Machine Learning for Crop Yield Prediction Market Is Set To Reach 581 Million By 2033, Growing At A CAGR Of 26.5

Key Insights

The size of the Machine Learning for Crop Yield Prediction Market was valued at USD 581 Million in 2023 and is projected to reach USD 3011.69 Million by 2032, with an expected CAGR of 26.5% during the forecast period. The Market for Machine Learning in Crop Yield Prediction uses advanced ML algorithms to enhance accuracy in predicting crop yields, leading to better decision-making in agriculture. This sector utilizes data analytics, remote sensing, and artificial intelligence to evaluate and forecast agricultural output using factors such as weather, soil, crops, and past information. By using machine learning models, farmers and agricultural companies can improve crop management, predict yields, and reduce risks related to climate change, pests, and diseases. The rise in precision agriculture needs, as well as the focus on enhancing food security and sustainability, is pushing for the use of machine learning technologies in predicting crop yields. Furthermore, improvements in big data, IoT gadgets, and satellite images are increasing the precision and effectiveness of ML-based forecasts. Anticipated market growth is likely due to farmers and agribusinesses seeking to boost crop yields, cut down on resource waste, and improve planning and resource distribution amidst worldwide agricultural hurdles.

Machine Learning for Crop Yield Prediction Market Concentration & Characteristics

The market exhibits a moderate level of concentration, with established players holding a significant market share. Key players include Ag Leader Technology, Blue River Technology, Corteva, SAP, Microsoft Azure, Taranis, and Ceres Imaging. Innovation is a key characteristic of the market, with players continually investing in research and development to enhance the accuracy and efficiency of yield prediction models. Government regulations play a limited role, while product substitutes, such as traditional crop forecasting methods, face competition due to the superior accuracy and data-driven insights offered by machine learning models. End-user concentration is moderate, with farmers and agricultural cooperatives accounting for a substantial portion of the market. Merger and acquisition activities are expected to increase as companies seek strategic alliances and expand their product portfolios.

Machine Learning for Crop Yield Prediction Market Trends

Advancements in machine learning algorithms and the availability of vast datasets are driving the development of more sophisticated and accurate yield prediction models. Integration with IoT devices and sensor networks enables real-time data collection, enhancing the predictive capabilities of these models. Precision farming techniques, coupled with machine learning-based yield optimization, allow for targeted interventions and efficient resource utilization. The adoption of cloud-based platforms and mobile applications provides farmers with convenient access to yield prediction tools and insights, fostering decision-making on the go.

Key Region or Country & Segment to Dominate the Market

North America holds a dominant position in the Machine Learning for Crop Yield Prediction Market due to the early adoption of precision farming practices and technological advancements. The Asia-Pacific region is expected to exhibit the highest growth rate, driven by rising food security concerns and government initiatives promoting agricultural productivity.

In terms of segments, software, particularly predictive modeling software, accounts for the largest market share, as it provides a framework for developing and deploying yield prediction models. The cloud-based deployment model is gaining popularity due to its scalability, cost-effectiveness, and ease of accessibility. Large farms are the primary users of these technologies, given their significant capital and resource investments in crop production.

Machine Learning for Crop Yield Prediction Market Analysis

The market analysis reveals that adoption of machine learning for crop yield prediction is primarily driven by the need for improved crop management, reduced production costs, and increased profitability. Key market players focus on strategic partnerships, mergers, and acquisitions to expand their market reach and strengthen their product portfolios. The analysis further provides insights into market size, market share, and growth drivers for each market segment.

Machine Learning for Crop Yield Prediction Market Regional Insights

North America: U.S., Canada 

Europe: UK, Germany, France, Italy, Spain, Russia, Nordics, Rest of Europe 

Asia Pacific: China, India, Japan, South Korea, ANZ, Southeast Asia, Rest of Asia Pacific 

Latin America: Brazil, Mexico, Argentina, Rest of Latin America 

MEA: South Africa, Saudi Arabia, UAE, Rest of MEA

Driving Forces: What's Propelling the Machine Learning for Crop Yield Prediction Market

• Rising food security concerns
• Technological advancements in machine learning and data analytics
• Government initiatives promoting sustainable farming practices
• Increasing demand for efficient crop management solutions
• Adoption of precision farming techniques

Challenges and Restraints in Machine Learning for Crop Yield Prediction Market

• Data quality and availability
• Lack of skilled professionals
• Limitations of machine learning algorithms in handling complex scenarios
• Cost of implementation and ongoing training

Emerging Trends in Machine Learning for Crop Yield Prediction Market

• Integration with IoT and sensor networks
• Predictive analytics for disease and pest management
• Adoption of AI-powered autonomous tractors
• Use of drones for data collection and monitoring
• Blockchain for secure data sharing and transparency

Market Segmentation: Machine Learning for Crop Yield Prediction

Component:

• Software 
  • Predictive modeling software
  • Data analytics platform
  • Others
• Services 
  • Professional
  • Managed

Deployment Model:

• Cloud-based
• On-premises 

Farm Size:

• Small
• Medium
• Large 

End User:

• Farmers
• Agricultural cooperatives
• Research institutions
• Government agencies
• Others

Machine Learning for Crop Yield Prediction Industry News

Recent Developments:

• March 2023: Corteva Agrisciences introduced an advanced gene editing technology for corn hybrids, integrating disease-resistant traits using machine learning data.
• December 2023: John Deere invested USD 180 million in constructing the Brazil Technology Development Center, aiming to reduce solution development time by up to 40%.

Leading Players in the Machine Learning for Crop Yield Prediction Market

• Ag Leader Technology
• Blue River Technology 
• Corteva 
• SAP
• Microsoft Azure
• Taranis
• Ceres Imaging