Generative AI In Agriculture Market Overview: Extensive Evaluation of Market Size, Growth Opportunities

 The global generative AI in agriculture market size was estimated at USD 226.2 million in 2024 and is projected to reach USD 2,158.9 million by 2033, growing at a CAGR of 28.7% from 2025 to 2033. Farmers are increasingly using generative models to predict yields, weather impacts, and input requirements. This trend supports precision agriculture by enabling scenario-based planning.

The global generative AI in agriculture industry is witnessing a shift toward AI-driven crop simulation and decision-making tools. The development of specialized, accessible, and efficient AI models for agricultural advisory, yield prediction, and climate adaptation is driving growth in the generative AI in agriculture market. This indicates growing demand for localized, multilingual, and climate-resilient advisory tools, especially for smallholder farmers in emerging regions, and signals a shift from general-purpose AI to frugal, scalable solutions suited for underserved agricultural ecosystems. Companies are increasingly developing lightweight, specialized models to address these needs at scale. For instance, in April 2024, Cropin Technology Solutions Private Limited, a software company in India, launched akṣara, an open-source micro language model built on Mistral, designed for climate-smart agriculture in the Global South. It supports nine key crops across five countries, offering localized, low-resource generative AI advisory to smallholder farmers and agricultural stakeholders. This initiative highlights the growing focus on making generative AI more inclusive, sustainable, and aligned with regional agrarian priorities.

The increasing integration of AI with existing digital agriculture platforms is propelling market growth as it enables timely and data-driven decisions throughout the farming cycle. These platforms use generative AI alongside satellite imagery, IoT data, and management systems to deliver context-specific recommendations. This improves resource efficiency, better crop outcomes, and reduced operational risks. Farmers can respond more effectively to dynamic factors such as weather changes or pest threats. The rising demand for intelligent, connected farming solutions is accelerating the adoption of generative AI in agriculture. For instance, in March 2024, Bayer AG, a German life science company, collaborated with Microsoft and Ernst & Young to launch a pilot generative AI system trained on proprietary agronomic data and expert insights. The tool provides fast, accurate recommendations and expands access to agronomic intelligence, especially for smallholder farmers, through customized, validated responses.

Advancements in machine learning, computer vision, and compute/cloud infrastructure are transforming agricultural practices. Machine learning algorithms enable accurate predictions for crop yields, disease outbreaks, and soil health. Computer vision systems process drone and satellite imagery to monitor crop conditions in real time. These tools help detect issues such as pest infestations or nutrient deficiencies with high precision. Meanwhile, improvements in cloud infrastructure provide scalable data storage and processing capabilities. Farmers can now access analytics platforms from remote locations, enabling informed decision-making. Integration with IoT devices further enhances real-time monitoring and automation. These technologies reduce manual intervention and improve operational efficiency. Cloud-based solutions also support collaboration between stakeholders across the value chain. Together, these advancements are driving a data-driven shift toward smarter, more sustainable agriculture.

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Key Company Insights

Some of the key companies in the Generative AI in Agriculture industry include Aptiv PLC, General Motors Company, Hyundai Motor Company, NVIDIA Corporation, Qualcomm Technologies, Inc., and others. Organizations are focusing on increasing customer base to gain a competitive edge in the industry. Therefore, key players are taking several strategic initiatives, such as mergers and acquisitions, and partnerships with other major companies.

• AgroScout uses generative AI to improve crop scouting by analyzing drone and satellite imagery. The AI models generate detailed insights on pest infestations, nutrient deficiencies, and plant diseases. These insights enable early intervention and reduce reliance on manual field inspections. The technology supports real-time decision-making for precision agriculture.

Research Methodology

We employ a comprehensive and iterative research methodology focused on minimizing deviance in order to provide the most accurate estimates and forecasts possible. We utilize a combination of bottom-up and top-down approaches for segmenting and estimating quantitative aspects of the market. Data is continuously filtered to ensure that only validated and authenticated sources are considered. In addition, data is also mined from a host of reports in our repository, as well as a number of reputed paid databases. Our market estimates and forecasts are derived through simulation models. A unique model is created and customized for each study. Gathered information for market dynamics, technology landscape, application development, and pricing trends are fed into the model and analyzed simultaneously.

About Grand View Research

Grand View Research provides syndicated as well as customized research reports and consulting services on 46 industries across 25 major countries worldwide. This U.S. based market research and consulting company is registered in California and headquartered in San Francisco. Comprising over 425 analysts and consultants, the company adds 1200+ market research reports to its extensive database each year. Supported by an interactive market intelligence platform, the team at Grand View Research guides Fortune 500 companies and prominent academic institutes in comprehending the global and regional business environment and carefully identifying future opportunities.

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