Machine Learning For Crop Yield Prediction Market Set to Reach $12.94 Billion by 2029 with 5.4% Yearly Growth

What industry-specific factors are fueling the growth of the machine learning for crop yield prediction market?
The drive towards sustainable farming practices is predicted to accelerate the expansion of the machine learning market for crop yield forecasting. Sustainable farming is a holistic approach to agriculture which aims to cultivate food and other agricultural goods whilst preserving resources, fostering biodiversity, safeguarding viable economies, and ensuring social equity for current and future generations. With increasing alarm about environmental damage, resource depletion, climate change, and the requirement for healthier, more robust food systems that bolster long-term food security and community prosperity, sustainable agriculture is gaining ground. Machine learning plays a crucial role in sustainable farming as it enables data-sourced decision-making to maximize resource use, limit waste, increase crop output, and improve efficiency while lessening environmental harm. For example, as reported by IFOAM Organics International, a German non-profit organization, in February 2024, the worldwide area dedicated to organic farming increased by over 20 million hectares in 2022, totaling 96 million hectares. There was a substantial rise in the number of organic farmers as well, surpassing 4.5 million. Sales of organic food also nearly hit 135 billion euros in 2022. Consequently, the push for sustainable farming practices is fuelling the growth of the market for machine learning in crop yield prediction.

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What Is the projected market size and growth rate for the machine learning for crop yield prediction market?
The market for machine learning in predicting crop yields has seen explosive growth in recent years. The market size is set to expand from $0.79 billion in 2024 to $1.01 billion in 2025, displaying a compound annual growth rate (CAGR) of 26.9%. The impressive growth witnessed in the past years owes itself to factors such as a surge in global population and the consequential food demand, increased reliance on historical data for creating predictive models, the growing acceptance of precision agricultural practices, escalating investments and funding in agtech, as well as the emerging trend of climate-smart agriculture.

It is anticipated that the machine learning market for crop yield predictions will witness a substantial increase in the upcoming years. The market worth is expected to swell to $2.58 billion by 2029, with a compound annual growth rate (CAGR) of 26.6%. The predicted surge in the forecast period is linked to the enhancement in accuracy and efficacy of ML-based forecasts, increasing global population versus limited resources, the emergence of big data in agriculture, climate shifts, and environmental pressure, along with the embrace of sustainable farming practices. Noteworthy trends during the forecast period encompass AI technology, the application of machine learning in crop yield predictions, the integration of the Internet of Things (IoT), technological progress, and the use of AI-powered autonomous tractors.

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What new trends are reshaping the machine learning for crop yield prediction market and its opportunities?
Leading entities in the market of machine learning for crop yield predictions are prioritizing the development of platforms integrated with GenAI to create innovative, data-centric solutions. GenAI-integrated platforms merge generative artificial intelligence with other technological advancements, allowing the generation, customization, and implementation of AI-created content and solutions in various applications and industries. To illustrate, CropIn, an agtech firm based in India, teamed up with Google (Gemini), a US technology organization, to inaugurate the GenAI-powered agricultural intelligence platform, Sage, in July 2024. The uniqueness of Sage lies in its capability to offer in-depth, grid-specific insights into crop activity across varied periods through the merger of generative AI, advanced weather and crop models, and Earth monitoring data. This amalgamation lets Sage create an exclusive grid-based map for agricultural information, rendering superior scale, accuracy, and quickness. It revolutionizes how stakeholders comprehend crop dynamics, impacts of climate, and best agricultural procedures, allowing for enlightened, data-based decisions in numerous languages across global farming procedures.

What major market segments define the scope and growth 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 region dominates 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.

Which key market leaders are driving the machine learning for crop yield prediction industry growth?
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

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What Is Covered In The Machine Learning For Crop Yield Prediction Global Market Report?

•Market Size Forecast: Examine the machine learning for crop yield prediction market size across key regions, countries, product categories, and applications.
•Segmentation Insights: Identify and classify subsegments within the machine learning for crop yield prediction market for a structured understanding.
•Key Players Overview: Analyze major players in the machine learning for crop yield prediction market, including their market value, share, and competitive positioning.
•Growth Trends Exploration: Assess individual growth patterns and future opportunities in the machine learning for crop yield prediction market.
•Segment Contributions: Evaluate how different segments drive overall growth in the machine learning for crop yield prediction market.
•Growth Factors: Highlight key drivers and opportunities influencing the expansion of the machine learning for crop yield prediction market.
•Industry Challenges: Identify potential risks and obstacles affecting the machine learning for crop yield prediction market.
•Competitive Landscape: Review strategic developments in the machine learning for crop yield prediction market, including expansions, agreements, and new product launches.

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