Thailand Application of AI in Agriculture Market

Thailand AI in Agriculture Market, valued at USD 80 million, grows via AI integration for crop yields, resource management, and food security, with key segments in precision farming and farms.

Region:Asia

Author(s):Rebecca

Product Code:KRAA4843

Pages:91

Published On:September 2025

About the Report

Base Year 2024

Thailand Application of AI in Agriculture Market Overview

  • The Thailand Application of AI in Agriculture Market is valued at USD 80 million, based on a five-year historical analysis. This growth is primarily driven by the increasing adoption of advanced technologies in farming practices, aimed at enhancing productivity and sustainability. The integration of AI solutions in agriculture has been propelled by the need for efficient resource management and improved crop yields, addressing the challenges posed by climate change, labor shortages, and population growth .
  • Key regions in this market include Bangkok, Chiang Mai, and Nakhon Ratchasima, which are prominent due to their strategic agricultural landscapes and access to technology hubs. These areas are pivotal in fostering innovation and collaboration between agricultural stakeholders and tech companies, facilitating the development and deployment of AI-driven solutions tailored to local farming needs .
  • The Thai government has advanced the adoption of AI technologies in farming through the Bio-Circular-Green (BCG) Economic Model and initiatives such as the AGROWTH platform, launched by the National Innovation Agency (NIA) in 2024. These programs provide financial incentives and support for integrating AI tools into agricultural operations, aiming to enhance productivity, sustainability, and food security. The BCG Economic Model, issued by the Ministry of Higher Education, Science, Research and Innovation in 2021, sets operational guidelines for smart agriculture, including compliance requirements for technology adoption and innovation grants .
Thailand Application of AI in Agriculture Market Size

Thailand Application of AI in Agriculture Market Segmentation

By Type:The market is segmented into various types of AI applications in agriculture, including Precision Farming Solutions, Crop Monitoring & Analytics Platforms, Soil Health & Nutrient Management Tools, Pest and Disease Detection Systems, Automated Irrigation & Water Management Systems, Livestock Monitoring & Management Solutions, Supply Chain & Post-Harvest Optimization, and Others. Among these, Precision Farming Solutions are leading due to their ability to optimize resource use and increase crop yields through data-driven decision-making .

Thailand Application of AI in Agriculture Market segmentation by Type.

By End-User:The end-user segmentation includes Farms (Commercial & Smallholder), Agri-businesses & Cooperatives, Government & Public Sector, Research & Academic Institutions, Agri-tech Startups, and Others. The segment of Farms (Commercial & Smallholder) is currently dominating the market, as these users are increasingly adopting AI technologies to enhance productivity and manage resources more effectively .

Thailand Application of AI in Agriculture Market segmentation by End-User.

Thailand Application of AI in Agriculture Market Competitive Landscape

The Thailand Application of AI in Agriculture Market is characterized by a dynamic mix of regional and international players. Leading participants such as Ricult (Thailand) Co., Ltd., AI and Robotics Ventures Co., Ltd. (ARV), Sertis Co., Ltd., FarmInno (Thailand) Co., Ltd., Kaset Inno Co., Ltd., Kubota Corporation (Thailand), Siam Kubota Corporation Co., Ltd., Precision Agriculture Co., Ltd., Agritech Startup Co., Ltd. (AGROWTH Platform), Charoen Pokphand Group (CP Group) – AgriTech Division, Erawan AI Co., Ltd., FarmTech Solutions (Thailand) Co., Ltd., Thai Rung Ruang Group – Smart Farming, Betagro Group – Digital Agriculture, InnoSpace (Thailand) Co., Ltd. contribute to innovation, geographic expansion, and service delivery in this space.

Ricult (Thailand) Co., Ltd.

2016

Bangkok, Thailand

AI and Robotics Ventures Co., Ltd. (ARV)

2017

Bangkok, Thailand

Sertis Co., Ltd.

2014

Bangkok, Thailand

FarmInno (Thailand) Co., Ltd.

2018

Bangkok, Thailand

Kaset Inno Co., Ltd.

