Key Factors Fueling the Growth of the Machine Learning For Crop Yield Prediction Market in 2025: Rising Demand For Sustainable Agriculture Driving The Growth Of The Market Due To Environmental And Food Security Concerns
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What is the Projected CAGR for the Machine Learning For Crop Yield Prediction Market Size from 2025 to 2034?
In recent times, there has been an exponential growth in the market size for machine learning in crop yield prediction. This market, which was valued at $0.79 billion in 2024, is expected to scale up to $1.01 billion in 2025, with a compound annual growth rate (CAGR) of 26.9%. This impressive growth recorded in the historic period can be linked to factors such as burgeoning global population and food demand, increased use of historical data for modeling, growing interest in precision agriculture, heightened investment and funding in agricultural technology (agtech), and the rise of climate-smart agriculture.
The market size for machine learning in crop yield prediction is anticipated to witness substantial growth in the coming years. The market is projected to surge to $2.58 billion by 2029, with a compound annual growth rate (CAGR) of 26.6%. The escalation during the estimated period can be credited to aspects such as enhanced precision and efficiency of ML-based predictions, increasing global population with limited resources, the surge of big data in farming, environmental stress due to climate change, and uptake of sustainable farming practices. Notable trends for the forecast period comprise AI technology, the application of machine learning to predict crop yields, the amalgamation of the Internet of Things (IoT), progressive technological improvements, and the adoption of tractors powered by AI.
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What are the Fundamental Drivers and Innovations Shaping the Machine Learning For Crop Yield Prediction Market?
The machine learning market for crop yield prediction is set to expand, driven by the increasing demand for sustainable farming methods. This approach involves an all-encompassing way of farming that prioritizes food and other agricultural product production while preserving resources, encouraging biodiversity, sustaining economic viability, and making sure that social fairness is maintained for current and future generations. As worries about environmental damage, resource shortage, climate change, and the requirement for healthier, more resilient food systems that can support long-term food security and boost community well-being intensify, sustainable farming is gaining traction. Machine learning for crop yield prediction plays a crucial role in sustainable farming as it enables evidence-based decision-making to maximize the use of resources, reduce wastage, increase crop yield, and improve efficiency while lessening environmental harm. For example, IFOAM Organics International, a German non-profit organization, reported in February 2024 that in 2022, the worldwide area for organic farming increased by over 20 million hectares to a total of 96 million hectares. Moreover, the number of organic producers also saw significant growth, with the number exceeding 4.5 million. Furthermore, 2022 saw organic food sales nearing 135 billion euros. As such, the rise in sustainable farming practices is propelling the machine learning for crop yield prediction market.
How Is the Machine Learning For Crop Yield Prediction Market Segmented?
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 Regions Are Driving the Next Phase of the Machine Learning For Crop Yield Prediction Market Growth?
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 Key Trends Are Shaping the Future of the Machine Learning For Crop Yield Prediction Market?
Leading enterprises in the machine learning sector for crop yield prediction are prioritizing the development of platforms integrated with GenAI to enhance the establishment of inventive, data-influenced solutions. Platforms integrated with GenAI are systems that bring together generative artificial intelligence and other technologies, facilitating the making, customization, and distribution of AI-produced content and solutions in various sectors and applications. For example, in July 2024, the agtech company CropIn, based in India, collaborated with the US tech company Google (Gemini) to present the agri-intelligence platform powered by GenAI, named Sage. The distinctive quality of Sage is its capability to offer comprehensive, grid-based perceptions of crop behavior over different timespans by incorporating generative AI, modern crop and climate models, and Earth observation data. This combination empowers Sage to create a distinct grid-based map for agricultural data, delivering unparalleled scale, precision, and speed. It revolutionizes the way stakeholders perceive crop dynamics, climate effects, and perfect farming practices, facilitating informed, data-driven decisions in numerous languages across worldwide farming activities.
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How Is the Machine Learning For Crop Yield Prediction Market Defined and What Are Its Core Parameters?
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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