Rising Demand For Sustainable Agriculture Driving The Growth Of The Market Due To Environmental And Food Security Concerns: The Driving Engine Behind Machine Learning For Crop Yield Prediction Market Evolution in 2025

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Machine Learning For Crop Yield Prediction Market Size Growth Forecast: What to Expect by 2025?
The exponential growth of the market size for machine learning in crop yield prediction has been notable in recent years. Projected growth from $0.79 billion in 2024 to $0.99 billion in 2025 indicates a compound annual growth rate (CAGR) of 25.4%. The surge during the historic period can be traced back to factors such as a ballooning global population and an increased demand for food, a climbing preference for using historical data for modeling, a rise in the popularity of precision agriculture, heightened levels of investment and funding in agtech, and an upswing in climate-smart agriculture.

How Will the Machine Learning For Crop Yield Prediction Market Size Evolve and Grow by 2029?
The market for machine learning in crop yield prognosis is projected to experience significant expansion in the coming years. Its value is set to reach $2.4 billion in 2029, with a Compound Annual Growth Rate (CAGR) of 24.6%. Factors contributing to this growth over the predicted period include increased accuracy and efficiency of machine learning-aided predictions, a burgeoning global population with finite resources, the emergence of big data in agriculture, climate change pressures, and the implementation of sustainable farming practices. Key trends for the forecasted interval are AI technology, the utilization of machine learning methodologies for predicting crop yields, integration of the Internet of Things (IoT), technological progression, and the uptake of AI-enabled autonomous tractors.

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What Drivers Are Propelling the Growth of Machine Learning For Crop Yield Prediction Market Forward?
It is anticipated that the machine learning market for crop yield prediction will see a significant boost due to the growing demand for sustainable farming methods. This type of agriculture embodies a comprehensive farming approach geared towards food production and other forms of agricultural produce, at the same time prioritizing resource conservation, biodiversity promotion, economical viability, and social equality for the current and future generations. There is an increased focus on sustainable agriculture due to mounting concerns related to environmental degradation, resource scarcity, climate change, and the necessity for robust, healthier food systems accommodating permanent food security and communal prosperity. Machine learning's role in crop yield prediction is crucial within sustainable agriculture, facilitating informed decision-making aimed at streamlining resource use, mitigating waste production, augmenting crop output and promoting efficiency, all the while minimizing environmental damage. For instance, IFOAM Organics International, a not-for-profit organization based in Germany reported in February 2024 that the global organic farming area witnessed an expansion of over 20 million hectares in 2022, hence totaling 96 million hectares. Organic producer count too saw notable growth surpassing 4.5 million producers. Furthermore, organic food sales in 2022 approached a nearly 135 billion euros. Thus, the escalating demand for sustainable farming practices is propelling the machine learning market for crop yield prediction.

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What Long-Term Trends Will Define the Future of the Machine Learning For Crop Yield Prediction Market?
Leading firms within the machine learning for crop yield prediction industry are concentrating their efforts on the development of platforms integrated with GenAI to facilitate the establishment of inventive, data-based solutions. These GenAI-integrated platforms are created by merging generative artificial intelligence with various other technologies, which creates, customizes, and deploys AI-engineered content and solutions within a diverse range of industries and applications. To illustrate, CropIn, an agtech company situated in India, entered a partnership with US-based tech firm Google (Gemini) in July 2024 to introduce the GenAI-empowered agri-intelligence platform, Sage. Sage's distinctive feature is its capability to offer comprehensive, grid-organized insights into crop behaviour over multiple time periods. This is achieved by incorporating generative AI, advanced crop and climate models, alongside Earth observation data. The integration enables the platform to produce its own grid-based map for farming data, which boasts superior scale, precision and speed. This ultimately adjusts the approach stakeholders have towards comprehending crop dynamics, climate effects and optimal farming practices, positioning them to make data-based decisions in a plethora of languages across international farming procedures.

What Are the Key Segments in 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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Who Are the Key Players Shaping the Machine Learning For Crop Yield Prediction Market's Competitive Landscape?
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 Geographic Markets Are Powering Growth 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.

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