Artificial Intelligence (AI) in Agriculture

Global Artificial Intelligence (AI) in Agriculture Market to Reach US$7.0 Billion by 2030

The global market for Artificial Intelligence (AI) in Agriculture estimated at US$1.8 Billion in the year 2024, is expected to reach US$7.0 Billion by 2030, growing at a CAGR of 24.8% over the analysis period 2024-2030. Software Component, one of the segments analyzed in the report, is expected to record a 21.8% CAGR and reach US$2.9 Billion by the end of the analysis period. Growth in the Hardware Component segment is estimated at 28.7% CAGR over the analysis period.

The U.S. Market is Estimated at US$594.7 Million While China is Forecast to Grow at 28.1% CAGR

The Artificial Intelligence (AI) in Agriculture market in the U.S. is estimated at US$594.7 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$924.4 Million by the year 2030 trailing a CAGR of 28.1% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 23.4% and 21.8% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 26.0% CAGR.

Global Artificial Intelligence (AI) in Agriculture Market - Key Trends and Drivers Summarized

How is AI Transforming Agricultural Practices?

Artificial intelligence is transforming agriculture by converting traditional, labor-intensive practices into highly efficient, data-driven processes that maximize productivity, conserve resources, and address critical environmental challenges. At the core of this transformation is the use of machine learning algorithms, computer vision, and predictive analytics, which together allow farmers to make smarter, more precise decisions on crop management and soil care. For instance, computer vision-powered drones can scan entire fields, identifying crop health issues like nutrient deficiencies, diseases, and pests with incredible accuracy, often before they become visible to the human eye. AI systems can also predict optimal planting and harvesting times based on historical and real-time data on weather patterns, soil conditions, and crop growth cycles. Additionally, autonomous machinery such as AI-guided tractors can plow, plant, and harvest fields with minimal human intervention, performing repetitive tasks with consistency and precision across vast landscapes. This shift towards automation and precision agriculture allows farmers to optimize resources, save time, and reduce labor costs, while also enhancing crop yields and quality. The AI-driven transformation in agriculture is enabling farms to adapt to environmental changes and scale their operations to meet rising global food demands more sustainably.

What Are the Advantages of AI for Crop Monitoring and Management?

AI offers powerful tools for crop monitoring and management, allowing farmers to monitor crop health, detect issues early, and make data-backed decisions to improve productivity. Through the use of drones, satellite imagery, and Internet of Things (IoT) sensors connected to AI platforms, farmers can collect real-time data on soil moisture, temperature, nutrient levels, and other key indicators across different field areas. Machine learning algorithms process this data to provide actionable insights, such as predicting pest infestations, identifying nutrient deficiencies, and monitoring crop growth rates. For instance, AI-driven pest recognition tools can analyze images of crops to detect signs of infestation or disease at an early stage, allowing farmers to apply targeted treatments only where necessary. This selective approach to pest and disease management reduces pesticide use, minimizes environmental impact, and lowers costs. Additionally, AI tools assist with soil management by analyzing soil health and suggesting precise fertilizer applications that avoid nutrient overloading and runoff, which can harm surrounding ecosystems. By delivering real-time insights on crop and soil health, AI technologies enable farmers to maintain healthier crops, achieve higher yields, and reduce resource waste, setting a new standard in efficient and sustainable crop management.

How is AI Enabling Sustainable Agriculture?

AI is driving a new era of sustainable agriculture, offering tools and practices that help farmers reduce their environmental footprint while maintaining productivity. Through precision agriculture, AI allows for the precise application of water, fertilizers, and pesticides, ensuring these resources are only used where they are needed most. For example, smart irrigation systems guided by AI algorithms monitor soil moisture, weather forecasts, and crop needs, adjusting water usage dynamically to prevent over-irrigation and conserve water—a crucial practice in drought-prone regions. Additionally, autonomous tractors and drones, driven by AI insights, apply fertilizers and pesticides selectively across fields, avoiding excess usage and reducing runoff that can contaminate water sources. AI-driven data analysis also supports sustainable crop rotation and soil regeneration practices by providing farmers with insights into soil health, nutrient levels, and the best crops for maintaining balanced ecosystems. Furthermore, AI helps farmers anticipate and prepare for climate variations, allowing them to schedule planting and harvesting more strategically. As agriculture faces increasing pressures to balance productivity with environmental responsibility, AI-enabled sustainable practices offer solutions for conserving natural resources, protecting ecosystems, and reducing carbon emissions, paving the way for a future where food production can meet global demands while safeguarding the planet.

What Factors Are Driving the Growth of AI in Agriculture?

Several factors are driving the rapid adoption of AI in agriculture, reflecting a convergence of technological advancements, environmental challenges, and rising global food demand. Technological innovations in AI, IoT, and cloud computing have made advanced data analytics and automation tools more accessible and cost-effective for farmers, enabling both small and large-scale operations to integrate precision agriculture. Environmental challenges, such as limited freshwater resources, soil degradation, and climate change, have accelerated the need for sustainable, resource-efficient farming methods that AI can facilitate. The global population is projected to surpass nine billion by 2050, sharply increasing food demand, and AI presents scalable solutions for boosting crop yields without expanding farmland, which is crucial for preserving biodiversity and natural habitats. Additionally, the rise of government incentives and investments in agri-tech has further driven AI adoption, with programs aimed at promoting digital transformation in farming and supporting innovations that address food security and climate resilience. The convergence of these factors is making AI a cornerstone of modern agriculture, transforming it into a high-tech, sustainable industry capable of meeting the complex challenges of feeding a growing world in an era of environmental unpredictability.

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence (AI) in Agriculture market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Component (Software Component, Hardware Component, Services Component); Technology (Machine Learning Technology, Predictive Analytics Technology, Computer Vision Technology); Application (Precision Farming Application, Drone Analytics Application, Livestock Monitoring Application, Agriculture Robots Application, Labor Management Application, Other Applications)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 213 Featured) -

• AgEagle Aerial Systems
• aWhere Inc.
• Autonomous Tractor Corporation
• Cainthus
• Connecterra
• Cropx Technologies
• Deere & Company (John Deere)
• Descartes Labs Inc.
• ec2ce
• Farmbot
• Farmers Edge Inc.
• Gamaya
• Harvest Croo
• International Business Machines Corp. (IBM)
• Microsoft Corporation
• Peat
• Precision Hawk
• Resson
• The Climate Corporation
• Trace Genomics
• Tule Technologies
• Vineview
• Vision Robotics

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