2015

Bangkok, Thailand

Company

Establishment Year

Headquarters

Company Size (Large, Medium, Small)

Annual Revenue from AI in Agriculture (USD)

Market Penetration (Number of Deployments/Clients in Thailand)

Year-on-Year Revenue Growth (%)

R&D Expenditure as % of Revenue

Product Portfolio Breadth (Number of AI Solutions Offered)

Thailand Application of AI in Agriculture Market Industry Analysis

Growth Drivers

  • Increasing Demand for Food Security:Thailand's population is projected to reach approximately 66 million by 2024, intensifying the need for sustainable food production. The World Bank estimates that food demand will increase by 50% globally in future. In response, Thailand's agricultural sector is focusing on AI technologies to enhance crop yields and ensure food security, with the government aiming for a 20% increase in agricultural productivity in future through innovative practices.
  • Adoption of Precision Farming Techniques:The Thai government has allocated around THB 1.5 billion (approximately USD 45 million) for the promotion of precision agriculture technologies in future. This investment aims to improve resource efficiency and crop management. Precision farming, which utilizes AI for data analysis, is expected to increase yields by 10-20% while reducing water usage by up to 30%, addressing both productivity and sustainability challenges in the sector.
  • Government Initiatives Promoting AI in Agriculture:The Thai government has launched the "Digital Thailand" initiative, with a budget of THB 3 billion (around USD 90 million) for future, aimed at integrating AI into agriculture. This initiative includes training programs for farmers and subsidies for AI technology adoption. By 2025, the government targets a 25% increase in the use of AI technologies in farming, fostering innovation and enhancing competitiveness in the agricultural sector.

Market Challenges

  • High Initial Investment Costs:The adoption of AI technologies in agriculture requires significant upfront investments, estimated at around THB 2 million (approximately USD 60,000) per farm for necessary equipment and software. Many smallholder farmers, who constitute about 70% of Thailand's agricultural workforce, struggle to afford these costs. This financial barrier limits the widespread implementation of AI solutions, hindering overall market growth and technological advancement in the sector.
  • Lack of Skilled Workforce:A report from the Ministry of Agriculture and Cooperatives indicates that only 15% of Thai farmers possess the necessary skills to operate AI technologies effectively. This skills gap poses a significant challenge to the successful implementation of AI in agriculture. The government aims to train 100,000 farmers in future, but the current shortage of qualified trainers and educational resources remains a critical obstacle to achieving this goal.

Thailand Application of AI in Agriculture Market Future Outlook

The future of AI in Thailand's agriculture sector appears promising, driven by technological advancements and increasing government support. By 2024, the integration of AI with IoT is expected to enhance real-time data collection and analysis, improving decision-making processes for farmers. Additionally, the rise of urban agriculture and vertical farming is anticipated to reshape traditional farming practices, promoting sustainability and resource efficiency. As these trends evolve, the agricultural landscape in Thailand will likely become more innovative and resilient.

Market Opportunities

  • Expansion of Smart Farming Technologies:The market for smart farming technologies is projected to grow significantly, with investments expected to reach THB 5 billion (approximately USD 150 million) in future. This growth presents opportunities for companies to develop AI-driven solutions that enhance productivity and sustainability, catering to the increasing demand for efficient agricultural practices.
  • Collaborations with Tech Startups:Collaborations between agricultural firms and tech startups are on the rise, with over 50 partnerships formed in future alone. These collaborations aim to leverage innovative AI solutions to address specific agricultural challenges, creating a dynamic ecosystem that fosters innovation and accelerates the adoption of advanced technologies in the sector.

Scope of the Report

SegmentSub-Segments
By Type

Precision Farming Solutions

Crop Monitoring & Analytics Platforms

Soil Health & Nutrient Management Tools

Pest and Disease Detection Systems

Automated Irrigation & Water Management Systems

Livestock Monitoring & Management Solutions

Supply Chain & Post-Harvest Optimization

Others

By End-User

Farms (Commercial & Smallholder)

Agri-businesses & Cooperatives

Government & Public Sector

Research & Academic Institutions

Agri-tech Startups

Others

By Application

Precision Farming

Crop Management

Livestock Monitoring

Supply Chain Optimization

Market Forecasting & Analytics

Resource & Input Management

Others

By Distribution Channel

Direct Sales

Online Platforms

Distributors & Dealers

Retail Outlets

Others

By Investment Source

Private Investments

Government Funding

International Aid & Development Agencies

Public-Private Partnerships

Others

By Policy Support

Subsidies for AI Adoption

Tax Incentives

Grants for Research and Development

Training & Capacity Building Programs

Others

By Technology

Machine Learning & Predictive Analytics

Computer Vision & Imaging Technologies

Robotics & Automation

Cloud-Based AI Solutions

IoT-Integrated AI Platforms

Others

Key Target Audience

Investors and Venture Capitalist Firms

Government and Regulatory Bodies (e.g., Ministry of Agriculture and Cooperatives, National Innovation Agency)

