Artificial Intelligence (AI) in Livestock Farming Market Report, By Type of Solution (Hardware, Software, Services), Application (Livestock Monitoring, Precision Farming, etc.), Livestock Type (cattle, poultry, etc.), and Function (Feeding Management, Health Monitoring, etc.); and Regions 2024-2032

Market Overview:

"As per Reports and Insights analysis, the global AI in livestock farming market is expected to register a CAGR of 27.2% over the forecast period of 2024-2032."

Report Attributes	Details
Base Year	2023
Forecast Years	2024-2032
Historical Years	2021-2023
AI in Livestock Farming Market Growth Rate (2024-2032)	27.2%

The global human population is projected to reach approximately 9 billion in 2050 from new estimates of over 8 billion (or 8,081,405,902) as of Thursday, 28 December 2023 according to United Nations estimates. Meeting global food demand has always been a significant challenge, and this along with meeting or complying with various regulations and norms have brought about major evolution in practices and approaches in traditional farming and agriculture, as well as those related to livestock rearing and consumption.

Emerging variations are expected to catalyze supply and demand, as along with global food demand rising steadily, changing food habits and preferences, health conscious consumers opting for vegan diets, others shifting to more protein-rich diets of meats, dairy, and fish, and vide-versa, are some key factors creating need for new research into the overall livestock farming market. Livestock farming and the practice of raising cows, sheep, goats, chickens, pigs, and other birds and animals for products such as meat, fiber, milk, and eggs, is undergoing rapid and positive transformations with the integration of Artificial Intelligence (AI). This technology is simplifying the daily care and management of livestock, providing farmers with enhanced efficiency in their operations.

The integration of AI in animal and livestock farming offers the potential to enhance overall farm and facility management, enhance quality of life for animals, preempt and treat potential disease outbreaks, optimize resource utilization, improve operational efficiency, and reduce costs, enable more efficient production, and lower environmental impact otherwise caused by traditional methods and approaches to farming. Livestock farmers and breeders are also able to efficiently manage livestock and flocks, gain insights into selective breeding and reproduction, and the advantages extend into the processing of animals for human consumption as well. Wearables designed for livestock serve to generate alerts for instances such as illness, vaccination schedules, or optimal breeding times, and is also employed in animal husbandry. In addition, companies supporting initiatives in Artificial Intelligence (AI) in veterinary medicine and AI in livestock farming are adopting various approaches to contribute to the development and adoption of AI technologies in these fields.

AI in Livestock Farming Market Trends and Drivers:

Companies continue to invest in research and development efforts to create innovative AI solutions tailored for veterinary applications. This involves developing algorithms, software, and hardware that can enhance diagnostic capabilities, treatment planning, and overall veterinary care.

A number of companies collaborate with research institutions, universities, and other industry players to leverage insights and pursue innovation. These collaborations may involve joint research projects, knowledge sharing, and the development of AI applications that benefit the veterinary community. Also, companies may provide educational resources and training programs to veterinarians to help them understand and use AI tools effectively. This includes workshops, online courses, and other forms of training to ensure that veterinary professionals are well-equipped to integrate AI into their practice.

In addition, companies work on integrating AI technologies seamlessly into existing veterinary workflows. This involves developing user-friendly interfaces, interoperable solutions, and integration with existing veterinary software and hardware systems. Data sharing and analytics play a major role as AI relies on large datasets for training and optimization. Companies may facilitate the sharing of anonymized veterinary data (with proper privacy and ethical considerations) to improve AI algorithms and enhance their accuracy and effectiveness. Product development and commercialization is another major factor and companies bring AI products and solutions to market, making them accessible to veterinary practices. This involves not only developing the technology, but also ensuring that it meets regulatory standards and can be easily adopted by veterinary professionals.

Companies work to ensure that their AI solutions comply with relevant regulations and standards in the veterinary industry, and obtain necessary approvals from regulatory bodies to ensure the safety and efficacy of AI applications in veterinary medicine. Offering robust customer support services, including technical assistance and training programs, helps veterinary professionals effectively implement and use AI solutions in their daily practice. Companies operating in the AI in livestock farming market also engage in awareness campaigns and advocacy efforts to promote the benefits of AI in veterinary medicine, and this is done through participation in industry events, conferences, and seminars to share knowledge and build awareness among veterinarians and other stakeholders.

