Machine Learning For Crop Yield Prediction Market Report 2026

Machine Learning For Crop Yield Prediction Market Overview

• Machine Learning For Crop Yield Prediction market size has reached to $0.99 billion in 2025 • Expected to grow to $2.95 billion in 2030 at a compound annual growth rate (CAGR) of 24.2% • Growth Driver: Rising Demand For Agriculture-global-market-report" target="_blank">Sustainable Agriculture Driving The Growth Of The Market Due To Environmental And Food Security Concerns • Market Trend: Leveraging GenAI For Enhanced Agricultural Insights And Decision-Making • North America was the largest region in 2025.

What Is Covered Under Machine Learning For Crop Yield Prediction Market?

Machine learning for crop yield prediction refers to the application of machine learning (ML) algorithms and models to forecast the quantity of crops that can be harvested from a specific area of farmland. This approach leverages historical and real-time data, including environmental factors, soil characteristics, weather conditions, crop type, and farming practices, to provide accurate and data-driven predictions. The main components of machine learning for crop yield prediction are software and services. Software is a set of programs and instructions that enable a computer to perform tasks and interact with hardware. It can be deployed both on the cloud and on-premises and is used for small, medium, and large-sized farms. It is used by various end users such as farmers, agricultural cooperatives, research institutions, government agencies, and others.

What Is The Machine Learning For Crop Yield Prediction Market Growth Forecast?

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.

What Is Driver Of The Machine Learning For Crop Yield Prediction Market?

The need for sustainable agriculture practices is expected to propel the growth of the machine learning for crop yield prediction market going forward. Sustainable agriculture is an integrated approach to farming that focuses on producing food and other agricultural products while conserving resources, promoting biodiversity, supporting economic viability, and ensuring social equity for present and future generations. Sustainable agriculture is rising due to growing concerns about environmental degradation, resource scarcity, climate change, and the need for healthier, more resilient food systems that support long-term food security and community well-being. Machine learning for crop yield prediction is essential for sustainable agriculture as it facilitates data-driven decision-making to optimize resource utilization, minimize waste, boost crop productivity, and enhance efficiency while reducing environmental impact. For instance, in February 2025, IFOAM Organics International, a Germany-based non-profit organization, reported that in 2023, around 98.9 million hectares of land were managed organically, reflecting a 2.6% increase (equivalent to 2.5 million hectares) compared to 2022. Therefore, the need for sustainable agriculture practices is driving the machine learning for crop yield prediction industry.

Key Players In The Global Machine Learning For Crop Yield Prediction Market

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

Global Machine Learning For Crop Yield Prediction Market Trends and Insights

Major companies operating in the machine learning for crop yield prediction market are focusing on developing GenAI-integrated platforms to streamline the creation of innovative, data-driven solutions. GenAI-integrated platforms are systems that combine generative artificial intelligence with other technologies, enabling the creation, customization, and deployment of AI-generated content and solutions across various industries and applications. For instance, in July 2024, CropIn, an India-based agtech company, partnered with Google (Gemini), a US-based technology company, to launch the GenAI-powered agri-intelligence platform, Sage. Sage's unique feature lies in its ability to provide detailed, grid-based insights into crop behavior over various timeframes by integrating generative AI, advanced crop and climate models, and Earth observation data. This integration allows Sage to generate a proprietary grid-based map for agricultural data, offering unmatched scale, accuracy, and speed. It transforms how stakeholders understand crop dynamics, climate impacts, and optimal agricultural practices, enabling informed, data-driven decisions in multiple languages across global farming operations.

What Are Latest Mergers And Acquisitions In The Machine Learning For Crop Yield Prediction Market? AGCO Corporation's Strategic Acquisition To Enhance Precision Agriculture And Machine Learning Solutions

In April 2024, AGCO Corporation, a US-based agricultural machinery manufacturer, acquired Trimble Agriculture for a deal of $2 billion. This acquisition allows AGCO to integrate Trimble's advanced precision agriculture technologies into its product offerings, which is expected to significantly benefit farmers by improving productivity and efficiency. Trimble Agriculture is a US-based provider of machine learning solutions for crop yield prediction.

What Defines the Machine Learning For Crop Yield Prediction Market?

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.

How is Market Value Defined and Measured?

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.

What Key Data and Analysis Are Included in the Machine Learning For Crop Yield Prediction Market Report 2026?

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 market statistics, including industry global market size, regional shares, competitors with the market share, detailed market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning for crop yield prediction industry. The market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future state of the industry.