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Machine Learning For Crop Yield Prediction Market Size Forecast: How Big Could The Market Get By 2030?
The machine learning for crop yield prediction market size has seen substantial expansion in recent years. It is anticipated to expand from $0.99 billion in 2025 to $1.24 billion in 2026, demonstrating a compound annual growth rate (CAGR) of 25.0%. This growth in previous periods is attributable to factors such as increasing variability in crop yields, a growing dependence on historical weather datasets, the early adoption of predictive modeling tools, rising demand for optimized farm inputs, and an elevated need for risk mitigation in farming.
The machine learning for crop yield prediction market is expected to demonstrate significant expansion in the next few years. It is projected to reach $2.95 billion by 2030, showing a compound annual growth rate (CAGR) of 24.2%. This growth over the forecast period is attributable to the increasing uptake of AI-powered yield prediction systems, enhanced integration of cloud-based analytics, a rising need for precision farming insights, the growing value derived from satellite and drone imaging data, and a wider deployment of real-time environmental monitoring. Key trends in the forecast period include the increasing utilization of multivariate environmental data inputs, deeper integration of remote sensing technologies into yield models, the expansion of real-time crop monitoring practices, a growing adoption of data-driven farm decision frameworks, and the greater application of advanced soil–crop relationship modeling.
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Machine Learning For Crop Yield Prediction Market Expansion Drivers: What’s Shaping Future Growth?
The increasing demand for sustainable farming methods is projected to boost the expansion of the machine learning for crop yield prediction market in the coming years. Sustainable agriculture represents a holistic farming strategy, centered on producing food and other farm goods while simultaneously preserving resources, fostering biodiversity, upholding economic viability, and guaranteeing social fairness for current and future generations. The adoption of sustainable agriculture is growing due to increasing worries regarding environmental harm, depletion of resources, climate shifts, and the necessity for healthier, more adaptable food systems that ensure lasting food security and communal welfare. Within sustainable agriculture, machine learning for crop yield prediction plays a crucial role by enabling data-driven choices to optimize resource use, reduce waste, increase crop output, and improve overall efficiency, all while lessening environmental effects. As an illustration, IFOAM Organics International, a non-profit organization based in Germany, stated in February 2025 that approximately 98.9 million hectares of land were managed organically in 2023, which signifies a 2.6% rise (equating to 2.5 million hectares) compared to the year 2022. Consequently, the imperative for sustainable agricultural practices is fueling the machine learning for crop yield prediction market.
Machine Learning For Crop Yield Prediction Market Breakdown By Product Type And Application
The machine learning for crop yield prediction market covered in this report is segmented –
1) By Component: Software, Services
2) 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 Software
2) 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
Machine Learning For Crop Yield Prediction Market Strategic Trends: What Defines The Next Growth Phase?
Major companies active in the machine learning for crop yield prediction market are concentrating on developing GenAI-integrated platforms to simplify the creation of inventive, data-driven solutions. GenAI-integrated platforms are systems that unite generative artificial intelligence with other technologies, allowing for the generation, customization, and deployment of AI-produced content and solutions across diverse industries and applications. For instance, in July 2024, CropIn, an India-based agtech company, partnered with Google (Gemini), a US-based technology company, to introduce Sage, a GenAI-powered agri-intelligence platform. Sage’s unique characteristic is its ability to deliver detailed, grid-based insights into crop behavior over different timeframes by integrating generative AI, sophisticated crop and climate models, and Earth observation data. This integration empowers Sage to create a proprietary grid-based map for agricultural data, providing unparalleled scale, accuracy, and speed. It transforms how stakeholders perceive crop dynamics, climate effects, and optimal agricultural practices, facilitating informed, data-led decisions in multiple languages across global farming operations.
Machine Learning For Crop Yield Prediction Market Company Landscape And Competitive Strategy
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
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Machine Learning For Crop Yield Prediction Market Regional Outlook: Where Is Opportunity Concentrated?
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.
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Wasay has over a decade of experience in market research, data modelling, and analytics, with prior experience at GlobalData and Decision Tree Consulting Services. At The Business Research Company , he leads research operations across syndicated studies, customized consulting engagements, and the Global Market Model platform. His professional experience includes supporting organizations such as Boston Consulting Group, KPMG, and Ernst & Young. Wasay holds a degree in Electronics and Communications Engineering, postgraduate management qualifications from International Management Institute Belgium and Indian School of Business and Entrepreneurship, and completed the Integrated Program in Business Analytics from Indian Institute of Management Indore.
