AI In Livestock Farming Statistics By Market Size And Country (2026)

Overview

AI In Livestock Farming Statistics: Livestock farming is moving beyond traditional methods as artificial intelligence brings new ways to manage animals and daily farm work. Farmers can now use smart cameras, sensors, and wearable devices to keep a closer watch on animal health, behavior, feeding, and growth. These tools can help identify health problems early, reduce waste, and save time on routine tasks. AI can also support breeding decisions and improve overall farm management by turning farm data into useful insights.

With rising production costs and pressure to improve efficiency, more livestock producers are exploring AI-based solutions. As adoption grows, AI is helping create smarter, more efficient, and more sustainable livestock farms.

Expert Pick

1. The market is expected to grow from USD 543.3 million in 2025 to USD 672.6 million in 2026.
2. The global smart dairy farming market is valued at USD 9.8 billion in 2026.
3. The TimeSformer computer-vision model identified beef cattle behaviors with 90.33% accuracy.
4. A 2026 poultry weight-monitoring system analyzed 12,640 images containing 20,138 labeled chicken instances.
5. BeeViz tracks 3 hive factors across 2 German hives over 2 years, using 730 training days, 130 validation days, and 860 testing days.
6. Aquatic animals supplied 89% of human consumption, supported 600+ million livelihoods, and reached 21.3 kg per person.
7. AI-powered dairy systems reached 22 lakh+ farmers and covered 20 million cattle in India.
8. The AI animal monitoring market is projected to reach USD 743.6 million in 2026.
9. A federated cattle-monitoring system achieved 93.1% accuracy and an F1 score of 0.91.

AI in Livestock Farming Market Size

(Source: market.us)

• The market is expected to grow from USD 543.3 million in 2025 to USD 672.6 million in 2026.
• By 2033, it is forecast to reach USD 2,997.9 million.
• Overall, the market is projected to expand at a CAGR of 23.8%, driven by software, IoT sensors, and services.

AI in Precision Livestock Farming Market

• DATAM Intelligence reported that the AI market in Precision Livestock Farming is valued at USD 2.97 billion in 2026.
• It is projected to reach USD 10.76 billion by 2035, growing at a 15.39% CAGR from 2026 to 2035.

AI in Dairy Farming Statistics

• According to Market Research, the global smart dairy farming market is valued at USD 9.8 billion in 2026. 
• It is projected to reach USD 29.4 billion by 2034, growing at a 14.6% CAGR.

(Source: wiseguyreports.com)

• The Precision Dairy Farming Market stood at USD 2.92 billion in 2025.
• It is expected to reach USD 6.5 billion by 2035, expanding at a CAGR of 8.4%. 
• USDA research found that robotic milking and multiple precision technologies were associated with a 13% increase in U.S. dairy net returns.
• Robotic milking alone increased returns by USD 3.15/cwt.
• A 2026 Journal of Dairy Science study found that 81.5% of surveyed U.S. dairy farmers used at least one precision dairy technology. Wearable technology adoption reached 64.2%.
• Other technologies included parlor systems at 41.9%, followed by cow collars (40.7%), electronic ear tags (22.2%), milk-quality sensors (14.8%), cameras (9.9%), rumen boluses (8.6%), pedometers (7.4%), and lameness monitoring (3.7%).
• An AI-assisted lameness detection system achieved 83.6% accuracy, with 86.4% sensitivity and 80.9% specificity.
• An AI-based approach to supplementation optimization improved milk production by up to 8.64%. The fastest reported computation time was 74.9 seconds/day.
• Deep-learning models for udder segmentation and milk yield prediction achieved an average accuracy of 98% in 2026.

In Meat Farming

• Computers and Electronics in Agriculture reported that the TimeSformer computer-vision model identified beef cattle behaviors with 90.33% accuracy.
• An IoT-enabled edge-AI system classified 4 cattle behaviors, standing, sitting, walking, and grazing, with 99.84% accuracy. Its preprocessing stages achieved accuracies of 98.98% and 98.65%.
• A computer-vision lameness system recorded accuracy ranging from 64.1% for leg-joint detection to 78.6% for whole-cow detection.
• Around 63% of AI systems used narrow AI for specific monitoring tasks, while 21% used context-aware AI.
• Taylor and Francis reported that many AI camera systems achieved more than 90% accuracy. 
• Camera-based pig-weight estimation also recorded a mean absolute percentage error of about 4%, indicating potential for automated livestock monitoring and weight assessment.