Agricultural Producers and Cooperatives

Agri-tech Startups

Farm Equipment Manufacturers

Supply Chain and Logistics Companies

Agrochemical Companies

Financial Institutions and Banks

Players Mentioned in the Report:

Ricult (Thailand) Co., Ltd.

AI and Robotics Ventures Co., Ltd. (ARV)

Sertis Co., Ltd.

FarmInno (Thailand) Co., Ltd.

Kaset Inno Co., Ltd.

Kubota Corporation (Thailand)

Siam Kubota Corporation Co., Ltd.

Precision Agriculture Co., Ltd.

Agritech Startup Co., Ltd. (AGROWTH Platform)

Charoen Pokphand Group (CP Group) AgriTech Division

Erawan AI Co., Ltd.

FarmTech Solutions (Thailand) Co., Ltd.

Thai Rung Ruang Group Smart Farming

Betagro Group Digital Agriculture

InnoSpace (Thailand) Co., Ltd.

Table of Contents

Market Assessment Phase

1. Executive Summary and Approach


2. Thailand Application of AI in Agriculture Market Overview

2.1 Key Insights and Strategic Recommendations

2.2 Thailand Application of AI in Agriculture Market Overview

2.3 Definition and Scope

2.4 Evolution of Market Ecosystem

2.5 Timeline of Key Regulatory Milestones

2.6 Value Chain & Stakeholder Mapping

2.7 Business Cycle Analysis

2.8 Policy & Incentive Landscape


3. Thailand Application of AI in Agriculture Market Analysis

3.1 Growth Drivers

3.1.1 Increasing demand for food security
3.1.2 Adoption of precision farming techniques
3.1.3 Government initiatives promoting AI in agriculture
3.1.4 Rising labor costs and need for automation

3.2 Market Challenges

3.2.1 High initial investment costs
3.2.2 Lack of skilled workforce
3.2.3 Data privacy and security concerns
3.2.4 Resistance to change among traditional farmers

3.3 Market Opportunities

3.3.1 Expansion of smart farming technologies
3.3.2 Collaborations with tech startups
3.3.3 Development of AI-driven supply chain solutions
3.3.4 Increasing investment in agri-tech innovations

3.4 Market Trends

3.4.1 Integration of IoT with AI in agriculture
3.4.2 Growth of data analytics for crop management
3.4.3 Rise of vertical farming and urban agriculture
3.4.4 Focus on sustainable agricultural practices

3.5 Government Regulation

3.5.1 Policies supporting digital agriculture
3.5.2 Regulations on data usage in agriculture
3.5.3 Standards for AI technology in farming
3.5.4 Incentives for adopting smart farming solutions

4. SWOT Analysis


5. Stakeholder Analysis


6. Porter's Five Forces Analysis


7. Thailand Application of AI in Agriculture Market Market Size, 2019-2024

7.1 By Value

7.2 By Volume

7.3 By Average Selling Price


8. Thailand Application of AI in Agriculture Market Segmentation

8.1 By Type

8.1.1 Precision Farming Solutions
8.1.2 Crop Monitoring & Analytics Platforms
8.1.3 Soil Health & Nutrient Management Tools
8.1.4 Pest and Disease Detection Systems
8.1.5 Automated Irrigation & Water Management Systems
8.1.6 Livestock Monitoring & Management Solutions
8.1.7 Supply Chain & Post-Harvest Optimization
8.1.8 Others

8.2 By End-User

8.2.1 Farms (Commercial & Smallholder)
8.2.2 Agri-businesses & Cooperatives
8.2.3 Government & Public Sector
8.2.4 Research & Academic Institutions
8.2.5 Agri-tech Startups
8.2.6 Others

