In the realm of agriculture, the proximity of livestock can lead to rapid disease transmission, posing a significant challenge for farmers. Swine producers, in particular, face the constant threat of respiratory illnesses that account for a substantial percentage of swine deaths worldwide. Detecting these illnesses early is crucial, as traditional methods often fall short in identifying subtle signs of disease, resulting in widespread outbreaks with severe economic and health implications.
By Jeffrey Schmitz, Senior Director of Strategic Accounts at Unigen
The problem: delayed illness detection in livestock
As most people in the agriculture industry know, livestock living in close quarters creates an environment where diseases can spread quickly. For swine producers, they must constantly be on the lookout for the spread of respiratory illness, which according to MACSO, accounts for 60% of swine deaths globally.
Traditional methods to detect disease like conducting visual inspections and checkups from the vet often fail to catch the early and more subtle signs of disease. Typically, by the time a swine’s symptoms are noticeable, the illness has already begun to spread, causing massive ripple effects.
Economic impact: In China, swine diseases are projected to cause 1.40 – 2.07% losses to GDP ($186 – 286 billion). In the US, a Porcine Reproductive and Respiratory Syndrome Virus (PRRS) outbreak would cost $50 billion in direct and indirect losses.
Livestock and human health impact: Not only is the spread of disease costly, but it also undermines farmers’ efforts to reduce antibiotics use to decrease the risk of bacteria that are resistant, also called antimicrobial resistance. In addition to posing a threat to swine, it can also be a risk to humans for crossover diseases that humans treat with antibiotics. Humans would also be at greater risks from consuming contaminated meat products.
Visual inspection: Managing visual inspections across multiple large barns, which often hold thousands of swine, makes human observation alone an ineffective way to consistently spot a sick animal. Additionally, there aren’t enough trained personnel to complete these visual inspections of livestock. The sheer number of farms and farm workers is declining, while livestock populations continue to rise.
Frequent vet visits: Periodic checkups from a veterinarian can be helpful, but this method of disease detection often fails to catch illness early because checkups are too infrequent. The cost of increasing the frequency of these visits will add up quickly, and costs may be passed down through the supply chain to the end customer.
A new solution is being proposed to solve the problem of swine producers needing to balance the rising demand for pork with fewer veterinary resources and increasing pressure to reduce antibiotic use. Adding an on-premise AI inference server and AI-enabled audio sensors to the barn gives farmers the tools to detect illness early and act quickly without relying on cloud connectivity.
Training AI to hear what farmers can’t
These audio-based AI sensors listen for signs of distress, coughing patterns, or environmental changes that may indicate signs of illness. Integrated with an AI inference server, the system enables early illness detection and real-time herd monitoring. In fact, MACSO studies have shown that this method can identify respiratory problems in pigs days before human detection would be possible.
AI that stays on the farm
To power this smart monitoring system, audio-based AI sensors are deployed across the barn, and they connect locally to an AI inference server. By using an on-premises solution and avoiding cloud dependency, farms gain reliability, speed, and data privacy, especially in areas with limited internet access.
In addition to detecting disease, the same AI inference server can also support other farm management applications, including feed optimization, environmental control, and behavioral tracking.
Turning real-time insights into results
For large-scale operations, early detection and rapid response can deliver measurable returns. Lower mortality rates, reduced antibiotic usage, and fewer disruptions all contribute to a stronger bottom line. By keeping compute and analytics on-site, this new AI solution helps farms act faster, operate more efficiently, and make better-informed decisions.
When companies with disparate technological core competencies work together using the latest tools toward a common cause, the result can be much greater than the sum of its parts.
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AI in agriculture | AI/ML | edge AI | edge computing | edge hardware | livestock disease detection | Smart Farming | Unigen