In Poultry Farming

• A 2026 poultry weight-monitoring system analyzed 12,640 images containing 20,138 labeled chicken instances. Its YOLOv12m model achieved mAP50 of 0.909 and mAP50-95 of 0.836. 
• Another review identified 408 poultry computer-vision publications from 2015 to October 15, 2024.
• A meta-analysis of 21 studies, including 10 in the final analysis, reported a pooled accuracy of 90.39%.
• A broiler dataset contained 1,487 images and 327,289 bird instances from 2 poultry houses, with up to 500 birds per image. Cameras operated 24 hours a day for 6 weeks at 30 FPS and 1,920 × 1,080 resolution.
• AI dead-bird detection achieved 88.64% accuracy for about 1,200 broilers and processed 34.12 FPS at 1,600 × 2,880 resolution.
• Federated AI improved poultry disease detection from 64.86% to 90.31%, compared with 95.10% with centralized training.
• Turkey chicken weight estimation achieved an R² of 0.96 ± 0.04 and a MAPE of 7.39% ± 4.06%.
• AI welfare monitoring achieved 85.7% MOTA, 94.7% precision, and 92.5% recall, while a 270-cage system achieved 93.3% accuracy in low-yield cage detection.

In Bee Farming

• BeeViz uses AI and sensors to track 3 hive factors: temperature, humidity, and weight for smarter forecasting.
• Testing covered 2 German hives over 2 years, with 730 training days, 130 validation days, and 860 testing days.
• BeeViz recorded anomaly-detection sensitivity of 38.33% for humidity and 34.16% for hive weight.
• Temperature sensitivity reached 20.06% with Isolation Forest and 0.28% with DBSCAN.
• 844 beekeepers across 18 countries found that 79.1% had no digital hive monitoring, while 20.9% used it to a limited extent.
• YOLOv10 achieved 93.9% accuracy and an F1 score of 0.92, while FrCNN achieved 93.4% accuracy and an F1 score of 0.91.
• A multimodal AI system tested 9 colonies and 26,272 observations. It reduced false alarms from 27.0% to 0.5%, achieving a precision of 0.995, a recall of 0.921, and an F1 score of 0.957.

In Aquaculture

• FAO reported that aquaculture produced 103 million tonnes of aquatic animals in 2024, accounting for 53% of global aquatic animal production. Including algae, output reached 141 million tonnes, valued at USD 391 billion.
• Aquatic animals accounted for 89% of human consumption, supporting more than 600 million livelihoods, with availability at 21.3 kg per person.
• FAO projects aquatic animal production to reach 214 million tonnes by 2034.
• Many computer vision applications achieved over 90% accuracy.
• A sea-bream AI system tested 100 images containing 193 fish against 220 ground-truth fish.
• It achieved 1.12% error and 1.38 g absolute error, while adding a body-midpoint keypoint improved accuracy by 6.4%.
• According to Nature, an AI feeding system achieved 98.93% accuracy using 5 layers and 5 frames over 15 seconds.
• A fish-disease model achieved 95.72% mAP50, 95.65% precision, 136.38 FPS, and 62.3 FPS on Jetson Nano.
• Shrimp AI achieved 99.1% detection accuracy, 94.8% precision, 88.1% recall, 91.3% F1, 98.6% mAP50, and 185 FPS.

Top AI Livestock Farming Players

Country-Wise AI Livestock Farming Statistics, 2026

• According to PIB.gov.in, AI-powered dairy systems reached 22 lakh+ farmers and covered 20 million cattle in India.
• They reportedly achieved a 45% reduction in methane and generated USD 2.1 million annually.
• Digital livestock systems registered 9.84 crore owners and issued 38.53 crore Pashu Aadhaar IDs.
• The country recorded 168.67 crore vaccinations through 4,019 mobile veterinary units across 29 States/UTs.
• In the United States, farms had 94.2 million cattle and calves in 2026.
• This included 9.65 million milk cows and 13.2 million cattle on feed. 
• In Brazil, a 2026 study monitored 9 heifers using sensors with 96 visual observations.

AI-Powered Animal Monitoring Market Growth

(Source: persistencemarketresearch.com)

• The AI animal monitoring market is projected to reach USD 743.6 million in 2026.
• It may reach USD 3,185.5 million by 2033, growing at a 23.1% CAGR.
• In 2026, North America leads with 32%, while Europe accounts for nearly 27% of the global market share.
• Livestock leads with an 82% share, while health monitoring accounts for 34% of the market in 2026.

AI Livestock Farming Tools Performance

• A 2026 federated AI system for cattle health monitoring achieved 93.1% accuracy and an F1 score of 0.91.
• Centralized machine learning achieved an F1 score of 0.86 and an accuracy of 91.2%.
• Federated LSTM achieved 89.5% accuracy and a 0.86 F1 score of 0.86.
• The federated LSTM + CNN model achieved an F1 score of 0.91, 93.1% accuracy, 0.92 precision, and 0.90 recall.
• It recorded 135 ms average latency and 8.4 MB bandwidth usage.
• Livestock foundation-model research identified 6 application subsystems, while non-contact cattle monitoring covered 5 core domains.

Summary

AI is helping farmers take better care of livestock and run farms more efficiently. It can spot health problems early, improve feeding, monitor animals, and reduce unnecessary costs. At the same time, adopting AI can be difficult because of high costs, limited technical skills, and weak digital infrastructure.

As technology becomes more affordable and easier to use, AI could play a bigger role in improving animal health, farm productivity, and the sustainability of livestock farming.

FAQ