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As per Intent Market Research, the Smart Agriculture Market was valued at USD 15.0 billion and will surpass USD 27.5 billion by 2030; growing at a CAGR of 9.1% during 2024 - 2030.
The smart agriculture market is transforming the agricultural sector by integrating advanced technologies such as IoT, AI, and data analytics. These innovations enable farmers to optimize resources, enhance productivity, and reduce environmental impacts. Key segments in this market include offerings like hardware, software, and services; diverse agricultural types; and applications ranging from precision farming to smart greenhouse management.
Among offerings, precision farming hardware stands out as the largest subsegment, thanks to its central role in enabling data-driven agricultural practices. Devices such as sensors, GPS-enabled equipment, and drones provide real-time insights into soil conditions, crop health, and field variability, helping farmers make informed decisions.
The demand for precision farming hardware is driven by the need to increase yield and minimize resource wastage in a growing population scenario. With advancements in sensor accuracy, autonomous machinery, and affordable technology solutions, this subsegment continues to attract significant investments from agritech companies and governments alike.
Precision aquaculture is the fastest-growing subsegment in the agriculture type category, driven by the need to sustainably meet global seafood demand. By leveraging technologies such as automated feeding systems, water quality sensors, and monitoring platforms, farmers can ensure optimal conditions for aquatic species.
This growth is particularly evident in regions where aquaculture is a significant contributor to food supply. With increasing investments in research and technology for sustainable aquaculture practices, precision aquaculture is becoming a vital part of the broader smart agriculture ecosystem.
Yield monitoring is the largest application subsegment in precision farming, as it provides critical data to enhance crop management and improve productivity. By analyzing real-time yield data, farmers can identify high-performing zones within fields and optimize planting and fertilization strategies.
The widespread adoption of yield monitoring systems is fueled by advancements in GPS technology and data analytics. These systems are particularly popular among large-scale farms aiming to maximize profits while minimizing environmental impacts, making yield monitoring a cornerstone of modern precision farming.
Smart greenhouse technology is rapidly growing in adoption, particularly for managing HVAC systems and water usage. Leveraging IoT-enabled sensors and automation tools, farmers can regulate temperature, humidity, and lighting to create optimal growth conditions.
This segment is crucial for urban and controlled-environment agriculture, which addresses land and resource constraints. With increasing consumer demand for year-round fresh produce and sustainable farming practices, the smart greenhouse segment remains an area of high growth potential.
North America is the largest region in the smart agriculture market, driven by its early adoption of precision agriculture technologies and strong R&D infrastructure. The region benefits from significant government initiatives promoting sustainable farming and the presence of leading agritech companies.
Farmers in North America are increasingly using IoT-based solutions, drones, and AI-driven platforms to enhance productivity and reduce resource usage. The combination of technological innovation, robust funding, and awareness about sustainable practices positions North America as a leader in this market.
The competitive landscape in the smart agriculture market features established players and innovative startups. Key companies include John Deere, Trimble, AGCO Corporation, Raven Industries, DeLaval, and Topcon Positioning Systems, known for their comprehensive product portfolios and customer-centric solutions.
Emerging players are focusing on niche areas such as aquaculture monitoring and greenhouse automation, while established firms are expanding their portfolios through R&D and acquisitions. The dynamic competition fosters continuous technological advancement, supporting the market's rapid growth and diversification.
Report Features |
Description |
Market Size (2023) |
USD 15.0 billion |
Forecasted Value (2030) |
USD 27.5 billion |
CAGR (2024 – 2030) |
9.1% |
Base Year for Estimation |
2023 |
Historic Year |
2022 |
Forecast Period |
2024 – 2030 |
Report Coverage |
Market Forecast, Market Dynamics, Competitive Landscape, Recent Developments |
Segments Covered |
Smart Agriculture Market By Offering (Hardware, Software, Services), By Agriculture Type (Precision Farming, Livestock Monitoring, Precision Aquaculture, Precision Forestry, Smart Greenhouse), By Application (Yield Monitoring, Field Mapping, Crop Scouting, Feeding Management, Breeding Management, HVAC Management, Water and Fertilizer Management) |
Regional Analysis |
North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, and Rest of Europe), Asia-Pacific (China, Japan, South Korea, Australia, India, and Rest of Asia-Pacific), Latin America (Brazil, Argentina, and Rest of Latin America), Middle East & Africa (Saudi Arabia, UAE, Rest of Middle East & Africa) |
Major Companies |
AGCO Corporation, AgEagle Aerial Systems Inc., Allflex Livestock Intelligence, Argus Control Systems Ltd, BouMatic Robotic B.V., CropMetrics, CropZilla, Deere & Company, DeLaval Inc, DICKEY-john, GEA Group Aktiengesellschaft, Innovasea Systems, Raven Industries, Topcon Positioning, Trimble |
Customization Scope |
Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements |
1. Introduction |
1.1. Market Definition |
