How large is the machine learning for crop yield prediction market, and what is its growth trajectory?
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 increasing global population and food demand, rising use of historical data for modeling, rising popularity of precision agriculture, rising investment and funding in agtech, climate-smart agriculture.
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 can be attributed to increasing the precision and effectiveness of ML-based forecasts, growing population around the work with less resources, rise of big data in agriculture, climate change and environmental stress, adoption of sustainable agriculture. Major trends in the forecast period include AI technology, machine learning technologies in predicting crop yields, integration of Internet of Things (IoT), technological advancements, adoption of AI-powered autonomous tractors.
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What are the key forces behind the machine learning for crop yield prediction market’s growth in recent years?
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 2024, according to IFOAM Organics International, a Germany-based non-profit organization, the global organic farming area grew by more than 20 million hectares in 2022, totaling 96 million hectares. The number of organic producers also experienced substantial growth, exceeding 4.5 million. Additionally, sales of organic food nearly reach 135 billion euros in 2022. Therefore, the need for sustainable agriculture practices is driving the machine learning for crop yield prediction market.
What are the major segments of the machine learning for crop yield prediction market?
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
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Which companies dominate the 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, 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
What major trends will shape the machine learning for crop yield prediction market during the forecast period?
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 the key regional dynamics of the machine learning for crop yield prediction market, and which region leads in market share?
North America was the largest region in the machine learning for crop yield prediction market in 2024. The regions covered in the machine learning for crop yield prediction market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Does The Machine Learning For Crop Yield Prediction Market Report 2025 Offer?
The machine learning for crop yield prediction market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
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.
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