8.3 By Application

8.3.1 Precision Farming
8.3.2 Crop Management
8.3.3 Livestock Monitoring
8.3.4 Supply Chain Optimization
8.3.5 Market Forecasting & Analytics
8.3.6 Resource & Input Management
8.3.7 Others

8.4 By Distribution Channel

8.4.1 Direct Sales
8.4.2 Online Platforms
8.4.3 Distributors & Dealers
8.4.4 Retail Outlets
8.4.5 Others

8.5 By Investment Source

8.5.1 Private Investments
8.5.2 Government Funding
8.5.3 International Aid & Development Agencies
8.5.4 Public-Private Partnerships
8.5.5 Others

8.6 By Policy Support

8.6.1 Subsidies for AI Adoption
8.6.2 Tax Incentives
8.6.3 Grants for Research and Development
8.6.4 Training & Capacity Building Programs
8.6.5 Others

8.7 By Technology

8.7.1 Machine Learning & Predictive Analytics
8.7.2 Computer Vision & Imaging Technologies
8.7.3 Robotics & Automation
8.7.4 Cloud-Based AI Solutions
8.7.5 IoT-Integrated AI Platforms
8.7.6 Others

9. Thailand Application of AI in Agriculture Market Competitive Analysis

9.1 Market Share of Key Players

9.2 Cross Comparison of Key Players

9.2.1 Company Name
9.2.2 Company Size (Large, Medium, Small)
9.2.3 Annual Revenue from AI in Agriculture (USD)
9.2.4 Market Penetration (Number of Deployments/Clients in Thailand)
9.2.5 Year-on-Year Revenue Growth (%)
9.2.6 R&D Expenditure as % of Revenue
9.2.7 Product Portfolio Breadth (Number of AI Solutions Offered)
9.2.8 Customer Retention Rate (%)
9.2.9 Strategic Partnerships/Collaborations (Number in Thailand)
9.2.10 Local Adaptation Score (Degree of Localization for Thai Market)
9.2.11 Brand Recognition Index (Survey-Based/Market Share Proxy)

9.3 SWOT Analysis of Top Players

9.4 Pricing Analysis

9.5 Detailed Profile of Major Companies

9.5.1 Ricult (Thailand) Co., Ltd.
9.5.2 AI and Robotics Ventures Co., Ltd. (ARV)
9.5.3 Sertis Co., Ltd.
9.5.4 FarmInno (Thailand) Co., Ltd.
9.5.5 Kaset Inno Co., Ltd.
9.5.6 Kubota Corporation (Thailand)
9.5.7 Siam Kubota Corporation Co., Ltd.
9.5.8 Precision Agriculture Co., Ltd.
9.5.9 Agritech Startup Co., Ltd. (AGROWTH Platform)
9.5.10 Charoen Pokphand Group (CP Group) – AgriTech Division
9.5.11 Erawan AI Co., Ltd.
9.5.12 FarmTech Solutions (Thailand) Co., Ltd.
9.5.13 Thai Rung Ruang Group – Smart Farming
9.5.14 Betagro Group – Digital Agriculture
9.5.15 InnoSpace (Thailand) Co., Ltd.

