This Machine Learning for Crop Yield Prediction Market report delivers an in-depth analysis of the market’s key characteristics, including size, growth potential, and segmentation. It provides a detailed breakdown of the market across major regions and leading countries, highlighting historical data and future growth projections. The report also examines the competitive landscape, market share insights, emerging trends, and strategic developments shaping the market.
The machine learning for crop yield prediction market size has grown exponentially in recent years. It will grow from $0.79 billion in 2024 to $1.01 billion in 2025 at a compound annual growth rate (CAGR) of 26.9%. The growth in the historic period can be attributed to the increasing global population and food demand, the rising use of historical data for modeling, the growing popularity of precision agriculture, increased investment and funding in agtech, and the adoption of climate-smart agriculture practices.
The machine learning for crop yield prediction market size is expected to see exponential growth in the next few years. It will grow to $2.58 billion in 2029 at a compound annual growth rate (CAGR) of 26.6%. The growth in the forecast period will be driven by improvements in the precision and effectiveness of ML-based forecasts, a growing global population with limited resources, the rise of big data in agriculture, the impact of climate change and environmental stress, and the increasing adoption of sustainable agricultural practices. Key trends include the integration of AI technology and machine learning for crop yield prediction, the adoption of IoT in agriculture, ongoing technological advancements, and the emergence of AI-powered autonomous tractors.
The growing need for sustainable agriculture practices is expected to drive the expansion of the machine learning market for crop yield prediction. Sustainable agriculture is a holistic farming approach that aims to produce food and other agricultural products while conserving resources, promoting biodiversity, maintaining economic viability, and ensuring social equity for current and future generations. The rise in sustainable agriculture is fueled by increasing concerns over environmental degradation, resource scarcity, climate change, and the demand for healthier, more resilient food systems that support long-term food security and community well-being. Machine learning for crop yield prediction plays a crucial role in sustainable agriculture by enabling data-driven decision-making to optimize resource use, reduce waste, enhance crop productivity, and improve efficiency while minimizing environmental impact. For example, in February 2024, IFOAM Organics International, a Germany-based non-profit organization, reported that the global organic farming area expanded by more than 20 million hectares in 2022, reaching a total of 96 million hectares. The number of organic producers also saw significant growth, surpassing 4.5 million, while organic food sales nearly hit 135 billion euros in the same year. Consequently, the increasing focus on sustainable agriculture is driving the adoption of machine learning for crop yield prediction.
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 players in the machine learning for crop yield prediction market are Microsoft Corp., BASF SE, International Business Machines Corp., Bayer AG, Ninjacart, 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., Keymakr Inc., Trace Genomics Inc., Bloomfield Robotics, Agrograph Inc., Xyonix Inc., AiDOOS Corp., and FruitSpec.
North America was the largest region in the machine learning for crop yield prediction market in 2024. The regions covered in machine learning for crop yield prediction report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the machine learning for crop yield prediction market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
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.
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.
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 3-5 business days.
The machine learning for crop yield prediction market size has grown exponentially in recent years. It will grow from $0.79 billion in 2024 to $1.01 billion in 2025 at a compound annual growth rate (CAGR) of 26.9%. The growth in the historic period can be attributed to the increasing global population and food demand, the rising use of historical data for modeling, the growing popularity of precision agriculture, increased investment and funding in agtech, and the adoption of climate-smart agriculture practices.
The machine learning for crop yield prediction market size is expected to see exponential growth in the next few years. It will grow to $2.58 billion in 2029 at a compound annual growth rate (CAGR) of 26.6%. The growth in the forecast period will be driven by improvements in the precision and effectiveness of ML-based forecasts, a growing global population with limited resources, the rise of big data in agriculture, the impact of climate change and environmental stress, and the increasing adoption of sustainable agricultural practices. Key trends include the integration of AI technology and machine learning for crop yield prediction, the adoption of IoT in agriculture, ongoing technological advancements, and the emergence of AI-powered autonomous tractors.
The growing need for sustainable agriculture practices is expected to drive the expansion of the machine learning market for crop yield prediction. Sustainable agriculture is a holistic farming approach that aims to produce food and other agricultural products while conserving resources, promoting biodiversity, maintaining economic viability, and ensuring social equity for current and future generations. The rise in sustainable agriculture is fueled by increasing concerns over environmental degradation, resource scarcity, climate change, and the demand for healthier, more resilient food systems that support long-term food security and community well-being. Machine learning for crop yield prediction plays a crucial role in sustainable agriculture by enabling data-driven decision-making to optimize resource use, reduce waste, enhance crop productivity, and improve efficiency while minimizing environmental impact. For example, in February 2024, IFOAM Organics International, a Germany-based non-profit organization, reported that the global organic farming area expanded by more than 20 million hectares in 2022, reaching a total of 96 million hectares. The number of organic producers also saw significant growth, surpassing 4.5 million, while organic food sales nearly hit 135 billion euros in the same year. Consequently, the increasing focus on sustainable agriculture is driving the adoption of machine learning for crop yield prediction.
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 players in the machine learning for crop yield prediction market are Microsoft Corp., BASF SE, International Business Machines Corp., Bayer AG, Ninjacart, 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., Keymakr Inc., Trace Genomics Inc., Bloomfield Robotics, Agrograph Inc., Xyonix Inc., AiDOOS Corp., and FruitSpec.
North America was the largest region in the machine learning for crop yield prediction market in 2024. The regions covered in machine learning for crop yield prediction report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the machine learning for crop yield prediction market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
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.
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.
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 3-5 business days.
Table of Contents
1. Executive Summary2. Machine Learning for Crop Yield Prediction Market Characteristics3. Machine Learning for Crop Yield Prediction Market Trends and Strategies4. Machine Learning for Crop Yield Prediction Market - Macro Economic Scenario Macro Economic Scenario Including the Impact of Interest Rates, Inflation, Geopolitics, and the Recovery from COVID-19 on the Market32. Global Machine Learning for Crop Yield Prediction Market Competitive Benchmarking and Dashboard33. Key Mergers and Acquisitions in the Machine Learning for Crop Yield Prediction Market34. Recent Developments in the Machine Learning for Crop Yield Prediction Market
5. Global Machine Learning for Crop Yield Prediction Growth Analysis and Strategic Analysis Framework
6. Machine Learning for Crop Yield Prediction Market Segmentation
7. Machine Learning for Crop Yield Prediction Market Regional and Country Analysis
8. Asia-Pacific Machine Learning for Crop Yield Prediction Market
9. China Machine Learning for Crop Yield Prediction Market
10. India Machine Learning for Crop Yield Prediction Market
11. Japan Machine Learning for Crop Yield Prediction Market
12. Australia Machine Learning for Crop Yield Prediction Market
13. Indonesia Machine Learning for Crop Yield Prediction Market
14. South Korea Machine Learning for Crop Yield Prediction Market
15. Western Europe Machine Learning for Crop Yield Prediction Market
16. UK Machine Learning for Crop Yield Prediction Market
17. Germany Machine Learning for Crop Yield Prediction Market
18. France Machine Learning for Crop Yield Prediction Market
19. Italy Machine Learning for Crop Yield Prediction Market
20. Spain Machine Learning for Crop Yield Prediction Market
21. Eastern Europe Machine Learning for Crop Yield Prediction Market
22. Russia Machine Learning for Crop Yield Prediction Market
23. North America Machine Learning for Crop Yield Prediction Market
24. USA Machine Learning for Crop Yield Prediction Market
25. Canada Machine Learning for Crop Yield Prediction Market
26. South America Machine Learning for Crop Yield Prediction Market
27. Brazil Machine Learning for Crop Yield Prediction Market
28. Middle East Machine Learning for Crop Yield Prediction Market
29. Africa Machine Learning for Crop Yield Prediction Market
30. Machine Learning for Crop Yield Prediction Market Competitive Landscape and Company Profiles
31. Machine Learning for Crop Yield Prediction Market Other Major and Innovative Companies
35. Machine Learning for Crop Yield Prediction Market High Potential Countries, Segments and Strategies
36. Appendix
Executive Summary
Machine Learning For Crop Yield Prediction Global Market Report 2025 provides strategists, marketers and senior management with the critical information they need to assess the market.This report focuses on 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? 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, competitive landscape, market shares, 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.
- 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 Russia-Ukraine war, rising inflation, higher interest rates, and the legacy of the COVID-19 pandemic.
- 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. It covers the growth trajectory of COVID-19 for all regions, key developed countries and major emerging markets.
- 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 trends and strategies section analyses the shape of the market as it emerges from the crisis and suggests how companies can grow as the market recovers.
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
Key Companies Profiled: Microsoft Corp.; BASF SE; International Business Machines Corp.; Bayer AG; Ninjacart
Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Russia; South Korea; UK; USA; Canada; Italy; Spain
Regions: Asia-Pacific; 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: PDF, Word and Excel Data Dashboard.
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
- Ninjacart
- 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.
- Keymakr Inc.
- Trace Genomics Inc.
- Bloomfield Robotics
- Agrograph Inc.
- Xyonix Inc.
- AiDOOS Corp.
- FruitSpec
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 175 |
| Published | May 2025 |
| Forecast Period | 2025 - 2029 |
| Estimated Market Value ( USD | $ 1.01 Billion |
| Forecasted Market Value ( USD | $ 2.58 Billion |
| Compound Annual Growth Rate | 26.6% |
| Regions Covered | Global |
| No. of Companies Mentioned | 26 |
