What Fueled The Previous Growth In The Machine Learning For Crop Yield Prediction Market?
The machine learning for crop yield prediction market has seen considerable growth due to a variety of factors.
• The market size for machine learning in crop yield prediction has seen a surge in growth over the recent years. It is projected to escalate from $0.79 billion in 2024 to $1.01 billion in 2025, with a notable compound annual growth rate (CAGR) of 26.9%.
This robust growth during the historic period can be linked to factors such as the global population increase and increased food demand, the use of historical data in modeling, the growing popularity of precision agriculture, increased investment and funding in agricultural technology (agtech), as well as the advent of climate-smart agriculture.
How Does the Forecast Look for the Machine Learning For Crop Yield Prediction Market?
The machine learning for crop yield prediction market is expected to maintain its strong growth trajectory in upcoming years.
• The market for machine learning in crop yield prediction is anticipated to experience a rapid expansion in the coming years, expecting to reach $2.58 billion in 2029 with a Compound Annual Growth Rate (CAGR) of 26.6%.
This expected surge in the forecast period could be caused by the increased precision and efficiency of machine learning-based predictions, a growing global population with limited resources, the emergence of big data in agriculture, environmental changes and stress, and the adoption of sustainable farming methods. Key trends in this projected period include the use of AI technology, the integration of the Internet of Things (IoT), technological progress, and the use of AI-controlled self-driving tractors.
What Are The Leading Drivers Of Growth In The Machine Learning For Crop Yield Prediction Market?
The expansion of the machine learning for crop yield prediction market is projected due to the increasing demand for sustainable farming methods. Sustainable farming adopts a comprehensive approach towards agriculture, emphasizing resource conservation, biodiversity promotion, economic stability, and equitable societal roles for contemporary and future generations. Concerns about environmental pollution, resource depletion, climate changes, and healthier and more robust food structures that guarantee long-term food security and community wellness have escalated the use of sustainable agriculture. Machine learning for crop yield prediction is crucial for sustainable farming because it enables decision-making derived from data analysis to optimize resource allocation, minimize waste, accelerate productivity, and improve efficacy while curtailing environmental damage. For instance, IFOAM Organics International, a non-profit organization from Germany, reported in February 2024 that the global area dedicated to organic farming expanded by over 20 million hectares in 2022, totaling 96 million hectares. The number of organic farmers also saw a significant increase, exceeding 4.5 million. Also, the sales of organic food soared, nearly touching 135 billion euros in 2022. Hence, sustainable farming practices stimulate growth in the machine learning for crop yield prediction market.
How Are The Segments Defined Within The Global 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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What Emerging Trends Are Influencing The Growth Of The Machine Learning For Crop Yield Prediction Market?
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.
Who Are the Key Players In 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 Is The Most Dominant Region In The Machine Learning For Crop Yield Prediction Market?
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.
