Global Artificial Intelligence (AI) in Agriculture Supply, Demand and Key Producers, 2026-2032
Description
The global Artificial Intelligence (AI) in Agriculture market size is expected to reach $ 10399 million by 2032, rising at a market growth of 20.5% CAGR during the forecast period (2026-2032).
Artificial intelligence in agriculture refers to the integrated use of machine learning, computer vision, and increasingly generative AI, deployed across cloud and edge environments, to convert heterogeneous agricultural data into actionable decisions and automated interventions. Typical data inputs include satellite and aerial imagery, drone scouting, in field sensor streams, machinery telematics, weather and soil datasets, and farm management records. The core objective is to improve yield and quality predictability, reduce water, fertilizer, and crop protection inputs, strengthen early warning for pest and disease pressure as well as weather risk, and shift agronomic operations from experience based practice to measurable, data driven precision management.
From a product form factor perspective, AI in agriculture is commonly delivered as a platform plus applications stack, spanning monitoring and diagnostic models, prescription generation and variable rate decisioning, autonomous and assisted machinery control, yield and quality forecasting, grading and inspection analytics, and farmer or agronomist copilots for advisory workflows. These capabilities are implemented through cloud analytics combined with edge devices to enable near real time sensing, decision execution, and feedback loops. Use cases cover precision planting and fertilization, smart irrigation, pest and weed detection with targeted treatment, greenhouse climate optimization, livestock health monitoring, and post harvest quality sorting and loss reduction, serving large farms, service providers, cooperatives, and smallholders through different commercial and deployment models.
Against a backdrop of climate volatility, input cost uncertainty, and persistent labor constraints, agriculture is accelerating adoption of sensing plus analytics as a structural pathway to productivity and sustainability gains. Higher frequency remote sensing, drone enabled field intelligence, connected equipment and IoT expansion, and the lowering of analytics barriers through generative AI are collectively moving AI in agriculture from pilots toward scaled deployment. As a result, AI is increasingly positioned as a primary growth engine within precision agriculture and automation, particularly where it can translate data into operational outcomes on a repeatable basis.
Commercialization is also evolving from single point software subscriptions toward ROI anchored, closed loop solutions focused on high frequency, high value operations such as variable rate application, pest and disease recognition with prescriptions, route and energy optimization, and continuous control in greenhouses and livestock operations. Major equipment and ag technology players are investing in automation and intelligent features, strengthening ecosystem collaboration across data, algorithms, and delivery. This improves bankability and exit potential, but it also raises the competitive bar around proprietary datasets, distribution reach, and field level implementation capability.
Key challenges remain material. Agricultural data are fragmented and highly context dependent by crop, region, and season, which increases the difficulty of model generalization and elevates the need for explainability and agronomic validation. Uneven connectivity and edge compute readiness at farm level, unclear data ownership and privacy expectations, responsibility boundaries when algorithms influence operational decisions, and interoperability constraints across equipment standards can all increase the cost of scaling. In addition, agtech funding cyclicality can slow expansion in certain segments, reinforcing the need for vendors to prove unit economics and field verified performance outcomes to unlock procurement at scale.
This report studies the global Artificial Intelligence (AI) in Agriculture demand, key companies, and key regions.
This report is a detailed and comprehensive analysis of the world market for Artificial Intelligence (AI) in Agriculture, and provides market size (US$ million) and Year-over-Year (YoY) growth, considering 2025 as the base year. This report explores demand trends and competition, as well as details the characteristics of Artificial Intelligence (AI) in Agriculture that contribute to its increasing demand across many markets.
Highlights and key features of the study
Global Artificial Intelligence (AI) in Agriculture total market, 2021-2032, (USD Million)
Global Artificial Intelligence (AI) in Agriculture total market by region & country, CAGR, 2021-2032, (USD Million)
U.S. VS China: Artificial Intelligence (AI) in Agriculture total market, key domestic companies, and share, (USD Million)
Global Artificial Intelligence (AI) in Agriculture revenue by player, revenue and market share 2021-2026, (USD Million)
Global Artificial Intelligence (AI) in Agriculture total market by Type, CAGR, 2021-2032, (USD Million)
Global Artificial Intelligence (AI) in Agriculture total market by Application, CAGR, 2021-2032, (USD Million)
This report profiles major players in the global Artificial Intelligence (AI) in Agriculture market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include John Deere, CNH Industrial, AGCO Corporation, Kubota Corporation, CLAAS KGaA mbH, Trimble Inc., Topcon Positioning Systems, Bayer, Corteva, Inc., Valmont Industries, Inc., etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Stakeholders would have ease in decision-making through various strategy matrices used in analyzing the world Artificial Intelligence (AI) in Agriculture market
Detailed Segmentation:
Each section contains quantitative market data including market by value (US$ Millions), by player, by regions, by Type, and by Application. Data is given for the years 2021-2032 by year with 2025 as the base year, 2026 as the estimate year, and 2027-2032 as the forecast year.
