Artificial Intelligence (AI) in Bovine Disease Prediction.
AI is the computer science discipline that teaches machines to recognize patterns in data and learn from them. In animal health, AI systems (and particularly machine learning) analyze large volumes of data from livestock to anticipate health problems.
ANIMAL PRODUCTION
11/6/20256 min read
AI is the computer science discipline that teaches machines to recognize patterns in data and "learn" from them. In animal health, AI systems (and particularly machine learning) analyze large volumes of data from livestock to anticipate health problems (1, 2). Combined with sensors (IoT) and big data, AI can monitor the physiological and behavioral parameters of each cow in real time. This way, very subtle anomalies are detected before clinical symptoms appear, allowing for preventive actions. For example, Allflex Livestock Intelligence (MSD) collects feeding, reproduction, and health data in real time; by analyzing it together with the farmer and veterinarian, it improves the farm's efficiency, animal welfare, and profitability (3).
Types of Data for Predictive Models
AI models in livestock are trained with varied data collected by sensors on collars, cameras, and milking equipment. Among the most frequent are:
Movement and Position: GPS, accelerometers, and pedometers on collars or electronic ear tags to record activity (steps, position, rest time) (4). Changes in movement patterns can indicate stress or lameness.
Feed and Water Consumption: Sensors on automatic feeders (e.g., GrowSafe) measure how much each cow eats and at what rate. This data, along with rumination time measured by smart collars, reflects appetite and digestive health (5, 6).
Rumination (cud chewing): Accelerometers and collars record rumination time. A sudden drop in rumination often precedes digestive or metabolic disturbances (5, 6).
Body Temperature: Subcutaneous sensors or thermal cameras measure the cow's temperature, a key indicator of fever or heat stress. Temperature increases can signal infections.
Milk Production and Quality: Robotic milking systems and computerized milk analyzers measure milk volume, flow, conductivity, and parameters (pH, color). These variables reflect mammary gland health; for example, electrical conductivity spikes in mastitis (7, 8).
Laboratory Parameters: Routine data (somatic cell count, fat content, urea, bacteriology) are integrated into the AI. For example, the Boalvet PAM software analyzes daily milk tests (somatic cells, urea, etc.) and immediately detects health deviations (9).
Bovine Diseases Detected by AI
AI has been applied to predict the most common diseases in cattle. These include: mastitis (udder inflammation), respiratory diseases, postpartum metabolic disorders (such as ketosis or milk fever), and locomotion diseases (lameness). For example, AI algorithms analyze consumption, rumination, and production patterns to predict clinical mastitis before it becomes evident (7, 10). Likewise, by detecting variations in production or temperament, risks of ketosis or milk fever can be predicted (11). Recent studies have used computer vision to monitor respiration: a deep learning-based model achieved $\sim 93\%$ accuracy in identifying abnormal respiratory patterns in dairy cows (12). Lameness detection (foot diseases) has also improved with AI: systems like CattleEye use cameras to flag cows with altered gaits before the farmer notices (13). Even facial recognition tools are being developed: the MyAnIML startup achieved $99.4\%$ accuracy in predicting infectious bovine keratoconjunctivitis (pink eye) using AI on muzzle images (14). In summary, AI can anticipate pathologies ranging from mastitis and pneumonia to reproductive, immunological, or stress alterations, always based on data captured by sensors and cameras.
Commercial AI Platforms and Devices
Several solutions are available on the market designed for farmers:
Boalvet PAM (Precision Animal Milking): Spanish app that processes daily milk laboratory data (somatic cells, urea, bacteriology, etc.) in the cloud to issue health or nutritional alerts (9). It sends notifications to the farmer and veterinarian about deviations, reducing antibiotic use (15).
DeLaval Plus (BioSense / DeepBlue): Integrated system that uses collars with sensors (BioSense) to record feeding and activity, along with AI algorithms (DeepBlue) that analyze this behavior. It allows for detecting cows in heat or sick cows with high precision, improving reproductive and health management (16).
Allflex Livestock Intelligence: MSD Animal Health platform that integrates data from smart tags and collars for health and reproduction tracking. Real-time information enables the farmer and veterinarian to make preventive decisions, increasing efficiency, welfare, and profitability (3).
Precision Livestock Technologies (PLT): Company offering smart feeding systems. Their AI algorithms, trained with thousands of days of feeding data, predict the optimal daily intake for each cow and automatically adjust the ration (17), mimicking the work of a nutrition expert.
CattleEye: Platform based on computer vision cameras that monitor cow movement in the barn. It uses AI to identify unusual gait patterns, providing early warnings about lameness or other health conditions (13).
MooCow: Software (academic initiative) that uses AI to individually recognize cows through video and track their feeding, movement, and rest patterns (18). It helps producers improve herd management.
Collaborative Projects: Initiatives like ClearFarm (Europe) combine environmental sensors, collars, and cameras with AI to measure the overall welfare of the herd. They generate immediate alerts for any health or behavior anomaly (19).
