Machine Learning for Crop Yield Prediction - Global Strategic Business Report

The global market for Machine Learning for Crop Yield Prediction was estimated at US$713.0 Million in 2024 and is projected to reach US$2.4 Billion by 2030, growing at a CAGR of 22.7% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions. The report includes the most recent global tariff developments and how they impact the Machine Learning for Crop Yield Prediction market.

Global Machine Learning for Crop Yield Prediction Market - Key Trends & Drivers Summarized

Why Is Machine Learning for Crop Yield Prediction Gaining Importance?

The agricultural sector is increasingly leveraging machine learning (ML) to enhance efficiency and accuracy in predicting crop yields. With the growing global population and food security concerns, optimizing agricultural productivity has become a top priority. Traditional yield estimation methods rely on historical data and manual observations, which are often inaccurate and time-consuming. Machine learning algorithms, combined with satellite imagery, soil data, climate patterns, and real-time sensor inputs, are revolutionizing how farmers and agribusinesses forecast crop output. This predictive capability allows for better decision-making regarding planting cycles, resource allocation, and supply chain planning, ultimately improving profitability and reducing waste.

How Are Innovations Enhancing the Performance of Crop Yield Prediction?

Recent advancements in artificial intelligence (AI) and big data analytics are enhancing the accuracy and efficiency of crop yield prediction models. Machine learning algorithms can process vast amounts of data, including temperature fluctuations, soil moisture levels, pest infestations, and irrigation patterns, to provide highly accurate yield forecasts. Additionally, remote sensing technology and drones equipped with multispectral cameras are being integrated with ML models to monitor plant health, detect diseases early, and assess nutrient deficiencies. Cloud computing and IoT-enabled sensors further enable real-time data collection and predictive analytics, allowing farmers to make proactive decisions to maximize yields.

What Are the Key Market Drivers?

The increasing adoption of precision agriculture, growing investments in agritech startups, and government initiatives promoting smart farming solutions are key drivers fueling market growth. The rising unpredictability of weather patterns due to climate change has also increased the need for advanced predictive models that can mitigate risks and enhance agricultural sustainability. Moreover, the integration of machine learning with blockchain technology is improving transparency and traceability in the agricultural supply chain, fostering trust among consumers and stakeholders.

What Challenges and Future Opportunities Exist?

Challenges include the high cost of implementing machine learning systems, the need for technical expertise among farmers, and the lack of standardized data collection methods across different agricultural regions. However, opportunities lie in the development of user-friendly AI-powered platforms that simplify data interpretation for farmers. The expansion of ML applications in small-scale farming, improvements in affordable precision agriculture tools, and collaborations between agribusiness firms and tech companies will further drive the growth of this market.

Scope of Study:

The report analyzes the Machine Learning for Crop Yield Prediction market in terms of units by the following Segments, and Geographic Regions/Countries:

• Segments: Component (Software Component, Services Component); Deployment (Cloud-based Deployment, On-Premise Deployment); Farm Size (Small Farm, Medium Farm, Large Farm); End-User (Farmers End-User, Agricultural Cooperatives End-User, Research Institutions End-User, Government Agencies End-User, Other End-Users)
• Geographic Regions/Countries: World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

Key Insights:

• Market Growth: Understand the significant growth trajectory of the Software Component segment, which is expected to reach US$1.5 Billion by 2030 with a CAGR of a 20.7%. The Services Component segment is also set to grow at 26.5% CAGR over the analysis period.
• Regional Analysis: Gain insights into the U.S. market, estimated at $194.3 Million in 2024, and China, forecasted to grow at an impressive 30.0% CAGR to reach $576.9 Million by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific.

Why You Should Buy This Report:

• Detailed Market Analysis: Access a thorough analysis of the Global Machine Learning for Crop Yield Prediction Market, covering all major geographic regions and market segments.
• Competitive Insights: Get an overview of the competitive landscape, including the market presence of major players across different geographies.
• Future Trends and Drivers: Understand the key trends and drivers shaping the future of the Global Machine Learning for Crop Yield Prediction Market.
• Actionable Insights: Benefit from actionable insights that can help you identify new revenue opportunities and make strategic business decisions.

Key Questions Answered:

• How is the Global Machine Learning for Crop Yield Prediction Market expected to evolve by 2030?
• What are the main drivers and restraints affecting the market?
• Which market segments will grow the most over the forecast period?
• How will market shares for different regions and segments change by 2030?
• Who are the leading players in the market, and what are their prospects?

Report Features:

• Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030.
• In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa.
• Company Profiles: Coverage of players such as Aerobotics Ltd., AgriWebb Pty Ltd., BASF SE, Bayer AG, Ceres Imaging Inc. and more.
• Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments.

Some of the 36 companies featured in this Machine Learning for Crop Yield Prediction market report include:

• Aerobotics Ltd.
• AgriWebb Pty Ltd.
• BASF SE
• Bayer AG
• Ceres Imaging Inc.
• Cropin Technology Solutions Pvt. Ltd.
• CropX Inc.
• FarmWise Labs Inc.
• Fasal
• International Business Machines Corp. (IBM)
• IUNU Inc.
• Keymakr Inc.
• Microsoft Corporation
• Ninjacart
• PrecisionHawk
• Raven Industries Inc.
• Sentera Inc.
• Taranis
• Terramera Inc.
• Trace Genomics Inc.

This edition integrates the latest global trade and economic shifts as of June 2025 into comprehensive market analysis. Key updates include:

• Tariff and Trade Impact: Insights into global tariff negotiations across 180+ countries, with analysis of supply chain turbulence, sourcing disruptions, and geographic realignment. Special focus on 2025 as a pivotal year for trade tensions, including updated perspectives on the Trump-era tariffs.
• Adjusted Forecasts and Analytics: Revised global and regional market forecasts through 2030, incorporating tariff effects, economic uncertainty, and structural changes in globalization. Includes segmentation by product, technology, type, material, distribution channel, application, and end-use, with historical analysis since 2015.
• Strategic Market Dynamics: Evaluation of revised market prospects, regional outlooks, and key economic indicators such as population and urbanization trends.
• Innovation & Technology Trends: Latest developments in product and process innovation, emerging technologies, and key industry drivers shaping the competitive landscape.
• Competitive Intelligence: Updated global market share estimates for 2025, competitive positioning of major players (Strong/Active/Niche/Trivial), and refined focus on leading global brands and core players.
• Expert Insight & Commentary: Strategic analysis from economists, trade experts, and domain specialists to contextualize market shifts and identify emerging opportunities.
• Complimentary Update: Buyers receive a free July 2025 update with finalized tariff impacts, new trade agreement effects, revised projections, and expanded country-level coverage.