Continuous Improvement is also key, and companies engage in continuous improvement processes to refine and enhance their AI solutions based on user feedback, evolving technology, and advancements in the field. This ensures that the AI tools remain cutting-edge and provide ongoing value to the veterinary community. By taking these initiatives, companies contribute to the growth and development of AI in veterinary medicine, thereby enabling pursuit of a more technologically advanced and efficient approach to animal healthcare.

AI in Livestock Farming Market Restraining Factors:

Implementation of AI solutions in livestock farming requires significant upfront investments in technologies such as sensors, AI algorithms, and data infrastructure. Initial high capital is a primary concern, with the cost of implementing AI solutions acting as a barrier for many small and medium-sized farms. Also, lack of digital literacy and technological know-how among some farmers and farming communities may hamper adoption of AI solutions, as understanding and effectively utilizing these technologies can be challenging for individuals not familiar with advanced digital tools.

Another major factor hampering adoption of AI solutions is concerns about the security and privacy of the sensitive data collected from their livestock. This is particularly relevant as AI is majorly dependent on data for analysis and decision-making. Integration challenges with existing farming practices or time-tested traditional farming practices can be complex and may require modifications to existing infrastructure and change of attitude. Farmers may resist adoption if they perceive the technology as disruptive to their established workflows.

In addition, the absence of clear regulatory frameworks and industry standards for AI in livestock farming can create uncertainty and inhibit widespread adoption. Farmers may be reluctant to invest in solutions without established guidelines, and convincing such communities can be time consuming, and result in negative outcomes after all efforts.

AI in Livestock Farming Market Opportunities:

Leading players can capitalize on developing advanced AI-powered monitoring systems that provide real-time insights into the health, behavior, and overall conditions of livestock. This can contribute to improved disease detection, early intervention, and optimized resource allocation. Development of more advanced AI-driven data analytics for predictive maintenance of farm equipment and facilities can minimize downtime and reduce operational costs. Leading players can offer integrated solutions that utilize AI algorithms to predict potential issues, schedule maintenance, and ensure the continuous functionality of essential infrastructure.

Also, developing AI applications to optimize the entire supply chain in livestock farming, from production to distribution, can streamline processes and reduce waste. Leading players can explore opportunities to implement AI-driven logistics, inventory management, and demand forecasting for a more efficient and sustainable supply chain. Companies can leverage AI to enhance breeding programs by analyzing genetic data, predicting traits, and optimizing mating strategies. This can lead to the development of healthier and more productive livestock breeds, meeting evolving market demands and ensuring long-term sustainability in livestock farming.

AI in Livestock Farming Market Segmentation:

By Type of Solution

• Hardware
• Software
• Services (Consulting, Maintenance, Training)

The software segment is expected to account for largest revenue share among the type of solutions segments over the forecast period, and this is due to AI software playing a key role in processing and analyzing vast amounts of data generated by livestock monitoring systems, and high demand for more advanced software and solutions in the market. Also, increasing shift towards data-driven decision-making for precision livestock farming is expected to continue to support demand for sophisticated AI algorithms and software solutions and drive revenue growth of this segment.

By Application

• Livestock Monitoring
• Precision Livestock Farming
• Animal Health and Welfare
• Others

The livestock monitoring segment is expected to continue to account for largest revenue share over the forecast period, and this is attributed increasing demand for real-time insights into health, behavior, and overall conditions of livestock. AI-powered monitoring systems enable proactive disease detection, efficient resource allocation, and improved overall management.

By Livestock Type

• Cattle
• Poultry
• Swine
• Others (Sheep, Goats, etc.)

The cattle segment is projected to account for largest revenue share, driven by the substantial global demand for beef and dairy products, prompting a heightened focus on optimizing cattle farming practices and improving the efficiency and productivity of cattle farming. AI technologies offer enhanced monitoring, predictive analytics for breeding, and health management, aligning with the specific needs of the cattle industry.

By Function

• Feeding Management
• Behavior Monitoring and Management
• Breeding and Reproduction
• Health Monitoring and Management

The health monitoring and management segment is expected to dominate other function segments in terms of revenue share size owing to high preference and adoption of this solution type due to the critical role AI plays in revolutionizing health monitoring for livestock. Advanced AI applications enable real-time disease detection, early intervention, and precise health management, leading to improved overall herd health.

