Machine Learning for Crop Yield Prediction

Global Machine Learning for Crop Yield Prediction Market to Reach US$2.4 Billion by 2030

The global market for Machine Learning for Crop Yield Prediction estimated at US$713.0 Million in the year 2024, is expected to reach US$2.4 Billion by 2030, growing at a CAGR of 22.7% over the analysis period 2024-2030. Software Component, one of the segments analyzed in the report, is expected to record a 20.7% CAGR and reach US$1.5 Billion by the end of the analysis period. Growth in the Services Component segment is estimated at 26.5% CAGR over the analysis period.

The U.S. Market is Estimated at US$194.3 Million While China is Forecast to Grow at 30.0% CAGR

The Machine Learning for Crop Yield Prediction market in the U.S. is estimated at US$194.3 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$576.9 Million by the year 2030 trailing a CAGR of 30.0% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 18.5% and 20.2% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 19.0% CAGR.

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.

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.

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