Global Artificial Intelligence (AI) in Agriculture Market, By Region:
United States
China
Europe
Japan
South Korea
ASEAN
India
Rest of World
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Type:
Machine Learning
Computer Vision
Generative Ai
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Application Scenario:
Cloud Based
Edge Based
Hybrid Cloud and Edge
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Crop and Livestock Focus:
Row Crops
Horticulture
Livestock
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Solution Form Factor:
Software Platforms
Embedded Ai Devices
Autonomous Machines and Robots
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Application:
Precision Crop Management
Smart Irrigation and Fertigation
Pest Disease and Weed Management
Others
Companies Profiled:
John Deere
CNH Industrial
AGCO Corporation
Kubota Corporation
CLAAS KGaA mbH
Trimble Inc.
Topcon Positioning Systems
Bayer
Corteva, Inc.
Valmont Industries, Inc.
DTN
Ever.Ag
Taranis
CropX
Gamaya
IBM
SAP
Monarch Tractor
Odd.Bot
AgEagle Aerial Systems Inc.
SZ DJI Technology Co., Ltd.
Guangzhou Xaircraft Technology Co., Ltd.
Zoomlion Smart Agriculture Co., Ltd.
YTO Group Corporation
Key Questions Answered
1. How big is the global Artificial Intelligence (AI) in Agriculture market?
2. What is the demand of the global Artificial Intelligence (AI) in Agriculture market?
3. What is the year over year growth of the global Artificial Intelligence (AI) in Agriculture market?
4. What is the total value of the global Artificial Intelligence (AI) in Agriculture market?
5. Who are the Major Players in the global Artificial Intelligence (AI) in Agriculture market?
6. What are the growth factors driving the market demand?
Artificial intelligence in agriculture refers to the integrated use of machine learning, computer vision, and increasingly generative AI, deployed across cloud and edge environments, to convert heterogeneous agricultural data into actionable decisions and automated interventions. Typical data inputs include satellite and aerial imagery, drone scouting, in field sensor streams, machinery telematics, weather and soil datasets, and farm management records. The core objective is to improve yield and quality predictability, reduce water, fertilizer, and crop protection inputs, strengthen early warning for pest and disease pressure as well as weather risk, and shift agronomic operations from experience based practice to measurable, data driven precision management.
From a product form factor perspective, AI in agriculture is commonly delivered as a platform plus applications stack, spanning monitoring and diagnostic models, prescription generation and variable rate decisioning, autonomous and assisted machinery control, yield and quality forecasting, grading and inspection analytics, and farmer or agronomist copilots for advisory workflows. These capabilities are implemented through cloud analytics combined with edge devices to enable near real time sensing, decision execution, and feedback loops. Use cases cover precision planting and fertilization, smart irrigation, pest and weed detection with targeted treatment, greenhouse climate optimization, livestock health monitoring, and post harvest quality sorting and loss reduction, serving large farms, service providers, cooperatives, and smallholders through different commercial and deployment models.
Against a backdrop of climate volatility, input cost uncertainty, and persistent labor constraints, agriculture is accelerating adoption of sensing plus analytics as a structural pathway to productivity and sustainability gains. Higher frequency remote sensing, drone enabled field intelligence, connected equipment and IoT expansion, and the lowering of analytics barriers through generative AI are collectively moving AI in agriculture from pilots toward scaled deployment. As a result, AI is increasingly positioned as a primary growth engine within precision agriculture and automation, particularly where it can translate data into operational outcomes on a repeatable basis.
Commercialization is also evolving from single point software subscriptions toward ROI anchored, closed loop solutions focused on high frequency, high value operations such as variable rate application, pest and disease recognition with prescriptions, route and energy optimization, and continuous control in greenhouses and livestock operations. Major equipment and ag technology players are investing in automation and intelligent features, strengthening ecosystem collaboration across data, algorithms, and delivery. This improves bankability and exit potential, but it also raises the competitive bar around proprietary datasets, distribution reach, and field level implementation capability.
