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AI, IoT, and Blockchain Market in Modern Agriculture Report Coverage

AI, IoT, and Blockchain Market in Modern Agriculture

Base Year

2024

Market Size in 2025

$24,301.3 Million

Forecast Period

2025-2035

Value Projection and Estimation by 2035

$154,546.5 Million

CAGR During Forecast Period

20.32%

Number of Pages

160

Number of Tables

 62 Number of Figures 47

 

Key Market Players and Competition Synopsis

The companies profiled in the AI, IoT, and blockchain market in modern agriculture have been selected based on expert inputs regarding their technological capabilities, solution breadth, global footprint, and market penetration across digital agriculture value chains.

Leading Players in the AI, IoT, and Blockchain Market in Modern Agriculture

• Deere & Company
• Robert Bosch GmbH
• CNH Industrial N.V
• Trimble Inc.
• Signify Holding
• Taranis
• CropIn Technology Solutions
• Plantix (PEAT GmbH)
• Ceres Imaging
• Climate LLC (The Climate Corporation)
• AGRIVI
• Regen Network Development
• SZ DJI Technology Co., Ltd. (DJI)
• OSRAM GmbH (ams OSRAM)
• Granular Inc.

Companies that are not a part of the aforementioned pool have been well represented across different sections of the AI, IoT, and blockchain market in modern agriculture report (wherever applicable).

How can this report add value to an organization?

Product/Innovation Strategy: This report offers organizations a detailed understanding of how AI, IoT, and blockchain technologies are transforming modern agriculture. It highlights emerging innovations such as AI-driven crop intelligence, IoT-enabled sensing networks, autonomous farm machinery, digital twins, and blockchain-based traceability systems. These technologies are enabling real-time farm monitoring, predictive analytics, and resource-efficient operations. By mapping technological advancements, ranging from machine vision for crop health to distributed ledger systems for supply chain transparency, the report provides actionable insights for product development teams, R&D departments, and innovation leaders. Companies can use these insights to design next-generation precision farming tools, enhance interoperability across devices and platforms, and build scalable digital agriculture solutions aligned with evolving market needs.

Growth/Marketing Strategy: The AI, IoT, and blockchain market in modern agriculture offers robust growth potential across all major agricultural regions. This report outlines key strategies adopted by leading players, including mergers and acquisitions (e.g., CNH Industrial’s acquisition of Raven), strategic partnerships (such as Deere & Company’s automation collaborations), and the expansion of cloud-based farm management platforms. It also identifies growth hotspots such as smart irrigation, livestock automation, autonomous tractors, greenhouse digitalization, and blockchain-enabled supply chain systems. With farmers, cooperatives, input suppliers, and food companies increasingly adopting data-driven practices, organizations can leverage the report to refine their market positioning, tailor their go-to-market strategies, and enter high-potential segments using targeted product offerings and value-added services.

Competitive Strategy: The report provides a comprehensive competitive landscape of the digital agriculture ecosystem, profiling major players across equipment manufacturers, agtech startups, IoT sensor providers, AI analytics companies, and blockchain-based platforms. It examines strategic moves such as partnerships, technology collaborations, joint ventures, platform integrations, and product launches that shape competitive dynamics. Through competitive benchmarking, organizations can identify white-space opportunities, assess competitor capabilities, and evaluate emerging threats. As agriculture rapidly shifts toward automation, remote sensing, cloud analytics, and decentralized data systems, competition will intensify around innovation speed, interoperability, data ownership, and ecosystem integration. The insights in this report help organizations strengthen their long-term competitive positioning and capture a larger share of the evolving digital agriculture market.

Research Methodology

Factors for Data Prediction and Modelling

• The base currency considered for the AI, IoT, and blockchain market in modern agriculture analysis is the US$. Currencies other than the US$ have been converted to the US$ for all statistical calculations, considering the average conversion rate for that particular year.
• The currency conversion rate has been taken from the historical exchange rate of the Oanda website. 
• Nearly all the recent developments from January 2021 to March 2024 have been considered in this research study.
• The information rendered in the report is a result of in-depth primary interviews, surveys, and secondary analysis.
• Where relevant information was not available, proxy indicators and extrapolation were employed.
• Any economic downturn in the future has not been taken into consideration for the market estimation and forecast.
• Technologies currently used are expected to persist through the forecast with no major technological breakthroughs.

Market Estimation and Forecast

This research study involves the usage of extensive secondary sources, such as certified publications, articles from recognized authors, white papers, annual reports of companies, directories, and major databases, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the AI, IoT, and blockchain market in modern agriculture

The market engineering process involves the calculation of the market statistics, market size estimation, market forecast, market crackdown, and data triangulation (the methodology for such quantitative data processes has been explained in further sections). The primary research study has been undertaken to gather information and validate the market numbers for segmentation types and industry trends of the key players in the market.

Primary Research

The primary sources involve industry experts from the AI, IoT, and blockchain market in modern agriculture and various stakeholders in the ecosystem. Respondents such as CEOs, vice presidents, marketing directors, and technology and innovation directors have been interviewed to obtain and verify both qualitative and quantitative aspects of this research study.

The key data points taken from primary sources include:

• validation and triangulation of all the numbers and graphs
• validation of report segmentations and key qualitative findings
• understanding the competitive landscape
• validation of the numbers of various markets for the market type
• percentage split of individual markets for geographical analysis

Secondary Research

This research study involves the usage of extensive secondary research, directories, company websites, and annual reports. It also makes use of databases, such as Hoovers, Bloomberg, Businessweek, and Factiva, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the global market. In addition to the data sources, the study has been undertaken with the help of other data sources and websites, such as the Census Bureau, OICA, and ACEA.

