The machine learning for crop yield prediction market size has grown exponentially in recent years. It will grow from $0.99 billion in 2025 to $1.24 billion in 2026 at a compound annual growth rate (CAGR) of 25%. The growth in the historic period can be attributed to increasing variability in crop yields, growing reliance on historical weather datasets, early adoption of predictive modeling tools, rising demand for optimized farm inputs, heightened need for risk mitigation in farming.
The machine learning for crop yield prediction market size is expected to see exponential growth in the next few years. It will grow to $2.95 billion in 2030 at a compound annual growth rate (CAGR) of 24.2%. The growth in the forecast period can be attributed to expanding adoption of AI-powered yield prediction systems, increasing integration of cloud-based analytics, rising demand for precision farming insights, growing value of satellite and drone imaging data, wider use of real-time environmental monitoring. Major trends in the forecast period include increasing use of multivariate environmental data inputs, growing integration of remote sensing into yield models, expansion of real-time crop monitoring practices, rising adoption of data-driven farm decision frameworks, greater use of advanced soil-crop relationship modeling.
The need for sustainable agriculture practices is expected to drive the growth of the machine learning for crop yield prediction market going forward. Sustainable agriculture is an integrated farming approach that focuses on producing food and other agricultural products while conserving resources, promoting biodiversity, supporting economic viability, and ensuring social equity for both present and future generations. The rise in sustainable agriculture is driven by concerns about environmental degradation, resource scarcity, climate change, and the need for healthier, more resilient food systems that ensure long-term food security and community well-being. Machine learning for crop yield prediction supports sustainable agriculture by enabling data-driven decision-making to optimize resource use, minimize waste, increase crop productivity, and improve efficiency while reducing environmental impact. For example, in February 2025, IFOAM Organics International, a Germany-based non-profit organization, reported that in 2023, approximately 98.9 million hectares of land were organically managed, marking a 2.6% increase (about 2.5 million hectares) compared to 2022. Therefore, the need for sustainable agriculture practices is fueling the machine learning for crop yield prediction market.
Leading companies in the machine learning for crop yield prediction market are prioritizing the development of GenAI-integrated platforms to enhance the creation of innovative, data-driven solutions. These platforms combine generative artificial intelligence with other technologies, allowing for the generation, customization, and deployment of AI-driven insights across various industries and applications. For instance, in July 2024, CropIn, an India-based agtech company, collaborated with Google (Gemini), a US-based technology company, to introduce Sage, a GenAI-powered agri-intelligence platform. Sage's key advantage lies in its ability to deliver detailed, grid-based insights into crop behavior over different time periods by integrating generative AI, advanced crop and climate models, and Earth observation data. This integration enables Sage to produce a proprietary grid-based agricultural data map, offering exceptional scale, accuracy, and speed. It revolutionizes the way stakeholders analyze crop dynamics, climate effects, and optimal agricultural practices, facilitating informed, data-driven decisions in multiple languages across global farming operations.
In April 2024, AGCO Corporation, a US-based agricultural machinery manufacturer, acquired Trimble Agriculture in a $2 billion deal. This acquisition enables AGCO to incorporate Trimble's cutting-edge precision agriculture technologies into its product portfolio, which is expected to enhance farming efficiency and productivity significantly. Trimble Agriculture, a US-based company, specializes in providing machine learning solutions for crop yield prediction.
Major companies operating in the machine learning for crop yield prediction market are Microsoft Corp., BASF SE, International Business Machines Corp., Bayer AG, Raven Industries Inc., Cropin Technology Solutions Pvt., Terramera Inc., FarmWise Labs Inc., Sentera Inc., Taranis, Ceres Imaging Inc., CropX Inc., PrecisionHawk, Aerobotics Ltd., Fasal, IUNU Inc., AgriWebb Pty Ltd., Trace Genomics Inc., Bloomfield Robotics, Agrograph Inc., AiDOOS Corp., FruitSpec.
Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.
Tariffs are influencing the machine learning for crop yield prediction market by increasing costs for imported sensors, data acquisition hardware, satellite imaging components, and cloud infrastructure equipment, slowing deployment for farmers, cooperatives, and government agencies. Regions reliant on foreign electronics particularly North America, Europe, and Asia-Pacific face higher operational expenses and delayed adoption of advanced analytics systems. However, tariffs can encourage domestic innovation in data platforms, strengthen local supplier ecosystems, and promote region-specific yield prediction solutions, ultimately supporting long-term competitiveness.