Key challenges remain material. Agricultural data are fragmented and highly context dependent by crop, region, and season, which increases the difficulty of model generalization and elevates the need for explainability and agronomic validation. Uneven connectivity and edge compute readiness at farm level, unclear data ownership and privacy expectations, responsibility boundaries when algorithms influence operational decisions, and interoperability constraints across equipment standards can all increase the cost of scaling. In addition, agtech funding cyclicality can slow expansion in certain segments, reinforcing the need for vendors to prove unit economics and field verified performance outcomes to unlock procurement at scale.
This report studies the global Artificial Intelligence (AI) in Agriculture demand, key companies, and key regions.
This report is a detailed and comprehensive analysis of the world market for Artificial Intelligence (AI) in Agriculture, and provides market size (US$ million) and Year-over-Year (YoY) growth, considering 2025 as the base year. This report explores demand trends and competition, as well as details the characteristics of Artificial Intelligence (AI) in Agriculture that contribute to its increasing demand across many markets.
Highlights and key features of the study
Global Artificial Intelligence (AI) in Agriculture total market, 2021-2032, (USD Million)
Global Artificial Intelligence (AI) in Agriculture total market by region & country, CAGR, 2021-2032, (USD Million)
U.S. VS China: Artificial Intelligence (AI) in Agriculture total market, key domestic companies, and share, (USD Million)
Global Artificial Intelligence (AI) in Agriculture revenue by player, revenue and market share 2021-2026, (USD Million)
Global Artificial Intelligence (AI) in Agriculture total market by Type, CAGR, 2021-2032, (USD Million)
Global Artificial Intelligence (AI) in Agriculture total market by Application, CAGR, 2021-2032, (USD Million)
This report profiles major players in the global Artificial Intelligence (AI) in Agriculture market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include John Deere, CNH Industrial, AGCO Corporation, Kubota Corporation, CLAAS KGaA mbH, Trimble Inc., Topcon Positioning Systems, Bayer, Corteva, Inc., Valmont Industries, Inc., etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Stakeholders would have ease in decision-making through various strategy matrices used in analyzing the world Artificial Intelligence (AI) in Agriculture market
Detailed Segmentation:
Each section contains quantitative market data including market by value (US$ Millions), by player, by regions, by Type, and by Application. Data is given for the years 2021-2032 by year with 2025 as the base year, 2026 as the estimate year, and 2027-2032 as the forecast year.
Global Artificial Intelligence (AI) in Agriculture Market, By Region:
United States
China
Europe
Japan
South Korea
ASEAN
India
Rest of World
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Type:
Machine Learning
Computer Vision
Generative Ai
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Application Scenario:
Cloud Based
Edge Based
Hybrid Cloud and Edge
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Crop and Livestock Focus:
Row Crops
Horticulture
Livestock
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Solution Form Factor:
Software Platforms
Embedded Ai Devices
Autonomous Machines and Robots
Others
Global Artificial Intelligence (AI) in Agriculture Market, Segmentation by Application:
Precision Crop Management
Smart Irrigation and Fertigation
Pest Disease and Weed Management
Others
Companies Profiled:
John Deere
CNH Industrial
AGCO Corporation
Kubota Corporation
CLAAS KGaA mbH
Trimble Inc.
Topcon Positioning Systems
Bayer
Corteva, Inc.
Valmont Industries, Inc.
DTN
Ever.Ag
Taranis
CropX
Gamaya
IBM
SAP
Monarch Tractor
Odd.Bot
AgEagle Aerial Systems Inc.
SZ DJI Technology Co., Ltd.
Guangzhou Xaircraft Technology Co., Ltd.
Zoomlion Smart Agriculture Co., Ltd.
YTO Group Corporation
Key Questions Answered
1. How big is the global Artificial Intelligence (AI) in Agriculture market?
2. What is the demand of the global Artificial Intelligence (AI) in Agriculture market?
3. What is the year over year growth of the global Artificial Intelligence (AI) in Agriculture market?
4. What is the total value of the global Artificial Intelligence (AI) in Agriculture market?
5. Who are the Major Players in the global Artificial Intelligence (AI) in Agriculture market?
6. What are the growth factors driving the market demand?
Table of Contents
168 Pages
- 1 Supply Summary
- 2 Demand Summary
- 3 World Artificial Intelligence (AI) in Agriculture Companies Competitive Analysis
- 4 United States VS China VS Rest of World (by Headquarter Location)
- 5 Market Analysis by Type
- 6 Market Analysis by Application Scenario
- 7 Market Analysis by Crop and Livestock Focus
- 8 Market Analysis by Solution Form Factor
- 9 Market Analysis by Application
- 10 Company Profiles
- 11 Industry Chain Analysis
- 12 Research Findings and Conclusion
- 13 Appendix
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