voice reached only 72.5%.

The researchers state clear limits: the study covered a single negative situation, isolation; the number of low-frequency calls was small (192); and commercial use would need software that filters out farm noise. So the phrase «cow emotion translator» runs ahead of the evidence. What is shown is that the voice carries a signal about the intensity of arousal, not that it can be translated into sentences.

A warning before inflammation sets in

Milking robots collect daily records for every cow. In a study of 1,790 cows on two German farms, with about 2.73 million milking records between 2019 and 2022, researchers tried six models for predicting mastitis. One day before the case was observed, accuracy ranged from 0.80 to 0.90, but sensitivity, the ability to catch actual cases, ranged from 0.64 to 0.78. In other words, from about a fifth of cases to more than a third could slip past the model. The authors consider that the models need further refinement and the integration of other sensors before they can be relied on in the field.

Feed and methane

In fattening, the farmer needs to know how much each animal eats. American researchers analysed data on 2,546 animals from 19 trials at a feedlot in Idaho, and the XGBoost model outperformed the others. Its average deviation from actual intake for a single animal was about 1.39 kg a day, falling to less than 0.2 kg per animal per day at pen level. The paper sits on the arXiv repository before peer review, its data are confidential and so cannot be re-checked, and it has not been tried on commercial farms. The figure is a research promise, not a production guarantee.

Methane from the rumen is a well-known environmental problem. Earlier work reported by the US Department of Agriculture's Agricultural Research Service indicates that bromoform, found in some seaweeds, reduces it considerably, but it is a carcinogen and so unsuitable as feed for food animals. For that reason researchers from the Service and Iowa State University used generative AI and computational models to shortlist less harmful molecules, and fifteen molecules clustered near bromoform in a «methanogenesis inhibition space». This step speeds up selection before the laboratory; it is not a ready-made feed additive.

In the Kingdom, and ideas awaiting a test

The Saudi example is not about cattle, but it shows the direction. In July 2025 the spokesperson of the Sustainable Rural Agricultural Development Programme (Saudi Reef) said that the «Saudi Smart Flock», a digital system for sheep and goats that identifies an animal by its features and tracks its productivity and health, was applied on six farms in 2024. Its aim is to reduce losses and support the farmer's decisions.

Among ideas put forward for the sector, none of which has a proven product behind it in our sources:

• Cooling sprays directed by thermal camera at cows showing heat stress, an idea suited to the region's heat.
• A national early-warning platform that gathers animal-health reports to track the geographic spread of infection.
• A QR code on milk packaging that tells the story of its journey from farm to consumer.
• Early detection of lameness from gait, so the animal can be directed to hoof care.

Before you buy a system

In the editorial to a special issue of Animal Frontiers (2026), researchers Victor Cabrera and Mutian Niu summarise recurring obstacles: fitting smart tools onto barns built before the digital age, data scattered across systems that do not talk to each other, and the fragility of relying on a single sensor in harsh farm conditions. They also warn of the risk of over-optimising output at the expense of animal welfare.

This accords with the Sharia's teaching of kindness to animals and good care of them. So ask the seller before you buy: has the system been tried on farms like mine? How does it behave on a false alarm? Who owns the data? In the end, the most useful question for the farmer is not «shall I buy artificial intelligence?» but «which problem in my barn do I want to measure first?».