By Region

North America

• United States
• Canada

Europe

• Germany
• United Kingdom
• France
• Italy
• Spain
• Russia
• Poland
• Benelux
• Nordic
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• South Korea
• ASEAN
• Australia & New Zealand
• Rest of Asia Pacific

Latin America

• Brazil
• Mexico
• Argentina

Middle East & Africa

• Saudi Arabia
• South Africa
• United Arab Emirates
• Israel
• Rest of MEA

The global Artificial Intelligence (AI) in livestock farming market is divided into five key regions: North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. North America, particularly the United States and Canada, ideally lead in terms of adoption of more advanced technologies and solutions across various sector, including mature agriculture and livestock sectors. In Europe, countries such as Germany, France, and the UK are prominent due to a focus on sustainable agriculture and livestock rearing, and food production practices. In Asia Pacific, countries such as China and Japan are key players with increasing technology adoption in agriculture. Some common factors driving overall growth in the AI in livestock farming markets across different regions include rising emphasis on precision farming practices to optimize resource utilization, improve productivity, and enhance overall livestock management. Also, ongoing advancements in AI technologies, including Machine Learning (ML) and data analytics, are supporting  the development of sophisticated solutions for livestock monitoring and management. In addition, rising focus on sustainable agriculture, and integration of AI in livestock farming addresses the need for efficient resource utilization, reduced environmental impact, and improved animal welfare.

Leading Companies in AI in Livestock Farming Market & Competitive Landscape:

The competitive landscape in the Artificial Intelligence (AI) in livestock farming market is dynamic, with companies adopting strategic measures to maintain and expand their market positions. Leading companies focus on continuous research and development to enhance AI solutions for livestock management. Strategic partnerships with technology providers and agricultural organizations help in accessing complementary technologies and expanding market reach. Also, effective marketing and educational initiatives aimed at demonstrating the tangible benefits of AI in livestock farming contribute to consumer trust and adoption. By prioritizing innovation, collaboration, and market education, leading companies stay competitive in this evolving landscape.

These companies include:

• Cainthus
• Connecterra
• Afimilk Ltd.
• Allflex Livestock Intelligence
• Quantified AG
• SCR Dairy
• Cowlar
• Vital Herd
• DeLaval
• Moocall
• EIO Diagnostics
• Vence
• Ripe Technology
• Octopus Robots
• Connect Robotics

Recent Development:

• May 2023: MyAnIML unveiled a hardware update aimed at facilitating the integration of its AI-driven predictive health platform into commercial feedlot and dairy operations. The newly introduced Bluetooth-enabled cattle ear tag not only incorporates geolocation and tracking features, but also seamlessly integrates with MyAnIML's robust predictive health modeling technology. This integration provides producers with an efficient means of managing herd productivity and welfare while optimizing costs. MyAnIML's pioneering technology utilizes AI and ML, and predicts an extensive range of total health indicators, utilizing the industry's first cattle facial - specifically muzzle - recognition library dataset, developed by MyAnIML.

• June 2020: Connecterra, the leader in Artificial Intelligence (AI) for the dairy sector closed a Series B funding round, securing €7.8 million. This funding round saw participation from new AgTech investors and industry experts. Notably, this marks the largest Series B funding ever achieved by a Dutch AgTech firm specializing in farm animal technology. Also, it stands as the most substantial Series B investment to date for a European livestock tech company. The substantial funds raised provide Connecterra with robust support from key AgTech and industry stakeholders to expedite the growth of its advanced predictive AI platform, Ida. Ida, initially rooted in sensor technology, has evolved into a comprehensive technology and AI platform, integrating proprietary sensor hardware, animal data, third-party enterprise data, and cutting-edge machine-learning algorithms.

AI in Livestock Farming Market Research Scope

Report Metric	Report Details
AI in Livestock Farming Market size available for the years	2021-2023
Base Year	2023
Forecast Period	2024-2032
Compound Annual Growth Rate (CAGR)	9.7%
Segment covered	Type Of Solution, Application, Livestock Type, Function
Regions Covered	North America:  The U.S. & Canada Latin America: Brazil, Mexico, Argentina, & Rest of Latin America Asia Pacific: China, India, Japan, Australia & New Zealand, ASEAN, & Rest of Asia Pacific Europe: Germany, The U.K., France, Spain, Italy, Russia, Poland, BENELUX, NORDIC, & Rest of Europe The Middle East & Africa:  Saudi Arabia, United Arab Emirates, South Africa, Egypt, Israel, and Rest of MEA
Fastest Growing Country in Europe	UK
Largest Market	North America
Key Players	Cainthus, Connecterra, Afimilk Ltd., Allflex Livestock Intelligence, Quantified AG, SCR Dairy, Cowlar, Vital Herd, DeLaval, Moocall, EIO Diagnostics, Vence, Ripe Technology, Octopus Robots, Connect Robotics