InsightAce Analytic Pvt. Ltd. announces the release of a market assessment report on the “Global AI in Livestock Welfare Monitoring Market Size, Share & Trends Analysis Report By Component (Software [Data Management Platforms, Behavior Analytics Software, AI & Machine Learning Models, Health Monitoring Algorithms], Hardware [Cameras, Sensors, Microphones, Gateways, RFID Tags], and Services [Maintenance & Support, Installation & Integration Services, Training & Consulting]), Type (Wearable Sensor-Based Systems, Thermal Imaging Systems, Vision-Based Systems, Integrated Multi-Sensor Platforms, and Audio-Based Monitoring Systems), Livestock Type (Swine, Poultry, Cattle, Sheep & Goats, and Others), Application (Health Monitoring, Environmental Monitoring, Behavior Analysis, Stress & Pain Detection, Feeding Pattern Monitoring, and Breeding Management), Deployment Mode (On-Premise, Cloud-Based, and Hybrid), Technology (Machine Learning, Edge AI, Computer Vision, IoT & Smart Sensors, and Data Analytics), End-user (Animal Welfare Organizations, Commercial Livestock Farms, Veterinary Clinics & Hospitals, Research Institutes & Universities, and Government & Regulatory Bodies),-Market Outlook And Industry Analysis 2034”

The Global AI in Livestock Welfare Monitoring Market is valued at US$ 2.3 Bn in 2024 and it is expected to reach US$ 11.8 Bn by the year 2034, with a CAGR of 18.4% during the forecast period of 2025-2034.

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AI in livestock welfare monitoring seeks to use intelligent technologies to enhance animal health, behaviour tracking, and environmental factors. It uses sensors, cameras, and algorithms to monitor livestock continuously without the need for human intervention.

This technique helps farmers identify early signs of illness, stress, or discomfort, allowing them to take precise action to prevent the spread of disease and boost productivity. The market for AI in livestock welfare management is growing quickly due to the need for efficient livestock management and technological advancements.

The need for sustainable agricultural methods, the growing demand for food as a result of the world’s population, and technological improvements are some of the main causes driving the growth of AI in livestock welfare management. AI helps address these needs by increasing efficiency and productivity, which results in higher outputs with less input.

Additionally, governments and the corporate sector are investing more in smart agricultural solutions as they recognize the potential of AI to transform agriculture and promote food security. This will boost the growth of AI in the livestock welfare management market in the coming years.

List of Prominent Players in the AI in Livestock Welfare Monitoring Market:

• Merck Animal Health

• Afimilk

• Connecterra

• DeLaval

• Vence (acquired by Merck)

• Gallagher Animal Management

• HerdDogg

• Lely

• Allflex

• PrecisionAG (formerly PrecisionHawk)

• Stellapps

• Zoetis

• Tri-Scan (acquired by Zoetis)

• AgriWebb

• Cainthus

• Nedap

• Silent Herdsman (acquired by Afimilk)

• Halo (livestock monitoring Al)

• SmartBow (by Allflex)

• Cargill (livestock Al division)

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Market Dynamics:

Drivers-

The market for AI in livestock welfare management is anticipated to grow in the future due to the rising demand for livestock products. Livestock products are a variety of goods derived from animals bred for agricultural purposes, including meat, dairy, eggs, and other commodities. Large amounts of data from sensors, drones, and satellite photos may be gathered, analysed, and interpreted by farmers thanks to artificial intelligence (AI) technologies.

Furthermore, improvements in machine learning techniques are driving the AI in livestock welfare management market. The behavior and health of livestock may now be predicted with greater accuracy due to these advancements. Businesses are focusing on developing user-friendly solutions that meet the needs of farmers.

Challenges:

There are many obstacles in the way of integrating AI in livestock welfare management. A primary obstacle is the high upfront cost of AI systems, which small and medium-sized farms may find unaffordable. Additionally, farmers must learn how to utilize advanced AI technology, which requires training and skill development.

Furthermore, because these systems frequently gather and handle vast volumes of sensitive data, worries regarding data security and privacy surface. To fully utilize AI in livestock welfare management, two more issues that must be resolved are technological dependability and the requirement for a strong infrastructure to support AI applications.

Regional Trends:

The region’s strong infrastructure and cutting-edge agricultural technology allowed North America to maintain its leading position in the AI in livestock welfare management market in 2024. The incorporation of AI into different livestock farming operations is further fueled by the fact that North American farmers are frequently early adopters of technology that promises more profitability and efficiency. Further supporting the adoption of AI technologies is the region’s significant emphasis on precision and sustainable agriculture.

The AI in livestock welfare management market in Asia Pacific is growing in strength as corporate parties and governments work to modernize livestock welfare management. Asia Pacific nations such as China, Japan, and India choose cost-effective aluminium solutions designed for intensive animal husbandry. The demand for cloud-based, mobile-enabled Al platforms that function well in a variety of infrastructure configurations is also rising in these locations.