The machine learning for crop yield prediction market research report is one of a series of new reports that provides machine learning for crop yield prediction market statistics, including machine learning for crop yield prediction industry global market size, regional shares, competitors with a machine learning for crop yield prediction market share, detailed machine learning for crop yield prediction market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning for crop yield prediction industry. This machine learning for crop yield prediction market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.
Machine learning for crop yield prediction involves using ML algorithms and models to estimate the quantity of crops that can be harvested from a given farmland area. This approach utilizes historical and real-time data, including environmental conditions, soil characteristics, weather patterns, crop types, and farming practices, to generate accurate and data-driven forecasts.
The primary components of machine learning for crop yield prediction include software and services. Software consists of programs and instructions that enable computers to analyze agricultural data and optimize predictions. These solutions can be deployed both on the cloud and on-premises, catering to small, medium, and large-sized farms. The key end users include farmers, agricultural cooperatives, research institutions, government agencies, and others.North America was the largest region in the machine learning for crop yield prediction market in 2025. The regions covered in the machine learning for crop yield prediction market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning for crop yield prediction market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
The machine learning for crop yield prediction market includes revenues earned by entities by providing services such as yield forecasting consulting, soil health and fertility analysis, weather impact analysis and field zone mapping. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 1-3 business days.
The machine learning for crop yield prediction market size is expected to see exponential growth in the next few years. It will grow to $2.95 billion in 2030 at a compound annual growth rate (CAGR) of 24.2%. The growth in the forecast period can be attributed to expanding adoption of AI-powered yield prediction systems, increasing integration of cloud-based analytics, rising demand for precision farming insights, growing value of satellite and drone imaging data, wider use of real-time environmental monitoring. Major trends in the forecast period include increasing use of multivariate environmental data inputs, growing integration of remote sensing into yield models, expansion of real-time crop monitoring practices, rising adoption of data-driven farm decision frameworks, greater use of advanced soil-crop relationship modeling.
The need for sustainable agriculture practices is expected to drive the growth of the machine learning for crop yield prediction market going forward. Sustainable agriculture is an integrated farming approach that focuses on producing food and other agricultural products while conserving resources, promoting biodiversity, supporting economic viability, and ensuring social equity for both present and future generations. The rise in sustainable agriculture is driven by concerns about environmental degradation, resource scarcity, climate change, and the need for healthier, more resilient food systems that ensure long-term food security and community well-being. Machine learning for crop yield prediction supports sustainable agriculture by enabling data-driven decision-making to optimize resource use, minimize waste, increase crop productivity, and improve efficiency while reducing environmental impact. For example, in February 2025, IFOAM Organics International, a Germany-based non-profit organization, reported that in 2023, approximately 98.9 million hectares of land were organically managed, marking a 2.6% increase (about 2.5 million hectares) compared to 2022. Therefore, the need for sustainable agriculture practices is fueling the machine learning for crop yield prediction market.
Leading companies in the machine learning for crop yield prediction market are prioritizing the development of GenAI-integrated platforms to enhance the creation of innovative, data-driven solutions. These platforms combine generative artificial intelligence with other technologies, allowing for the generation, customization, and deployment of AI-driven insights across various industries and applications. For instance, in July 2024, CropIn, an India-based agtech company, collaborated with Google (Gemini), a US-based technology company, to introduce Sage, a GenAI-powered agri-intelligence platform. Sage's key advantage lies in its ability to deliver detailed, grid-based insights into crop behavior over different time periods by integrating generative AI, advanced crop and climate models, and Earth observation data. This integration enables Sage to produce a proprietary grid-based agricultural data map, offering exceptional scale, accuracy, and speed. It revolutionizes the way stakeholders analyze crop dynamics, climate effects, and optimal agricultural practices, facilitating informed, data-driven decisions in multiple languages across global farming operations.
In April 2024, AGCO Corporation, a US-based agricultural machinery manufacturer, acquired Trimble Agriculture in a $2 billion deal. This acquisition enables AGCO to incorporate Trimble's cutting-edge precision agriculture technologies into its product portfolio, which is expected to enhance farming efficiency and productivity significantly. Trimble Agriculture, a US-based company, specializes in providing machine learning solutions for crop yield prediction.
Major companies operating in the machine learning for crop yield prediction market are Microsoft Corp., BASF SE, International Business Machines Corp., Bayer AG, Raven Industries Inc., Cropin Technology Solutions Pvt., Terramera Inc., FarmWise Labs Inc., Sentera Inc., Taranis, Ceres Imaging Inc., CropX Inc., PrecisionHawk, Aerobotics Ltd., Fasal, IUNU Inc., AgriWebb Pty Ltd., Trace Genomics Inc., Bloomfield Robotics, Agrograph Inc., AiDOOS Corp., FruitSpec.
Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.
Tariffs are influencing the machine learning for crop yield prediction market by increasing costs for imported sensors, data acquisition hardware, satellite imaging components, and cloud infrastructure equipment, slowing deployment for farmers, cooperatives, and government agencies. Regions reliant on foreign electronics particularly North America, Europe, and Asia-Pacific face higher operational expenses and delayed adoption of advanced analytics systems. However, tariffs can encourage domestic innovation in data platforms, strengthen local supplier ecosystems, and promote region-specific yield prediction solutions, ultimately supporting long-term competitiveness.
The machine learning for crop yield prediction market research report is one of a series of new reports that provides machine learning for crop yield prediction market statistics, including machine learning for crop yield prediction industry global market size, regional shares, competitors with a machine learning for crop yield prediction market share, detailed machine learning for crop yield prediction market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning for crop yield prediction industry. This machine learning for crop yield prediction market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.
Machine learning for crop yield prediction involves using ML algorithms and models to estimate the quantity of crops that can be harvested from a given farmland area. This approach utilizes historical and real-time data, including environmental conditions, soil characteristics, weather patterns, crop types, and farming practices, to generate accurate and data-driven forecasts.
The primary components of machine learning for crop yield prediction include software and services. Software consists of programs and instructions that enable computers to analyze agricultural data and optimize predictions. These solutions can be deployed both on the cloud and on-premises, catering to small, medium, and large-sized farms. The key end users include farmers, agricultural cooperatives, research institutions, government agencies, and others.North America was the largest region in the machine learning for crop yield prediction market in 2025. The regions covered in the machine learning for crop yield prediction market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning for crop yield prediction market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
The machine learning for crop yield prediction market includes revenues earned by entities by providing services such as yield forecasting consulting, soil health and fertility analysis, weather impact analysis and field zone mapping. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 1-3 business days.
Table of Contents
1. Executive Summary
2. Machine Learning for Crop Yield Prediction Market Characteristics
3. Machine Learning for Crop Yield Prediction Market Supply Chain Analysis
4. Global Machine Learning for Crop Yield Prediction Market Trends and Strategies
5. Machine Learning for Crop Yield Prediction Market Analysis of End Use Industries
7. Global Machine Learning for Crop Yield Prediction Strategic Analysis Framework, Current Market Size, Market Comparisons and Growth Rate Analysis
8. Global Machine Learning for Crop Yield Prediction Total Addressable Market (TAM) Analysis for the Market
9. Machine Learning for Crop Yield Prediction Market Segmentation
10. Machine Learning for Crop Yield Prediction Market Regional and Country Analysis
11. Asia-Pacific Machine Learning for Crop Yield Prediction Market
12. China Machine Learning for Crop Yield Prediction Market
13. India Machine Learning for Crop Yield Prediction Market
14. Japan Machine Learning for Crop Yield Prediction Market
15. Australia Machine Learning for Crop Yield Prediction Market
16. Indonesia Machine Learning for Crop Yield Prediction Market
17. South Korea Machine Learning for Crop Yield Prediction Market
18. Taiwan Machine Learning for Crop Yield Prediction Market
19. South East Asia Machine Learning for Crop Yield Prediction Market
20. Western Europe Machine Learning for Crop Yield Prediction Market
21. UK Machine Learning for Crop Yield Prediction Market
22. Germany Machine Learning for Crop Yield Prediction Market
23. France Machine Learning for Crop Yield Prediction Market
24. Italy Machine Learning for Crop Yield Prediction Market
25. Spain Machine Learning for Crop Yield Prediction Market
26. Eastern Europe Machine Learning for Crop Yield Prediction Market
27. Russia Machine Learning for Crop Yield Prediction Market
28. North America Machine Learning for Crop Yield Prediction Market
29. USA Machine Learning for Crop Yield Prediction Market
30. Canada Machine Learning for Crop Yield Prediction Market
31. South America Machine Learning for Crop Yield Prediction Market
32. Brazil Machine Learning for Crop Yield Prediction Market
33. Middle East Machine Learning for Crop Yield Prediction Market
34. Africa Machine Learning for Crop Yield Prediction Market
36. Machine Learning for Crop Yield Prediction Market Competitive Landscape and Company Profiles
37. Machine Learning for Crop Yield Prediction Market Other Major and Innovative Companies
40. Machine Learning for Crop Yield Prediction Market High Potential Countries, Segments and Strategies
41. Appendix
Executive Summary
Machine Learning For Crop Yield Prediction Market Global Report 2026 provides strategists, marketers and senior management with the critical information they need to assess the market.This report focuses machine learning for crop yield prediction market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.
