Artificial Intelligence (AI) in Agriculture - Thematic Intelligence
Summary
Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own. With recent progress in machine learning (ML) on the back of improved algorithms (e.g., OpenAI’s GPT-3) and increasing computing power, AI is now able to solve real-life problems.
GlobalData estimates the total AI market will grow from $81 billion in 2022 to $909 billion by 2030, growing at a compound annual growth rate (CAGR) of 35% over the period.
The report outlines the Impact of AI on the Agriculture Sector. AI will help farmers deliver precision agriculture solutions.
Precision agriculture requires a vast amount of data from onsite sensors and satellite imagery. This data can be quickly analysed using AI. Ultimately, this will help farmers make good, timely decisions about their crop or livestock management.
AI will also help in various agricultural management techniques that observe, measure, and respond to crops' inter- and intra-field variability for improved resource use and efficiency.
AI can further address the extreme variability inherent in the agricultural sector, exacerbated by climate change and geopolitical events. For example, it can be used to optimize farm planning, assess climate risks, handle disease management, and much more.
The recent impact of generative AI will also benefit the agriculture industry as it can be used from the operation of smart chatbots to the discovery of new seed variations.
Scope
The report provides a detailed analysis of the key challenges for the agriculture industry including climate change, geopolitics, disease, pressure on limited resources, environmental degradation, and much more. Along with the impact of AI in agriculture sector.
The report includes Market Size and Growth Forecasts for the AI market, split into its key platforms and services.
Evaluation of the Signals, AI value chain and Companies, which will help understand where to invest, explore, or ignore - for agriculture players.
The report identifies leading adopters of AI in the agriculture sector, the top specialist vendors for the AI in the agriculture sector, and cross-sector AI vendors.
Reasons to Buy
- Position yourself for success by understanding the ways in which AI can help to solve the major challenges for the agriculture industry.
- Identify the leading and specialist vendors of AI solutions for the agriculture industry.
- Discover what each vendor offers and who some of their existing clients are.
- Quickly identify attractive investment targets in the agriculture industry by understanding which companies are most likely to be winners in the future based on our thematic scorecard.
- Gain a competitive advantage in the agriculture industry over your competitors by understanding the potential of AI solutions in the future.
- Executive Summary
- Players
- Agriculture Challenges
- The Green Revolution’s second act
- The Impact of AI on Agriculture
- How AI helps tackle the challenges of climate change and environmental degradation
- How AI helps resolve the challenge of geopolitics and market transparency
- How AI helps resolve the challenge of land and resource availability
- How AI helps resolve the challenge of disease
- How AI helps resolve the challenge of spoilage and waste
- Case Studies
- AGCO deploys a conversational platform
- Bayer’s innovative use of AI for plant breeding
- Cargill uses AI to improve poultry flock management
- CNH Industrials invests in California-based start-up for smart agriculture equipment
- Syngenta partners with PEAT for AI powered diagnostics
- AI Timeline
- Market Size and Growth Forecasts
- Signals
- Mergers and acquisitions – all sectors
- Mergers and acquisitions in the agriculture sector
- Patent trends
- Company filings trends
- Hiring trends
- AI Value Chain
- Hardware
- Semiconductors
- Cameras
- Sensors and lasers
- Servers
- Storage devices
- Networking equipment
- Edge equipment
- Data management
- Data governance and security
- Data storage
- Data processing
- Data aggregation
- Data integration
- Foundational AI
- Data science
- Machine learning
- 3D modeling
- Knowledge representation and reasoning
- Visualization engines
- Advanced AI capabilities
- Human-AI interaction
- Decision-making
- Motion
- Creation (also known as generative AI)
- Sentience
- Delivery
- Hardware appliance
- Licensed software
- Artificial intelligence as a service
- Companies
- Leading AI adopters in agriculture
- Leading AI vendors
- Specialist AI vendors in agriculture
- Glossary
- Further Reading
- GlobalData reports
- Our Thematic Research Methodology
- About GlobalData
- Contact Us
- List of Tables
- Table 1: Key challenges facing the agricultural sector
- Table 2: Mergers and acquisitions – all sectors
- Table 3: Mergers and acquisitions in the agriculture sector
- Table 4: Leading AI adopters in agriculture
- Table 5: Leading AI vendors
- Table 6: Specialist AI vendors in agriculture
- Table 7: Glossary
- Table 8: GlobalData reports
- List of Figures
- Figure 1: Key players in the AI value chain
- Figure 2: The agricultural land area has declined since 2010, but the undernourished population has grown
- Figure 3: The share of internet users was significantly higher in urban areas than in rural areas in 2020
- Figure 4: AI is important across the entire agriculture value chain
- Figure 5: The conversational platform noticeably improved CX
- Figure 6: Bayer offers farmers thousands of seed varieties
- Figure 7: Cargill uses AI decision-making to secure flock health
- Figure 8: CNH Industrials invests in computer vision and AI motion
- Figure 9: Crop diseases, deficiencies, or pests can be diagnosed in under five seconds
- Figure 10: The AI story
- Figure 11: By 2030, global AI market revenue will reach $909 billion
- Figure 12: The number of AI patents in the agriculture sector grew by 467% between April 2021 and April 2022
- Figure 13: Agriculture companies regularly mention AI in their filings
- Figure 14: AI-related jobs continue to increase in the agriculture sector
- Figure 15: The AI value chain - An overview
- Figure 16: The AI value chain - Hardware - semiconductors
- Figure 17: The AI value chain - Hardware - cameras
- Figure 18: The AI value chain - Hardware – sensors and lasers
- Figure 19: The AI value chain - Hardware – servers
- Figure 20: The AI value chain - Hardware – storage devices
- Figure 21: The AI value chain - Hardware – networking equipment
- Figure 22: The AI value chain - Hardware – edge equipment
- Figure 23: The AI value chain - Data management
- Figure 24: The AI value chain - Foundational AI – data science
- Figure 25: The AI value chain - Foundational AI – machine learning
- Figure 26: The AI value chain - Foundational AI – 3D modeling
- Figure 27: The AI value chain - Foundational AI – knowledge representation and reasoning
- Figure 28: The AI value chain - Foundational AI – visualization engines
- Figure 29: The AI value chain - Advanced AI capabilities– human-AI interaction
- Figure 30: The AI value chain - Advanced AI capabilities– decision-making
- Figure 31: The AI value chain - Advanced AI capabilities– motion
- Figure 32: The AI value chain - Advanced AI capabilities– creation
- Figure 33: The AI value chain - Advanced AI capabilities– sentience
- Figure 34: The AI value chain - Delivery
- Figure 35: Our five-step approach for generating a sector scorecard
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