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1702402

全球精準畜牧業人工智慧（AI）市場－2025-2032

Global Artificial Intelligence (AI) In Precision Livestock Farming Market- 2025-2032

出版日期: 2025年04月10日 | 出版商:

DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球精準畜牧業人工智慧 (AI) 市場規模達到 22.3 億美元，預計到 2032 年將達到 198.7 億美元，在 2025-2032 年預測期內的複合年成長率為 15.39%。

受對高效和永續動物養殖實踐日益成長的需求的推動，精準畜牧業中的全球人工智慧 (AI) 市場正在經歷快速成長。人工智慧技術正在透過即時監控、預測分析和自動化徹底改變畜牧業管理，提高動物健康、生產力和資源最佳化。

飼養管理、疾病檢測和行為監測等應用越來越受到關注，尤其是在大型農場。隨著全球肉類和乳製品消費量的增加、勞動力短缺以及數據驅動農業的推動，預計市場將在北美、歐洲和亞太新興經濟體大幅擴張。

市場趨勢

基於人工智慧的電腦視覺工具正在與攝影機整合，以追蹤動物的行為和健康狀況。愛爾蘭公司 Cainthus 使用臉部辨識和視覺監控來評估乳牛行為、識別異常情況，並透過檢測乳牛的不適或壓力來提高產奶產量。

雲端運算與人工智慧結合，實現了跨多個地點的綜合牲畜管理。 Afimilk 的 AfiFarm 軟體提供了一個集中式平台，可以分析來自感測器、擠奶機和餵食系統的資料，從而提供可行的見解和遠端決策。

動力學

對高效能牲畜監控的需求不斷成長

隨著全球對牛奶、肉類和蛋類等動物產品的需求不斷增加，農民面臨著在保持動物健康的同時最大限度地提高生產力的壓力。人工智慧工具有助於即時監控牲畜，從而實現早期疾病檢測、最佳化餵食和及時繁殖週期。

例如，ZenaDrone 1000 透過即時 GPS 技術簡化了牲畜追蹤，即使在廣闊或難以進入的區域也能確保精確的位置資料。配備GPS技術和大規模監控的無人機在這些地區具有很大的優勢。 Zenadrone 的 GPS 追蹤為牧場主提供了丟失動物的精確座標。

初始投資和維護成本高

高昂的初始投資和維護成本嚴重限制了人工智慧在精準畜牧業的應用，尤其是在中小型農戶中。自動擠乳機、健康感測器和智慧監控平台等人工智慧整合系統價格昂貴，造成了財務障礙，尤其是在發展中地區。

此外，較長的投資回收期以及維護、軟體訂閱和資料管理的經常性成本阻礙了其廣泛實施。融資管道有限和農村基礎設施薄弱進一步增加了挑戰，使得許多農民難以證明或承擔此類先進技術的成本。

The global artificial intelligence (AI) in precision livestock farming market is experiencing rapid growth, driven by the increasing demand for efficient and sustainable animal farming practices. AI technologies are revolutionizing livestock management through real-time monitoring, predictive analytics, and automation, enhancing animal health, productivity, and resource optimization.

Applications like feeding management, disease detection, and behavior monitoring are gaining traction, especially in large-scale farms. With rising global meat and dairy consumption, labor shortages, and the push for data-driven farming, the market is projected to expand significantly across North America, Europe, and emerging economies in Asia-Pacific.

Market Trend

AI-based computer vision tools are being integrated with cameras to track animal behavior and well-being. Cainthus, an Irish company, uses facial recognition and visual monitoring to assess cow behavior, identify abnormalities, and improve milk yield by detecting discomfort or stress in dairy cattle.

Cloud computing combined with AI is enabling integrated livestock management across multiple locations. Afimilk's AfiFarm software offers a centralized platform that analyzes data from sensors, milking machines, and feeding systems, enabling actionable insights and remote decision-making.

Dynamics

Rising Demand for Efficient Livestock Monitoring

With the global increase in demand for animal products such as milk, meat, and eggs, there is pressure on farmers to maximize productivity while maintaining animal health. AI tools help monitor livestock in real-time, enabling early disease detection, optimized feeding, and timely reproduction cycles.

For instance, the ZenaDrone 1000 simplifies livestock tracking with real-time GPS technology, ensuring precise location data even in expansive or inaccessible areas. UAVs equipped with GPS technology and large-scale monitoring are highly advantageous in these areas. Zenadrone's GPS tracking provides ranchers with the exact coordinates of lost animals.

High Initial Investment and Maintenance Costs

High initial investment and maintenance costs significantly restrain the adoption of AI in precision livestock farming, particularly among small and medium-scale farmers. The expensive nature of AI-integrated systems-such as automated milking machines, health sensors, and smart monitoring platforms-creates a financial barrier, especially in developing regions.

Additionally, the long payback period and recurring expenses for maintenance, software subscriptions, and data management deter widespread implementation. Limited access to financing options and poor rural infrastructure further add to the challenge, making it difficult for many farmers to justify or sustain the cost of such advanced technologies.

Segment Analysis

The global artificial intelligence (AI) in precision livestock farming market is segmented based on component, deployment mode, livestock type, application and region.

Cloud-Based Solutions Accelerate Adoption of AI in Precision Livestock Farming

The cloud-based deployment mode is a key driver in the AI precision livestock farming market due to its scalability, real-time data access, and lower upfront infrastructure costs. Cloud platforms enable farmers to monitor and manage livestock remotely through smartphones or computers, making operations more efficient and responsive.