Technical and Economic Benefits
The adoption of AI in livestock farming offers clear benefits. Technically, it allows for detecting diseases at subclinical stages, which reduces mortality and improves treatment effectiveness (15, 20). For example, intervening before an outbreak of mastitis or respiratory stress minimizes production losses and avoids the excessive use of medications (15, 20). This results in better animal welfare (less suffering and stress) and higher- quality milk. Economically, prevention and optimization reduce operating costs. Studies indicate that farms using AI increase their productivity: they better adjust feeding, increase pregnancy rates, and avoid unnecessary veterinary expenses (3, 21). In summary, AI helps in making informed decisions based on real data, which elevates herd efficiency and farm profitability (20, 21).
Challenges and Considerations for Adoption
Despite its advantages, AI in the field faces challenges. Firstly, initial costs are high: sensors, smart collars, and communication infrastructure (servers, connectivity) require significant investment (22). Data networks (Wi-Fi, $3\text{G}/4\text{G}$, or $5\text{G}$) are still limited in rural areas, which can hinder real-time data transmission (22). Furthermore, livestock farming must incorporate technical profiles: farmers and their staff need to be trained in these tools, and leaders must be designated to manage the data and interpret the alerts (23). Another challenge is managing the volume of generated data: correctly integrating and analyzing signals from multiple sensors requires robust platforms and analytical expertise. Overall, the technological maturity of some solutions is still nascent and depends on specialized human resources (23). However, these challenges are usually overcome with the support of training projects, public-private alliances, and gradual implementation.
Future Perspectives
The horizon of AI in livestock points towards increasingly automated and connected farms. With the mass adoption of high-speed networks ($5\text{G}$), each animal will be able to wear devices (collars, tags, "bovine Fitbit") that transmit instant data to the control center (24, 25). For example, pilot tests in the UK used $5\text{G}$ collars that inform when a cow should be milked, activating automatic gates and robotic arms in the milking parlor (26). This allows for milking and feeding the cow without human intervention. Concurrently, widespread use of drones equipped with thermal cameras and sensors is anticipated for monitoring large herds: detecting sick animals, counting heads, guiding stray cows, and surveying pasture availability (27). Robotics will continue to advance (milking robots, automatic cleaning systems, robotic feeding), as will the application of Big Data: integrated analysis of genetic data, climate, nutrition, and animal welfare. In short, AI, IoT, and robotics are converging towards Precision Livestock Farming 5.0. As experience highlights, “we can connect every cow… that’s what $5\text{G}$ can do for agriculture” (25): unleashing the potential of data to optimize health and production. Thus, future farms will be increasingly "remote and connected," increasing the preventive capacity and sustainability of the livestock system.
References: Recent studies and industry sources present real use cases and data updated through 2025. For example, reports from MSD Animal Health Intelligence (3), academic research on precision livestock farming (20, 28), and specialized livestock news (CONtexto Ganadero, Dellait, The Animal Echo, etc.) support the above claims (5, 9, 12, 14). Every aspect described here is based on these reliable sources from the veterinary and technology fields.
(1, 18) La IA aplicada a la sanidad animal: las herramientas que todo veterinario debe conocer | IM Veterinaria
(2, 3) Inteligencia artificial garantiza la salud animal | MSD Animal Health España
(4, 23, 28) innovasciencejournal.omeditorial.com
https://innovasciencejournal.omeditorial.com/index.php/home/article/download/64/196
(5, 6, 7, 8, 11) Estas tecnologías de punta en lechería permiten tomar mejores decisiones | CONtexto Ganadero
(9, 15) Inteligencia artificial para detectar enfermedades de forma temprana | CONtexto Ganadero
(10) Uso de algoritmos matemáticos para predecir las vacas que tendrán mastitis - Campo Galego
(12) La enfermedad respiratoria bovina y cómo detectarla | Dellait
https://dellait.com/es/la-neumonia-bovina-y-como-detectarla-dellait/
(13) Granjas inteligentes, perreras digitales: cómo la IA está revolucionando silenciosamente el cuidado animal - The Animal Echo
(14) Desarrollan tecnología con reconocimiento facial para predecir enfermedades en el ganado
(16, 17, 19) Ganadero La inteligencia artificial revoluciona la ganadería con estas herramientas | CONtexto
(20) revistas.um.edu.uy
https://revistas.um.edu.uy/index.php/ingenieria/article/download/1412/1792/4578
(21) Futuro ganadero: Revolucionando el sector con Inteligencia Artificial | MIOTI
https://mioti.es/es/blog-futuro-ganadero-revolucionando-el-sector-con-inteligencia-artificial/
(22) IoT para la gestión ganadera: beneficios, desafíos y futuro
https://richestsoft.com/es/blog/iot-for-livestock-management/
(24, 25, 26) ¿Cómo contribuirá la red 5G a hacer ganadería de forma remota? | CONtexto Ganadero
(3, 27) Ejemplos de tecnologías en ganadería inteligente - Club ganadero
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