AI Tech Can Reduce Antibiotic Treatment In Dairy Cows


AI technologies are helping farmers minimize antibiotic treatment in dairy farms, supporting improved health outcomes for humans and animals. The use of antibiotics in livestock is a serious concern today due to the growth of antibiotic resistance in humans. Agricultural technology developers are leveraging AI and machine learning to help cattle farmers minimize their need for antibiotics in cows.

How could farmers apply this technology? Is AI effective enough to deliver a good ROI for cattle farmers? There are several innovative applications for AI on dairy and cattle farms, ranging from predictive analytics to facial recognition.

Bovine Illness Prediction with AI

Identifying cows suffering from illnesses is critical since contagious diseases threaten entire herds. Unfortunately, it can be exceedingly difficult for farmers to monitor their cattle efficiently since herds can number into the hundreds. Agricultural technology developers are addressing this issue using AI predictive analytics.

Identifying bovine illnesses sooner is essential because it allows cattle farmers and veterinarians to treat the cow as quickly as possible. This can minimize the days a cow has to receive antibiotic treatment. While antibiotics can be invaluable for ensuring livestock health, too much treatment can contribute to antimicrobial resistance in humans.

Antimicrobial resistance is a serious medical condition that makes certain life-saving drugs ineffective. Exposure to high amounts of antimicrobial treatments — such as antibiotics — can allow harmful bacteria to resist those medications. So, minimizing humans contact with antibiotics is vital to ensure they still work when patients need them.

“Cows need antibiotics, too, but because humans rely on cattle as a food source, it’s essential to reduce antibiotic treatment in cattle as much as possible.” 

AI is helping farmers accomplish this by providing a simple and efficient way to monitor cows for signs of illness.

The Process in Action

For example, one multi-year trial on two European cattle farms recorded a 68% reduction in treatment days for cows subject to AI monitoring. The cows wear AI-powered sensors that monitor their behaviour for key signs of a potential illness, such as lethargy or unusual eating habits. If the AI detects any red-flag behaviour, it triggers an alert so farmers can check on the cow.

The system makes it significantly easier for farmers to catch early warning signs in their herds. Not only is this good for the individual cow — it it’s also good for the group. Early isolation and treatment prevent contagious diseases from spreading to other cows.

This reduces the amount of antibiotics needed across the herd and the cost of medical expenses for the farmer. The end consumer benefits from eating meat or drinking milk from cows that consumed fewer antibiotics, thereby reducing the likelihood of developing antimicrobial resistance.

Facial Recognition for Cows

Behaviour monitoring isn’t the only application for AI in cattle health care. Farmers and vets can also use AI facial recognition to identify early warning signs of illnesses like bovine respiratory disease.

BRD symptoms often include facial symptoms such as drooping ears, coughing or dull eyes. It’s one of the most common causes of death in cows. In fact, respiratory infections are the number one cause of antibiotic treatment for calves. BRD alone can cause up to 80% of illnesses in cattle feedlots.

Strategically placed cameras mounted around farms can capture numerous pictures of every cow’s face daily. An AI algorithm analyzes the images and processes them using computer vision. The algorithm can identify markers of potential illness in the photos, such as the drooping ears common in cows with BRD.

One study using this technology combined it with RFID tags that can show the exact location of individual cows at a given time. Farmers can rapidly identify potentially sick cows and find them in the herd with the two technologies.

Early intervention can save a cow’s life regarding serious illnesses like BRD. The fact that BRD is among the most typical causes for antibiotic treatments in cattle means there is vast potential for using AI to minimize treatment times. This technology could significantly improve the health outcomes of cattle.

AI Herd Analytics

Cattle farmers can use AI to predict the outcomes of different care strategies that may help reduce antibiotic resistance. Breeding livestock strategically is standard practice to minimize the likelihood of inherited disease vulnerabilities. Modern agricultural technology is taking this a step further by quantifying key genetic markers for cattle health.

Genomic selection involves using these genetic markers, known as genomic breeding values, to make breeding decisions based on concrete data. For example, a 2019 study modelled the effects of genomic selection in a simulated cattle herd over 15 years. The study found utilizing genomic selection decreased antibiotic use and medical costs per cow.

“AI is a powerful tool for analyzing genomic breeding values and genetic data so farmers can conduct genomic selection breeding successfully.” 

Making sense of a massive data set of genetic information for hundreds of cows would be challenging for anyone. AI can simplify extracting important trends and connections in this data.

For instance, a farmer could use AI data analytics to identify the cows in their herd with the best genomic breeding values. The AI could compare the herd’s data to the National Lifetime Net Merit selection index to pinpoint particularly healthy cows. The farmer could then use this information to identify the best breeding pairs in their herd. Over time, this analytical approach to breeding could lead to naturally healthier cows with stronger immune systems, reducing the need for antibiotic treatments.

Modelling the Herd

Farmers can also use AI to model herd data and predict health outcomes. For instance, they could use a digital twin to simulate the effects of using genomic selection on their farm over a 10- or 20-year period. Machine learning can create realistic models based on real-world data and adapt as the model gets new information.

Herd modelling is beneficial for farm operations well beyond reducing antibiotic treatment days. Farmers can use AI digital twins to improve their farm layout, feed mixes, land management, supplement treatments, breeding and more.

In fact, predictive analytics could help farmers acquire more funding and investment. Concrete data can go a long way toward demonstrating the value of a farming operation to potential investors and business partners. Farmers can use digital twins to show prospective partners exactly how their investments will improve processes, cattle health and revenue.

AI Tech Is the Future of Cattle Farming

Farming has a reputation as a low-tech industry, but this couldn’t be further from the truth. AI technology is revolutionizing cattle farming, helping farmers maintain healthier herds and deliver more nutritious food to consumers.

Studies show leveraging AI to monitor cattle for early warning signs of disease can significantly reduce the need for antibiotic treatments. RFID tags, Bluetooth and GPS are helpful supplements to this technology, allowing farmers to locate potentially ill cows quickly. Using machine learning and AI analytics to predict health outcomes and identify symptoms has enormous potential to minimize the need for antibiotics in dairy cows.

Also, Read 5 Things You Need to Know About AI Analyzing Coffee Flavor Profiles

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