10. Thailand Application of AI in Agriculture Market End-User Analysis

10.1 Procurement Behavior of Key Ministries

10.1.1 Ministry of Agriculture and Cooperatives
10.1.2 Ministry of Science and Technology
10.1.3 Ministry of Commerce

10.2 Corporate Spend on Infrastructure & Energy

10.2.1 Investment in Smart Farming Infrastructure
10.2.2 Budget Allocation for AI Technologies

10.3 Pain Point Analysis by End-User Category

10.3.1 Smallholder Farmers
10.3.2 Large Enterprises

10.4 User Readiness for Adoption

10.4.1 Awareness of AI Benefits
10.4.2 Training Needs Assessment

10.5 Post-Deployment ROI and Use Case Expansion

10.5.1 Measurement of ROI
10.5.2 Potential for Scaling Solutions

11. Thailand Application of AI in Agriculture Market Future Size, 2025-2030

11.1 By Value

11.2 By Volume

11.3 By Average Selling Price


Go-To-Market Strategy Phase

1. Whitespace Analysis + Business Model Canvas

1.1 Market Gaps Identification

1.2 Business Model Development


2. Marketing and Positioning Recommendations

2.1 Branding Strategies

2.2 Product USPs


3. Distribution Plan

3.1 Urban Retail Strategies

3.2 Rural NGO Tie-ups


4. Channel & Pricing Gaps

4.1 Underserved Routes

4.2 Pricing Bands


5. Unmet Demand & Latent Needs

5.1 Category Gaps

5.2 Consumer Segments


6. Customer Relationship

6.1 Loyalty Programs

6.2 After-sales Service


7. Value Proposition

7.1 Sustainability Initiatives

7.2 Integrated Supply Chains


8. Key Activities

8.1 Regulatory Compliance

8.2 Branding Efforts

8.3 Distribution Setup


9. Entry Strategy Evaluation

9.1 Domestic Market Entry Strategy

9.1.1 Product Mix Considerations
9.1.2 Pricing Band Strategy
9.1.3 Packaging Solutions

9.2 Export Entry Strategy

9.2.1 Target Countries
9.2.2 Compliance Roadmap

10. Entry Mode Assessment

10.1 Joint Ventures

10.2 Greenfield Investments

10.3 Mergers & Acquisitions

10.4 Distributor Model


11. Capital and Timeline Estimation

11.1 Capital Requirements

11.2 Timelines for Implementation


12. Control vs Risk Trade-Off

12.1 Ownership Considerations

12.2 Partnerships Evaluation


13. Profitability Outlook

13.1 Breakeven Analysis

13.2 Long-term Sustainability


14. Potential Partner List

14.1 Distributors

14.2 Joint Ventures

14.3 Acquisition Targets


15. Execution Roadmap

15.1 Phased Plan for Market Entry

15.1.1 Market Setup
15.1.2 Market Entry
15.1.3 Growth Acceleration
15.1.4 Scale & Stabilize

15.2 Key Activities and Milestones

15.2.1 Milestone Planning
15.2.2 Activity Tracking

Research Methodology

ApproachModellingSample

Phase 1: Approach1

Desk Research

  • Analysis of agricultural production statistics from the Ministry of Agriculture and Cooperatives, Thailand
  • Review of academic journals and publications on AI applications in agriculture
  • Examination of reports from agricultural technology associations and government agencies

Primary Research

  • Interviews with agricultural technology providers and AI solution developers
  • Surveys with farmers and agricultural cooperatives utilizing AI tools
  • Focus groups with agricultural extension officers and industry experts

Validation & Triangulation

  • Cross-validation of findings through multiple data sources including government reports and industry publications
  • Triangulation of insights from primary interviews with secondary data trends
  • Sanity checks conducted through expert panel discussions and feedback sessions

Phase 2: Market Size Estimation1

Top-down Assessment

  • Estimation of the overall agricultural market size in Thailand and its growth rate
  • Segmentation of the market by AI application areas such as precision farming, crop monitoring, and pest control
  • Incorporation of government initiatives promoting AI in agriculture and their impact on market growth

Bottom-up Modeling

  • Collection of data on the number of farms adopting AI technologies and their average spending
  • Estimation of market penetration rates for various AI solutions across different crop types
  • Calculation of revenue generated from AI services based on subscription and usage models

Forecasting & Scenario Analysis

  • Development of predictive models using historical growth data and emerging trends in AI technology
  • Scenario analysis based on varying levels of technology adoption and regulatory support
  • Projections of market growth through 2030 under different economic conditions and technological advancements

Phase 3: CATI Sample Composition1

Scope Item/SegmentSample SizeTarget Respondent Profiles
Precision Farming Technologies100Farm Owners, Agronomists
AI-Driven Crop Monitoring Solutions80Technology Providers, Agricultural Researchers
Pest Control AI Applications70Farm Managers, Pest Control Specialists
Data Analytics in Agriculture90Data Scientists, Agricultural Economists
AI Adoption Barriers in Agriculture50Policy Makers, Agricultural Extension Officers

Frequently Asked Questions

What is the current value of the Thailand Application of AI in Agriculture Market?

The Thailand Application of AI in Agriculture Market is valued at approximately USD 80 million, reflecting a significant growth driven by the adoption of advanced technologies aimed at enhancing agricultural productivity and sustainability.

What are the key regions driving the AI in Agriculture Market in Thailand?

How is the Thai government supporting AI adoption in agriculture?

What types of AI applications are prevalent in Thailand's agriculture sector?

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