Global Artificial Intelligence (AI) in Agriculture Market Growth (Status and Outlook) 2026-2032
Description
The global Artificial Intelligence (AI) in Agriculture market size is predicted to grow from US$ 2739 million in 2025 to US$ 10532 million in 2032; it is expected to grow at a CAGR of 21.8% from 2026 to 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.
LPI (LP Information)' newest research report, the “Artificial Intelligence (AI) in Agriculture Industry Forecast” looks at past sales and reviews total world Artificial Intelligence (AI) in Agriculture sales in 2025, providing a comprehensive analysis by region and market sector of projected Artificial Intelligence (AI) in Agriculture sales for 2026 through 2032. With Artificial Intelligence (AI) in Agriculture sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Artificial Intelligence (AI) in Agriculture industry.
This Insight Report provides a comprehensive analysis of the global Artificial Intelligence (AI) in Agriculture landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on Artificial Intelligence (AI) in Agriculture portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Artificial Intelligence (AI) in Agriculture market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Artificial Intelligence (AI) in Agriculture and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global Artificial Intelligence (AI) in Agriculture.
This report presents a comprehensive overview, market shares, and growth opportunities of Artificial Intelligence (AI) in Agriculture market by product type, application, key players and key regions and countries.
Segmentation by Type:
Machine Learning
Computer Vision
Generative Ai
Others
Segmentation by Application Scenario:
Cloud Based
Edge Based
Hybrid Cloud and Edge
Others
Segmentation by Crop and Livestock Focus:
Row Crops
Horticulture
Livestock
Others
Segmentation by Solution Form Factor:
Software Platforms
Embedded Ai Devices
Autonomous Machines and Robots
Others
Segmentation by Application:
Precision Crop Management
Smart Irrigation and Fertigation
Pest Disease and Weed Management
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
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
Please note: The report will take approximately 2 business days to prepare and deliver.
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.
LPI (LP Information)' newest research report, the “Artificial Intelligence (AI) in Agriculture Industry Forecast” looks at past sales and reviews total world Artificial Intelligence (AI) in Agriculture sales in 2025, providing a comprehensive analysis by region and market sector of projected Artificial Intelligence (AI) in Agriculture sales for 2026 through 2032. With Artificial Intelligence (AI) in Agriculture sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Artificial Intelligence (AI) in Agriculture industry.
This Insight Report provides a comprehensive analysis of the global Artificial Intelligence (AI) in Agriculture landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on Artificial Intelligence (AI) in Agriculture portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Artificial Intelligence (AI) in Agriculture market.
This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Artificial Intelligence (AI) in Agriculture and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global Artificial Intelligence (AI) in Agriculture.
This report presents a comprehensive overview, market shares, and growth opportunities of Artificial Intelligence (AI) in Agriculture market by product type, application, key players and key regions and countries.
Segmentation by Type:
Machine Learning
Computer Vision
Generative Ai
Others
Segmentation by Application Scenario:
Cloud Based
Edge Based
Hybrid Cloud and Edge
Others
Segmentation by Crop and Livestock Focus:
Row Crops
Horticulture
Livestock
Others
Segmentation by Solution Form Factor:
Software Platforms
Embedded Ai Devices
Autonomous Machines and Robots
Others
Segmentation by Application:
Precision Crop Management
Smart Irrigation and Fertigation
Pest Disease and Weed Management
Others
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
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
Please note: The report will take approximately 2 business days to prepare and deliver.
Table of Contents
141 Pages
- *This is a tentative TOC and the final deliverable is subject to change.*
- 1 Scope of the Report
- 2 Executive Summary
- 3 Artificial Intelligence (AI) in Agriculture Market Size by Player
- 4 Artificial Intelligence (AI) in Agriculture by Region
- 5 Americas
- 6 APAC
- 7 Europe
- 8 Middle East & Africa
- 9 Market Drivers, Challenges and Trends
- 10 Global Artificial Intelligence (AI) in Agriculture Market Forecast
- 11 Key Players Analysis
- 12 Research Findings and Conclusion
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