1.2. Scope of the Study |
1.3. Research Assumptions |
1.4. Study Limitations |
2. Research Methodology |
2.1. Research Approach |
2.1.1. Top-Down Method |
2.1.2. Bottom-Up Method |
2.1.3. Factor Impact Analysis |
2.2. Insights & Data Collection Process |
2.2.1. Secondary Research |
2.2.2. Primary Research |
2.3. Data Mining Process |
2.3.1. Data Analysis |
2.3.2. Data Validation and Revalidation |
2.3.3. Data Triangulation |
3. Executive Summary |
3.1. Major Markets & Segments |
3.2. Highest Growing Regions and Respective Countries |
3.3. Impact of Growth Drivers & Inhibitors |
3.4. Regulatory Overview by Country |
4. Smart Agriculture Market, by Offering (Market Size & Forecast: USD Million, 2022 – 2030) |
4.1. Hardware |
4.1.1. Precision Farming Hardware |
4.1.2. Livestock Monitoring |
4.1.3. Precision Forestry |
4.1.4. Precision Aquaculture |
4.1.5. Smart Greenhouse |
4.2. Software |
4.2.1. On-premises |
4.2.2. On-cloud |
4.3. Services |
4.3.1. System Integration and Consulting Services |
4.3.2. Managed Services |
4.3.3. Connectivity Services |
4.3.4. Maintenance & Support Services |
4.3.5. Installation and Deployment Services |
5. Smart Agriculture Market, by Agriculture Type (Market Size & Forecast: USD Million, 2022 – 2030) |
5.1. Precision Farming |
5.2. Precision Aquaculture |
5.3. Livestock Monitoring |
5.4. Precision Forestry |
5.5. Smart Greenhouse |
5.6. Others |
6. Smart Agriculture Market, by Application (Market Size & Forecast: USD Million, 2022 – 2030) |
6.1. Precision Farming |
6.1.1. Yield Monitoring |
6.1.2. Field Mapping |
6.1.3. Crop Scouting |
6.1.4. Others |
6.2. Livestock Monitoring |
6.2.1. Feeding Management |
6.2.2. Breeding Management |
6.2.3. Others |
6.3. Precision Aquaculture |
6.3.1. Feeding Management |
6.3.2. Monitoring, Control, & Surveillance |
6.4. Precision Forestry |
6.4.1. Genetics and Nurseries |
6.4.2. Harvesting Management |
6.4.3. Others |
6.5. Smart Greenhouse |
6.5.1. HVAC Management |
6.5.2. Yield Monitoring & Harvesting |
6.5.3. Water and Fertilizer Management |
6.5.4. Others |
6.6. Others |
7. Regional Analysis (Market Size & Forecast: USD Million, 2022 – 2030) |
7.1. Regional Overview |
7.2. North America |
7.2.1. Regional Trends & Growth Drivers |
7.2.2. Barriers & Challenges |
7.2.3. Opportunities |
7.2.4. Factor Impact Analysis |
7.2.5. Technology Trends |
7.2.6. North America Smart Agriculture Market, by Offering |
7.2.7. North America Smart Agriculture Market, by Agriculture Type |
7.2.8. North America Smart Agriculture Market, by Application |
7.2.9. By Country |
7.2.9.1. US |
7.2.9.1.1. US Smart Agriculture Market, by Offering |
7.2.9.1.2. US Smart Agriculture Market, by Agriculture Type |
7.2.9.1.3. US Smart Agriculture Market, by Application |
7.2.9.2. Canada |
7.2.9.3. Mexico |
*Similar segmentation will be provided for each region and country |
7.3. Europe |
7.4. Asia-Pacific |
7.5. Latin America |
7.6. Middle East & Africa |
8. Competitive Landscape |
8.1. Overview of the Key Players |
8.2. Competitive Ecosystem |
8.2.1. Level of Fragmentation |
8.2.2. Market Consolidation |
8.2.3. Product Innovation |
8.3. Company Share Analysis |
8.4. Company Benchmarking Matrix |
8.4.1. Strategic Overview |
8.4.2. Product Innovations |
8.5. Start-up Ecosystem |
8.6. Strategic Competitive Insights/ Customer Imperatives |
8.7. ESG Matrix/ Sustainability Matrix |
8.8. Manufacturing Network |
8.8.1. Locations |
8.8.2. Supply Chain and Logistics |
8.8.3. Product Flexibility/Customization |
8.8.4. Digital Transformation and Connectivity |
8.8.5. Environmental and Regulatory Compliance |
8.9. Technology Readiness Level Matrix |
8.10. Technology Maturity Curve |
8.11. Buying Criteria |
9. Company Profiles |
9.1. AGCO Corporation |
9.1.1. Company Overview |
9.1.2. Company Financials |
9.1.3. Product/Service Portfolio |
9.1.4. Recent Developments |
9.1.5. IMR Analysis |
*Similar information will be provided for other companies |
9.2. AgEagle Aerial Systems Inc. |
9.3. Allflex Livestock Intelligence |
9.4. Argus Control Systems Ltd |
9.5. BouMatic Robotic B.V. |
9.6. CropMetrics |
9.7. CropZilla |
9.8. Deere & Company |
9.9. DeLaval Inc |
9.10. DICKEY-john |
9.11. GEA Group Aktiengesellschaft |
9.12. Innovasea Systems |
9.13. Raven Industries |
9.14. Topcon Positioning |
9.15. Trimble |
10. Appendix |
A comprehensive market research approach was employed to gather and analyze data on the Smart Agriculture Market. In the process, the analysis was also done to analyze the parent market and relevant adjacencies to measure the impact of them on the Smart Agriculture Market. The research methodology encompassed both secondary and primary research techniques, ensuring the accuracy and credibility of the findings.
Secondary research involved a thorough review of pertinent industry reports, journals, articles, and publications. Additionally, annual reports, press releases, and investor presentations of industry players were scrutinized to gain insights into their market positioning and strategies.
Primary research involved conducting in-depth interviews with industry experts, stakeholders, and market participants across the Smart Agriculture ecosystem. The primary research objectives included:
A combination of top-down and bottom-up approaches was utilized to analyze the overall size of the Smart Agriculture Market. These methods were also employed to assess the size of various subsegments within the market. The market size assessment methodology encompassed the following steps:
To ensure the accuracy and reliability of the market size, data triangulation was implemented. This involved cross-referencing data from various sources, including demand and supply side factors, market trends, and expert opinions. Additionally, top-down and bottom-up approaches were employed to validate the market size assessment.