Machine Learning for Crop Yield Prediction - Global Stategic 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.

Report Scope

The report analyzes the Machine Learning for Crop Yield Prediction market, presented in terms of market value (US$ Thousand). The analysis covers the key segments and geographic regions outlined below.

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.

Select Competitors (Total 36 Featured):

• 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.

Tariff Impact Analysis: Key Insights for 2025

Global tariff negotiations across 180+ countries are reshaping supply chains, costs, and competitiveness. This report reflects the latest developments as of April 2025 and incorporates forward-looking insights into the market outlook.

The analysts continuously track trade developments worldwide, drawing insights from leading global economists and over 200 industry and policy institutions, including think tanks, trade organizations, and national economic advisory bodies. This intelligence is integrated into forecasting models to provide timely, data-driven analysis of emerging risks and opportunities.

What’s Included in This Edition:

• Tariff-adjusted market forecasts by region and segment
• Analysis of cost and supply chain implications by sourcing and trade exposure
• Strategic insights into geographic shifts

Buyers receive a free July 2025 update with:

• Finalized tariff impacts and new trade agreement effects
• Updated projections reflecting global sourcing and cost shifts
• Expanded country-specific coverage across the industry