Artificial Intelligence in Agriculture Market Forecasts to 2030 – Global Analysis By Crop Type (Cereals & Grains, Oilseeds & Pulses, Fruits & Vegetables and Other Crop Types), Component, Deployment Mode, Technology, Application, End User and By Geography

Artificial Intelligence in Agriculture Market Forecasts to 2030 – Global Analysis By Crop Type (Cereals & Grains, Oilseeds & Pulses, Fruits & Vegetables and Other Crop Types), Component, Deployment Mode, Technology, Application, End User and By Geography

According to Stratistics MRC, the Global Artificial Intelligence in Agriculture Market is accounted for $1.95 billion in 2024 and is expected to reach $6.53 billion by 2030 growing at a CAGR of 25.2% during the forecast period. Artificial Intelligence in agriculture refers to the application of machine learning, computer vision, robotics, and data analytics to enhance farming practices. AI-driven technologies enable precision farming by analyzing data from various sources such as soil sensors, weather forecasts, and satellite imagery. These technologies assist in optimizing crop yields, reducing resource usage, and minimizing environmental impact. Tasks such as pest detection, crop monitoring, and automated harvesting are streamlined through AI, leading to improved efficiency, sustainability, and profitability in agricultural operations.

According to NASSCOM, by 2025, approximately USD 90 billion of value will be added to the agriculture sector through data and AI technologies in India. With all the sectors combined, artificial intelligence is projected to add approximately USD 500 billion to India's GDP by 2025.

Market Dynamics:

Driver:

Increasing demand for food production

Increasing food production demand drives AI growth in agriculture by necessitating efficient resource use, yield maximization, and sustainable practices. AI technologies, such as precision farming, predictive analytics, and automated machinery, optimize resource use, improve crop yields, and reduce waste. As the global population rises, farmers adopt AI to meet food supply demands sustainably. Advanced AI applications facilitate real-time monitoring, pest and disease management, and data-driven decision-making, making agriculture more resilient and responsive to challenges, thereby propelling market growth.

Restraint:

Lack of technical expertise

The lack of technical expertise in Artificial Intelligence (AI) in agriculture stems from the sector's traditional reliance on conventional farming methods and limited exposure to advanced technologies. Insufficient technical know-how leads to underutilization of AI's potential, hindering innovation, data-driven decision-making and overall productivity improvements in agriculture. Consequently, the adoption rate of AI technologies slows, limiting the market's expansion and its transformative impact on the sector.

Opportunity:

Rising investments in agritech start-ups

Rising investments in agritech start-ups fosters innovation and development of advanced AI-driven solutions. These investments enable start-ups to enhance precision farming, optimize resource utilization, and improve crop yield through AI technologies like machine learning, computer vision, and data analytics. Increased funding accelerates research and development, leading to more robust and scalable AI applications, thereby transforming agricultural practices, boosting productivity, and addressing challenges such as climate change and food security.

Threat:

High initial investment costs

Artificial Intelligence in agriculture involves high initial investment costs due to the need for advanced technologies, infrastructure, and skilled personnel. Developing and implementing AI systems, such as machine learning algorithms, robotics, and IoT devices, requires substantial financial resources. Consequently, market growth is hampered as widespread implementation is slowed, creating a barrier to entry and reducing the overall pace of technological advancement and productivity improvements in the agricultural sector.

Covid-19 Impact

The covid-19 pandemic accelerated the adoption of AI in agriculture by highlighting the need for automation and resilience in food supply chains. Labor shortages and disrupted logistics spurred interest in AI-driven solutions for precision farming, remote monitoring, and automated harvesting. However, economic uncertainties and disrupted supply chains also posed challenges, affecting investment and implementation timelines for AI technologies in the agricultural sector.

The robotics & automation segment is expected to be the largest during the forecast period

The robotics & automation segment is estimated to have a lucrative growth. Robotics and automation in agriculture leverage AI to enhance efficiency and productivity. Autonomous tractors, drones, and robotic harvesters use AI for precision tasks like planting, watering, and harvesting. These technologies enable real-time monitoring and management of crops, reducing labor costs and increasing yields. AI-driven automation ensures optimal use of resources, minimizes waste, and helps in making data-driven decisions for better crop management and sustainability.

The field preparation segment is expected to have the highest CAGR during the forecast period

The field preparation segment is anticipated to witness the highest CAGR growth during the forecast period. Field preparation in AI-driven agriculture involves using technologies like soil sensors, drones, and machine learning algorithms to analyze soil health, moisture levels, and nutrient content. This data guides farmers in optimizing tillage, planting schedules, and soil treatment, leading to improved crop yields, reduced input costs, and sustainable farming practices. AI aids in precise field mapping and decision-making, enhancing overall efficiency and productivity in agriculture.

Region with largest share:

Asia Pacific is projected to hold the largest market share during the forecast period due to increasing food demand, government initiatives, and advancements in technology. Countries like China, India, and Japan are leading in adopting AI for precision farming, crop monitoring, and automated machinery. Rapid urbanization, technological advancements, and shifting dietary preferences are reshaping the market dynamics. The region's large agricultural base, coupled with rising investments in AgriTech start-ups, fosters innovation and implementation of AI solutions.

Region with highest CAGR:

Europe is projected to have the highest CAGR over the forecast period, driven by the region’s precision farming techniques. Europe is marked by a mix of small-scale family farms and large commercial operations, with an increasing focus on sustainability and organic production methods. Europe's supportive regulatory environment and government initiatives are highly promoting digital agriculture. This trend indicates a promising future for AI integration in European agriculture, poised to revolutionize the sector's operational landscape.

Key players in the market

Some of the key players profiled in the Artificial Intelligence in Agriculture Market include IBM Corporation, Microsoft Corporation, Deere & Company, Bayer AG, Trimble Inc., AG Leader Technology, Cropin Technology Solutions Pvt. Ltd., Agribotix LLC, Prospera Technologies, Descartes Labs, Taranis, Corteva, aWhere Inc., Ceres Imaging and Gamaya.

Key Developments:

In April 2024, Cropin launched Aksara, a generative AI system for climate smart agriculture. Aksara will cover nine crops such as paddy, wheat, maize, sorghum, barley, cotton, sugarcane, soybean, and millets for 5 countries in the Indian subcontinent. This generative AI system can suggest farmers which inputs to use for crops like rice or maize under specific agro-climatic conditions or provide climate smart agri-advisories, the company said in a statement.

In June 2023, Deere & Company has unveiled its first fully autonomous tractor, which is already operational on select farms and available for purchase. This tractor is a product of 20 years of AI development and is designed to complete tasks on time, every time, and at a high level of quality.

Crop Types Covered:
• Cereals & Grains
• Oilseeds & Pulses
• Fruits & Vegetables
• Other Crop Types

Components Covered:
• Hardware
• Software
• Services

Deployment Modes Covered:
• Cloud-Based
• On-Premises

Technologies Covered:
• Machine Learning
• Computer Vision
• Predictive Analytics
• Natural Language Processing (NLP)
• Robotics & Automation
• Other Technologies

Applications Covered:
• Precision Farming
• Livestock Monitoring
• Soil Management
• Field Preparation
• Other Applications

End Users Covered:
• Farmers
• Agribusinesses
• Research Organizations
• Government Bodies
• Other End Users

Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa

What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements