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Machine Learning For Crop Yield Prediction Global Market Report 2025
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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 from The Business Research Company 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 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.

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.

Machine Learning For Crop Yield Prediction Global Market Report 2025 from The Business Research Company 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.

Reasons to Purchase

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 from the Business Research Company 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 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.

Scope

Table of Contents

1. Executive Summary

2. Machine Learning For Crop Yield Prediction Market Characteristics

3. Machine Learning For Crop Yield Prediction Market Trends And Strategies

4. Machine Learning For Crop Yield Prediction Market - Macro Economic Scenario Macro Economic Scenario Including The Impact Of Interest Rates, Inflation, Geopolitics And Covid And Recovery On The 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

32. Global Machine Learning For Crop Yield Prediction Market Competitive Benchmarking And Dashboard

33. Key Mergers And Acquisitions In The Machine Learning For Crop Yield Prediction Market

34. Recent Developments In The Machine Learning For Crop Yield Prediction Market

35. Machine Learning For Crop Yield Prediction Market High Potential Countries, Segments and Strategies

36. Appendix

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