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Recent Developments:

• In October 2024, Merck Animal Health officially introduced SenseHub Cow Calf, a remote livestock monitoring system designed for cow/calf operations. The solution automatically detects estrus, identifies optimal insemination times, tracks activity and rumination using ear-mounted accelerometers, and delivers insights via cloud-based dashboards to improve breeding efficiency and reduce labor.

Segmentation of AI in Livestock Welfare Monitoring Market-

By Component-

• Software

o Data Management Platforms

o Behavior Analytics Software

o AI & Machine Learning Models

o Health Monitoring Algorithms

• Hardware

o Cameras

o Sensors

o Microphones

o Gateways

o RFID Tags

• Services

o Maintenance & Support

o Installation & Integration Services

o Training & Consulting

By Type –

• Wearable Sensor-Based Systems

• Thermal Imaging Systems

• Vision-Based Systems

• Integrated Multi-Sensor Platforms

• Audio-Based Monitoring Systems

By Livestock Type-

• Swine

• Poultry

• Cattle

• Sheep & Goats

• Others

By Application-

• Health Monitoring

• Environmental Monitoring

• Behavior Analysis

• Stress & Pain Detection

• Feeding Pattern Monitoring

• Breeding Management

By Deployment Type-

• On-Premise

• Cloud-Based

• Hybrid

By Technology-

• Machine Learning

• Edge AI

• Computer Vision

• IoT & Smart Sensors

• Data Analytics

By End-use-

• Animal Welfare Organizations

• Commercial Livestock Farms

• Veterinary Clinics & Hospitals

• Research Institutes & Universities

• Government & Regulatory Bodies

By Region-

North America-

• The US

• Canada

Europe-

• Germany

• The UK

• France

• Italy

• Spain

• Rest of Europe

Asia-Pacific-

• China

• Japan

• India

• South Korea

• South East Asia

• Rest of Asia Pacific

Latin America-

• Brazil

• Argentina

• Mexico

• Rest of Latin America

Middle East & Africa-

• GCC Countries

• South Africa

• Rest of Middle East and Africa

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Changes in Learning: Cheating and Artificial Intelligence

When reading the news, one gets the impression that all students use artificial intelligence to cheat in their studies. Headlines in newspapers such as The Wall Street Journal or the New York Times often mention ‘cheating’ and ‘AI’. Many stories, similar to a publication in New York Magazine, describe students who openly testify about using generative AI to complete assignments.

With the rise of such headlines, it seems that education is under threat: traditional exams, readings, and essays are filled with cheating through AI. In the worst cases, students use tools like ChatGPT to write complete works.

This seems frustrating, but such a thought is only part of the story.

Cheating has always existed. As an educational researcher studying cheating with AI, I can assert that preliminary data indicate that AI has changed the methods of cheating, but not its volumes.

Our early data suggest that AI has changed the method, but not necessarily the scale of cheating that was already taking place.

This does not mean that cheating using AI is not a serious problem. Important questions are raised: Will cheating increase in the future due to AI? Is the use of AI in education cheating? How should parents and schools respond to prepare children for a life that is significantly different from our experience?

The Pervasiveness of Cheating

Cheating has existed for a very long Time — probably since the creation of educational institutions. In the 1990s and 2000s, Don McCabe, a business school professor at Rutgers University, recorded high levels of cheating among students. One of his studies showed that up to 96% of business students admitted to engaging in ‘cheating behavior’.

McCabe used anonymous surveys where students had to indicate how often they engaged in cheating. This allowed for high cheating rates, which varied from 61.3% to 82.7% before the pandemic.

Cheating in the AI Era

Has cheating using AI increased? Analyzing data from over 1900 students from three schools before and after the introduction of ChatGPT, we found no significant changes in cheating behavior. In particular, 11% of students used AI to write their papers.

Our diligent work showed that AI is becoming a popular tool for cheating, but many questions remain to be explored. For example, in 2024 and 2025, we studied the behavior of another 28000-39000 students, where 15% admitted to using AI to create their work.

Challenges of Using AI

Students are accustomed to using AI but understand that there are boundaries between acceptable and unacceptable use. Reports indicate that many use AI to avoid doing homework or to gain ideas for creative work.

Students feel that their teachers use AI, and many consider it unfair when they are punished for using AI in education.

What Will AI Use Mean for Schools?

The modern education system was not designed with generative AI in mind. Traditionally, educational tasks are seen as the result of intensive work, but now this work is increasingly blurred.

It is important to understand what the main reasons for cheating are, how it relates to stress, time management, and the curriculum. Protecting students from cheating is important, but ways of teaching and the use of AI in classrooms also need to be rethought.

Four Future Questions

AI has not caused cheating in educational institutions but has only opened new possibilities. Here are questions worth considering:

• Why do students resort to cheating? The stress of studying may lead them to seek easier solutions.
• Do teachers adhere to their rules? Hypocrisy in demands on students can shape false perceptions of AI use in education.
• Are the rules concerning AI clearly stated? Determining the acceptability of AI use in education may be vague.
• What is important for students to know in a future rich in AI? Educational methods must be timely adapted to the new reality.

The future of education in the age of AI requires an open dialogue between teachers and students. This will allow for the development of new skills and knowledge necessary for successful learning.