For instance, Afimilk's AfiCloud and Connecterra's Ida platform offer cloud-based solutions that collect and analyze data from sensors and wearable devices to deliver actionable insights on animal health, feeding, and reproduction. These systems allow continuous updates, seamless integration with multiple devices, and data storage without the need for expensive local servers.

Additionally, the cloud model supports multi-location farm management, which is increasingly essential for large commercial operations. As internet connectivity improves in rural areas and subscription-based pricing models become more accessible, cloud deployment continues to gain traction, driving digital transformation across the livestock farming industry.

Geographical Penetration

North America Leads AI Adoption in Precision Livestock Farming with Strong Tech Infrastructure and Agri-Tech Investments

North America dominates the AI in precision livestock farming market due to its advanced technological infrastructure, early adoption of smart farming solutions, and significant investments in agri-tech innovation. The US, in particular, is home to major players like Connecterra, Cargill, and Allflex, which are actively deploying AI tools for health monitoring, feeding optimization, and productivity tracking.

For instance, in 2024, Precision Livestock Technologies (PLT), a provider of software and hardware solutions for livestock feeding and health, has recently announced the launch of a new system that integrates artificial intelligence (AI) to forecast cattle feed intake and generate feeding recommendations. This system represents a significant advancement in the use of technology within the livestock industry. High digital literacy and greater access to funding make North America a frontrunner in this evolving market.

Sustainability Analysis

AI in precision livestock farming plays a crucial role in promoting sustainability by enabling resource-efficient, eco-friendly, and welfare-centric agricultural practices. Through real-time monitoring and predictive analytics, farmers can reduce overfeeding, optimize water usage, and minimize waste generation, thereby lowering the environmental footprint.

For example, AI-driven feeding systems ensure precise nutrient delivery, which reduces methane emissions and enhances feed conversion efficiency. Automated health monitoring helps detect diseases early, minimizing the need for antibiotics and veterinary interventions.

Competitive Landscape

The major global players in the market include Connecterra, Cainthus, Vence, DeLaval, Afimilk Ltd, BouMatic, Allflex Livestock Intelligence (MSD Animal Health), Quantified Ag, Cargill, Incorporated, GEA Group, Moocall and among others.

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Target Audience 2024

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Component
3.2. Snippet by Deployment Mode
3.3. Snippet by Livestock Type
3.4. Snippet by Application
3.5. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Rising Demand for Efficient Livestock Monitoring
- 4.1.2. Restraints
  - 4.1.2.1. High Initial Investment and Maintenance Costs
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory and Compliance Analysis
5.5. Sustainability Analysis
5.6. DMI Opinion

6. By Component

6.1. Introduction
- 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 6.1.2. Market Attractiveness Index, By Component
6.2. Hardware *
- 6.2.1. Introduction
- 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
- 6.2.3. Sensors
- 6.2.4. Smart Cameras
- 6.2.5. Drones
- 6.2.6. RFID Tags & Readers
- 6.2.7. GPS Devices
6.3. Software
- 6.3.1. AI Algorithms
- 6.3.2. Predictive Analytics
- 6.3.3. Farm Management Software
6.4. Services

7. By Deployment Mode

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 7.1.2. Market Attractiveness Index, By Deployment Mode
7.2. Cloud-Based *
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. On-Premise

8. By Livestock Type

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 8.1.2. Market Attractiveness Index, By Livestock Type
8.2. Cattle *
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Poultry
8.4. Swine
8.5. Sheep & Goats
8.6. Others

9. By Application

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.1.2. Market Attractiveness Index, By Application
9.2. Feeding Management *
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Milk Harvesting & Monitoring
9.4. Reproduction Management
9.5. Animal Health Monitoring & Disease Detection
9.6. Livestock Behavior & Welfare Monitoring
9.7. Supply Chain & Farm Management
9.8. Others

10. By Region

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 10.1.2. Market Attractiveness Index, By Region
10.2. North America
- 10.2.1. Introduction
- 10.2.2. Key Region-Specific Dynamics
- 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.2.7.1. US
  - 10.2.7.2. Canada
  - 10.2.7.3. Mexico
10.3. Europe
- 10.3.1. Introduction
- 10.3.2. Key Region-Specific Dynamics
- 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.3.7.1. Germany
  - 10.3.7.2. UK
  - 10.3.7.3. France
  - 10.3.7.4. Italy
  - 10.3.7.5. Spain
  - 10.3.7.6. Rest of Europe
10.4. South America
- 10.4.1. Introduction
- 10.4.2. Key Region-Specific Dynamics
- 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.4.7.1. Brazil
  - 10.4.7.2. Argentina
  - 10.4.7.3. Rest of South America
10.5. Asia-Pacific
- 10.5.1. Introduction
- 10.5.2. Key Region-Specific Dynamics
- 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.5.7.1. China
  - 10.5.7.2. India
  - 10.5.7.3. Japan
  - 10.5.7.4. Australia
  - 10.5.7.5. Rest of Asia-Pacific
10.6. Middle East and Africa
- 10.6.1. Introduction
- 10.6.2. Key Region-Specific Dynamics
- 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

11. Company Profiles

11.1. Connecterra *
- 11.1.1. Company Overview
- 11.1.2. Product Portfolio and Description
- 11.1.3. Financial Overview
- 11.1.4. Key Developments
11.2. Cainthus
11.3. Vence
11.4. DeLaval
11.5. Afimilk Ltd.
11.6. BouMatic
11.7. Allflex Livestock Intelligence (MSD Animal Health)
11.8. Quantified Ag
11.9. Cargill, Incorporated
11.10. GEA Group
11.11. Moocall