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Description
Where is the largest and fastest growing market for machine learning for crop yield prediction? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward, including technological disruption, regulatory shifts, and changing consumer preferences? The machine learning for crop yield prediction market global report answers all these questions and many more.The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, total addressable market (TAM), market attractiveness score (MAS), competitive landscape, market shares, company scoring matrix, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.
- The market characteristics section of the report defines and explains the market. This section also examines key products and services offered in the market, evaluates brand-level differentiation, compares product features, and highlights major innovation and product development trends.
- The supply chain analysis section provides an overview of the entire value chain, including key raw materials, resources, and supplier analysis. It also provides a list competitor at each level of the supply chain.
- The updated trends and strategies section analyses the shape of the market as it evolves and highlights emerging technology trends such as digital transformation, automation, sustainability initiatives, and AI-driven innovation. It suggests how companies can leverage these advancements to strengthen their market position and achieve competitive differentiation.
- The regulatory and investment landscape section provides an overview of the key regulatory frameworks, regularity bodies, associations, and government policies influencing the market. It also examines major investment flows, incentives, and funding trends shaping industry growth and innovation.
- The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
- The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.
- The total addressable market (TAM) analysis section defines and estimates the market potential compares it with the current market size, and provides strategic insights and growth opportunities based on this evaluation.
- The market attractiveness scoring section evaluates the market based on a quantitative scoring framework that considers growth potential, competitive dynamics, strategic fit, and risk profile. It also provides interpretive insights and strategic implications for decision-makers.
- Market segmentations break down the market into sub markets.
- The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth.
- Expanded geographical coverage includes Taiwan and Southeast Asia, reflecting recent supply chain realignments and manufacturing shifts in the region. This section analyzes how these markets are becoming increasingly important hubs in the global value chain.
- The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
- The company scoring matrix section evaluates and ranks leading companies based on a multi-parameter framework that includes market share or revenues, product innovation, and brand recognition.
Scope
Markets Covered:
1) By Component: Software; Services2) By Deployment Model: Cloud-Based; On-Premises
3) By Farm Size: Small; Medium; Large
4) By End User: Farmers; Agricultural Cooperatives; Research Institutions; Government Agencies; Other End Users
Subsegments:
1) By Software: Predictive Analytics Software; AI-Powered Crop Monitoring Software; Weather And Climate Data Analytics Software; Remote Sensing And Satellite Imaging Software; Farm Management Software2) By Services: Consulting And Advisory Services; Implementation And Integration Services; Training And Support Services; Data Analytics And Custom Modeling Services; Cloud-Based Agricultural AI Services
Companies Mentioned: Microsoft Corp.; BASF SE; International Business Machines Corp.; Bayer AG; Raven Industries Inc.; Cropin Technology Solutions Pvt.; Terramera Inc.; FarmWise Labs Inc.; Sentera Inc.; Taranis; Ceres Imaging Inc.; CropX Inc.; PrecisionHawk; Aerobotics Ltd.; Fasal; IUNU Inc.; AgriWebb Pty Ltd.; Trace Genomics Inc.; Bloomfield Robotics; Agrograph Inc.; AiDOOS Corp.; FruitSpec
Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Taiwan; Russia; South Korea; UK; USA; Canada; Italy; Spain
Regions: Asia-Pacific; South East Asia; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
Delivery Format: Word, PDF or Interactive Report + Excel Dashboard
Added Benefits:
- Bi-Annual Data Update
- Customisation
- Expert Consultant Support
Companies Mentioned
The companies featured in this Machine Learning for Crop Yield Prediction market report include:- Microsoft Corp.
- BASF SE
- International Business Machines Corp.
- Bayer AG
- Raven Industries Inc.
- Cropin Technology Solutions Pvt.
- Terramera Inc.
- FarmWise Labs Inc.
- Sentera Inc.
- Taranis
- Ceres Imaging Inc.
- CropX Inc.
- PrecisionHawk
- Aerobotics Ltd.
- Fasal
- IUNU Inc.
- AgriWebb Pty Ltd.
- Trace Genomics Inc.
- Bloomfield Robotics
- Agrograph Inc.
- AiDOOS Corp.
- FruitSpec
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 250 |
| Published | January 2026 |
| Forecast Period | 2026 - 2030 |
| Estimated Market Value ( USD | $ 1.24 Billion |
| Forecasted Market Value ( USD | $ 2.95 Billion |
| Compound Annual Growth Rate | 24.2% |
| Regions Covered | Global |
| No. of Companies Mentioned | 23 |