Secondary research has been done to obtain crucial information about the industry’s value chain, revenue models, the market’s monetary chain, the total pool of key players, and the current and potential use cases and applications.

The key data points taken from secondary research include:

• segmentations and percentage shares
• data for market value
• key industry trends of the top players in the market
• qualitative insights into various aspects of the market, key trends, and emerging areas of innovation
•  quantitative data for mathematical and statistical calculations

Data Triangulation

This research study involves the usage of extensive secondary sources, such as certified publications, articles from recognized authors, white papers, annual reports of companies, directories, and major databases, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the AI, IoT, and blockchain market in modern agriculture.

The process of market engineering involves the calculation of the market statistics, market size estimation, market forecast, market crackdown, and data triangulation (the methodology for such quantitative data processes has been explained in further sections). The primary research study has been undertaken to gather information and validate the market numbers for segmentation types and industry trends of the key players in the market.

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AI, IoT, and Blockchain Market in Modern Agriculture - A Global and Regional Analysis

Focus on Application, Product, and Country Analysis - Analysis and Forecast, 2025-2035

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Some Faq's

Frequently Asked Questions

The AI, IoT, and blockchain market in modern agriculture refers to the integration of advanced digital technologies designed to optimize farm productivity, resource use, and traceability across the agricultural value chain. It includes:

•    AI-driven analytics and predictive models for yield forecasting, pest detection, crop health monitoring, and autonomous decision-making.
•    IoT sensors and connected devices for monitoring soil moisture, weather conditions, livestock health, irrigation systems, machinery performance, and environmental parameters in real time.
•    Blockchain platforms for secure, tamper-proof traceability, supply chain transparency, smart contracts, and verification of sustainability practices.

Together, these technologies enhance precision farming, reduce input costs, improve sustainability, and support data-driven agricultural operations at scale.

The market is shaped by a combination of global technology providers, agricultural equipment manufacturers, and emerging ag-tech innovators.

Tech giants, including IBM, Microsoft, SAP, Oracle, and Amazon Web Services, are leading through cloud-based analytics, AI tools, and blockchain traceability solutions.

Agriculture equipment and input companies such as John Deere, CNH Industrial, Trimble, Corteva, and Bayer are integrating IoT sensors, automation systems, and machine-learning platforms into their machinery and tools.

Ag-tech startups like CropX, Arable, Farmers Edge, AgNext, EOS Data Analytics, and Chainvine add innovation in soil sensing, satellite crop analytics, and blockchain-based supply chain platforms. This ecosystem is accelerating digital transformation across farms globally.

Three strong forces are propelling market expansion:

1.    Rising Demand for Precision Agriculture: With pressures to increase yields and optimize inputs, farmers increasingly rely on digital decision-support tools and automated systems.
2.    Global Challenges around Food Security and Climate Change: AI and IoT tools help reduce waste, optimize water use, monitor soil conditions, and support climate-resilient farming.
3.    Government Incentives and Corporate Sustainability Goals: Many nations are funding smart farming programs, while food companies seek transparent, traceable, low-carbon supply chains, boosting blockchain adoption and digital MRV systems.

The combination of technology maturity, sustainability requirements, and policy support is accelerating adoption worldwide.

Despite rapid growth, several barriers remain:

•    Digital divide and lack of farmer training: Many farmers, especially in emerging markets, lack access to high-speed internet, digital skills, or capital to adopt smart technologies.
•    High upfront investment and maintenance costs for sensors, connectivity solutions, and data platforms.
•    Interoperability issues across devices, platforms, and legacy systems create data silos.
•    Cybersecurity concerns, as connected systems are vulnerable to data breaches and hacking.
•    Fragmented regulatory frameworks for data ownership, privacy, and blockchain usage.

Solving these challenges requires improved affordability, digital literacy programs, and harmonized standards.

Key enabling technologies include:

•    AI-based computer vision for automated crop scouting, disease detection, and livestock monitoring.
•    Advanced IoT sensors for soil nutrients, moisture, CO?, and microclimate monitoring.
•    Satellite imagery and drone-based sensing combined with machine learning for high-resolution crop analytics.
•    Edge computing enabling local, real-time decision-making in low-connectivity environments.
•    Blockchain for tamper-proof supply chain traceability, automated smart contracts, and digital identity of farm products.

These technologies reduce uncertainty, increase efficiency, and support scalable digital transformation.

Blockchain ensures transparency and trust by:

•    Recording every step of the food supply chain
•    Enabling smart contracts for input purchases and crop sales
•    Certifying sustainability practices (organic, low-carbon, regenerative, etc.)
•    Supporting farm-to-fork traceability for food safety and export compliance

Companies such as IBM Food Trust, AgriDigital, and TE-FOOD are working with producers and food companies to authenticate origin, quality, and environmental impact.

The market outlook is highly positive. The global AI, IoT, and blockchain market in modern agriculture is projected to grow from $24.30 billion in 2025 to $154.55 billion in 2035, registering a strong CAGR of 20.32%.

Who should use this report?

  • Ag-tech and software companies
  • Agriculture equipment manufacturers
  • Sensor and IoT device providers
  • Blockchain and traceability solution providers
  • Food and beverage corporations with sustainability commitments
  • Investors, policymakers, and research institutions
  • Agribusiness consultants and supply chain strategists

This report provides insights into technologies, opportunities, competitive landscapes, and strategic direction for the next-generation digital agriculture ecosystem.